In general, NIR reflectance spectra (whether recorded using log(1/R) or the Kubelka-Munk function) are not linear functions of the concentration of the absorbers which we are measuring. There are several causes for this non-linearity, the most commonly cited one being front surface reflection. However, non-linearity also arises from the effects of particle size, sample thickness, void fraction, and experimental arrangement. In this talk, we will attempt to isolate the effects of the various causes, and show the effects of each, using both theoretical calculations and actual data. The listener should then be able to assess where we stand in our quest to produce “linear” data through pre-processing and/or alternate collection schemes.

The elegant early experiments of Herschel demonstrated that there is light after the visible spectrum in a region we call the near infrared (NIR). This was followed by the work which showed that the spectrum went further into what we call the mid infrared (MIR). The MIR has been used for many years as a qualitative and quantitative region to measure constituent values. The MIR region contains the fundamental vibrations which can be theoretically calculated from symmetry rules and harmonic oscillator equations. The NIR is not as straight forward because the region from 400-2500 nm does not contain any of the fundamental vibrations only combination bands and overtones. Over the past fifty years efforts to understand the NIR have largely been ignored while the quantitative aspects of the region have been utilized. This presentation will focus on the efforts to define terms for NIR, examine the calculation of combination bands and overtones and ways to interpret the spectra. The interpretation of the NIR has been aided greatly in recent years by the use of two dimensional spectroscopy which allows the correlation of bands in one spectral region with that of the NIR.

Sample presentation, which means how to set samples to an NIR instrument, is very important in Near Infrared (NIR) Spectroscopy. When sample presentation is not suitable for the samples that you use, very good spectra can not be obtained even if you use a sophisticated NIR instrument. In my presentation, various NIR sample presentations for agricultural products such as intact fruits, single grains, vegetable juice and the others will be explained. In case of peaches with thin peel, the fiber optics of Interactance can be used. However, the fiber optics are not suitable for oranges with relatively thick peel. In this case, transmittance method is useful. As for a small sample such as single grains, a specially designed cell is needed. The cell in transmittance mode has been developed and then applied to single kernels of rice and soybean. In this case we also used the fiber optics. As regards liquid type of sample, a cuvette cell made of quartz in transmittance mode is popular. However, it is time-consuming to wash and dry it. In order to compensate this disadvantage the sample presentation using normal test tubes as sample cells have been developed and applied to milk, rumen juice and urine of a milking cow. An individual test tube can be used for each sample if you use the calibration equation with sample cell compensation. The test tube cell has also been applied to spinach juice for determination of undesirable constituents. It is concluded that sample presentation is most important for NIR Spectroscopy.

To obtain accurate and repeatable analyses using NIR technology it is important to select an NIR instrument and / or its sample presentation attachments which allow the operator to minimize sampling errors without compromising the benefits of NIR analysis -namely rapid, low cost, minimal sample preparation, minimal structural facilities, minimal hazards. For each sample type and consistency there may be different optimal combinations of instrument, sample presentation attachment, and sample preparation. This paper will consider options available to NIR users in the area of plant and soil analysis and evaluate the potential benefits and disadvantages of crop nutrient diagnoses using laboratory based and airborne imaging techniques.

The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.

Artificial Neural Network (ANN) calibration techniques have been used commercially for agricultural applications since the mid-nineties. Global models, based on transmission data from 850 to 1050 nm, are used routinely to measure protein and moisture in wheat and barley and also moisture in triticale, rye, and oats. These models are currently used commercially in approx. 15 countries throughout the world. Results concerning earlier European ANN models are being published elsewhere. Some of the findings from that study will be discussed here. ANN models have also been developed for coarsely ground samples of compound feed and feed ingredients, again measured in transmission mode from 850 to 1050 nm. The performance of models for pig- and poultry feed will be discussed briefly. These models were developed from a very large data set (more than 20,000 records), and cover a very broad range of finished products. The prediction curves are linear over the entire range for protein, fat moisture, fibre, and starch (measured only on poultry feed), and accuracy is in line with the performance of smaller models based on Partial Least Squares (PLS). A simple bias adjustment is sufficient for calibration transfer across instruments. Recently, we have investigated the possible use of ANN for a different type of NIR spectrometer, based on reflectance data from 1100 to 2500 nm. In one study, based on data for protein, fat, and moisture measured on unground compound feed samples, dedicated ANN models for specific product classes (cattle feed, pig feed, broiler feed, and layers feed) gave moderately better Standard Errors of Prediction (SEP) compared to modified PLS (MPLS). However, if the four product classes were combined into one general calibration model, the performance of the ANN model deteriorated only slightly compared to the class-specific models, while the SEP values for the MPLS predictions doubled. Brix value in molasses is a measure of sugar content. Even with a huge dataset, PLS models were not sufficiently accurate for commercial use. In contrast an ANN model based on the same data improved the accuracy considerably and straightened out non-linearity in the prediction plot. The work of Mr. David Funk (GIPSA, U. S. Department of Agriculture) who has studied the influence of various types of spectral distortions on ANN- and PLS models, thereby providing comparative information on the robustness of these models towards instrument differences, will be discussed. This study was based on data from different classes of North American wheat measured in transmission from 850 to 1050 nm. The distortions studied included the effect of absorbance offset pathlength variation, presence of stray light bandwidth, and wavelength stretch and offset (either individually or combined). It was shown that a global ANN model was much less sensitive to most perturbations than class-specific GIPSA PLS calibrations. It is concluded that ANN models based on large data sets offer substantial advantages over PLS models with respect to accuracy, range of materials that can be handled by a single calibration, stability, transferability, and sensitivity to perturbations.

Removal of variability in spectra data before the application of chemometric modeling will generally result in simpler (and presumably more robust) models. Particularly for sparsely sampled data, such as typically encountered in diode array instruments, the use of Savitzky-Golay (S-G) derivatives offers an effective method to remove effects of shifting baselines and sloping or curving apparent baselines often observed with scattering samples. The application of these convolution functions is equivalent to fitting a selected polynomial to a number of points in the spectrum, usually 5 to 25 points. The value of the polynomial evaluated at its mid-point, or its derivative, is taken as the (smoothed) spectrum or its derivative at the mid-point of the wavelength window. The process is continued for successive windows along the spectrum. The original paper, published in 1964 [1] presented these convolution functions as integers to be used as multipliers for the spectral values at equal intervals in the window, with a normalization integer to divide the sum of the products, to determine the result for each point. Steinier et al. [2] published corrections to errors in the original presentation [1], and a vector formulation for obtaining the coefficients. The actual selection of the degree of polynomial and number of points in the window determines whether closely situated bands and shoulders are resolved in the derivatives. Furthermore, the actual noise reduction in the derivatives may be estimated from the square root of the sums of the coefficients, divided by the NORM value. A simple technique to evaluate the actual convolution factors employed in the calculation by the software will be presented. It has been found that some software packages do not properly account for the sampling interval of the spectral data (Equation Ⅶ in [1]). While this is not a problem in the construction and implementation of chemometric models, it may be noticed in comparing models at differing spectral resolutions. Also, the effects on parameters of PLS models of choosing various polynomials and numbers of points in the window will be presented.

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

In 1989, at the 2nd ICNIRS Meeting in Tsukuba, I projected certain directions for NIR technology would take in the future. Among those projections were: (1) An inrush of companies producing FT-NIR instruments. (2) Hybrid NIR Systems (3) Hand-held NIR Technology. All three thrusts have resulted in numerous commercial offerings over the last 12 years Hand-held technology for all fields is growing at an astonishing rate. To date, NIR work at North Carolina State University has produced four (4) hand-held NIR units for: (1) Nicotine and Moisture in tobacco, (2) Vanillin and Moisture in Vanilla Beans, (3) Protein, Moisture and Nitrogen in plant tissue, (4) Chlorophyll and Moisture in Growing Plants: A NIR Spectrometer for Developing Countries. This paper will discuss these developments, including design and performance data.

Hear infrared (NIR) spectra were measured, at five temperatures, for forty-eight samples of honey, from a variety of geographical and botanical sources, and the data has been used to explore the possibility of using NIR spectroscopy for testing label claims concerning the geographical and botanical source of honey being offered for sale to the public. These results demonstrate that the successful characterization of the botanical source of a honey may be obtained by NIR spectroscopy. Further work with large numbers of samples and groups will be required to realized this potential. Additional analysis of these data suggest that research into new ways of obtaining information on the change of absorption with temperature might be beneficial for a range of technologies.

The scope of precision agriculture is to reach the put up cultivation goals by adjusting inputs as precise as possible after what is required by the soil and crop potentials, on a high spatial resolution. Consequently, precision agriculture is also often called site specific agriculture. Regulation of field inputs “on the run” has been made possible by the GPS (Geographical Position System)-technology, which gives the farmer his exact real time positioning in the field. The general goal with precision agriculture is to apply inputs where they best fill their purpose. Thus, resources could be saved, and nutrient losses as well as the impact on the environment could be minimized without lowering total yields or putting product quality at risk. As already indicated the technology exists to regulate the input based on beforehand decisions. However, the real challenge is to provide a reliable basis for decision-making. To support high spatial resolution, extensive sampling and analysis is required for many soil and plant characteristics. The potential of the NIR-technology to provide rapid, low cost analyses with a minimum of sample preparation for a multitude of characteristics therefore constitutes a far to irresistible opportunity to be un-scrutinized. In our work we have concentrated on soil-analysis. The instrument we have used is a Bran Lubbe InfraAlyzer 500 (1300-2500 nm). Clay- and organic matter-contents are soil constituents with major implications for most properties and processes in the soil system. For these constituents we had a 3000-sample material provided. High performance models for the agricultural areas in Sweden have been constructed for clay-content, but a rather large reference material is required, probably due to the large variability of Swedish soils. By subdividing Sweden into six areas the total performance was improved. Unfortunately organic matter was not as easy to get at. Reliable models for larger areas could not be constructed. However, through keeping the mineral fraction of the soil at minimal variation good performance could be achieved locally. The influence of a highly variable mineral fraction is probably one of the reasons for the contradictory results found in the literature regarding organic matter content. Tentative studies have also been performed to elucidate the potential performance in contexts with direct operational implications: lime requirement and prediction of plant uptake of soil nitrogen. In both cases there is no definite reference method, but there are numerous indirect, or indicator, methods suggested. In our study, field experiments where used as references and NIR was compared with methods normally used in Sweden. The NIR-models performed equally or slightly better as the standard methods in both situations. However, whether this is good enough is open for evaluation.

The concept of “precision agriculture” or “site-specific farming” is usually confined to the fields of soil science, crop science and agronomy. However, because plants grow in soil, animals eat plants, and humans eat animal products, it could be argued (perhaps with some poetic licence) that the fields of feed quality, animal nutrition and animal production should also be considered in this context. NIR spectroscopy has proved over the last 20 years that it can provide a firm foundation for quality measurement across all of these fields, and with the continuing developments in instrumentation, computer capacity and software, is now a major cog in the wheel of precision agriculture. There have been a few giant leaps and a lot of small steps in the impact of NIR on the animal world. These have not been confined to the amazing advances in hardware and software, although would not have occurred without them. Rapid testing of forages, grains and mixed feeds by NIR for nutritional value to livestock is now commonplace in commercial laboratories world-wide. This would never have been possible without the pioneering work done by the USDA NIR Forage Research Network in the 1980's, following the landmark paper of Norris et al. in 1976. The advent of calibration transfer between instruments, algorithms which utilize huge databases for calibration and prediction, and the ability to directly scan whole grains and fresh forages can also be considered as major steps, if not leaps. More adventurous NIR applications have emerged in animal nutrition, with emphasis on estimating the functional properties of feeds, such as in vivo digestibility, voluntary intake, protein degradability and in vitro assays to simulate starch digestion. The potential to monitor the diets of grazing animals by using faecal NIR spectra is also now being realized. NIR measurements on animal carcasses and even live animals have also been attempted, with varying degrees of success, The use of discriminant analysis in these fields is proving a useful tool. The latest giant leap is likely to be the advent of relatively low-cost, portable and ultra-fast diode array NIR instruments, which can be used “on-site” and also be fitted to forage or grain harvesters. The fodder and livestock industries are no longer satisfied with what we once thought was revolutionary: a 2-3 day laboratory turnaround for fred quality testing. This means that the instrument needs to be taken to the samples rather than vice versa. Considerable research is underway in this area, but the challenge of calibration transfer and maintenance of instrument networks of this type remains. The animal world is currently facing its biggest challenges ever; animal welfare, alleged effects of animal products on human health, environmental and economic issues are difficult enough, but the current calamities of BSE and foot and mouth disease are “the last straw” NIR will not of course solve all these problems, but is already proving useful in some of these areas and will continue to do so.

Near-infrared spectroscopy (NIRS) is potentially a powerful and revolutionary technology for environmental analysis. It is supported by a large body of scientific and experiential knowledge. The instrumentation is well-developed, with easy-to-use, highly dependable instruments, but at the same time it is still developing, particularly with the production of more portable and rapid instruments, and more powerful software. NIRS is used globally in numerous industries for commodity analysis. Yet NIRS is largely unknown in the field of environmental chemistry and monitoring, and is not even routinely used in soil analysis, where the research literature on NIRS extends over four decades. Part of the explanation for the poor visibility of NIRS is the fact that NIRS is not routinely taught in Chemistry programs in universities, where most environmental chemists and environmental technicians are trained. This presentation examines the unique capabilities of NIRS, such as rapid, real-time analysis; analysis of whole samples; simultaneous analysis of multiple constituents; cost-effectiveness, and portability, as they match needs for analysis in several environmental areas. Examples of NIRS usage and published and unpublished results will be described for such areas as soil and sediment analysis; water quality monitoring; and nutrient loading in application of manures and sewage sludge (biosolids) to land. Present barriers to the use of NIRS in environmental analysis will be discussed. It is argued that emerging environmental problems and increasing attention to some traditional problems will enhance the application of NIRS in the future.

The purpose of this research is to evaluate rapid determination of phosphorus in soils using NIR spectroscopy. The soil samples from the fields subject to different crops and land-use in Kyeongbook province, Korea were used to make the calibration and validation of the calibration set estimating phosphorus in soil. The NIR reflectance was scanned at 2nm intervals from 1100 to 2500nm with an InfraAlyzer 500 (Bran+Luebbe Co.). Various regression analyses were used to evaluate a NIRS method for determination of phosphorus in the soil. NIR absorption approach requires many soil samples to obtain optimal prediction. Applicability of NIR spectra technique may allow for the analysis of available soil phosphorus rapidly as well as total component within a few seconds.

The constant need for quality improvement and production rationalization in the chemical and related industries has led to the increasing replacement of conservative control procedures by more specific and environmentally compatible analytical techniques. In this respect, vibrational spectroscopy has developed over the last yews - in combination with new instrumental accessories and statistical evaluation procedures - to one of the most important analytical tools for industrial chemical quality control and process monitoring in a wide field of applications. In the present communication this potential is demonstrated in order to further support the implementation of mid-infrared (MIR), near-infrared (NIR) and Raman spectroscopy Primarily as industrial on-line tools. To this end the data of selected feasibility studies will be discussed in terms of the individual strengths of the different techniques for the respective application.

Extrusion is one of the most important processes in polymer industry. The characterization of the polymer melt during processing will improve this process noticeably, One possibility of characterizing the actual processed polymer melt is the inline near infrared (NIR) spectroscopy, With this method several polymer properties can be observed during processing, e.g. composition, moisture ormechanical properties of the melt. For this purpose probes for transmission and reflection measurements have been developed, withstanding the high temperatures and pressures appearing during extrusion process (tested up to 300$^{\circ}C$ and 10 ㎫). For the transmission system an optical bypass was developed to eliminate disturbing spectral influences and hence increase the long term stability, which is the prerequisite for an industrial application. Measurements in transmission and reflection produced comparable results (or blending processes, where the prediction error was less than 1%. An optimum RMSEP of only 0.24% was found for preprocessed polymer blends measured in transmission on a laboratory extruder. A transflection measurement allowed for the first time the recording of relevant NIR-spectra in the screw area of an extruder. The application to a (PE+PP) blending process delivered promising results. This new measurement mode allows the observation of the ongoing processes within the screw area, which is of maximum Interest for reactive extrusion processes. Due to economic reasons the calibration transfer between different extrusion systems is also of high importance. Investigations on simulated and real-world spectra showed that a calibration transfer is possible. A new method alternatively to the well-known direct standardization procedures was developed, which is based on an automatic data pretreatment. This procedure delivers comparable results for the calibration transfer. Overall this paper presents concepts, components and algorithms for the inline near infrared (NIR) spectroscopy for polymer extrusion, which allows the use of it in a real industrial extrusion process.

Previous reports have shown that Near Infrared Spectroscopy (NIRS) can be used to assess physical and chemical properties of flax fibre and fabric quality. Currently, spinners assess yarn quality mainly based on strength and regularity measurements. There two key characteristics are influenced by quality of raw fibres used, especially the degree of rotting and strength. The aim of this investigation was to evaluate the use of NIRS for assessing quality of weft grade yarn available on the commercial market. In order to develop the NIR calibrations, a range of samples representing poor, medium and good quality weft yarn samples was included in the calibration and validation sample sets. The samples were analysed for physical and chemical parameters including caustic weight loss, fibre fractions, lipid, ash and minerals. A detailed protocol for assessing yarn quality has been developed to maximize the accuracy of the reflectance spectra. The development of partial least squares regression models and validation of the calibration equations using blind samples will be presented and discussed.

A previous study (Berzaghi et al., 2001) evaluated the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural networks (ANN) on the prediction of the chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with reference values for moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected using 10 different Foss NIR Systems instruments, only some of which had been standardized to one master instrument. The aim of the present study was to evaluate the behaviour of these different calibration methods when predicting the same samples measured on different instruments. Twenty-two sealed samples of different kind of forages were measured in duplicate on seven instruments (one master and six slaves). Three sets of near infrared spectra (1100 to 2500nm) were created. The first set consisted of the spectra in their original form (unstandardized); the second set was created using a single sample standardization (Clone1); the third was created using a multiple sample procedure (Clone6). WinISI software (Infrasoft International Inc., Port Mathilda, PA, USA) was used to perform both types of standardization, Clone1 is just a photometric offset between a “master” instrument and the “slave” instrument. Clone6 modifies both the X-axis through a wavelength adjustment and the Y-axis through a simple regression wavelength by wavelength. The Clone1 procedure used one sample spectrally close to the centre of the population. The six samples used in Clone 6 were selected to cover the range of spectral variation in the sample set. The remaining fifteen samples were used to evaluate the performances of the different models. The predicted values for dry matter, protein and neutral detergent fibre from the master Instrument were considered as “reference Y values” when computing the statistics RMSEP, SEPC, R, Bias, Slope, mean GH (global Mahalanobis distance) and mean NH (neighbourhood Mahalanobis distance) for the 6 slave instruments. From the results we conclude that i) all the calibration techniques gave satisfactory results after standardization. Without standardization the predicted data from the slaves would have required slope and bias correction to produce acceptable statistics. ii) Standardization reduced the errors for all calibration methods and parameters tested, reducing not only systematic biases but also random errors. iii) Standardization removed slope effects that were significantly different from 1.0 in most of the cases. iv) Clone1 and Clone6 gave similar results except for NDF where Clone6 gave better RMSEP values than Clone1. v) GH and NH were reduced by half even with very large data sets including unstandardized spectra.

A compact and handhold near infrared (NIR) system using microspectrometer was developed. This system was suitable not only in the laboratory, but also in the field or in the process. This system was first applied for classification of geographical origin of herbal medicine such as ginseng and sesame. To identify the origin of ginseng on site, the portable NIR system is more suitable for real field application. For this study, using the compact NIR system, soft independent modeling of class analogies (SIMCA) with 1100-1750 nm NIR spectra was utilized for classification of geographical origin (Korea and China) of both ginseng and sesame. The accuracy of results is more than 90%. Quantitative analysis for petroleum such as toluene, benzene, tri-methyl benzene, and ethyl benzene was performed with partial least squares (PLS) regression with NIR 1100-1750 nm spectra. This study showed that the NIR method and gas chromatography (GC), which is a standard method, have good correlations. Furthermore, the ash content of Cornu Cervi Parvum was analyzed and the accuracy was confirmed by the developed compact NIR system.

In recent years, a miniature spectrometer has been extensively developed due to the marriage of fiber optics and semiconductor detector array. This type of miniature spectrometer has advantages of low price and robustness due to the capability of mass production and no moving parts are required such as lenses, mirrors and scanning monochromator. These systems are ideal for use in teaching labs, process monitoring and field analyses. A portable near infrared (NIR) system has been developed for qualitative and quantitative analysis. This system includes a tungsten halogen lamp for light source, a fiber optics connected a light source, and a sample module to the microspectrometer, The size of spectrometer can be as small as 2.5 cm x 1.5 cm x 0.1 cm. Wavelength ranges can be chosen as 360-800 nm, 800-1100 nm and 1100-1900 nm depending on the type of detector. The software consists of various tools for multivariate analysis and pattern recognition techniques. To evaluate the system, long and short-term stability, wavelength accuracy, and stray light have been investigated and compared with conventional scanning type NIR spectrometer. This developed system can be sufficiently used for quantitative and qualitative analysis for various samples such as agricultural product, herbal medicine, food, petroleum, and pharmaceuticals, etc.

CARNAC is a procedure for obtaining quantitative analysis of a sample by comparison of the NIR spectra of the unknown sample with a database of a large number of samples with NIR spectral and compositional data. The method depends on the compression of the NIR database followed by a modification of the compressed data which emphasizes the required analyte. The method identifies a few very similar samples and the value of the required analyte is calculated from a weighed average of the analyte values for the selected similar samples. The method was originally described at Chambersburg IDRC in 1986 and in the Proceedings of the FT Conference of 1987. This contribution will describe recent work on utilising new methods for both compression and modification.

Micro-scale test methods are producing small-sample size where the conventional physical and chemical tests can not be used (high standard deviation, uncertain sampling conditions, low repeatability). Different small-scale test methods were developed recently for determination of physico-chemical, functional, rheological properties of wheat or wheat dough using miniaturized instruments with sophisticated sample preparation/handling and mechanics (RVA, 2 g mixograph, micro-Z-arm mixer, small-scale noodle maker, micro-baking method etc.). The small-scale methodologies can be used as basic research tools or as technology supported measurements and can be also essential in the early selection for quality traits in breeding programs. The milling as a sample preparation step is essential procedure providing good quality flour or semolina samples from small amount of grain (5-10 g) in a reproducible and reliable way. The aim of present study was to use NIR as quality control tool, and to evaluate the recently developed and manufactured micro-scale lab mill (FQC-2000) produced by Inter-Labor Co. Ltd., Hungary. The milling characteristics of the new instrument were compared to other laboratory mills and the effects of milling action on the chemical composition of fractions were analysed. The fractions were tested with both chemical and near infrared spectroscopic methods. The micro-scale milling resulted significantly different yields, particle size distributions and different fractions from compositional point of view. The near infrared spectra were sensitive enough to distinguish the fractions obtained by different milling procedures. Quantitative NIR calibration equations were developed and tested in order to measure the chemical composition of characteristic milling fractions. Special qualification procedure the PQS (Polar Qualification System) method was used for detecting the differences between fractions obtained by macro and micro-milling procedures. The results and the limitations of PQS method in this application will be discussed.

The paper presented here is the initial part of a larger study, in which it was determined which quality parameters in cheese powder could already be predicted by NIR at an early stage in the process and which could only be predicted at the final stages of the process. This initial study was performed in order to establish the levels and nature of variation within and between batches such that the subsequent data collection could be tackled optimally. The perspectives evolved into more than was originally planned and revealed some interesting uses of NIR-technology. Cheese powder production starts as a batch process, where waste cheese from other dairies is melted down in a vat. The process then turns into a continual process as the vat is emptied and the melted cheese is then filtered, homogenized, pasteurized and finally spray dried. Between each batch the powder is to a greater or lesser degree a mixture of 2 batches. This paper is divided into 2 aspects, one regarding the optimization of sampling time and the other is a study of process dynamics. Optimizing sampling time This initial study included 9 powder samples from 9 different batches produced during one day. The raw materials for the batches were chosen with the aim of creating a relatively high level of variation in the data. The total of 81 samples were taken out at regular intervals and spectra were collected on a NIR-systems 6500 instrument. The subsequent reduction of the data by PCA to score values shows the power of NIR as a tool to determine not only when samples are representative of a certain batch, but also which batches are stable enough to include in a further study. Studying process dynamics To take this experiment a step further 1 of the 81 samples were sent to the laboratory for further analyses. The samples were chosen on the criteria that they covered the spectral variation in the dataset. These samples were analysed for 4 chemical components and 5 physical attributes, which are essential for describing the quality of the product. The latent structure of the 7 samples, using the chemical and physical variables, is totally comparable to the latent structure of the NIR spectra. This outcome makes it possible to describe the dynamics of one day's production both chemically and physically with relatively little resources. Additionally it raises the question as to whether reference values are needed, as the latent structure of the NIR-spectra appears to be sufficient in providing information on the quality of the product. To be able to use NIR in this way would require defining quality limits in the principal component space as opposed to each of the reference values. The potential of NIR applied in an explorative fashion with batch processes opens a whole new gateway for the use of this technology. This study explains yet again after so many years in the field “why I'm crazy about NIR!”.

The transfer of predictive models using various chemometric techniques has been reported for FTNIR and scanning-grating based NIR instruments with respect relatively dry samples (<10% water). Some of the currently used transfer techniques include slope and bias correction (SBC), direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT) and application of neural networks. In a previous study (Greensill et at., 2001) on calibration transfer for wet samples (intact melons) across silicon diode array instrumentation, we reported on the performance of various techniques (SBC, DS, PDS, double window PDS (DWPDS), OSC, FIR, WT, a simple photometric response correction and wavelength interpolative method and a model updating method) in terms of RMSEP and Fearns criterion for comparison of RMSEP. In the current study, we compare these melon transfer results to a similar study employing pairs of spectrometers for non-invasive prediction of soluble solid content of peaches.

We have developed a probe for measuring the light levels inside illuminated fruit. The probe has minimal effect on the light levels being measured and enables the sampling of the light flux at any point within the fruit. We present experimental light extinction rates within apple, nashi, kiwifruit, and mandarin fruit. Moving from the illuminated side to the far side of the fruit, the extinction level follows an initial power law decay as the light diffuses into the fruit then reduces to an exponential decay through the rest of the fruit. Significant variations in the rates of light extinction are found in the core, skin and differing flesh regions. Monte Carlo simulations of the light distribution in fruit, which use scattering and absorption coefficients for the diffusely scattering tissue, and boundary conditions for the skin effects, produce results that follow the experimental results closely.

Maize, in Hungary, is the fodder-plant grown in the biggest quantity. It is not only used as a fodder but other products such as iso-sugar are made from it, too. The quality of the fodder and the produce is largely dependent on the composition of the supplied maize to the processing site. The examination of quality parameters besides conventional methods are investigated and measured by NIR spectroscopy on a routine basis. The investigated parameters are the following: water, total protein, starch and oil content. The accuracy and precision of determining these parameters we, apart from the wet chemical methods, influenced by sample preparation to a great extent. One of the main features of this is the sample particle size and its distribution across the sample. The uneven distribution of particle size negatively influences the measurement accuracy, decreases model robustness and prediction ability. With these in mind the aim of our experiment was to investigate the effect of particle size on the accuracy of maize composition determination using reflection measurement setup. In addition, we tested different spectrum transformations, which are suitable for canceling this effect. In our experiment 47 samples were analyzed with three different mesh sizes (1.5mm, 1.8mm and 2mm). The results of our findings are presented here.

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

This work is a further development of the method created by G. Krivoshiev in 1996 for elimination of peel interference and prediction of fruit flesh optical density. In this investigation, as it was earlier, the objects are observed as being structured by three successive layer “AlongrightarrowOlongrightarrowB” denoting “peel-flesh-peel”. In the first version of the method the transmittances of the surface layers A and B were measured according to Kubelka-Munk theory by means of their diffuse reflectance. At that the overall transmittance T was approximated in the form of a multiplication approximation being valid for plane-parallel layers of a non-scattering material. In this work this approximation was done away with applying the theory of discontinuum, respectively Benfor's equations. As a result two mathematical models were created for non-destructive prediction of fruit flesh optical density. These models are different from the ones based solely on Kubelka-Munk theory, the destruction being marked by the terms 1n (1 - $R_{A}R_{0}$) and 1n (1 - $R_{A}R_{B}$), where: $R_{A}$ and $R_{B}$ are reflectance values for the surface layers A and B; $R_{0}$ is the average reflectance of the internal layer that could be obtained empirically by means of a preliminary measurement of sufficiently large number of physically peeled fruits of a given species and variety.

Fruit sweetness, as indexed by total soluble solids (TSS), and fruit acidity are key factors in the description of the fruit eating quality. Our group has been using short wave NIR spectroscopy (SW-NIR; 700-1100 nm) in combination with chemometric methods (PLS and MLR) for the non-invasive determination of the fruit eating quality (1,2). In order to further improve calibration performance, we have investigated SW-NIR spectra of sucrose and D-glucose. In previous reports on the band assignment for these sugars in the 1100-2500 nm spectral region (3-7), it has been established that change in concentration, temperature and physical state of sugars reflects on the shape and position of the spectral bands in the whole NIR region(5-7). The effect of change in concentration and temperature of individual sugar solutions and sugar spiked Juice samples was analysed using combined spectroscopic (derivative, difference, 2D spectroscopy) and linear regression chemometric (PLS, MLR) techniques. The results have been compared with the spectral data of a range of fruit types, varying in TSS content and temperature. In the 800-950 nm spectral region, the B-coefficients for apples, peaches and nectarines resemble those generated in a calibration of pure sucrose in water (Fig. 1). As expected, these fruits exhibit better calibration and prediction results than those in which the B-coefficients were poorly related to those for sugar.(Figure omitted).

The aim of the study was to evaluate the performance of 3 calibration methods, modified partial least squares (MPLS), local PLS (LOCAL) and artificial neural network (ANN) on the prediction of chemical composition of forages, using a large NIR database. The study used forage samples (n=25,977) from Australia, Europe (Belgium, Germany, Italy and Sweden) and North America (Canada and U.S.A) with information relative to moisture, crude protein and neutral detergent fibre content. The spectra of the samples were collected with 10 different Foss NIR Systems instruments, which were either standardized or not standardized to one master instrument. The spectra were trimmed to a wavelength range between 1100 and 2498 nm. Two data sets, one standardized (IVAL) and the other not standardized (SVAL) were used as independent validation sets, but 10% of both sets were omitted and kept for later expansion of the calibration database. The remaining samples were combined into one database (n=21,696), which was split into 75% calibration (CALBASE) and 25% validation (VALBASE). The chemical components in the 3 validation data sets were predicted with each model derived from CALBASE using the calibration database before and after it was expanded with 10% of the samples from IVAL and SVAL data sets. Calibration performance was evaluated using standard error of prediction corrected for bias (SEP(C)), bias, slope and R2. None of the models appeared to be consistently better across all validation sets. VALBASE was predicted well by all models, with smaller SEP(C) and bias values than for IVAL and SVAL. This was not surprising as VALBASE was selected from the calibration database and it had a sample population similar to CALBASE, whereas IVAL and SVAL were completely independent validation sets. In most cases, Local and ANN models, but not modified PLS, showed considerable improvement in the prediction of IVAL and SVAL after the calibration database had been expanded with the 10% samples of IVAL and SVAL reserved for calibration expansion. The effects of sample processing, instrument standardization and differences in reference procedure were partially confounded in the validation sets, so it was not possible to determine which factors were most important. Further work on the development of large databases must address the problems of standardization of instruments, harmonization and standardization of laboratory procedures and even more importantly, the definition of the database population.

The international coffee trade is conducted almost exclusively with green coffee. The main coffee producing countries include Brazil, Columbia, Indonesia, Mexico and the Ivory Coast. About 99 % of the coffee grown throughout the world belong to two coffee plant varieties that are commonly known as Arabica and Robusta. The quality of green coffee can be assessed according to several ISO standards (1,2,3,4,5). However, no official international standards for the authenticity of green coffee have been issued. It is important to know the country of origin of the coffee for the purposes of fair international trade. The geographic origin of the coffee is often stated on the label of coffee products such as speciality roasted and soluble coffees. Near Infrared Spectroscopy (NIR) is an accepted technique for quantitative analysis of various parameters in routine QC analysis of food products. It would appear to be a promising candidate as a tool for identification of green coffee origin and numerous feasibility studies have appeared in the literature on its use for soluble, roasted and green coffee variety identification as well as identification of arabica or robusta coffees. NIR spectrophotometers when configured in the reflectance mode are able to perform a complete profile of the NIR spectrum on whole beans. The data can then be interpreted by discriminant chemometrics data analysis. This is the approach used in the present study.

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145 154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

Directive 79/373/EEC on the marketing of compound feeding stuffs, provided far a flexible declaration arrangement confined to the indication of the feed materials without stating their quantity and the possibility was retained to declare categories of feed materials instead of declaring the feed materials themselves. However, the BSE (Bovine Spongiform Encephalopathy) and the dioxin crisis have demonstrated the inadequacy of the current provisions and the need of detailed qualitative and quantitative information. On 10 January 2000 the Commission submitted to the Council a proposal for a Directive related to the marketing of compound feeding stuffs and the Council adopted a Common Position (EC N$^{\circ}$/2001) published at the Official Journal of the European Communities of 2. 2. 2001. According to the EC (EC N$^{\circ}$ 6/2001) the feeds material contained in compound feeding stufs intended for animals other than pets must be declared according to their percentage by weight, by descending order of weight and within the following brackets (I :< 30%; II :> 15 to 30%; III :> 5 to 15%; IV : 2% to 5%; V: < 2%). For practical reasons, it shall be allowed that the declarations of feed materials included in the compound feeding stuffs are provided on an ad hoc label or accompanying document. However, documents alone will not be sufficient to restore public confidence on the animal feed industry. The objective of the present work is to obtain calibration equations fur the instanteneous and simultaneous prediction of the chemical composition and the percentage of ingredients of unground compound feeding stuffs. A total of 287 samples of unground compound feeds marketed in Spain were scanned in a FOSS-NIR Systems 6500 monochromator using a rectangular cup with a quartz window (16 $\times$ 3.5 cm). Calibration equations were obtained for the prediction of moisture ($R^2$= 0.84, SECV = 0.54), crude protein ($R^2$= 0.96, SECV = 0.75), fat ($R^2$= 0.86, SECV = 0.54), crude fiber ($R^2$= 0.97, SECV = 0.63) and ashes ($R^2$= 0.86, SECV = 0.83). The sane set of spectroscopic data was used to predict the ingredient composition of the compound feeds. The preliminary results show that NIRS has an excellent ability ($r^2$$\geq$ 0, 9; RPD $\geq$ 3) for the prediction of the percentage of inclusion of alfalfa, sunflower meal, gluten meal, sugar beet pulp, palm meal, poultry meal, total meat meal (meat and bone meal and poultry meal) and whey. Other equations with a good predictive performance ($R^2$$\geq$0, 7; 2$\leq$RPD$\leq$3) were the obtained for the prediction of soya bean meal, corn, molasses, animal fat and lupin meal. The equations obtained for the prediction of other constituents (barley, bran, rice, manioc, meat and bone meal, fish meal, calcium carbonate, ammonium clorure and salt have an accuracy enough to fulfill the requirements layed down by the Common Position (EC Nº 6/2001). NIRS technology should be considered as an essential tool in food Safety Programs.

Near infra-red (NIR) spectroscopy has been used for the non-invasive assessment of intact fruit for eating quality attributes such as total soluble solids (TSS) content. However, little information is available in the literature with respect to the robustness of such calibration models validated against independent populations (however, see Peiris et al. 1998 and Guthrie et al. 1998). Many studies report ‘prediction’ statistics in which the calibration and prediction sets are subsets of the same population (e. g. a three year calibration validated against a set from the same population, Peiris et al. 1998; calibration and validation subsets of the same initial population, Guthrie and Walsh 1997 and McGlone and Kawano 1998). In this study, a calibration was developed across 84 melon fruit (R$^2$= 0.86$^{\circ}$Brix, SECV = 0.38$^{\circ}$Brix), which predicted well on fruit excluded from the calibration set but taken from the same population (n = 24, SEP = 0.38$^{\circ}$Brix with 0.1$^{\circ}$Brix bias), relative to an independent group (same variety and farm but different harvest date) (n = 24, SEP= 0.66$^{\circ}$ Brix with 0.1$^{\circ}$Brix bias). Prediction on a different variety, different growing district and time was worse (n = 24, SEP = 1.2$^{\circ}$Brix with 0.9$^{\circ}$Brix bias). Using an ‘in-line’ unit based on a silicon diode array spectrometer, as described in Walsh et al. (2000), we collected spectra from fruit populations covering different varieties, growing districts and time. The calibration procedure was optimized in terms of spectral window, derivative function and scatter correction. Performance of a calibration across new populations of fruit (different varieties, growing districts and harvest date) is reported. Various calibration sample selection techniques (primarily based on Mahalanobis distances), were trialled to structure the calibration population to improve robustness of prediction on independent sets. Optimization of calibration population structure (using the ISI protocols of neighbourhood and global distances) resulted in the elimination of over 50% of the initial data set. The use of the ISI Local Calibration routine was also investigated.

The quality of agricultural products is very important factor for consumers. In Japan, quality is sometimes more important than cost. Usually, the quality of fresh food products is determined in terms of shape, color, size, etc. However, these indices are not always associated with taste, leaving consumers to complain. Recently, two types of the fruit quality meter (a tabletop type - K-FS200 and a portable type - K-BA100, Kubota Corp.) using NIR technology were introduced in Japan. A tabletop instrument is for post harvest use and a portable one is for precision agriculture use. The both meters use the NIR region from 600nm to 1000nm in the interactance mode to determine quality factors related to taste. The instruments can measure sugar content and acidity of such fruit as apples, tomatoes, tangerines and other fruits. The measurement is timely, nondestructive and precise. For example, the coefficient of variation (CV) is less than 6% for sugar in most fruits. The K-FS200 has been evaluated in supermarkets, grading facilities, and wholesalers in Japan. The introduction of the K-FS200) has drawn attention to taste quality and its use is becoming more popular. In addition, researchers or farmers are becoming interested in measuring product ingredient not only after harvest but also during growing in the field so that they can make intelligent judgements concerning soil amendments, such as fertilizers and water, employs the fiber probe for flexible measurement and is battery powered for field use. Design of the fruit quality meters will be discussed. Applications to fruit quality will be presented.

This work aimed to prove the feasibility of NIR spectroscopy to detect vegetable protein isolates (soy, pea and wheat) in milk powder. Two hundred and thirty-nine samples of genuine and adulterated milk powder (NIZO, Ede, NL) were analysed by NIRS using an InfraAlyzer 500 (Bran+Luebbe). NIR spectra were collected at room temperature, and data were processed by using Sesame Software (Bran+Luebbe). Separated calibrations for each non-milk protein added, in the range of 0-5%, were calculated. NIR data were processed by using Sesame Software (Bran+Luebbe). Prediction and validation were made by using a set of samples not included into the calibration set. The best calibrations were obtained by the PLSR. The type of data pre-treatment (normalisation, 1$\^$st/ derivative, etc..) was chosen to optimize the calibration parameters. NIRS technique was able to predict with good accuracy the percentage of each vegetable protein added to milk powder (soy: R$^2$ 0.994, SEE 0.193, SEcv 0.301, RMSEPall 0.148; pea: R$^2$ 0.997, SEE 0.1498, SEcv 0.207, RMSEPall 0.148, wheat: R$^2$ 0.997, SEE 0.1418, SEcv 0.335, RMSEPall 0.149). Prediction results were compared to those obtained using other two techniques: capillary electrophoresis and competitive ELISA. On the basis of the known true values of non-vegetable protein contents, the NIRS was able to determine more accurately than the other two techniques the percentage of adulteration in the analysed samples.

Yeast, Kurtzurnanomyces sp. I-11, produces biosurfactant, mannosyl erythritol lipid (MEL), from soya oil. The properties of biosurfactant MEL include low-toxicity and high biodegradability. MEL provides new possibilities for a wide range of industrial applications, especially food, cosmetic, pharmaceutical fields and chemicals for biotechnology. In the fermentation process, techniques of measuring and controlling substrates and products are important to obtain high productivity with optimum concentrations of substrate and product in the culture broth. The measurement system for the concentrations of soya oil and MEL in the fermentation process was developed using near-infrared spectroscopy (NIRS). Soya oil and MEL in the culture broth were extracted with ethyl acetate and NIR spectra was carried out between the second derivative NIR spectral data at 1312 and 2040 nm and MEL concentrations obtained using a thin-layer chromatography with a flame-ionization detector (TLC/FID) method. A calibration equation for soya oil was results of the validation of the calibration equation, good agreement was observed between the results of the TLD/FID method and those of the NIRS method for both constituents. NIR method was applied to the measurement of the concentrations of MEL and soya oil in the practical fermentation and good results were obtained. The study indicates that NIRS is a useful method for measurement of the substrate and product in the glycolipid fermentation.

Fast and dynamic biochemical, enzymatic and morphological changes occur during the so-called generative development and during the vegetative processes in seeds. The most characteristic biochemical and compositional changes of this period are the formation and decline of storage components or their precursors, the change of their degree in polymerization and an extensive change in water content. The aim of the present study was to detect the maturation processes in seed nondestructively and to verify the applicability of near infrared spectroscopic methods in the measurement of physiological, chemical and biochemical changes in wheat seed. The amount and variation of different water “species” has been changed intensively during maturation. Characteristic changes of three water absorption bands (1920, 1420 and 1150 nm) during maturation were analysed. It was concluded that the free/bound transition of water molecules could be followed sensitively in different region of NIR spectra. Kinetic changes of carbohydrate reserves were characteristic during maturation. An intensive formation and decline of carbohydrate reserves were observed during early stage of maturation (0 -13 days, high energy demand). An accelerated formation of storage carbohydrates (starch) was detected in the second phase of maturation. Five characteristic absorption bands were analysed which were sensitive indicators the changes of carbohydrates occurred during maturation. Precursors of protein synthesis and the synthesis of reserve proteins and their kinetic changes during maturation were followed from NIR spectra qualitative and qualitatively. Dynamic formation of amino acids and the changes of N forms were detected by spectroscopic, chromatographic and by capillary electrophoresis methods. Calibration equations were developed and validated in order to measure the optimal maturation time protein and moisture content of developing wheat seeds. The spectroscopic methods are offering chance and measurement potential in order to detect fine details of physiological processes. The spectra have many hidden details, which can help to understand the biochemical background of processes.

Rapid cost-effective methods of measuring wood quality are extremely important to tree improvement programs where it is necessary to test large numbers of trees. Non-destructive sampling of a forest can be achieved by using increment cores generally removed at breast height. At CSIRO Forestry and Forest Products methods for the rapid, non-destructive measurement of wood properties and wood chemistry based on increment core samples have been developed. In this paper the application of near infrared (NIR) spectroscopy to the prediction of a range solid wood properties, including density, longitudinal modulus of elasticity (E$\sub$L/) and microfibril angle (MFA), is described. Experiments conducted on individual species (Eucalyptus delegatensis and Pinus radiata), the two species combined and a number of mixed species from several genera are reported. NIR spectra were obtained from the radial/longitudinal face of each sample and used to develop calibrations for the measured physical properties. When the individual species were used the relationships between laboratory determined data and NIR fitted data were good in all cases. Coefficients of determination (R$^2$) ranging from 0.77 for MFA to 0.93 for stick density were obtained for E. delegatensis and R$^2$ ranging from 0.68 for MFA to 0.94 for strip density were obtained for P. radiata. The calibration statistics for the combined E. delegatensis and P. radiata samples were similar to those found for the individual species. As these results indicated that it might be possible to produce general calibrations based on samples from a number of species of a single genus or samples from a number of different genera, a wide range of species was subsequently tested. Good relationships were obtained for both density and E$\sub$L/. These calibrations had R$^2$ that were slightly lower than those determined using individual species and standard errors that were higher. The mixed species calibrations, when applied to the E. delegatensis and P. radiata sample sets, provided good estimates of density (stick and strip) and E$\sub$L/. The results demonstrated that a mixed species calibration, that encompasses wide variation in terms of, wood anatomy, chemistry and physical properties, could be used to rank trees. Experiments reported in this paper demonstrate that solid wood properties can be estimated by NIR spectroscopy. The method offers a rapid and non-destructive alternative to traditional methods of analysis and is applicable to large-scale non-destructive forest resource assessment, and to tree breeding and silvicultural programs.

Whereas NIR spectroscopy has been applied in agriculture for more than 20 years, few studies refer to those plant substances occurring only in smaller amounts. Nevertheless there is a growing interest today to support efficiently activities in the production of high-quality medicinal and spice plants by this fast and non-invasive method. Therefore, it was the aim of this study to develop new NIR methods for the reliable prediction of secondary metabolites found as valuable substances in various plant species. First, sophisticated NIR methods were established to perform fast quality analyses of intact fennel, caraway and dill fruits deriving from single-plants [1]. Later on, a characterization of several leaf drugs and the corresponding fresh material has been successfully performed. In this context robust calibrations have been developed for dried peppermint, rosemary and sage leaves for the determination of their individual essential oil content and composition [2]. A specially adopted NIR method has been developed also for the analysis of carnosic acid in the leaves of numerous rosemary and sage gene bank accessions. Carnosic acid is an antioxidative substance for which several health promoting properties including cancer preservation are assumed. Also some other calibrations have been developed for non-volatile substances such as aspalathin (in unfermented rooibos leaves), catechins (in green tea) and echinacoside (in different Echinacea species) [3]. Some NIR analyses have also been successfully performed on fresh material, too. In spite of the fact that these measurements showed less accuracy in comparison to dried samples, the calibration equations are precise enough to register the individual plant ontogenesis and genetic background. Based on the information received, the farmers and breeders are able to determine the right harvest time (when the valuable components have reached their optimum profile) and to select high-quality genotypes during breeding experiments, respectively. First promising attempts have also been made to introduce mobile diode array spectrometers to collect the spectral data directly on the field or in the individual natural habitats. Since the development of reliable NIRS methods in this special field of application is very time-consuming and needs continuous maintenance of the calibration equations over a longer period, it is convenient to supply the corresponding calibration data to interested user via NIRS network. The present status of all activities, preformed in this context during the last three years, will be presented in detail.

Quantitative analysis is an important requirement in exploration, mining and processing of minerals. There is an increasing need for the use of quantitative mineralogical data to assist with bore hole logging, deposit delineation, grade control, feed to processing plants and monitoring of solid process residues. Quantitative analysis using X-Ray Powder Diffraction (XRD) requires fine grinding and the addition of a reference material, or the application of Rietveld analysis to XRD patterns to provide accurate analysis of the suite of minerals present. Whilst accurate quantitative data can be obtained in this manner, the method is time consuming and limited to the laboratory. Mid infrared when combined with multivariant analysis has also been used for quantitative analysis. However, factors such as the absorption coefficients and refractive index of the minerals requires special sample preparation and dilution in a dispersive medium, such as KBr to minimize distortion of spectral features. In contrast, the lower intensity of the overtones and combinations of the fundamental vibrations in the near infrared allow direct measurement of virtually any solid without special sample preparation or dilution. Thus Near Infrared Spectroscopy (NIR) has found application for quantitative on-line/in line analysis and control in a range of processing applications which include, moisture control in clay and textile processing, fermentation processes, wheat analysis, gasoline analysis and chemicals and polymers. It is developing rapidly in the mineral exploration industry and has been underpinned by the development of portable NIR spectrometers and spectral libraries of a wide range of minerals. For example, iron ores have been identified and characterized in terms of the individual mineral components using field spectrometers. Data acquisition time of NIR field instruments is of the order of seconds and sample preparation is minimal. Consequently these types of spectrometers have great potential for in-line or on-line application in the minerals industry. To demonstrate the applicability of NIR field spectroscopy for quantitative analysis of minerals, a specific example on the quantification of lateritic bauxites will be presented. It has been shown that the application of Partial Least Squares regression analysis (PLS) to the NIR spectra can be used to quantify chemistry and mineralogy in a range of lateritic bauxites. Important, issues such as sampling, precision, repeatability, and replication which influence the results will be discussed.

Compact Near Infrared Diode Array Spectrometers offer new possibilities for on line quality assurance in the agricultural sector. Due to their speed and complete robustness towards temperature fluctuations and mechanical shock Diode Array Spectrometers are suitable for the use on Agricultural Harvest Machines. The growing consumer consciousness of food quality in combination with falling manufacturing prices demands procedures for an effective quality control system. The various conventional types of NIR instruments which have so far been used in laboratories are unsuitable for mobile applications under the rough conditions of field cropping not only because of their slow speed of measurement but also because of their shock sensitive filter wheels and monochromators necessary for fractionating polychromatic light. Another advantage of the on line use is the reduction of the sampling error because of the continuously measurement of the whole product. Considering the large economic importance of the dry matter content on agricultural products it is of particular advantage that water belongs to those constituents which are most easily assessed in the near infrared. While other constituents of economic importance such as starch, oil and protein in grains and seeds have a much lesser effect on NIR signals, their contents can nonetheless be assessed with high analytical precision on freshly harvested grains and seeds. In the last years several applications for on line quality assessment on harvesting machines were developed and tested. The talk will give an overview and outlook on existing and future possibilities of this new field of NIR applications.

Robinson with ${coworkers}^{1}$ have introduced two-state outer-neighbor bonding model to explain the anomalies of water. The studies on the properties of water as a function of temperature and pressure revealed that, unlike other ideas, all $H_2O$ molecules in liquid are tetrabonded. On the average they are forming two different bonding types. One type is the regular tetrahedral water-water bonding similar to that found in the ordinary ice Ih, whereas the other is a more dense nonregular tetrahedral bonding similar to that appearing in the ice II. The transformation between these two bonding forms is evidenced by FT-NIR experiment. The FT-NIR measurements were done for liquid water in the temperature range from $20^{\circ}C$ up to $80^{\circ}C$ in a wide extent of frequencies: 12 000 - 4000 $cm^{-1}$ /. Temperature dependent variations in the volume fraction of these two structures are directly related to the spectral changes. The absorbance variations are explored by means of the two-dimensional correlation spectroscopy (2DCOS), principal component analysis (PCA), curve fitting and second derivatives. The presence of the isosbestic points in a range of the combination and overtone transitions indicates that the experimental spectra are a superposition of two temperature independent components. One component of diminishing intensity with temperature increase, is assigned to a stronger hydrogen bonds occurred in the Ih type, whereas the second component showing an opposite behavior, one can attribute to a weaker H-bonds characteristic for the II type. The understanding of the hydrogen bonding network in the liquid water is very important in interpretation of the interaction between water and protein chain. The two-state model of water surrounding the protein surface could advance an understanding of the hydration process.

In this study, the newly constructed optical measurement system, which was mainly composed of a parametric tunable laser and a near infrared photoelectric multiplier, was introduced to clarify the optical characteristics of wood as discontinuous body with anisotropic cellular structure from the viewpoint of the time-of-flight near infrared spectroscopy (TOF-NIRS). The combined effects of the cellular structure of wood sample, the wavelength of the laser beam λ, and the detection position of transmitted light on the time resolved profiles were investigated in detail. The variation of the attenuance of peak maxima At, the time delay of peak maxima Δt and the variation of full width at half maximum Δw were strongly dependent on the feature of cellular structure of a sample and the wavelength of the laser beam. The substantial optical path length became about 30 to 35 times as long as sample thickness except the absorption band of water. Δt ${\times}$ Δw representing the light scattering condition increased exponentially with the sample thickness or the distance between the irradiation point and the end of sample. Around the λ=900-950 nm, there may be considerable light scattering in the lumen of tracheid, which is multiple specular reflection and easy to propagate along the length of wood fiber. Such tendency was remarkable for soft wood with the aggregate of thin layers of cell walls. When we apply TOF-NIRS to the cellular structural materials like wood, it is very important to give attention to the difference in the light scattering within cell wall and the multiple specular-like reflections between cell walls. We tried to express the characteristics of the time resolved profile on the basis of the optical parameters for light propagation determined by the previous studies, which were absorption coefficient K and scattering coefficient S from Kubelka-Munk theory and n from nth power cosine model of radiant intensity. The wavelength dependency of the product of K/S and n, which expressed the light-absorbing and -scattering condition and the degree of anisotropy, respectively, was similar to that of the time delay of peak maxima Δt. The variation of the time resolved profile is governed by the combination of these parameters. So, we can easily find the set of parameters for light propagation synthetically from Δt.

Generation of precise, accurate, and robust calibration models for spectroscopic methods of analysis can be time-consuming, expensive, and sometimes difficult to achieve. For these reasons, efforts have been made to find ways in which the calibration from one instrument can be moved to another with minimal performance reduction. A slight shift in nomenclature from the common term calibration transfer to the term calibration transport is used here to help resolve the subtle difference between two means of moving a calibration from one instrument to another. The former term denotes a transfer procedure that includes mathematical manipulation of the calibration data via some determined transfer function, whereas the latter term does not. Todays generation of process and laboratory FTNIR analyzers is capable of not only achieving calibration transfer, but also calibration transport often without the need of slope or bias adjustments. Several studies are used to examine the boundaries of the extent to which calibration transport is achieved in the refining industry. Data collected on multiple on-line and laboratory FTNIR analyzers located in multiple countries are considered, and the ultimate limitations discussed.

Usually there are many furnaces in a ethylene plant and the performance of total furnaces can be improved if that of each furnace is monitored and controlled. For this purpose real-time data for the effluent of each furnace is necessary. However, it is very difficult to analyze the total effluent stream of a ethylene furnace by real-time because it is composed of so many components including heavy hydrocarbons. Fortunately, component data for lighter hydrocarbons is much more important than that of heavier ones for ethylene furnace. In ordinary case, the on-line measurement of light hydrocarbons is performed by on-stream gas chromatography, after separating gas-phase part from effluent. The main and important components of gas-phase are Methane, Ethane, Ethylene, and Propylene. If we can use Near-infrared spectroscopy for measuring those components within good reproducibility, shorter analysis time, better repeatability, easier maintenance and lower cost will make Near-infrared (NIR) analyzer replace on-stream gas chromatography in this process. Although it is known to be very difficult to measure gas components because of very weak absorption in Near-infrared region, we have studied the feasibility of the application of NIR for the measurement of gas-phase hydrocarbon in the effluent of ethylene furnace. The samples were obtained from actual process and NIR spectra were collected over 1100 to 2500nm range. NIR spectra and calibrations showed and demonstrated the possibility of extending NIR spectroscopy to the measurement of gas-phase hydrocarbon in the effluent of ethylene furnace.

For NIR measurements of papers normally diffuse reflectance accessories are used which can provide a large sampling area. The in-line process control FT-NIR spectrometer MATRIX-E enables the contactless measurement of paper samples of low silicone coat weights on label-stocks in a paper converting factory. For this study concentrations of silicone between 0 and 2 g/㎡ on various paper substrates were included in a quantitative method. The aim was to achieve an absolute value for the deviation from the target value of 1 g/㎡ during continuous movement of the paper with velocities around 400 m/minute. Influences from the uncoated paper type due to supplier, color, opacity, area densities, pre-coating as well as different compounds of the agent silicone were investigated and it was found that all these papers can be represented in one PLS-model. Especially the fact that silicone as an element is present in clay coated papers is of no consequence to the measurements with MATRIX-E. Moreover during in-line installations the variation of the moisture contents in the moving paper due to variable machine velocities as well as the reflecting material of the cylinder have to be considered. It is shown that the result of the in-line calibration has the same prediction ability compared to lab scale results (Root Mean Square Error of Cross-Validation RMSECV = 0.034 g/㎡).

On farm analysis of protein, moisture and oil in cereals and oil seeds is quickly being adopted by Australian farmers. The benefits of being able to measure protein and oil in grains and oil seeds are several : $\square$ Optimize crop payments $\square$ Monitor effects of fertilization $\square$ Blend on farm to meet market requirements $\square$ Off farm marketing - sell crop with load by load analysis However farmers are not NIR spectroscopists and the process of calibrating instruments has to the duty of the supplier. With the potential number of On Farm analyser being in the thousands, then the task of calibrating each instrument would be impossible, let alone the problems encountered with updating calibrations from season to season. As such, NIR technology Australia has developed a mechanism for \ulcorner\ulcorner\ulcorner their range of Cropscan 2000G NIR analysers so that a single calibration can be transferred from the master instrument to every slave instrument. Whole grain analysis has been developed over the last 10 years using Near Infrared Transmission through a sample of grain with a pathlength varying from 5-30mm. A continuous spectrum from 800-1100nm is the optimal wavelength coverage fro these applications and a grating based spectrophotometer has proven to provide the best means of producing this spectrum. The most important aspect of standardizing NIB instruments is to duplicate the spectral information. The task is to align spectrum from the slave instruments to the master instrument in terms of wavelength positioning and then to adjust the spectral response at each wavelength in order that the slave instruments mimic the master instrument. The Cropscan 2000G and 2000B Whole Grain Analyser use flat field spectrographs to produce a spectrum from 720-1100nm and a silicon photodiode array detector to collect the spectrum at approximately 10nm intervals. The concave holographic gratings used in the flat field spectrographs are produced by a process of photo lithography. As such each grating is an exact replica of the original. To align wavelengths in these instruments, NIR wheat sample scanned on the master and the slave instruments provides three check points in the spectrum to make a more exact alignment. Once the wavelengths are matched then many samples of wheat, approximately 10, exhibiting absorbances from 2 to 4.5 Abu, are scanned on the master and then on each slave. Using a simple linear regression technique, a slope and bias adjustment is made for each pixel of the detector. This process corrects the spectral response at each wavelength so that the slave instruments produce the same spectra as the master instrument. It is important to use as broad a range of absorbances in the samples so that a good slope and bias estimate can be calculated. These Slope and Bias (S'||'&'||'B) factors are then downloaded into the slave instruments. Calibrations developed on the master instrument can then be downloaded onto the slave instruments and perform similarly to the master instrument. The data shown in this paper illustrates the process of calculating these S'||'&'||'B factors and the transfer of calibrations for wheat, barley and sorghum between several instruments.

We propose a new calibration method, which uses the linearization method for spectral responses and the repetitive adoptions of the linearization weight matrices to construct a frature. Weight matrices are estimated through multiple linear regression (or principal component regression or partial least squares) with forward variable selection. The proposed method is applied to three data sets. The first is FTIR spectral data set for FeO content from sinter process and the second is NIR spectra from trans-alkylation process having two constituent variables. The third is NIR spectra of crude oil with three physical property variables. To see the calibration performance, we compare the new method with the PLS. It is found that the new method gives a little better performance than the PLS and the calibration result is stable in spite of the collinearity among each selected spectral responses. Furthermore, doing the repetitive adoptions of linearization matrices in the proposed methods, uninformative variables are disregarded. That is, the new methods include the effect of variables subset selection, simultaneously.

The use of fibre optic probes for NIR quality control in the industry is becoming very important, as it provides a powerful tool to reduce sample analysis time and it facilitates the implementation of on-line analyses. However, most of the applications of fibre optics and probes have been done on suspensions, clear liquids and films, chemical and pharmaceutical products and also on fruits and animal products. Traditional applications of near infrared spectroscopy in agriculture have been developed in reflectance mode and calibration transfer could be an interesting way to reduce efforts. Classical methods for calibration transfer between different instruments involve the use of sealed reference cups, but, as fibre optic analysis does not use cups, it is necessary to develop new methods for calibration transfer without standards (Blank et al., 1996). In this paper, we have studied how the most used mathematical pretreatments (three methods of Multiplicative Scatter Correction, Standard Normal Variate, Detrending and derivatives) and their combinations applied to calibration development can contribute to reduce spectral differences between instruments. Calibration equations were obtained for three sets of cereals (barley, wheat and maize) scanned in reflectance mode and then they were validated with samples analysed in reflectance and interactance-reflectance mode (fibre optic). Preliminary results show how some combination of pretreatments reduce the differences in the predicted values, measured as standard error of differences, facilitating the use of calibrations obtained in reflectance for samples analysed by interactance-reflectance. However, the application of pretreatments is not enough to satisfy the control limits for calibration transfer suggested by Shenk et al. (1992), and it should be necessary to combine them with a specific algorithm for instruments standardization.

During two consecutive years, it was developed global calibrations for the prediction of fatty acids on Iberian pig fat. These equations should analyse well samples of that animal fat because of their high accuracy (SECV/sub C16:0/ = 0.26%; SECV/sub C18:0/ = 0.28%; SECV/sub C18:1/ = 0.26%; SECV/sub C18:2/ = 0.15%) and their broad covering composition range. In some cases, when new samples are predicted H (Mahalanobis distance) values higher than 3 (recommended value for agricultural products by the ISI software) are obtained. However, there are not any obvious factors which tells that samples scanned are very different to the spectral mean of the calibration population. Furthermore, these samples are well predicted according to the SEP values. The objective of the present work is to deepen the understanding of the H statistic when analysing animal fats. Three different validation files were predicted with equations obtained from January '97 to April '98. The Set A has spectra of 20 samples not included on the calibration file and scanned in May of 1998. The Set B has spectra of 20 samples included on the calibration file and scanned again in November '99. The Set C contains 150 spectra of one sample representative of the mean values (for fatty acids composition) of the calibration file. This sample was analysed three times per week during June '99 to July '00. The H mean values for the Set A, Set B and Set C were respectively 1.35, 14.39 and 11.71. These anomalous values for the Set B and C make not sense because Set B contains replicate subsamples of the same samples scanned during calibration development and Set C only contains spectra of one sample which represent the mean spectrum of the calibration files. Results will be shown to demonstrate that small day to day variations are responsible of the high H values. When a PCA and LIB file are created with calibration samples and spectra of the Set C modelling day to day variations, the H values for Set A, Set B and Set C were respectively 1.83, 2.16 and 0.93.

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145-154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

The quality of meat is highly variable in many properties. This variability originates from both animal production and meat processing. At the pre-slaughter stage, animal factors such as breed, sex, age contribute to this variability. Environmental factors include feeding, rearing, transport and conditions just before slaughter (Hildrum et al., 1995). Meat can be presented in a variety of forms, each offering different opportunities for adulteration and contamination. This has imposed great pressure on the food manufacturing industry to guarantee the safety of meat. Tissue and muscle speciation of flesh foods, as well as speciation of animal derived by-products fed to all classes of domestic animals, are now perhaps the most important uncertainty which the food industry must resolve to allay consumer concern. Recently, there is a demand for rapid and low cost methods of direct quality measurements in both food and food ingredients (including high performance liquid chromatography (HPLC), thin layer chromatography (TLC), enzymatic and inmunological tests (e.g. ELISA test) and physical tests) to establish their authenticity and hence guarantee the quality of products manufactured for consumers (Holland et al., 1998). The use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise and non-destructive analysis of a wide range of organic materials has been comprehensively documented (Osborne et at., 1993). Most of the established methods have involved the development of NIRS calibrations for the quantitative prediction of composition in meat (Ben-Gera and Norris, 1968; Lanza, 1983; Clark and Short, 1994). This was a rational strategy to pursue during the initial stages of its application, given the type of equipment available, the state of development of the emerging discipline of chemometrics and the overwhelming commercial interest in solving such problems (Downey, 1994). One of the advantages of NIRS technology is not only to assess chemical structures through the analysis of the molecular bonds in the near infrared spectrum, but also to build an optical model characteristic of the sample which behaves like the “finger print” of the sample. This opens the possibility of using spectra to determine complex attributes of organic structures, which are related to molecular chromophores, organoleptic scores and sensory characteristics (Hildrum et al., 1994, 1995; Park et al., 1998). In addition, the application of statistical packages like principal component or discriminant analysis provides the possibility to understand the optical properties of the sample and make a classification without the chemical information. The objectives of this present work were: (1) to examine two methods of sample presentation to the instrument (intact and minced) and (2) to explore the use of principal component analysis (PCA) and Soft Independent Modelling of class Analogy (SIMCA) to classify muscles by quality attributes. Seventy-eight (n: 78) beef muscles (m. longissimus dorsi) from Hereford breed of cattle were used. The samples were scanned in a NIRS monochromator instrument (NIR Systems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced presentation to the instrument were explored. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

Constituents of animal biofluids such as milk, blood and urine contain information specifically related to metabolic and health status of the ruminant animals. Some changes in composition of biofluids can be attributed to disease response of the animals. Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and reducing milk quality. The purpose of this study was to investigate potential of NIRS combined with multivariate analysis for cow's mastitis diagnosis based on NIR spectra of milk, blood and urine. A total of 112 bulk milk, urine and blood samples from 4 Holstein cows were analyzed. The milk samples were collected from morning milking. The urine samples were collected before morning milking and stored at -35$^{\circ}C$ until spectral analysis. The blood samples were collected before morning milking using a catheter inserted into the carotid vein. Heparin was added to blood samples to prevent coagulation. All milk samples were analyzed for somatic cell count (SCC). The SCC content in milk was used as indicator of mastitis and as quantitative parameter for respective urine and blood samples collected at same time. NIR spectra of blood and milk samples were obtained by InfraAlyzer 500 spectrophotometer, using a transflectance mode. NIR spectra of urine samples were obtained by NIR System 6500 spectrophotometer, using 1 mm sample thickness. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. SIMCA was implemented to create models of the respective classes based on NIR spectra of milk, blood or urine. For the calibration set of samples, SIMCA models (model for samples from healthy cows and model for samples from mastitic cows), correctly classified from 97.33 to 98.67% of milk samples, from 97.33 to 98.61% of urine samples and from 96.00 to 94.67% of blood samples. From samples in the test set, the percent of correctly classified samples varied from 70.27 to 89.19, depending mainly on spectral data pretreatment. The best results for all data sets were obtained when first derivative spectral data pretreatment was used. The incorrect classified samples were 5 from milk samples,5 and 4 from urine and blood samples, respectively. The analysis of changes in the loading of first PC factor for group of samples from healthy cows and group of samples from mastitic cows showed, that separation between classes was indirect and based on influence of mastitis on the milk, blood and urine components. Results from the present investigation showed that the changes that occur when a cow gets mastitis influence her milk, urine and blood spectra in a specific way. SIMCA allowed extraction of available spectral information from the milk, urine and blood spectra connected with mastitis. The obtained results could be used for development of a new method for mastitis detection.

Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and considerable compositional changes in milk, reducing milk quality. The potential of near infrared (NIR) spectroscopy in the region from 1100 to 2500nm and chemometric method for classification to detect milk from mastitic cows was investigated. A total of 189 milk samples from 7 Holstein cows were collected for 27 days, consecutively, and analyzed for somatic cells (SCC). Three of the cows were healthy, and the rest had mastitis periods during the experiment. NIR transflectance milk spectra were obtained by the InfraAlyzer 500 spectrophotometer in the spectral range from 1100 to 2500nm. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. The classification of the samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two concentration of SCC - 200 000 cells/ml and 300 000 cells/ml, respectively, were used as thresholds fer separation of healthy and mastitis cows. The best detection accuracy was found for models, obtained using 200 000 cells/ml as threshold and smoothed absorbance data - 98.41% from samples in the calibration set and 87.30% from the samples in the independent test set were correctly classified. SIMCA results for classes, based on 300 000 cells/ml threshold, showed a little lower accuracy of classification. The analysis of changes in the loading of first PC factor for group of healthy milk and group of mastitic milk showed, that separation between classes was indirect and based on influence of mastitis on the milk components. The accuracy of mastitis detection by SIMCA method, based on NIR spectra of milk would allow health screening of cows and differentiation between healthy and mastitic milk samples. Having SIMCA models, mastitis detection would be possible by using only DIR spectra of milk, without any other analyses.

The routine analysis of milk chemical components is of major importance both for the management of animals in dairy farms and for quality control in dairy industries. NIRS technology is an analytical technique which greatly simplifies this routine. One of the most critical aspects in NIRS analysis of milk is sample preparation and analysis modes which should be fast and straightforward. An important difficulty when obtaining NIR spectra of milk is the high water content (80 to 90%) of this product, since water absorbs most of the infrared radiation, and, therefore, limits the accuracy of calibrating for other constituents. To avoid this problem, the DESIR system was set up. Other ways of radiation-sample interaction adapted for liquids or semi-liquids exist, which are practically instantaneous and with limited or null necessity of sample preparation: Transmission and Folded Transmission or Transflectance. The objective of the present work is to compare the precision and accuracy of milk calibration equations in two analysis modes: Reflectance (dry milk) and Folded Transmission (liquid milk). A FOSS-NIR Systems 6500 I spectrophotometer (400-2500 nm) provided with a spinning module was used. Two NIR spectroscopic methods for milk analysis were compared: a) folded transmission: liquid milk samples in a 0.1 pathlength sample cell (ref. IH-0345) and b) reflectance: dried milk samples in glass fibre filters placed in a standard ring cell. A set of 101 milk samples was used to develop the calibration equations, for the two NIR analysis modes, to predict casein, protein, fat and dry matter contents, and 48 milk samples to predict Somatic Cell Count (SCC). The calibrations obtained for protein, fat and dry matter have an excellent quantitative prediction power, since they present $r^2$ values higher than 0.9. The $r^2$ values are slightly lower for casein and SCC (0.88 and 0.89 respectively), but they still are sufficiently high. The accuracy of casein, protein and SCC equations is not affected by the analysis modes, since their ETVC values are very similar in reflectance and folded transmission (0.19% vs 0.21%; 0.16% vs 0.19% and 55.57% vs 53.11% respectively), Lower SECV values were obtained for the prediction of fat and dry matter with the folded transmission equations (0.14% and 0.25% respectively) compared to the results with the reflectance ones (0.43% and 0.34% respectively). In terms of accuracy and speed of analytical response, NIRS analysis of liquid milk is recommended (folded transmission), since the drying procedure takes 24 hours. However, both analysis modes offer satisfactory results.

The aim of this study was to evaluate the possibility to characterize and classify waxes applied on some type of cheeses to obtain good stability during handling and transportation. Generally, waxes are obtained from the petrochemical industry, nowadays there is the possibility to also use biodegradable waxes produced from microorganisms. Preliminary studies were carried out to optimize sample presentation in NIR analysis, such as melting conditions (influence of temperature) and coat thickness of wax. 12 waxes (biodegradable or not) were analysed by using an InfraAlyzer 500 (Bran+Luebbe). The sample size was performed cutting pieces of 1.5 cm (height) x 1.5 cm (width) x 1.5 mm (thickness), previously melted at 9$0^{\circ}C$. NIR spectra were collected at room temperature, and data were processed by Sesame Software (Bran+Luebbe) to evaluate qualitative differences among samples by cluster analysis. Waxes were gathered on the basis of their origin (petrochemical or microbial). To better understand the significance of the NIRS bands discriminating among waxes, a two-dimensional correlation with FT-IR spectra, collected by a FT-IR/ATR 420 (JASCO) instrument, was made using 2DCORR program (Galactic Industries). On the basis of its classification power, NIRS appears to be a promising tool when used in routine analysis for a qualitative control of raw materials.

The ability to accurately assess wine quality is important during the wine making process, particularly when allocating batches of wines to styles determined by consumer requirements. Grape payments are often determined by the quality category of the wine that is produced from them. Wine quality, in terms of sensory characteristics, is normally a subjective measure, performed by experienced winemakers, wine competition judges or winetasting panellists. By nature, such assessments can be biased by individual preferences and may be subject to day-to-day variation. Taste and aroma compounds are often present in concentrations below the detection limit of near infrared (NIR) spectroscopy but the more abundant organic compounds offer potential for objective quality grading by this technique. Samples were drawn from one of Australia's major wine shows and from BRL Hardy's post-vintage wine quality allocation tastings. The samples were scanned in transmission mode with a FOSS NIR Systems 6500, over the wavelength range 400-2500 ㎚. Data analysis was performed with the Vision chemometrics package. With samples from the allocation tastings, the best correlations between NIR spectra and tasting data were obtained with dry red wines. These calibrations used loadings in the wavelengths related to anthocyanins, ethanol and possibly tannins. Anthocyanins are a group of compounds responsible for colour in red wines - restricting the wavelengths to those relating to anthocyanins produced calibrations of similar accuracy to those using the full wavelength range. This was particularly marked with Merlot, a variety that tends to have relatively lower anthocyanin levels than Cabernet Sauvignon and Shiraz. For dry white wines, calibrations appeared to be more dependent on ethanol characteristics of the spectrum, implying that quality correlated with fruit maturity. The correlations between NIR spectra and sensory data obtained using the wine show samples were less significant in general. This may be related to the fact that within most classes in the show, the samples may span vintages, glowing areas and winemaking styles, even though they may be made from only one grape variety. For dry red wines, the best calibrations were obtained with a class of Pinot Noir - a variety that tends to be produced in limited areas in Australia and would represent the least matrix variation. Good correlations were obtained with a tawny port class - these wines are sweet, fortified wines, that are aged for long periods in wooden barrels. During the ageing process Maillard browning compounds are formed and the water is lost through the barrels in preference to ethanol, producing “concentrated” darkly coloured wines with high alcohol content. These calibrations indicated heaviest loadings in the water regions of the spectrum, suggesting that “concentration” of the wines was important, whilst the visible and alcohol regions of the spectrum also featured as important factors. NIR calibrations based on sensory scores will always be difficult to obtain due to variation between individual winetasters. Nevertheless, these results warrant further investigation and may provide valuable Insight into the main parameters affecting wine quality.

Near infrared spectroscopy (NIRS) was applied to determination of the lipid content of compost during compost fermentation of tofu(soybean-curd) refuse. The reflected rays in the wavelength range between 800 and 2500 nm were measured at 2 nm intervals. The absorption of lipid observed at 4 wavelengths, 1208, 1712, 2312 and 2352 nm on the second derivative spectra. To formulate a calibration equation, a multiple linear regression analysis was carried out between the near-infrared spectral data and on the lipid content in the calibration sample set (sample number, n=60) obtained using a Soxhlet extraction method. The calibration equation for prediction of lipid, the value of the multiple correlation coefficient (R) was 0.975 when using the wavelengths of 1208 and 1712nm. To validate the calibration equation obtained, the lipid content in the validation sample set (n=35) not used for formulating the calibration equation were calculated using the calibration equations, and compared with the values obtained using the Soxhlet extraction method. Good agreement were observed between the results of the Soxhlet extraction method and those values of the NIRS method. The simple correlation coefficient (r) and standard error of prediction (SEP) were 0.964 and 0.815 %, respectively. Then, the NIRS method was applied to a compost fermentation in which the time course the lipid content were measured and good results were obtained. The study indicates that NIRS is a useful method for process management of the compost fermentation of tofu refuse.

The study used 356 veal calf meat samples received from Finland (n=16), France (n=109), Italy (n=81) and The Netherlands (n=150). Calves were raised under experimental protocols that compared feeding and housing practices normally used in each county to treatments aiming at improving animal welfare. Samples were taken at the $8^{th}$ rib of Longissimus thoracis muscle 24h after slaughter, They were kept refrigerated ( $2-4^{\circ}C$) under vacuum package for 6d and then frozen ($-20^{\circ}C$) until meat quality evaluation. Measurements included pH, color (Hunter Lab system), shear force, chemical composition (DM, Ash, Ether Extract, collagen and haematin content), weight and area cooking losses and a sensory evaluation by a group of panelists. A sample of meat was ground with a blade mill and scanned in duplicate between 1100 and 1498 nm (FOSS NIR Systems 5000). WinISI software was used to develop a discriminating equation using NIR spectra (SNV-detrend, derivative=1, gap=4nm, smooth=4nm). The Proc ANOVA and DISCRIM of SAS were used for all the laboratory determinations. County of production had a significant (P<0.01) effect on all the parameters. However, discriminant analysis using any or few laboratory parameters resulted in great errors of county classification. A more accurate (98.8%) classification was obtained only when using all the laboratory parameters. NIRS classified correctly 354 of the 356 samples (99.4%). Provided with a larger data set, NIRS could be used to identify country of production of veal meat.

The physico-chemical and texture characteristics of meat determine the nutritional, technological and sensory quality. However, the analysis of meat quality requires expensive, laborious and time consuming analytical methods. The objective of this study was to evaluate NIR spectroscopy using transmittance for determining the moisture, fat, protein and total pigment content, the water holding capacity (WHC) and the toughness of beef meat. A total of 318 spectra were recorded from ground beef samples by a Feed Analyzer 1265 of Infratec. The samples were obtained from the Longissimus muscle of the 10$^{th}$ rib of yearling bulls, ground with an electrical chopper, vacuum packaged, aged during 7 days and frozen at -24$^{\circ}C$ until the analyses were done. Moisture content was measured by oven drying at 10$0^{\circ}C$, fat content was determined by Soxhlet extraction and protein content was estimated from nitrogen content using the Kjeldahl analysis. The total pigment content was determined by the method of Hornsey and the WHC using the method of filter paper press. The instrumental evaluation of texture (maximum load WB, maximum stress MS and toughness) was conducted in an Instron equipment with a Warner-Bratzler shearing device. This analysis was performed on a chop of 3.5 cm obtained from the longissimus of the 8$^{th}$ rib, aged during 7 days, kept frozen at -24$^{\circ}C$ and cooked before the analysis. Near infrared spectra were recorded as log 1/T (T=transmittance) at 2 nm intervals from 850 to 1050 nm using a Feed Analyzer 1265 of Infratec. Calibrations were performed with the WinISI software (vs. 1.02) using the MPLS method. To examine the effect of scatter correction o. derivation of spectra on the calibration performance, calibrations were calculated with the crude spectra or pretreated with different mathematical treatments (inverse MSC, SNVD) and/or second derivative operation. For chemical composition, the use of the scatter corrections improved the calibration statistics, in terms of lower SECV and higher $r^2$. In most of the variables, the use of the 2$^{nd}$ derivative improved the predictions, mainly when combined with the SNVD treatment. However, for predicting the texture traits, the best estimation was obtained from the crude spectrum. These results showed that the equations obtained for predicting moisture, fat and total pigments were very accurate, with $r^2$ being higher that 0.9. However, the prediction of the texture traits (WB, MS, toughness) from ground meat was poor.

Fourier transform near infrared (FT-NIR) spectroscopy was used as a rapid method to measure the $^{o}Brix$ content and to discriminate between different must samples in terms of their fee amino nitrogen (FAN) values. FT-NIR spectroscopy was also used as a rapid method to discriminate between Chardonnay wine samples in terms of the status of the male-lactic fermentation (MLF). This was done by monitoring the conversion of malic to lactic acid and thereby determining whether MLF has started, is underway or has been completed followed by classification of the samples. Furthermore, FT-NIR spectroscopy was applied as a rapid method to discriminate between table wine samples in terms of the ethyl carbamate (EC) content. EC in wine can pose a health threat and need to be monitored by determining the EC content in relation to the regulatory limits set by the authorities. For each of the above mentioned parameters, $QUANT+^{TM}$ methods were built and calibrations derived and it was found that a very strong correlation existed in the sample set for the FT-NIR spectroscopic predictions of $^{o}Brix$ (r = 0.99, SECV = 0.306), but the correlations for the FAN (r = 0.61, SECV = 272.1), malic acid (r = 0.58, SECV = 1.06), lactic acid (r = 0.51, SECV = 1.14) and EC predictions (r = 0.47, SECV = 3.67) were not as good. Soft Independent Modeling by Class Analogy (SIMCA) diagnostics and validation was applied as a sophisticated discrimination method. The must samples could be classified in terms of their FAN values when SIMCA was applied, obtaining results with recognition rates exceeding 80%. When SIMCA diagnostics and validation were applied to determine the progress of conversion of malic to lactic acid and the EC content, again results with recognition rates exceeding 80% were obtained. The evaluation of the applicability of FT-NIR spectroscopy measurement of FAN, $^{o}Brix$ values, malic acid, lactic acid and EC content in must and wine shows considerable promise. FT-NIR spectroscopy has the potential to reduce the analytical times considerably in a range of measurements commonly used during the wine making process. Where conventional FT-NIR calibrations are not effective, SIMCA methods can be used as a discriminative method for rapid classification of samples. SIMCA can replace expensive, time-consuming, quantitative analytical methods, if not completely, at least to some extent, because in many processes it is only needed to know whether a specific cut off point has been reach or not or whether a sample belongs to a certain class or not.

Estimation of sugar and reducing sugar content in molasses is very important task in sugar refineries. Conventional methods of determination of sugar content in molasses samples are highly time consuming and employ hazardous chemicals. Due to the physical properties of molasses, probability of error in conventional analytical techniques is high. These methods have proven to be inefficient for a process control in any sugar industry. Hence development of a rapid, inexpensive, physical and also accurate method for sugar determination in molasses will be highly useful. Near Infrared spectroscopy is being widely used worldwide as an analytical technique in food industry. The technique offers the advantage of being non-destructive and rapid. The present paper highlights the potential of near infrared reflectance spectroscopy as a rapid and automated analytical technique for determination of sugar and reducing sugar content in molasses. A number of molasses samples were collected during and after the sugar season from Havana Sugar Industry, Havana. The samples were chosen so as to obtain a wide range of concentration of sugar and reducing sugars. This was done in order to achieve a good calibration curve with widely spread data points. These samples were scanned in the region of 1100 - 2500 nm in diffuse reflectance mode. An indigenous ELICO NIR spectrophotometer, modified according to the requirements of sugar industry was used for this purpose. Each sample was also analyzed simultaneously by standard chemical methods. Chemical values were taken as reference for near infrared analysis. In order to obtain the most accurate calibration for the set of samples, various mathematical treatments were employed. Partial Least Square method was found to be most suitable for the analysis. A comparison is made between the actual values (chemical values) and the predicted values (NIR values). The actual values agree very well with the predicted values showing the accuracy of the technique. The validity of the technique is checked by predicting the concentration of sugar in unknown molasses samples using the calibration curve. The present investigation assesses the feasibility of the technique for on-line monitoring of sugars present in molasses in sugar industries.

Meat becomes brown and rancid during storage in the refrigerator and display in the case. Color changes, metmyoglobin formation and lipid oxidation are the important problems in the transportation / distribution of meat and retail display. The freshness of meat is determined by the sense of vision and smell. Since conventional method determining lipid oxidation is time consuming and destructive (it needs to homogenize meat with reagents, filtrate, time for reaction and read optical density using spectroscopy), more rapid and nondestructive technical tools are desired. The objective of this work was to evaluate near-infrared spectroscopy as an analytical tool for determining meat color, metmyoglobin formation and lipid oxidation. in beef, pork and chicken. Semitendinosus and longissimus thoracis muscles from six beef steers, biceps femoris and longissimus thoracis muscles from twelve LWD crossbred pigs, and superficial pectoral muscles from twenty-four broilers were used. About a 5-cm diameter and 1-cm thick sample (20.0g) was cut from the muscle and placed on plastic foam, over-wrapped with PVC film, and displayed under flourescent lights at 4 degrees C. during 10 days for beef and pork or 4 days for chicken. The spectra was measured by NIR systems Model 5500 Spectrophotometer using fiber optic scan at range of 400 - 1100 nm. Data were recorded at 2 nm intervals and 10 scans / 10 sec were averaged for every sample. Data obtained were saved as log 1/Re, where Re is the reflectance energy, and then mathematically transformed to second derivatives to reduce effects of differences in particle size. $L^{*}$, $a^{*}$ and $b^{*}$, and metmyoglobin formation were determined by conventional spectrophotometer using the integrating sphere unit. 2-Thiobarbituric acid reactive substances (TBARS) were measured for lipid oxidation. A multiple linear regression was used to find the equation which would best fit the data. The number of wavelengths used in the equation was selected based on the fewer number compared to the increasing multiple correlation and Decreasing standard error. (omitted)

Mixed feeds and their components are a very diverse matrix compared to other agricultural products worked on with NIRS classically. On a database of mixed feeds and their components (n=2.500) universal PLS calibrations and “local” calibrations were compared. The results from validation (n=600) show the potential of the calibrations and their limitations. Crude protein, crude fiber, crude fat, sugar and starch are predicted with SEPs of 0.6%, 1%, 0.3%, 1% and 1.5%, respectively. Ash content of 15% and more in several mixed feeds or components as well as rare components limit the use of NIRS for routine analyses.

The rice plant is one of the important staple crops in Korea. The high yield with low cost in rice is required the soil fertility and the development of new precise method of fertilizer application by nutritional diagnosis. Now, in Korea, the nitrogen application system for the rice plant is composed of the basal fertilization, fertilization at tillering stage and fertilization at panicle stage, which the nitrogen fertilization at panicle stage amount to about 30 percent in the total amount. Thus, this experiment carried out to the development of the system that can measure the nitrogen content in the rice plant at panicle stage rapidly with the near infrared spectroscopy, and to predict the appropriate quantity of the nitrogen fertilization at panicle stage based on calibration model for test of nitrogen content in rice plant. The samples were collected from 48 varieties in 4 regions which are mainly cultivated in the southern part of Korea. And then, it collected by classifying into the leaf, the whole plant and the stem since 7 days before the nitrogen fertilization at panicle stage. The ranges of the nitrogen contents were 1.6∼4.0%, 1.7∼3.0% and 1.4∼2.7% in the leaf, the whole plant and the stem, respectively. In the calibration models created by each part of the plant under the Multiple Linear Regression(MLR) method, the calibration model for the leaf recorded the relatively high accuracy. The mutual crossing test on unknown samples were carried out using Partial Least Square(PLS) calibration model. That is, the nitrogen content in the stem was tested by calibration model made by the leaf model and that of stem was tested by calibration model made by whole plant sample. When unknown leaf sample was tested by calibration model made by all sample that collected from each part in rice plant such as leaf, stem and whole plant, it recorded the highest accuracy. As a result, to test the nitrogen content in the rice plant at panicle stage, the nitrogen content in the leaf shall be tested by the calibration model composed of the leaf, the stem and the whole plant. In future, to estimated the amount of nitrogen fertilization at panicle stage for rice plant , it will be calculated based on regression model between rice yield and nitrogen content of leaf measured by calibration model made by mixed sample including leaf, stem and whole plant.

The amounts of organic matter present in soil and the rate of soil organic matter (SOM) turnover are influenced by agricultural management practice, such as rotation, tillage, forage plow down direct seeding and manure application. The amount of nutrients released from SOM is highly dependent upon the state of the organic matter. If it contains a large proportion of light fractions (low-density) more nutrients will be available to the glowing crops. However, if it contains mostly heavy fractions (high-density) that are difficult to breakdown, then lesser amounts of nutrients will be available. The state of the SOM and subsequent release of nutrients into the soil can be predicted by NIRS as long as a robust regression equation is developed. The NIRS method is known for its rapidity, convenience, simplicity, accuracy and ability to analyze many constituents at the same time. Our hypothesis is that the NIRS technique allows researchers to investigate fully and in more detail each field for the status of SOM, available moisture and other soil properties in Alberta soils for precision farming in the near future. One hundred thirty one (131) Alberta soils with various levels (low 2-6%, medium 6-10%, and high >10%) of organic matter content and most of dry land soils, including some irrigated soils from Southern Alberta, under various management practices were collected throughout Northern, Central and Southern Alberta. Two depths (0- 15 cm and 15-30 cm) of soils from Northern Alberta were also collected. These air-dried soil samples were ground through 2 mm sieve and scanned using Foss NIR System 6500 with transport module and natural product cell. With particle size above 150 microns only, the “Ludox” method (Meijboom, Hassink and van Noorwijk, Soil Biol. Biochem.27: 1109-1111, 1995) which uses stable silica, was used to fractionate SOM into light, medium and heavy fractions with densities of <1.13, 1.13-1.37 and >1.37 respectively, The SOM fraction with the particle size below 150 microns was discarded because practically, this fraction with very fine particles can't be further separated by wet sieving based on density. Total organic matter content, mechanical texture, ash after 375$^{\circ}C$, and dry matter (DM) were also determined by “standard” soil analysis methods. The NIRS regression equations were developed using Infra-Soft-International (ISI) software, version 3.11.

The affect of surface temperatures of fruits on spectrum which measures actual sugar contents was observed. PLSR was applied to develop the sugar content evaluation system that was not affected by temperature. The reflected spectrum was used at the wavelengths of 654 and 1052nm with the separation distance of 2.5nm. To increase the conformance of a model using unknown samples, let the minimum value of PRESS be an optimum factor. 71 Shingo pears stored in a refrigerator were left in a room temperature for a while and these temperatures and reflected spectrums were measured. Reflected spectrums were measured at the wavelengths of 654 and 1052nm, 3 samples in one second. To measure these at different temperatures, the experiment was repeated hourly and four times. Starting temperatures of 2-3 were increased up to 17. The total number of measured spectrum was 284. To develop a sugar content evaluation system model using measured reflected spectrum, three groups of samples were considered. First group had 51 samples at 14 and second group had 141 samples with lower or higher temperatures than 14. Third group had 155 samples with well distributed temperatures. Other samples were used as validations to ensure the conformance. Measuring the sugar contents of samples with surface temperatures other than 14 were difficult with PLS model I, developed by using a sample temperature of 14. If the sugar contents were compensated using samples' temperatures, results of prediction would be close to the expected results and it would be one of the most important factors to develop this system. PLS models I and II could compensate the temperature but the precision would not come up to the standard. High precision was expected by using samples with wide ranges of temperatures and sugar contents. Both models showed the possibility of an improvement of a sugar content evaluation system disregarding the temperature. For practical use of a system, selecting samples should be done carefully to reduce the effect of the temperature.

The organic materials included in excreta of livestock are important resources for organic manure and for improving soil quality, although there is still far from effective using. One reason for this is still unclearly standard of quality for evaluation of manure made from excreta of livestock. Therefore, the objective of this study is to develop rapid and accurate analytical method for analyzing organic compositions of manure made from excreta of livestock, and to establish quality evaluation method based on the compositions predicted by near infrared reflectance spectroscopy (NIRS). Sixteen samples of manure, each eight samples prepared from two treatments, were used in this study. The manure samples were prepared by mixing 560 kg feces of swine,60 kg sawdust with moisture content was adjusted to be 65%. The mixture was then keep under two kinds of shelter, black and clear sheets, as a treatment on the effect of sunlight. Samples were taken in every week (form week-0 to 7) during the process of manure making. Samples were analyzed to determine neutral detergent fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL) by detergent methods, and organic cell wall (OCW) and fibrous content of low digestibility in OCW (Ob) by enzymatic methods. Biological oxygen demand (BOD) was analyzed by coulometric respirometer method. These compositions were carbohydrateds and lignin that were hardly digested. Spectra of samples were scanned by NIR instrument model 6500 (Pacific Scientific) and read over the range of wavelength between 400 and 2500nm. Calibration equations were developed using eight manure samples collected from black sheet shelter, while prediction was conducted to the other eight samples from clear sheet shelter. Accuracy of NTRS prediction was evaluated by correlation coefficients (r), standard error of prediction (SEP) and ration of standard deviation of reference data in prediction sample set to SEP (RPD). The r, SEP and RPD value of forage were 0.99, 0.69 and 7.6 for ADL, 0.96, 1.03 and 4.1 for NDF, 0.98, 0.60 and 4.9 for ADF, 0.92, 1.24 and 2.6 for Ob, and 0.91, 1.02 and 7.3 for BOD, respectively. The results indicated that NIRS could be used to measure the organic composition of forage used in manure samples.

Pasture composition, an important attribute determining sward condition and value, is normally assessed by hand separation, drying and measuring weight contribution of each species in the mixture. This is a tedious, time and labour consuming procedure. NIRS has demonstrated the potential for predicting botanical composition of swards, but most of the work has been carried out on dry samples. The aim of this work was to evaluate the feasibility of developing NIR models for predicting the white clover and ryegrass content in fresh or dry mixtures artificially prepared from pure samples of both species. Mixtures from pure stands of white clover(Trifolium repens) and perennial ryegrass (Lolium perenne) were prepared with different proportions (0 to 100%) of each species (fresh weight). A total of 55 samples were made (11 mixtures,5 cuts). Spectra (400 to 2500 nm) were taken from fresh chopped (rectangular cuvettes, transport sample module) samples, in a NIR Systems 6500 scanning monochromator controlled by the software NIRS 3 (Infrasoft International), which was also utilized for calibration development. Different math treatments (derivative order, subtraction gap and smooth segment) and a scatter correction treatment of the spectra (SNV and Detrend) were tested. Equations were developed by modified partial least squares. Prediction accuracy evaluated by cross-validation, showed that percentage of clover or ryegrass, as contribution in dry weight, can be successfully percentage of clover or ryegrass, as contribution in dry weight, can be successfully predicted either on fresh or dried samples, with equations developed by different math treatments. Best equations for fresh samples were developed including a first, second, or third derivative, whereas for dry samples best equations included a second or third derivative. Standard errors of ross validation were about 6% for fresh and 3.6% for dry samples, Coefficient of determination of cross validation (1-VR) were over 0.95 times the value of SECV for fresh samples and over 8 times the value of SECV for dry samples. Scatter correction (SNV and Detrend) in general improved prediction accuracy. It is concluded more precise on dried and ground samples, it can be used with an acceptable error level and less time and labour, on fresh samples.

The existing fruit sorter has the method of tilting tray and extracting fruits by the action of solenoid or springs. In peaches, the most sort processing is supported by man because the sorter make fatal damage to peaches. In order to sustain commodity and quality of peach non-destructive, non-contact and real time based sorter was needed. This study was performed to develop peach sorter using near-infrared spectroscopy in real time and nondestructively. The prototype was developed to decrease internal and external damage of peach caused by the sorter, which had a way of extracting tray with it. To decrease positioning error of measuring sugar contents in peaches, fiber optic with two direction diverged was developed and attached to the prototype. The program for sorting and operating the prototype was developed using visual basic 6.0 language to measure several quality index such as chlorophyll, some defect, sugar contents. The all sorting result was saved to return farmers for being index of good quality production. Using the prototype, program and MLR(multiple linear regression) model, it was possible to estimate sugar content of peaches with the determination coefficient of 0.71 and SEC of 0.42bx using 16 wavelengths. The developed MLR model had determination coefficient of 0.69, and SEP of 0.49bx, it was better result than single point measurement of 1999's. The peach sweetness grading system based on NIR reflectance method, which consists of photodiode-array sensor, quartz-halogen lamp and fiber optic diverged two bundles for transmitting the light and detecting the reflected light, was developed and evaluated. It was possible to predict the soluble solid contents of peaches in real time and nondestructively using the system which had the accuracy of 91 percentage and the capacity of 7,200 peaches per an hour for grading 2 classes by sugar contents. Draining is one of important factors for production peaches having good qualities. The reason why one farm's product belows others could be estimated for bad draining, over-much nitrogen fertilizer, soil characteristics, etc. After this, the report saved by the peach grading system will have to be good materials to farmers for production high quality peaches. They could share the result or compare with others and diagnose their cultural practice.

The availability of in vivo and in sacco degradability values are limited because those methods require work with fistulated animals and are rather complicated, labour intensive and expensive. That is to say, the dynamics and logistics of the methodology result in considerable work, due to limitations on the amount of samples, number of bags that can be placed in an animal and different time intervals to perform kinetic studies. Therefore, a simpler method is necessary to estimate the degradation characteristics of the feed. In this way, near infrared reflectance spectroscopy has been used to predict degradation characteristics of forages. In other hand, the possibility of achieving successful transfer of spectra and equations between instruments is closely related. The objective of this study was to confirm the potential of NIR to optimize work conditions to avoid duplicated efforts in collaborative trials on animal feeds evaluation between research institutions. For this purpose, one set with forty hays and dehydrated forages samples from SERIDA and ten samples with the same characteristics from SIA, were be used to create a spectral database. A calibration was developed using samples from degradation essays made in SERIDA to predict dry matter and crude protein degradability. With the addition of five samples from SIA in original calibration set, the effect of different origin and location was compensated.

The Bovine Spongiform Encephalopathy (BSE) is one of the more important problems that have affected the economy of european cattles and the Public Safety. Their transmission is mainly through digestive system, and the compound feeds made with animal proteins are one source of infection for healthy cows. Nowadays the official method for meat and bone meal (MBM) detection in compound feeds is a microscopy technique. However, this methodology is subjective, and that alter the fact to make one exhaustive quantitative analysis and one differentiation between mammalian and poultry bones. In addition, the separation of the differents fractions in a sample by density before the analysis, requires the use of organochlorates products as $CCl_4$, which produce serious damages in the atmosphere ozone content. NIR methodology is another possible way to confirm and identifying animal ingredients in compound feeds, Its capabilities for quantitative and qualitative analysis of foods and feeds has been enought demonstrated. The objective of this work was to use NIR as a tool to make an qualitative and quantitative analysis and a prediction of the meat and bone meal presence in compound feeds from North Spain cattle farms. Using a global population of compound feeds, on make three different groups depending of MBM percentage presence (0, 0-100, 100), to build and validate one calibration equation to determine MBM content and make one discriminant analysis between these three groups. The preliminary dates obtained with another differents samples of known composition showed promising results.

Water pollutants in drainage mainly consist of organic compounds. Hence, total organic carbon (TOC), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) were generally used as the indices of pollution. However, these values are determined by special analyzer (TOC), titration method (COD), or microbe culture (BOD). Therefore, the development of simple and easy methods for the determination of water pollution is required. The authors reported the evaluation of water pollution by near infrared (NIR) spectroscopy in a model system with food components (Takamura et al. (200) Near Infrared Spectroscopy: Proceedings of 9th International Conference, pp. 503-507). In this study, the relationship between NIR spectra and drainage was investigated in order to develop a method for evaluation of drainage by NIR. Drainage was obtained in Nara Purification Center. The ranges of TOC, COD, and BOD were 0-130, 0-100 and 0-200, respectively. NIR transmittance spectra were recorded on NIR Systems Model 6250 Research Composition Analyzer in the wavelength range of 680-1235 and 1100-2500 nm with a quartz cell (light path: 0.5, 1, 2, 4 and 10mm) at 10-40. Statistical analysis was performed using NSAS program. A partial least squares (PLS) regression analysis was used for calibration. As the result, a good correlation between the raw NIR spectra and OC was obtained in the calibration. The best light path was 10 and 0.5mm in the wavelength range of 680-1235 and 110-2500nm, respectively. In the calibration, correlation coefficients(R) were 096-0.97 in the both range. In the prediction, however, a good correlation (R=0.89-0.96) was obtained only in the range of 6801235 nm, Similar results were obtained in the cases of COD and BOD. These results suggest the possibility that NIR spectroscopy can be used to evaluate drainage.

Eutrophication processes of aquatic environment are strictly correlated with the concentration levels of nitrogen, phosphorous, organic matter and biological parameters such as phytoplankton and chlorophylla (Tremel, 1996; Burns et al., 1997; Young et al. 1999; Wei et al.,2000). Accordingly, the monitoring and evaluation of these factors will provide useful information about the health of aquatic ecosystem. However, the traditional types of auqatic chemistry analysis and ecological monitoring of phytoplankton are time-consuming, costly, and further resulting in secondary pollution due to the use of reagents. NIR (near-infrared) spectroscopy, as a rapid, non-destructive, little sample preparation and reagents-free technology (Hildrum et al., 1992), has been extensively applied to the characterization of food (Osborne and Fearn, 1988), pharmaceutical (Morisseau and Rhodes, 1995) and textile materials (Clove et al.,2000). Currently, NIR technology has been used indirectly in inferring lake water chemistry by two approaches, suspended (Malley et al., 1996) or seston (Dabakk et al., 1999), and sediments (Korsman et al., 1992; Malley et al., 1999). In addition, the evaluation of trophic state and the identification of the key factors contributed to the trophication are the key step to restore the damaged aquatic environment. Moreover, an understanding of the factors, which regulate the algal proliferation, is crucial to the successful management of aquatic ecosystem. In the paper, NIR technology will be used to study the environmental factors affecting the algal proliferation in combination with the trophic state index and diversity index. This novel developed system can be applied in monitoring and evaluating allopathic water environment and provide real time information services for the aquatic environment management.

The efficiency of the luminal fermentation process influences overall efficiency of luminal production, animal health and reproduction. Ruminant production systems have a significant impact on the global environment, as well. Animal wastes contribute to pollution of the environment as ammonia volatilized to the air and nitrate leached to ground water. Microbial protein synthesis in the rumen satisfies a large proportion of the protein requirements of animals. Quantifying the microbial synthesis is possible by using markers for lumen bacteria and protozoa such as nucleic acids, purine bases, some specific amino acids, or by isotopic $^{15}N,^{32}P,\;and\;^{35}S$ labelled feeds. All those methods require cannulated animals, they are time-consuming and some methods are very expensive as well. Many attempts have been made to find an alternative method for indirect measurement of microbial synthesis in intact animals. The present investigations aimed to assess possibilities of NIRS for prediction of purine nitrogen excretion and ruminal microbial nitrogen synthesis by NIR spectra of urine. Urine samples were collected from 12 growing sheep,6 of them male, and 6- female. The sheep were included in feeding experiment. The ration consisted of sorghum silage and protein supplements -70:30 on dry matter basis. The protein supplements were chosen to differ in protein degradability. The urine samples were collected daily in a vessel containing $60m{\ell}$ 10% sulphuric acid to reduce pH below 3 and diluted with tap water to 4 liters. Samples were stored in plastic bottles and frozen at $-20^{\circ}C$ until chemical and NIRS analysis. The urine samples were analyzed for purine derivates - allantoin, uric acid, xantine and hypoxantine content. Microbial nitrogen synthesis in the lumen was calculated according to Chen and Gomes, 1995. Transmittance urine spectra with sample thickness 1mm were obtained by NIR System 6500 spectrophotometer in the spectral range 1100-2500nm. The calibration was performed using ISI software and PLS regression, respectively. The following statistical results of NIRS calibration for prediction of purine derivatives and microbial protein synthesis were obtained.(Table Omitted). The result of estimation of purine nitrogen excretion and microbial protein synthesis by NIR spectra of urine showed accuracy, adequate for rapid evaluation of microbial protein synthesis for a large number of animals and different diets. The results indicate that the advantages of the NIRS technology can be extended into animal physiological studies. The fast and low cost NIRS analyses could be used with no significant loss of accuracy when microbial protein synthesis in the lumen and the microbial protein flow in the duodenum are to be assessed by NIRS.

An anharmonicity is a fundamental quantity shaping the potential for stretching OH vibration in phenol and its derivatives. The phenomenon is examined both by experimental and theoretical methods. FT-IR and NIR spectra of series of phenols derivatives were measured in the range of fundamental and first two Overtones of $_{s}(OH)$ Vibrations in $CCl_4$ solutions. The electronic influence of substituents on the analyzed frequencies is discussed and correlated with $pK_{a}$ parameters. Ab initio MP2/6-31G(d,p) and B3LYP/6-31G(g,p) calculations of the potential for proton movement in OH group were performed. Equilibrium structures were also determined. The frequencies of fundamental and overtones were calculated by Numerov-type procedure. The results of calculations are compared with the experimental data. The best linear correlations were obtained for the results of MP2/6-31G(d,p) calculations. It was shown that some structural parameters are especially sensitive on substitution. The linear correlations were found between those parameters and spectroscopic data. The results of calculation are compared with available crystallographic data.

The pH value of grass silages is one important parameter to determine the quality of the forages. In an attempt to use NIRS spectra taken for other quality parameter of grass silage it has been shown that a good correlation between NIR spectra of the dried forage and pH value of the fresh forage could be determined. Further investigations revealed that the B coefficients of the pH value calibration were almost the same as the B coefficients of the sugar calibration multiplied with -1. And indead the pH value - in the fresh sample material - of the calibration set is strongly correlated with the sugar concentration - in the dried sample material. It is concluded that next to scientific tools in research the scientist and the user of NTRS equippment has to scrutinze his own work. Examples are given. NIRS is a powerfull technique, but pitfalls are present in surplus.

Polycyclic aromatic hydrocarbons(PAHs) are widely distributed in the environment and are often implicated as potential carcinogens. The chromatographic methods of detection and quantitative determination of PAHs in environmental samples are costly, time consuming, and do not account for all kinds of PAHs. This work describes a quantitative spectroscopic method for the analysis of mixtures of eight PAHs using multivariate calibration models for Fourier transform near infrared(FT-NIR) spectral data. The NIR spectra of mixtures of PAHs (anthracene, pyrene, 1,2-benzanthracene, perylene, chrysene, benzo(a)pyrene, 1-methylanthracene and benzo(ghi)perylene) were measured in the wavelength range from 1100 nm to 2500 nm. The spectral data were processed using a partial least squares regression. We have studied the spectral characteristics of NIR spectra of mixtures of PAHs. It was possible to determine each PAM used in this study at the environmental level(mg L-1) in the laboratory samples. Further development may lead to the rapid determination of more PAHs in typical environmental samples.

Near-infrared (NIR) spectra have bean measured for high-density (HDPE), linear low-density (LLDPE), and low-density (LDPE) polyethylene in pellet or thin films. The obtained spectra have been analyzed by conventional spectroscopic analysis methods and chemometrics. By using the second derivative, principal component analysis (PCA), and two-dimensional (2D) correlation analysis, we could separate many overlapped bands in the NIR. It was found that the intensities of some bands are sensitive to density and crystallinity of PE. This may be the first time that such bands in the NIR region have ever been discussed. Correlations of such marker bands among the NIR spectra have also been investigated. This sort of investigation is very important not only for further understanding of vibration spectra of various of PE but also for quality control of PE by vibrational spectroscopy. Figure 1 (a) and (b) shows a NIR reflectance spectrum of one of the LLDPE samples and that of PE, respectively. Figure 2 shows a PC weight loadings plot of factor 1 for a score plot of PCA for the 16 kinds of LLDPE and PE based upon their 51 NIR spectra in the 1100-1900 nm region. The PC loadings plot separates the bands due to the $CH_3$ groups and those arising form the $CH_2$ groups, allowing one to make band assignments. The 2D correlation analysis is also powerful in band enhancement, and the band assignments based upon PCA are in good agreement with those by the 2D correlation analysis.(Figure omitted). We have made a calibration model, which predicts the density of LLDPE by use of partial least square (PLS) regression. From the loadings plot of regression coefficients for the model , we suggest that the band at 1542, 1728, and 1764 nm very sensitive to the changes in density and crystalinity.

Near Infrared (NIR) spectra has long been used in industry to monitor rates of reactions via calculation of analyte concentrations. However, the kinetic information is inherent in the data through spectral ratios. Two-dimensional correlation spectroscopy (2D-COS) is a spectral method that is based on changes (e.g. concentration) in time and is therefore uniquely suited for reaction monitoring. This method is especially useful in the understanding of how the reaction(s) proceeds. We will show the application of 2D-COS to synthetic kinetic data from different reaction orders to illustrate the method. We will then show application to real reactions of various orders. Finally, we will illustrate how 2D-COS will be of specific interest to developing optimized industrial reactions.

NIR spectroscopy has been used extensively to investigate the structure of water, alcohol and other self-associate molecules because the frequencies of NIR bands due to OH and NH groups strength of hydrogen bonds. We have studied the structure of water -methanol mixtures by use of NIR spectroscopy. Strong features in the 7200-6300 $cm^{-1}$ / region consist of a number of overlapped bands due to the combination of OH antisymmetric and symmetric stretching modes of water and the first overtone of the OH stretching modes of free and hydrogen bonded methanol, while weak fratures in the 6000-5800 cm-1 region are ascribed to the first overtones of $CH_3$ stretching modes of methanol. We will focus the discussion on the $CH_3$ stretching bands. They seem to show a significant shift is not clear from the spectra shown in figure 1(a). Figure 1(b) depicts the second derivative in the 6000-5700 $cm^{-1}$ / region. Now, it is clear from the second derivative that there are two major bands near 5950 and 5900 $cm^{-1}$ / and that they do show a shift be about 30 $cm^{-1}$ / Why do the $CH_3$ bands show the shift with increasing concentration of methanol\ulcorner Probably, the CH, group interacts directly with OH groups of water. The results in figure 1(b) demonstrate the usefulness of the second derivative in resolution enhancement as well as the potential of NIR spectroscopy in the studies of molecular interactions.(Figure omitted).

The transesterification of molten ethylene/vinylacetate (EVA) copolymers by octanol as a reagent and sodium methoxide as a catalyst in an extruder has been monitored by on-line near infrared (NIR) spectroscopy. A total of 60 NIR spectra were acquired for 37 minutes with the last spectrum recorded 31 minutes after the addition of octanol and catalyst was stopped. The experimental spectra show strong baseline fluctuations which are corrected for by multiplicative scatter correction (MSC). The chemometric methods of orthogonal projection approach (OPA) and multivariate curve resolution (MCR) were used to resolve the spectra and to derive concentration profiles of the species. The detailed analysis reveals the absence of completely pure variables that leads to small errors in the calculation of pure spectra. The initial estimation of a concentration that is necessary as an input parameter for MCR also presents a non-trivial task. We obtained results that were not ideal but applicable for practical concentration control. They enable a fast monitoring of the process in real-time and resolve the spectra of the EVA copolymer and the ethylene/vinyl alcohol (EVAL) copolymer to be very close to the reference spectra. The chemometric methods used and the decomposed spectra are discussed in detail.

For NIR measurements of papers normally diffuse reflectance accessories are used which can provide a large sampling area. The in-line process control FT-NIR spectrometer MATRIX-E enables the contactless measurement of paper samples of low silicone coat weights on label-stocks in a paper converting factory. For this study concentrations of silicone between 0 and 2 g/$m^2$ on various paper substrates were included in a quantitative method. The aim was to achieve an absolute value for the deviation from the target value of 1 g/$m^2$ during continuous movement of the paper with velocities around 400 numinute. Influences from the uncoated paper type due to supplier, color, opacity, area densities, pre-coating as well as different compounds of the agent silicone were investigated and it was found that all these papers can be represented in one PLS-model. Especially the fact that silicone as an element is present in clay coated papers is of no consequence to the measurements with MATRIX-E. Moreover during in-line installations the variation of the moisture contents in the moving paper due to variable machine velocities as well as the reflecting material of the cylinder have to be considered. It is shown that the result of the in-line calibration has the same prediction ability compared to lab scale results(Root Mean Square Error of Cross-Validation RMSECV = 0.034 g/$m^2$).

Despite extensive theoretical and experimental studies the structure of the protein-solvent interface is subject of many controversy. Understanding the processes that occur in aqueous solution requires understanding of the solvent influence on the structure of protein. The aim of this study is to investigate the applicability of NIR methods in the study of hydration phenomena in protein solutions. Temperature-induced changes in NIR spectra of -lactoglobulin (BLG) in aqueous solutions have been investigated by means of two-dimensional correlation spectroscopy (2DCOS) and principal component analysis (PCA). With the temperature increase the balance of forces between the BLG's interaction with itself and the BLGs interaction with its environment is disrupted leading to BLG unfolding. Significant differences of 2D signals and distinct discrepancies of loading on PC1 and PC2 were observed as a result of temperature increase. In the native folded conformation of BLC, most of the nonpolar amino acids are hidden in the centre of the structure, out of contact with water molecules, while charged groups are outside, in the contact with water. The polar groups promote low density Ih-type structure in the water outside this first hydration shell. When BLG unfolds it assumes a more extended configuration on which the previously buried nonpolar groups are exposed to water and promote the higher density II-type structure outside its first shell. Detailed assignments of bands attributed to the bulk water, different states of the hydrated water and the changed conformation of BLG are proposed.

The structure of water molecules in the pure liquid state has been subjected to extensive research for several decades. Questions still remain unanswered, however, and no single model has been found capable of explaining all the anomalies of water. In the present study near-infrared spectra of water in the temperature region 6-$80^{\circ}C$ have been analysed by use of principal component analysis (PCA) and two-dimensional correlation spectroscopy in order to study the dynamic behaviour of the water band centred at 1440 nm, which is due to the combination of symmetric and antisymmetric O-H stretching modes. It has been found that the wavelengths 1412 and 1491 nm account for more than 99% of the spectral variation, representing two major water species with weaker and stronger hydrogen bonds, respectively. A third species located at 1438 nm, whose concentration was relatively constant as a function of temperature, is also indicated. A somewhat distorted two-state structural model for water is suggested.

X-ray powder diffraction (XRD) is utilized for determination of polymorphism in crystalline organic materials. Though convenient to use in a laboratory setting, XRD is not easily adapted to in situ monitoring of synthetic chemical production applications. Near-Infrared spectroscopy (NIR) can be adapted to in situ manufacturing schemes by use of a source/detector probe. Conversely, NIR is unable to conclusively define the existence of polymorphism in crystalline materials. By combining the two techniques, a novel simultaneous NIR/XRD instrument has been developed. During material's analysis, results from XRD allow for defining the polymorphic phase present, and NIR data are collected as a fingerprint for each of the observed polymorphs. These NIR fingerprints will allow for the development of a library, which can be referenced during the use of a NIR probe in manufacturing settings.

Water-methanol and water-acetonitrile mixtures are frequently used as HPLC solvent system and strong hydrogen bonding is well-known. But a detailed aspect of water-methanol and/or water-acetonitrile mixtures have not been shown with direct spectral evidence. Recently, near infrared spectroscopy and chemometric data refinery have been successfully combined in many applications. On the basis of factor analytical methods, the spectral features of water-methanol and water-acetonitrile mixtures were studied to reveal the detail of mixtures. Water-methanol and water-acetonitrile mixtures were prepared with varying concentration of each constituent and near infrared spectral data were acquired in the range of 1100-2500nm with 2-nm interval. The data matrices were analysed with ITTFA(Iterative Target Transform Factor Analysis) algorithm implemented as MATLAB codes. As a result, the concentration profiles of water, methanol and water-methanol complex were resolved and the spectra of water-methanol complexes were calculated, which cannot be acquired with pure complexes. A similar result was obtained with NIR spectral data of water-acetonitrile mixtures. Moreover, pure spectra of hydrogen-bonding complexes of water-methanol and water-acetonitrile can be computed, while any other usual physical methods cannot isolated those complexes for acquiring pure component spectra.

NIRS uses reflectance signals resulting from bending and stretching vibrations in chemical bonds between carbon, nitrogen, hydrogen, sulfur and oxygen. These reflectance signals are used to measure the concentration of major chemical composition and other descriptors of homogenized and freeze-dried whole broiler carcasses. Six strains of chicken were analyzed and the NIRS model predictions compared to reference data. The results of this comparison indicate that NIRS is a rapid tool for predicting dry matter (DM), fat, crude protein (CP) and ash content in the broiler carcass. Males and females of six commercial strain crosses of broiler chicken (Gallus domesticus) were used in this study (6$\times$2 factorial design). Each strain was grown to 16 weeks of age, and duplicate serial samples were taken for body composition analysis. Each whole carcass was pressure-cooked, homogenized, and a representative sample was freeze-dried. Body composition determined as follows: DM by oven dried method at 105$^{\circ}C$ for 3 hours, fat by Mojonnier diethyl ether extraction, CP by measuring nitrogen content using an auto-analyzer with Kjeldhal digest and ash by combustion in a muffle furnace for 24 hour at 55$0^{\circ}C$. These homogenized and freeze-dried carcass samples were then scanned with a Foss NIR Systems 6500 visible-NIR spectrophotometer (400-2500nm) (Foss NIR Systems, Silver Spring, MD., US) using Infra-Soft-International, ISI, WinISl software (ISI, Port Matilda, US). The NIRS spectra were analyzed using principal component (PC) analysis. This data was corrected for scatter using standard normal “Variate” and “Detrend” technique. The accuracy of the NIRS calibration equations developed using Partial Least Squares (PLS) for predicting major chemical composition and carcass descriptors- such as body mass (BM), bird dry matter and moisture content was tested using cross validation. Discrimination analysis was also used for sex and strain identification. According to Dr John Shenk, the creator of the ISI software, the calibration equations with the correlation coefficient, $R^2$, between reference data and NIRS predicted results of above 0.90 is excellent and between 0.70 to 0.89 is a good quantifying guideline. The excellent calibration equations for DM ($R^2$= 0.99), fat (0.98) and CP (0.92) and a good quantifying guideline equation for ash (0.80) were developed in this study. The results of cross validation statistics for carcass descriptors, body composition using reference methods, inter-correlation between carcass descriptors and NIRS calibration, and the results of discrimination analysis for sex and strain identification will also be presented in the poster. The NIRS predicted daily gain and calculated daily gain from this experiment, and true daily gain (using data from another experiment with closely related broiler chicken from each of the six strains) will also be discussed in the paper.

Energy content, expressed as calories per gram, is an important part of the evaluation and marketing of foods in developed countries. Currently accepted methods of measurement of energy by U.S. food labeling legislation include measurement of gross calories by bomb calorimetry with an adjustment for undigested protein and by calculation using specific factors for the energy values of protein, carbohydrate less the amount of insoluble dietary fiber, and total fat. The ability of NIRS to predict the energy value of diverse, processed and unprocessed cereal food products was investigated. NIR spectra of cereal products were obtained with an NIR Systems monochromator and the wavelength range used for analysis was 1104-2494 nm. Gross energy of the foods was measured by oxygen bomb calorimetry (Parr Manual No. 120) and expressed as calories per gram (CPGI, range 4.05-5.49 cal/g). Energy value was adjusted for undigested protein (CPG2, range 3.99-5.38 cal/g) and undigested protein and insoluble dietary fiber (CPG3, range 2.42-5.35 cal/g). Using a multivariate analysis software package (ISI International, Inc.) partial least squares models were developed for the prediction of energy content. The standard error of cross validation and multiple coefficient of determination for CPGI using modified partial least squares regression (n=127) was 0.060 cal/g and 0.95, respectively, and the standard error of performance, coefficient of determination, bias and slope using an independent validation set (n=59) were 0.057 cal/g, 0.98, -0.027 cal/g and 1.05 respectively. The PLS loading for factor 1 (Pearson correlation coefficient 0.92) had significant absorption peaks correlated to C-H stretch groups in lipid at 1722/1764 nm and 2304/2346 nm and O-H groups in carbohydrate at 1434 and 2076 nm. Thus the model appeared to be predominantly influenced by lipid and carbohydrate. Models for CPG2 and CPG3 showed similar trends with standard errors of performance, using the independent validation set, of 0.058 and 0.088 cal/g, respectively, and coefficients of determination of 0.96. Thus NIRS provides a rapid and efficient method of predicting energy content of diverse cereal foods.

The University of Cordoba conducts since 1991 a breeding program to obtain new olive cultivars from intraspecific crosses. The objective is to obtain new early bearing and high-quality cultivars. In plant breeding, many seedlings must be tested to increased the chance of getting desirable genotypes. Therefore, fast, cheap and accurate methods of analysis are necessary. The conventional laboratory techniques are costly and time-consuming. Near Infrared Spectroscopy (NIRS) can satisfy the characteristics requested by plant breeders and offers many advantages such as the simultaneous analysis of many traits and cheap cost. The objective of this work was to asses the performance of NIRS to estimate oil fruit components (fruit weight, flesh moisture, flesh/stone ratio and oil flesh content in dry weight basis) and fatty acid composition in olive fruit. Genotypes from reciprocal crosses between ‘Arbequina’, ‘Frantoio’ and ‘Picual’ cultivars have been used in this study. A total of 287 samples, each from a single plant, were scanned using a DA-7000 Diode Array VIS/NIR Analysis System (Perten Instruments), which covers the visible and NIR range from 400-1700 nm. All samples were analysed for fatty acid composition (gas chromatography) and 220 for oil fruit components (oil content by nuclear magnetic resonance), 70% and 30% of samples were randomly assign for the calibration and validation sets respectively. The preliminary results shows that calibration for palmitic, oleic and linoleic acids were highly accurate with calibration and validation values of $r^2$ from 0.85 to 0.95 and 0.76 to 0.91 respectively. Calibration for palmitoleic and estearic acids were less accurate, probably because of the narrow range of variability available for these fatty acids. For the oil fruit components, calibration were high accurate for flesh moisture and oil flesh content in dry weight basis ($r^2$ higher than 0.90 in both calibration and validation sets) and less accurate for the other characteristics evaluated. The first results obtained indicate that NIRS analysis could be an ideal technique to reduce the cost, time and chemical wasted necessary to evaluate a large number of genotypes and it is accurate enough to use for pre-selecting genotypes in a breeding program.

Present Food Legislation compels dairy industry to carry out analyses in order to guarantee the food safety and quality of products. Furthermore, in many cases industry pays milk according to bacteriological or/and nutritional quality. In order to do these analyses, several expensive instruments are needed (Milkoscan, Fossomatic, Bactoscan). NIRS technology Provides a unique instrument to deal with all analytical requirements. It offers as main advantages its speed and, specially, its versatility, since not only allows determine all the parameters required in milk analysis, but also allows analyse other dairy products, like cheese or whey. The objective of this study is to develop NIRS calibration equations to predict several quality parameters in goat milk, cheese and whey. Three sets of 123 milk samples, 190 cheese samples and 109 whey samples, have been analysed in a FOSS NIR Systems 6500 I spectrophotometer equipped with a spinning module. Milk and whey were analysed by folded transmission, using circular cells with gold surface and pathlength of 0.1 m, while intact cheese was analysed by reflectance using standard circular cells. NIRS calibrations were obtained for the prediction of chemical composition in goat milk, for fat (r$^2$=0.92; SECV=0.20%), total solids (r$^2$=0.95: SECV=0.22%), protein (r$^2$=0.94; SECV=0.07%), casein (r$^2$=0.93; SECV=0.07%) and lactose (r$^2$=0.89; SECV=0.05%). Moreover, equations have been performed to determine somatic cells (r$^2$=0.81; SECV=276.89%) and total bacteria (r$^2$=0.58; SECV=499.32%) counts in goat milk. In the case of cheese, calibrations were obtained for the prediction of fat (r$^2$=0.92; SECV=0.57), total solids (r$^2$=0.80; SECV=0.92%) and protein (r$^2$=0.70; SECV=0.63%). In whey, fat (r$^2$=0.66; SECV=0.08%), total solids (r$^2$=0.67; SECV=0.19%) and protein (r$^2$=0.76; SECV=0.07%) NIRS equations were obtained. These results proved the viability of NIRS technology to predict chemical and microbiological parameters and somatic cells count in goat milk, as well as chemical composition of goat cheese and whey.

In this paper a fast non-destructive analytical method to measure various nutrients in the intact tomato---Near infrared Spectrometry NIRs was introduced Using this method the content of some organic acid, vitamin C, reductive sugar, and solid soluble were determined simultaneously. Screen out four wavelengths at 916nm, 1000nm, 1004nm and 832nm to present optimum four optical terms of d$^2$ log(1/R) with second derivative spectra treating data scanned under these wavelengths. The multiple correlation coefficients between these values and those obtained on chemical analysis were 0.983, 0.990, 0.987, and 0.994, respectively, and the standard errors of prediction (SEP) were 0.007, 0.440, 0.037, and 0.057, respectively. These results indicate that NIRs is comparable to chemical methods in both accuracy and precision and is reliable method for determination of nutrients in intact tomato.

Retrogradation of starch is one of important quality indexes for food based on starch such as rice. Therefore, in this research, possibility of near infrared spectroscopy to determine the degree of the retrogradation was examined. The degree of the retrogradation was indicated as the degree of geratinization analyzed by BAP(-amylase-pullulanase) method. 20 samples which have a wide range of the degree of the retrogradation were prepared and the NIR spectra of the samples were measured in reflectance mode with the NIR Systems 6500. In the correlation plots calculated from the 2nd derivative values of the MSC treated spectra and the degree of the geratinization, the major negative peaks of 1544 nm and 2258 nm, and the major positive peaks of 1460 nm, 1602 nm, 1766 nm and 2136 nm could be observed, indicating that NIR absorption at the positive peak wavelengths became strong while the absorption at the negative peak wavelengths became weak as the degree of the retrogradation increased. Because there is negative correlation between the degree of the retrogradation and the degree of the geratinization. As a result of MLR using the 2nd derivative values of the MSC treated spectra and the degree of the geratinization, good calibration equation which include 2258 nm as the first wavelength and 1764 nm as the second one could be obtained, indicating that NIR spectroscopy has a possibility to detect the retrogradation of starch. In order to find the assignment of the bands observed in the correlation plots, further study may be needed.

During the recent years, wine analysis has played an increasing role due the health benefits of phenolic ingredients in red wine [1]. On the other hand there is the need to be able to distinguish between different wine varieties. Consumers want to know if a wine is an adulterated one or if it is based on the pure grape. Producers need to certificate their wines in order to ensure compliance with legal regulations. Up to now, the attempts to investigate the origin of wines were based on high-performance liquid chromatography (HPLC), gas chromatography (GC) and pyrolysis mass spectrometry (PMS) [l,2,3]. These methods need sample pretreatment, long analysis times and therefore lack of high sample throughput. In contradiction to these techniques using near infrared spectroscopy (NIRS), no sample pretreatment is necessary and the analysis time for one sample is only about 10 seconds. Hence, a near infrared spectroscopic method is presented that allows a fast classification of wine varieties in bottled red wines. For this, the spectra of 50 bottles of Cabernet Sauvignon, Lagrein and Sangiovese (Chianti) were recorded without any sample pretreatment over a wavelength range from 1000 to 2500 nm with a resolution of 12 cm$\^$-1/. 10 scans were used for an average spectrum. In order to yield best reproducibility, wines were thermostated at 23$^{\circ}C$ and a optical layer thickness of 3 mm was used. All recorded spectra were partitioned into a calibration and validation set (70% and 30%). Finally, a 3d scatter plot of the different investigated varieties allowed to distinguish between Cabernet Sauvignon, Lagrein and Sangiovese (Chianti). Considering the short analysis times this NRS-method will be an interesting tool for the quality control of wine verification and also for experienced sommeliers.

Cultural conditions during production of compost, using wheat straw and chicken litter as raw materials, will affect the microbial and biochemical characteristics, leading to a wide variation in mushroom productivity. Over the past 10 years, chemical and instrumental methods, suitable for assessing compost quality have been studied in Northern Ireland. In addition, the use of near subject of investigation over the past 4 years. Previous studies have shown that NIRS can be used fer assessing quality of dried and milled composts. The aim of the current investigation is to develop NIR calibrations for key quality parameters such as dry matter, pH, nitrogen, carbon, ash, microbial population and fibre factions during the two stages of production using spectra of fresh composts. Near infrared reflectance measurements of fresh composts prepared by 6 producers were made during a two-year period. Although the spectra of fresh composts were dominated by two moisture peaks at 1450 nm and 1940 nm, good calibrations for determining moisture content, conductivity, pH, nitrogen, carbon and fibre fractions were developed. The results of quality assessment during commercial production using the calibrations will be presented and discussed.

In the area of the destruction-free NIR analysis of fruit and vegetables development has not yet progressed as far as in grain and similar products. One reason for that is, that in contrast to grains, in fruit and vegetables water appears as the outstanding main-component making up typically 80% by weight of the fruit. Of the M absorption spectrum of pure water the bands at 1450, 970 and 760 nm are the first, second and third overtones respectively of O-H stretch while those at 1940 and 1190 are combination bands involving O-H stretch and O-H bend. The choice of band for spectrometry is governed by considerations of sensitivity and selectivity. The overtone bands are satisfactory for use in moisture measurements from 0 to 4 % depending on path length. Measurements in fruits and vegetables at wavelength areas that are also important for the determination of carbohydrates (sucrose, glucose, fructose) often lead to total absorption in the presence of significant water even if short path lengths are possible. In this work model systems are used containing different carbohydrates in solvents like heavy water (D$_2$O) or dimethylsulfoxide (DMSO) that do not contain O-H functional groups.

The aim of this work was to test the feasibility of NIRS in analysing textural characteristics of “Pasta Filata” cheese during the shelf-life. For this purpose, 128 samples of “Pasta Filata” cheese, subdivided into two sets on the basis of the wax used to avoid mechanical damages (paraffin, biodegradable wax), were analysed by using an InfraAlyzer 500 (Bran+Luebbe). Analyses were performed at room temperature. Samples were cut into small cylinders (D=3.2 cm, height = 1 cm), in agreement with literature information. Data were processed by using Sesame Software (Bran+Luebbe). Samples were analysed, during the shelf-life, at 90 and 120 days. In parallel, textural characteristics were detected carrying out a compression method by using an Universal Testing Machine Instron model 4301 (Instron Corporation, Canton, Massachusetts). As compression probe was used a cylinder (D = 5.8 cm, height = 3.7 cm) and a speed rate of 20mm/min was applied. The load at 20 mm of compression was recorded on sample cylinders of 1.7 cm (D) by 2 cm (height). Qualitative analysis of full spectra showed the possibility to gather samples on the basis of the days of shelf-life. The textural characteristics of cheese during the shelf-life was evaluated by comparing NIRS data with rheological results. The best correlation was obtained applying MLR to the first derivative of normalized absorbance values at seven wavelengths. Load values were plotted against the NIR prediction values based on first derivatives. NIRS proved to be an useful tool in classifying samples on the basis of the shelf-life period as well as in predicting their textural characteristics ($R^2$= 0.916, SEC = 0.192, SEP = 0.248, SEV = 0.345).

Food adulteration is a serious consumer fraud and a potentially dangerous practice. Regulatory authorities and food processors require a rapid, non-destructive test to accurately confirm authenticity in a range of food products and raw materials. Olive oil is prime target for adulteration either on the basis of the processing treatments used for its extraction (extra virgin vs virgin vs ordinary oil) or its geographical origin (e.g. Greek vs Italian vs Spanish). As part of an investigation into this problem, some preliminary work focused on the ability of near infrared spectroscopy to discriminate between virgin olive oils from separate regions of the Mediterranean i. e. Crete and the Peloponese. A total of 46 oils were collected: 18 originated in Crete and 28 in the Peloponese. Oils were stored in a temperature-controlled room at 2$0^{\circ}C$ prior to spectral collection at room temperature (15-18$^{\circ}C$). Samples (approximately 0.5$m\ell$) were placed in the centre of the quartz window in a camlock reflectance cell; the gold-plated baking plate was then gently placed into the cell against the glass so as to minimize the formation of air bubbles. The rear of the camlock cell was then screwed into place producing a sample thickness of 0.5mm. Spectra were recorded between 400 and 2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. Spectral collection took place over 2-3 days. Data were analysed using both WINISI and The Unscrambler software to investigate the possibility of discriminating between the oils from Crete and the Peloponese. A number of data pre-treatments were used and discriminant models were developed using discriminant PLS (WINISI & Unscrambler) and SIMCA (Unscrambler). Despite the small number of samples involved, a satisfactory discrimination between these two oil types was achieved. Graphical examination of principal component scores for each oil type also holds out the possibility of separating oils from either Crete and the Peloponese on the basis of districts within each region. These preliminary data suggest the potential of near infrared spectroscopy to act as a screening technique for the confirmation of geographic origin of extra virgin olive oils. The sample presentation strategy adopted uses only small volumes of material and produces high quality spectra.

The objective of the research being presented was to construct and calibrate a spectrometer for the analysis of single kernels of corn. In light of the difficulties associated with capturing the spatial variability in composition of corn kernels by single-beam spectrometry, a hyperspectral imaging spectrometer was constructed with the intention that it would be used to analyze single kernels of corn for the prediction of moisture and oil content. The spectrometer operated in the range of 750- 1090 nanometers. After evaluating four methods of standardizing the output from the spectrometer, calibrations were made to predict whole-kernel moisture and oil content from the hyperspectral image data. A genetic algorithm was employed to reduce the number of wavelengths imaged and to optimize the calibrations. The final standard errors of prediction during cross-validation (SEPCV) were 1.22% and 1.25% for moisture and oil content, respectively. It was determined, by analysis of variance, that the accuracy and precision of single-kernel corn analysis by hyperspectral imaging is superior to the single kernel reference chemistry method (as tested).

Beverages based on fruit juices are among the most popular commercially available drinks. There is an ever-increasing demand for these juices in the market. Orange juice is one of the most common as well as most favorite flavor. The fruit processing industries have a tremendous responsibility of quality control. For quality evaluation estimation of various components of the juice is necessary. Sucrose, glucose, fructose, citric acid and malic acid are the prime components of orange juice. Little information is available on analysis of orange juice. However, conventional and general wet chemistry procedures are currently being used which are no longer desired by the industry owing to the time involved, labor input and harmful chemicals required for each analysis. Need to replace these techniques with new, highly specific and automated sophisticated techniques viz. HPLC and spectroscopy has been realized since long time. Potential of Near Infrared Spectroscopy in quantitative analysis of different components of food samples has also been well established. A rapid, non-destructive and accurate technique based on Near Infrared Spectroscopy for determination of sugars and organic acids in orange juice will be highly useful. The current study is an investigation into the potential of Near Infrared Diffuse Reflectance Spectroscopy for rapid quantitative analysis of sucrose, glucose, fructose citric acid and malic acid in orange juice. All the Near Infrared measurements were peformed on a dispersive NIR spectrophotometer (ELICO 153) in diffuse reflectance mode. The spectral region from 1100 to 2500nm has been explored. The calibration has been performed on synthetic samples that are mixtures of sucrose, glucose, fructose, citric acid and malic acid in different concentration ranges typically encountered real orange juice. These synthetic samples are therefore considered to be representatives of natural juices. All the Near Infrared spectra of synthetic samples were subjected to mathematical analysis using Partial Least Square (PLS) algorithm. After the validation, calibration was applied to commercially available real samples and freshly squeezed natural juice samples. The actual concentrations were compared with those predicted from calibration curve. A good correlation is obtained between actual and predicted values as indicated by correlation coefficient ($R^2$) value, which is close to unity, showing the feasibility of the technique.

The wine industry requires practical methods for objectively measuring the composition of both red wine grapes on the vine to determine optimal harvest time; and of freshly harvested grapes for efficient allocation to vinery process streams for particular red wine products, and to determine payment of contract grapegrowers. To be practical for industry application these methods must be rapid, inexpensive and accurate. In most cases this restricts the analyses available to measurement of TSS (total soluble solids, predominantly sugars) by refractometry and pH by electropotentiometry. These two parameters, however, do not provide a comprehensive compositional characterization for the purpose of winemaking. The concentration of anthocyanin pigment in red wine grapes is an accepted indicator of potential wine quality and price. However, routine analysis for total anthocyanins is not considered as a practical option by the wider wine industry because of the high cost and slow turnaround time of this multi-step wet chemical laboratory analysis. Recent work by this ${group}^{l,2}$ has established the capability of near infrared (NIR) spectroscopy to provide rapid, accurate and simultaneous measurement of total anthocyanins, TSS and pH in red wine grapes. The analyses may be carried out equally well using either research grade scanning spectrometers or much simpler reduced spectral range portable diode-array based instrumentation. We have recently expanded on this work by collecting thousands of red wine grape samples in Australia. The sample set spans two vintages (1999 and 2000), five distinct geographical winegrowing regions and three main red wine grape varieties used in Australia (Cabernet Sauvignon, Shiraz and Merlot). Homogenized grape samples were scanned in diffuse reflectance mode on a FOSE NIR Systems6500 spectrometer and subject to laboratory analysis by the traditional methods for total anthocyanins, TSS and pH. We report here an analysis of the correlations between the NIR spectra and the laboratory data using standard chemometric algorithms within The Unscrambler software package. In particular, various subsets of the total data set are considered in turn to elucidate the effects of vintage, geographical area and grape variety on the measurement of grape composition by NIR spectroscopy. The relative ability of discrete calibrations to predict within and across these differences is considered. The results are then used to propose an optimal calibration strategy for red wine grape analysis.

A previous paper(Ito et al., 2000) has described the improvement of the standard error(SEC and SEP) of the predicted soluble solids(Brix) in a melon cultivar by non-contact mode with a fiber optic probe. Then we examined the immature and mature fruits. The objective of this study was to determine if non-contact mode could improve the standard error of the predicted Brix of matured melon fruits from cross progeny as well as the contact mode(usual method). The optical absorption spectrum was measured using a NIR Systems model 6500 spectrophotometer. A commercial spectral program(NSAS ver. 3.27) was used for multiple linear regression analysis. Absorbances of 902 and in the vicinity of 877 nm were included as the independent variables in both multiple regression equations. These wavelengths are key wavelengths for non-destructive Brix determination. When the results for the contact mode and non-contact mode are compared, the latter mode improved the former standard error(SEP and RMS).

Non-destructive determination of soluble solids(Brix) in harvested fruits using near infrared(hereafter, NIR) spectroscopy has been reported by many researchers. We have just reported on non-destructive estimation of Brix in harvested melons using a NIR Systems Model 6500 spectrophotometer(Ito et al., 2000). There is a melon cultivar that is difficult to judge the harvest time from the external appearance. If we can determine Brix in growing fruits non-destructively in the field, immature fruits will not be harvested. A portable m spectrophotometer for field use has been just developed by Kubota Corporation. The spectral data of growing melons were measured by the portable spectrophotometer. A commercial program was used for multiple linear regression analysis. Brix in growing melons could be estimated by a multiple regression equation calibrated with harvested melons. Absorbances of 906 and 874 nm were included as the independent variables in the multiple regression equation, and these wavelengths are key wavelengths for non-destructive Brix determination.

To improve the accuracy of sweetness sensor in automated sorting operations, it is necessary to clarify unevenness of the sugar content distribution within fruits. And it is expected that the technique to evaluate the content distribution in fruits contribute to the development of the near-infrared (NIR) imaging spectroscopy. Sugiyama (1999) had succeeded to visualize the distribution of the sugar content on the surface of a half-cut green fresh melon. However, this method cannot be applied to red flesh melons because it depends on information of the absorption band of chlorophyll (676 nm), which is affected by the color of the fresh. The objective of this study was to develop the universal visualization method depends on the absorption band of sugar, which can be applied to various kinds of melons and other fruits. The relationship between the sugar contents and absorption spectra of both green and red fresh melons were investigated by using a NIR spectrometer to determine the absorption band of sugar. The combination of 2$\^$nd/ derivative absorbances at 902 nm and 874 nm was highly correlated with the sugar contents. The wavelength of 902 nm is attributed to the absorption band of sugar. A cooled charge-coupled device (CCD) imaging camera which has 16 bit (65536 steps) A/D resolution was equipped with rotating band-pass filter wheel and used to capture the spectral absorption images of the flesh of a vertically half-cut red fresh melon. The advantage of the high A/D resolution in this research is that each pixel of the CCD is expected to function as a detector of the NIR spectrometer for quantitative analysis. Images at 846 nm, 874 nm, 902 nm and 930 nm were acquired using this CCD camera. Then the 2$\^$nd/ derivative absorbances at 902 nm and 874 nm at each pixel were calculated using these four images. On the other hand, parts of the same melon were extracted for capturing the images and squeezed for the measurement of sugar content. Then the calibration curve between the combination of 2$\^$nd/ derivative absorbances at 902 nm and 874 nm and sugar content was developed. The calibration method based on NIR spectroscopy techniques was applied to each pixel of the images to convert the 2$\^$nd/ derivative absorbances into the Brix sugar content. Mapping the sugar content value of each pixel with linear color scale, the distribution of the sugar content was visualized. As a result of the visualization, it was quantitatively confirmed that the Brix sugar contents are low at the near of the skin and become higher towards the seeds. This result suggests that the visualization technique by the NIR imaging spectroscopy could become a new useful method fer quality evaluation of melons.

Glucose, fructose and sucrose being the main sugars that can be found in natural fruit juice. Many instrumental methods, such as GC, LC, electrochemical or spectrometric methods provide information about both the total content of sugars and the specific concentration of each carbohydrate[1]. The simplicity of sample handling and measurement in the near IR(NIR) wavelength region, which allows the use of long pathlength, optical glass cells and optical fibers, makes NIR a good alternative for sugar determination [2]. In the present study, six varieties of persian grapes were harvested at intervals through august to october and analysed for sugars by NIR. The results were processed by principal component regression (PCR) and partial least squares (PLS) analysis. Sample juice was prepared by squeezing through gauze from crashed grape. This solution was treated by zinc ferrocyanide prior to analysis in order to eliminate colored compounds and all optically active nonsugar substances. For glucose and fructose the most characteristic wavelengths were 1456nm corresponding to the first harmonic O-H stretching and the second at 2062nm corresponding to O-H stretching and deformation; secondary characteristic combination bands were also seen at 2265 nm (O-H and C-C stretching) and at 2240 nm (C-H and C-C stretching). However these spectra were taken over a wavelength range from 1100-2500nm at room temperature of 25-$30^{\circ}C$. To test the accuracy of the described procedure, samples of six varieties of grape were analysed by the proposed NIR and a standard method[2]. Good agreement were found between these two sets of the results. To perform the recovery studies , samples of grape juices previously analysed by the proposed method, were spiked with known amounts of each individual sugars and then analysed again. Relative standard deviations varied from 1.4 to 1.8% for six independent measurements of individual and total sugar concentration. In the analysis of real and synthetic samples, precise and accurate results were obtained , providing accuracy errors lower than 1.9% in all cases. Average recoveries of ${97}{\pm}{4%}$ for total sugar and between ${95}{\pm}{5%}$ and ${99}{\pm}{2%}$ for sing1e sugars demonstrate the applicability of the methodology developed to the direct analysis of grape Juice.

Ginseng cultivated in different country or growing condition has generally different components such as saponin and protein, and it relates to efficacy and action. Protein content assumes by nitrogen content in ginseng radix. Nitrogen content could be determined by chemical analysis such as kjeldahl or extraction methods. However, these methods require long analysis time and result environmental pollution and sample damage. In this work we investigated possibility of non-destructive determination of nitrogen content in ginseng radix using near-infrared spectroscopy. Ginseng radix, root of Panax ginseng C. A. Meyer, was studied. Total 120 samples were used in this study and it was consisted of 6 sample sets, 4, 5 and 6-year-old Korea ginseng and 7, 8 and 9-year-old China ginseng, respectively. Each sample set has 20 sample. Nigrogen content was measured by electronic analysis. NIR reflectance spectra were collected over the 1100 to 2500 nm spectral region with a InfraAlyzer 500C (Bran+Luebbe, Germany) equipped with a halogen lapmp and PbS detector and data were collected every 2 nm data point intervals. The calibration models were carried out by multiple linear regression (MLR) and partial least squares (PLS) analysis using IDAS and SESAME software. Result of electronic analysis, Korean ginseng were different mean value in nitrogen content of China ginseng. Ginseng tend to generally decrease the nitrogen content according as cultivation year is over 6 years. The MLR calibration model with 8 wavelengths using IDAS software accurately predicted nitrogen contents with correlation coefficient (R) and standard error of prediction of 0.985 and 0.855%, respectively. In case of SESAME software, the MLR calibration with 9 wavelength was selected the best calibration, R and SEP were 0.972 and 0.596%, respectively. The PLSR calibration model result in 0.969 of R and 0.630 of RMSEP. This study shows the NIR spectroscopy could be applied to determine the nitrogen content in ginseng radix with high accuracy.

The watercore in apple is very important factor in storage and sorting of fruit. Most consumers tend to prefer the apple included watercore in immediately after harvest, however the watercore causes fruit flesh to brown during storage and lose the worth after all. But it is practically impossible to judge to the naked eye whether an apple has watercore or not. Therefore, the rapid, accurate and non-destructive analysis method for discrimination of watercore should be settled without delay. In this study we attempted the discrimination and quantitative analysis of watercore in apple fruit using near-infrared transmittance spectroscopy ‘Fuji’ apple fruits produced in Kyungpook of Korea was used in this experiment. The watercore content in apple was evaluated by graphic treatment of culled slice sections(10 mm). NIR transmittance spectra were collected over the 500 to 1000 nm spectral region with a spectrometer (Sentronic Co., Germany). The calibration models were carried out by partial least squares (PLS) analysis between NIR spectra data of apples and chemical data of watercore content. The spectra were different in absorbance between apple included watercore and not included one. Apple included watercore had higher absorption band than sample not included one at 732 and 820 nm. The calibration model seems to be accurate to predict the watercore content in apple fruit, the correlation coefficient (R) and root mean square error of prediction (RMSEP) were 0.99 and 0.93%, respectively. This result indicates that the PLSR calibration model by using NIR transmittance spectroscopy could be used for discrimination of watercore in apple fruit.

The objectives of this study were to develop models to predict quality parameters of Korean bee-honeys by visible and NIR spectroscopic technique. Two kinds of bee-honey fronl acacia and polyflower sources were tested in this study. The honeys were harvested in the spring of 2000 and stored in the storage facility at 20$^{\circ}C$ during experiments. Total of 394 samples of honey were analyzed. Reflectance spectra, moisture contents, ash, invert sugar, sucrose, F/G (fructose/glucose) ratio, HMF (hydroxymethyl furfural), and C12/C13 ratio of honeys were measured. The average values for the tested honeys were 19.9% of moisture contents, 0.12% of ash, 68.4% of invert sugar, 5.7% of sucrose, 1.27 of F/G(fructose/glucose) ratio, 14.4 mg/kg of HMF, and -19.1 of C12/C13 ratio. A spectrophotometer, equipped with a single-beam scanning monochromator (NIR Systems, Model 6500, USA) and a horizontal setup module, was used to collect reflectance data from honey. The reflectance spectra were measured in wavelength ranges of 400∼2,498 nm. with 2 nm of interval. Thirty-two repetitive scans were averaged, transformed to log(1/Reflectance), and then were stored in a microcomputer file, forming one spectrum per measurement. A sample cell and reflectance plate were made to hold honey samples constantly. Spectra of honey samples were divided into a calibration set and a validation set. The calibration set was used during model development, and the validation set was used to predict quality parameters from unknown spectra. The PLS(Partial Least Square) models were developed to predict the quality parameters of honeys. The first and the second derivatives of raw spectra were also used to develop the models with proper smoothing gap. The MSC (multiplicative scatter correction) and the SNV & Dtr.(standard normal variate and detranding) preprocessing were applied to all spectra to minimize sample-to-sample light scatter differences. The PLS models showed good relationships between predicted and measured quality parameters of honeys in the wavelength range of 1100∼2200 nm. However, the PLS analysis was not good enough to predict HMF of honeys.

A novel technique has been developed to observe the three-dimensional (3-D) distribution of chemical components in biological materials using both automatic sectioning microtome and infrared microscope. The 3-D image was reconstructed based on the relationship between the content and the absorbancy of specified wavelength for chemical components. By using the automatic sectioning microtome, the kernel of rice sample fixed in paraffin was sequentially sliced with the thickness of $5\;\mutextrm{m}$ after pasting the sliced sectional specimens on an adhesive tape. The chemical components of the specimens, which are placed on the X-Y controlling stage with positioning accuracy of ${\pm}10\;\mutextrm{m}$, were analyzed by the infrared microscope. The 3-D images demonstrated that the zonal protein about $200\mutextrm{m}$ in width was observed mainly at the outer parts of a rice particle, and carbohydrates entirely. These images can be observed by choosing arbitrary observation angle. The result indicated that the developed technique could be applied 3-D information to investigate intrinsic chemical components but also residual pesticides as well as bacteria contamination for agricultural produce.

Microbiological examination of silage is of little value in gauging the outcome of silage, and so chemical analysis is more reliable and meaningful indicator of quality. On the other hand chemical assessments of the principal fermentation products provide an unequivocal basis on which to judge quality. Livestock require energy, protein, minerals and vitamins from their food. While fresh forages provide these essential items, conserved forages on the other hand may be deficient in one or more of them. The aim of the conservation process is to preserve as many of the original nutrients as possible, particularly energy and protein components (Woolford, 1984). Silage fermentation is important to preservation of forage with respect of feeding value and animal performance. Chemical and bacteriological changes in the silo during the fermentation process can affect adversely nutrient yield and quality (Moe and Carr, 1984). Many of the important chemical components of silage must be assayed in fresh or by extraction of the fresh material, since drying either by heat or lyophilisation, volatilises components such as acids or nitrogenous components, or effects conversion to other compounds (Abrams et al., 1987). Maize silage dorms the basis of winter rations for the vast majority of dairy and beef cattle production in Uruguay. Since nutrient intake, particularly energy, from forages is influenced by both voluntary dry matter intake and digestibility; there is a need for a rapid technique for predicting these parameters in farm advisory systems. Near Infrared Reflectance Spectroscopy (NIRS) is increasingly used as a rapid, accurate method of evaluating chemical constituents in cereals and dried forages. For many years NIRS was applied to assess chemical composition in dry materials (Norris et al., 1976, Flinn et al., 1992; Murray, 1993, De Boever et al., 1996, De la Roza et al., 1998). The objectives of this study were (1) to determine the potential of NIRS to assess the chemical composition of dried maize samples and (2) to attempt calibrations on undried samples either for farm advisory systems or for animal nutrition research purposes in Uruguay. NIRS were used to assess the chemical composition of whole - plant maize silage samples (Zea mays, L). A representative population of samples (n = 350) covering a wide distribution in chemical characteristics were used. Samples were scanned at 2 nm intervals over the wavelength range 400-2500 nm in a NIRS 6500 (NIRSystems, Silver Spring, MD, USA) in reflectance mode. Cross validation was used to avoid overfitting of the equations. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The calibration statistics were R$^2$ 0. 86 (SECV: 11.4), 0.90 (SECV: 5.7), 0.90 (SECV: 16.9) for dry matter (DM), crude protein (CP), acid detergent fiber (ADF) in g kg$\^$-1/ on dry matter, respectively for maize silage samples. This work demonstrates the potential of NIRS to analyse whole - maize silage in a wide range of chemical characteristics for both advisory farm and nutritive evaluation.

The main selection parameters used by forage grass (rye and Italian rye grass) breeders are dry-matter yield, seasonal growth, persistency, disease resistance, heading date, and heading. These characteristics can all be identified usually in the segregating F2 population, however characteristics such as soluble carbohydrate level, protein, lipid and digestibility cannot be identified. The emphasis of this work is to introduce a quantitative selection process for characterization of herbage quality e.g. protein, water-soluble carbohydrates, fiber fractions, dry matter digestibility. NIRS calibrations are currently being developed for identifying grass genotypes to assist the selection process, thereby allowing the opportunity to actively breed improved herbage quality. The changes in fibre fractions, associated components and digestibility of a number of grass clones at different growth stages are being assessed changes taking place during a growing season. This will provide a database of the major changes taking place during a growing season. Attempts to classify quality differences between genotypes will be carried out using multivariate analysis of the spectral data. I addition changes associated with maturity of grass will be considered in order to develop robust calibrations.

The production of grain for export and domestic use is one of Australia's most important agricultural industries, and the NIR technique has been used extensively over many years for the routine monitoring of grain quality, particularly moisture and protein content. Because most Australian grain is intended for human food production, the determinants of grain quality for livestock feed, apart from protein, have been largely ignored. However the increasing use of grain for feeding to pigs, poultry, beef cattle and dairy cows has led to an important national research project entitled “Premium Grains for Livestock”. Two of the objectives of this project are to determine the compositional and functional characteristics of grains which influence their nutritional quality for the various classes of livestock, and to adopt rapid and objective analytical tests for these quality criteria. NIR has been used in this project firstly to identify a set of grain samples from a large population of breeders' lines which showed a wide spectral variation, and hence a potentially wide variation in nutritional value. The selected samples were not only subjected to an extensive array of chemical, physical and in vitro analyses, but also were grown out to produce sufficient quantities of grain to feed to animals in vivo studies. Additional grains were also strategically selected from farms in order to include the effect of weather damage, such as rain, drought and frost. In this study to date, NIR calibrations have been derived or attempted, on both ground and whole grains, for in vivo dry matter digestibility (DMD), pepsin-cellulase dry matter disappearance, protein, fat, acid detergent fibre, neutral detergent fibre, starch, in sacco DMD and in vitro assays to simulate starch digestion in the lumen and small intestine. Results so far indicate high calibration accuracy for chemical components (SECV 0.3 to 2.6%) and very promising statistics for in vivo DMD (SECV 1.8, $R^2$ 0.93, SD 7.0, range 61.9 to 92.3, n=60). There appears to be some potential for NIR to estimate some in vitro properties, depending upon the accuracy of reference methods and appropriate sample populations. Current work is in progress to extend the range of grains with in vivo DMD values (a very laborious and expensive process) and to increase the robustness of the various NIR calibrations, with the aim of implementing uniform testing procedures for nutritional value of grains throughout Australia.

Fats and molasses are used, at the present time, in a considerable proportion as ingredients for the animal feed industry. They are mainly used as energy sources, but also they provide other characteristics of technological and nutritional interest (dust reduction, increase in palatability, etc). Both semi-liquid ingredients have numerous aspects in common from the point of view of their use in livestock feeds, as well as of their analytical control. Feed manufacturers use several criteria to evaluate the quality of fat and molasses. Furthermore, the traditional methods currently used, for their evaluation (eg. fatty acids, sugars, etc) are expensive and more sophisticated that the traditionally used for solid ingredients. The objective of the present work is to carry out a viability study to evaluate the ability of NIRS technology for the quality control of fat and molasses. Samples of liquid molasses (n = 42) and liquid fat ( n = 61), provided by a feed manufacturer, were scanned in a FOSS-NIR Systems 6500 monochromator equipped with a spinning module. The samples were analysed by folded transmission, using a sample cup of 0.1mm pathlength and gold surface reflector. For molasses, calibration equations were developed for the prediction of moisture (SECV=1.69%; $r^2$=0, 42), gross protein (SECV=0, 14%; $r^2$=0, 99), ashy (SECV=0, 60%; $r^2$=0, 84), NaCl (SECV=0, 05%; $r^2$=0, 99) and sugars (SECV=1, 04%; $r^2$=0, 86). For animal fats calibrations were obtained for the prediction of moisture (SECV=0, 14%, $r^2$=0, 88), acidity index (SECV=0, 83%, $r^2$=0, 82), MIU (SECV=0, 38%, $r^2$=0, 94) and unsaponifiables (SECV=0, 45%, $r^2$=0, 87). High accuracy calibration equations were also obtained for the prediction of the fatty acid profile. The equations have $r^2$values around 0.9 or highest. The results showed that NIRS technology could provide rapid and accurate results and reduce analytical costs associated to the quality control of two Important feed ingredients of a well known chemical variability.

The sweetness (。Bix) of fruit is the main quality factor contributing to the fruit taste. The brix of the apple fruit can be measured non-destructively by near infrared (NIR) spectroscopy, allowing the sweetness grading of individual apple fruit. However, the fruit quality is influenced by various factors such as growing location, producing year, variety and harvest time etc., accordingly the robust NIR calibration is required. In this experimental results are presented the influence of two variations such as growing location and producing year of apple fruit in establishing of calibrations for sweetness, and developed a stable and highly accurate calibration. Apple fruit (Fuji) was collected every year from 1995 to 1997 in 3 different growing locations (Andong, Youngchun and Chungsong) of Kyungpook in Korea. NIR reflectance spectra of apple fruit were scanned in wavelength range of 1100∼2500nm using an InfraAlyzer 500C (Bran+Luebbe) with halogen lamp and PbS detector. The multiple linear regression and stepwise was carried out between the NIR raw spectra and the brix measured by refractometer to select the best regression equations. The calibration models by each growing district were well predicted to dependent sample set, but poorly predicted to independent sample set. Combined calibration model using data of three growing districts predicted reasonable well to a population set drawn from all growing districts(SEP = 0.69%, Bias=-0.075). The calibration models by each harvest year were not transferable across harvest year, however a combined calibration model using data of three harvest years was sufficiently robust to predict each sample sets(SEP = 0.53%, Bias = 0.004).

During the past ten years, Near Infrared Spectroscopy has been successfully applied to the analysis of a great variety of agriculture products. Previous works (Morra et al., 1991; Salgo et al., 1998) have shown the potential of this technology for soil analysis, estimating different parameters just with one single scan. The main advantages of NIR applications in soils are the speed of response, allowing the increase of the number of samples analysed to define a particular soil, and the instantaneous elaboration of recommendations for fertilization and soil amendment. Another advantage is to avoid the use of chemical reagents at all, being an environmentally safe technique. In this paper, we have studied a set of 129 soil samples selected from representative glasshouse soils from Southern Spain. The samples were dried, milled, and sieved to pass a 2 mm sieve and then analysed for organic carbon, total nitrogen, inorganic nitrogen (nitrate ammonium), hygroscopic humidity, pH and electrical conductivity in the 1:1 extract. NIR spectra of all samples were obtained in reflectance mode using a Foss NIR Systems 6500 spectrophotometer equipped with a spinning module. Calibration equations were developed for seven analytical parameters (ph, Total nitrogen, organic nitrogen, organic carbon, C/N ratio and Electric Conductivity). Preliminary results show good correlation coefficients and standard errors of cross validation in equations obtained for Organic Carbon, Organic Nitrogen, Total Nitrogen and C/N ratio. Calibrations for nitrates and nitrites, ammonia and electric conductivity were not acceptable. Calibration obtained for pH had an acceptable SECV, but the determination coefficient was found very poor probably due to the reduced range in reference values. Since the estimation of Organic Carbon and C/N ratio are acceptable NIIRS could be used as a fast method to assess the necessity of organic amendments in soils from Mediterranean regions where the low level of organic matter in soils constitutes an important agronomic problem. Furthermore, the possibility of a single and fast estimation of Total Nitrogen (tedious determination by modifications of the Kjeldahl procedure) could provide and interesting data to use in the estimation of nitrogen fertilizer rates by means of nitrogen balances.

To select kernels for breeding that have required constituent content from either naturally distributed samples or artificially mutated ones, it is necessary to process batch samples in a short time. The constituent content of single-kernel grains such as wheat and rice has been determined using conventional bench type NIR instruments; however, it takes a lot of time and effort. Shizuoka Seiki (Fukuroi-city, Japan) and NFRI (National Food Research Institute) of MAFF (Ministry of Agriculture, forestry and Fisheries of Japan) have jointly developed a continuous high-speed single-kernel brown rice sorting machine based on rice protein content. It consists of several sections such as a feeding mechanism, measuring unit, sorting mechanism and controlling PC. The feeding mechanism picks up single-kernel brown rice from the hopper (maximum of 5kg storage capacity) and sends it to the measuring unit. A spectrum of the brown rice is obtained in the measuring unit, which consists of a near-infrared array sensor. The brown rice is then sorted in the sorting mechanism based on its protein content estimated by the controlling PC. In the present study, measuring speed was approximately 500ms for the full spectrum range and overall sorting speed was approximately 2.8s for one kernel. Accuracy of estimation was approximately SEP=0.5% of dry matter protein content for nonglutinous rice.

It is well known now that near infrared spectroscopy (NIRS) is a fast, no destructive, and inexpensive analytical technique that could be used to classify, identify, and authenticate a wide range of foods and food items. Therefore, the main aims of this study were to provide a new insight into the authentication of five strawberry (Fragaria x ananassa) varieties and to correlate them with geographical zones and the propagating methods used. Three weeks plants of five different strawberry varieties (F. x ananassa Duch. cv Camarosa, Seascape, Chandler, F. Chiloensis, and F. Virginiana) were cultivated in vitro first and then transferred to pots with special soil, and grown in a greenhouse at CINVESTAV, all varieties were acquired from California (USA). After 18 months, ten leaves from each variety were collected. Transmission spectra from each leave were recorded over a range of 10, 000-4, 000 cm$-^{1}$, 32 scans of each strawberry leave were collected using a resolution of 4 cm$-^{1}$ with a Paragon IdentiCheck FT-NIR System Spectrometer. Triplicates of each strawberry leave were used. All spectra were analyzed using principal component analysis (PCA) and soft independent modeling class analogy (SIMCA). The optimum number of components to be used in the regression was automatically determined by the software. Camarosa was the only variety grown from the same shoot but propagated by a different method (direct or in vitro). Five different classes (varieties) or clusters were observed among samples, however, larger inter class distances were presented by the two wildtype samples (F. Chiloensis and F. Virginiana). Camarosa direct and Camarosa in vitro displayed a small overlapping region between them. On the other hand, Seascape variety presented the smallest rejection percentage among all varieties (more similarities with the rest of the samples). Therefore, it can be concluded that the application of NIRS technique allowed the authentication of all strawberry varieties and geographical origin as well. It was also possible to form subclasses of the same materials. The results presented here demonstrate that NIRS is a very powerful and promising analytical tool since all materials were authenticated and classified based on their variety, origin, and treatment. This is of a tremendous relevance since the variety and origin of a plant material can be established even before it gives its typical fruit or flower.

Predicting quality traits using near infrared (NIR) spectroscopy on whole grain samples has gained wide acceptance as a non-destructive, rapid and cost effective technique. Barley breeding programs throughout southern Australia currently use this technology as a tool for selecting malting quality lines. For the past 3 years whole grain barley calibrations have been developed at VIDA to predict malting quality traits in the early generation selections of the breeding program. More recently calibrations for whole grain malt have been developed and introduced to aid in selecting malted samples at the mid-generation stage for more complex malting quality traits. Using the same population set, barley and malt calibrations were developed to predict hot water extracts (EBC and IoB), diastatic power, free $\alpha$-amino nitrogen, soluble protein, wort $\beta$-glucan and $\beta$-glucanase. The correlation coefficients between NIR predicted values and laboratory methods for malt were all highly significant ($R^2$ > 0.84), whereas the correlation coefficients for the barley calibrations were lower ($R^2$ > 0.57) but still significant. The magnitude of the error in predicting hot water extract, diastatic power and wort $\beta$-glucan using whole grain malt was reduced by 50% when compared with predicting the same trait using whole grain barley. This can be explained by the complex nature of attempting to develop calibrations on whole grain barley utilizing malt data. During malting, the composition of barley is modified by the action of enzymes throughout the steeping and germination stages and by heating during the kilning stage. Predicting malting quality on whole grain malt is a more reliable alternative to predicting whole grain barley, although there is the added expense of micro-malting the samples. The ability to apply barley and malt calibrations to different generations is an advantage to a barley breeding program that requires thousands of samples to be assessed each year.

Ourwork aims to assess the content of dry matter, protein, cell wall parameters and water soluble carbohydrates in forages without having to handle samples, transport them to a laboratory, dry, grind and chemically analyze them. for this purpose, the concept of fresh forage analysis under field conditions by means of compact integrated NIRS InGaAs-diode array instruments on small plot harvesters is being evaluated for plant breeding trials. This work was performed with the world first commercial experimental forage plot harvester equipped with a NIRS module for the collection, compression, and scanning of forage samples (including automatic referencing and dark current measure ments). It was used for harvesting and analyzing a number of typical forage grass and forage legume plot trials. After NIRS measurements in the field each sample was again analyzed in the laboratory by means of a conventional grating spectrometer equipped with Si-and PbS-detectors. Conventional laboratory analysis of the samples was restricted to dry matter (DM) content by means of oven drying at 105. Routine chemometric procedures were then employed to assess the comparative accuracy and precision of the DM assessments in the spectral range between 950 and 1650nm by the NIRS diode array as well as by the conventional NIRS scanning instrument. The results of this study confirmed that the type of NIRS diode array instrument employed here functioned well even in rugged field operations. further refinements proved to be necessary for optimizing the automatic filling of the sample compartment to adjust for the wide variation in forage material under conditions of extremely low or high harvest yields. The error achieved in calibrating the apparatus for forages of typical DM content proved to be satisfactory (SECV < 1.0). Possibly as a consequence of higher sampling errors, its performance in atypical forages with elevated DM contents was less satisfactory. The error level obtained on the conventional grating NIR spectrometer was similar to that of the diode array instrument for both types of forage.

Forages, both grazed and conserved, provide the basis of ruminant production systems throughout the world. More than 90 per cent of the feed energy consumed by herbivorous animals world - wide were provided by forages. With such world - wide dependence on forages, the economic and nutritional necessity of been able to characterize them in a meaningful way is vital. The characterization of forages for productive animals is becoming important for several reasons. Relative to conventional laboratory procedures, Near Infrared Reflectance Spectroscopy (NIRS) offers advantages of simplicity, speed, reduced chemical waste, and more cost-effective prediction of product functionality. NIR spectroscopy represents a radical departure from conventional analytical methods, in that entire sample of forage is characterized in terms of its absorption properties in the near infrared region, rather than separate subsamples being treated with various chemicals to isolate specific components. This forces the analyst to abandon his/her traditional narrow focus on the sample (one analyte at a time) and to take a broader view of the relationship between components within the sample and between the sample and the population from which it comes. forage is usually analysed by NIRS in dry and ground presentation. Initial success of NIRS analysis of coarse forages suggest a need to better understand the potential for analysis of minimally processed samples. Preparation costs and possible compositional alterations could be reduced by samples presented to the instrument in undried and unground conditions. NIRS has gained widespread acceptance for the analysis of forage quality constituents on dry material, however little attention has been given to the use of NIRS for chemical determinations on undried and unground forages. Relatively few works reported the use of NIRS to determine quality parameters on undried materials, most of them on both grass and corn silage. Only two works have been found on the determination of quality parameters on fresh forages. The objectives of this paper were (1) to evaluate the use of NIRS for determination of nitrogen and moisture on undried and unground forage samples and (2) to explore two mathematical treatments and two NIR regions to predict chemical parameters on fresh forage. Four hundred forage samples (n: 400) were analysed in a NIRS 6500 instrument (NIR Systems, PA, USA) in reflectance mode. Two mathematical treatments were applied: 1,4,4,1 and 2,5,5,2. Predictive equations were developed using modified partial least squares (MPLS) with internal cross - validation. Coefficient of determination in calibration (${R^2}_{CAL}$) and standard error in cross-validation (SECV) for moisture were 0.92 (12.4) and 0.92 (12.4) for 1,4,4,1 and 2,5,5,2 respectively, on g $kg^{-1}$ dry weight. For crude protein NIRS calibration statistics yield a (${R^2}_{CAL}$) and (SECV) of 0.85 (19.8) and 0.85 (19.6) for 1,4,4,1 and 2,5,5,2 respectively, on a dry weight. It was concluded that NIRS is a suitable method to predict moisture and nitrogen on fresh forage without samples preparation.

The quality of sugarcane grown on the NE Australian tropical coast ($16^{\circ}$15'- $18^{\circ}$15' S Lat.) has declined markedly in the past seven years. This has been linked to dilution of mill-supply cane with increasing levels of non mature-stalk material consisting of leaves and sucker culms. The prime research objective was to examine the transition from the pre-harvest, in-field crop to harvested material sent for processing, in terms of quality and crop fraction proportions. A secondary objective was to quantify the effects of preharvest-season crop habit and culm condition on crop quality. Ten quadrat samples from each of 54 random crop sites (17 in 1999 and 37 in 2000), covering a wide range of variables (cultivar, crop class, and edaphic, topographic, climatic, and temporal factors) were collected immediately before harvest. Samples were partitioned into four fractions:- sound and unsound mature stalks (culms), sucker culms, and extraneous matter (leaves). Material harvested from each site was sampled and partitioned into four fractions:- sound and unsound billets (culm pieces), culm-spindle pieces, and leaf. In 2000, before harvest, 14 additional sites were sampled monthly, on three occasions, from March - June. Erect and non-erect culms were divided into sound and unsound classes. All samples were disintegrated and presented to a remote reflectance module of a scanning spectrophotometer using the BSES large cassette module. Near infra-red spectroscopic (NIS) analyses were developed for the rapid determination of quality components (Brix, commercial cane sugar (CCS), fibre, moisture, and polariscope reading). Calibrations for three material groups (culm (n = 639), non-culm (n = 496), and combined) were developed for all components using the 1999 data set. Two sub-sets (n = 178, and 190) of about 10% of the preharvest-season and harvest populations scanned in 2000 also were subjected to full routine laboratory analyses. The 1999 combined calibrations were excellent, but the culm calibrations produced consistently lower standard errors. Non-culm calibrations were marginally better than the combined for only CCS and pol. reading. Analysis of the 2000 culm data with calibrations using all 1999 and 2000 culm data resulted in better predictions relative to the 1999 culm calibrations. This also was true for the combined calibrations. Assessment of quality components in pre- and post-harvest sugarcane using NIS calibrations was more cost effective than using routine laboratory techniques. Outcomes from this NIS-facilitated research will have important economic consequences for the Australian sugarcane industry. Potential CCS present in mature culms is being discounted by dilution with leaves and sucker culms, threatening farm viability. The results question the efficacy of current harvesting technology. The CCS of harvested cane is improved only marginally over that of the in-field crop. Current harvesting technology requires either supplementary, innovative pre-mill processing or a design revolution to improve mill-supply cane quality, and therefore whole of industry economics. NIS-facilitated analyses, before the harvest season, highlighted the benefits of growing erect, sound crops. Loss of CCS then, can be minimized only by a combination of crop improvement and agronomic solutions, applied as part of sound on-farm management.

The purpose of this research was to develop a the reflection technique with near infrared (NIR) radiation for estimating physico-chemical properties in compost. The composts (cattle, pig, chicken and waste composts) were air dried and then ground to pass through a 0.5 or 2mm sieve for the physico-chemical properties and spectroscopic determinations. The physico-chemical properties of compost were shown high values ; moisture(30-60%), T-N(0.8-2.9%), organic matter(29-89%), pH(5.89-9.60) K$_2$O(0.27-5.66%), P2O$\sub$5/(0.07-2.62%), CaO(0.03-4.80%), MgO(0.09-1.56%), NaCl(0.01-1.13%), EC(1.41-13.76dS/m). Generally, we should select a simple calibration and prediction method for determining physico-chemical properties in compost under similar accuracy and precision of prediction. It should be remembered that the NIRS approach will never replace the traditional methods. However, NIRS technique may be an effective method for rapid and nondestructive measurements of a large number of compost samples. Near infrared reflectance spectra of composts was obtained by Infra Alyzer 500 scanning spectrophotometer at 2-nm intervals from 1100 to 2500nm. Multiple linear regression(MLR) or partial least square regression (PLSR) was used to evaluate a NIRS method for the rapid and nondestructive determination of physico-chemical properties and humic acid contents in composts. The standard error of prediction(SEP) for finely sized sample(<0.5mm) and coarsely sized sample(<2mm) did not show much difference. The NIR instrument of filter type showed the same accuracy of the monochromator scanning type to estimate the compost properties. The results summarized that NIR spectroscopy can be used as a routine testing method to determine quantitatively the OM, moisture, T-N, color, pH, cation content in the compost samples nondestructively.

Composting is a biological method used to transform the organic waste into stable, humified organic amendments. Humification is indicated as the key factor in improving the quality of compost, because of the importance of humic substances to soil ecology, fertility and structure, and their beneficial effects on plant growth The compost constituents vary widely, however, the degree of maturity is very important factor in compost quality. So this experiment carried out to determine the rapid estimation of the quality in cattle, pig, chicken and waste composts using near infrared reflectance spectroscopy(NIRS). Near infrared reflectance spectra of composts was obtained by Infra Alyzer 500 scanning spectrophotometer at 2-nm intervals from 1100 to 2500nm. Multiple linear regression(MLR) or partial least square regression (PLSR) was used to evaluate a NIRS method for the rapid and nondestructive determination of humic acid contents in composts. The results summarized that NIR spectroscopy can be used as a routine testing method to determine quantitatively the humic acid content in the compost samples ondestructively. Especially, we supposed that absorbance around 2300nm is related to humic acid as a factor of compost maturity. However the NIR absorption approach is empirical, it actually requires many combinations of samples and data manipulations to obtain optimal prediction.

Near-infrared spectroscopy (NIRS) was used to investigate the possibility for application in identification of apple cultivars. Three apple cultivars ‘Kamhong, Hwahong, and Fuji’ produced in Korea were scanned over the range of 1100-2500nm using NIRS (Infra Alzer 500). Two types of samples were used for scanning; one was apple with skin and the other was apple without skin. For cultivar identification, the NIR absorbance spectrums were analyzed by qualitative calibration in “Sesame” analysis program, and the various influence properties such as sugar contents, acidity, color, firmness, and micro-structure were compared in scanned samples. The ‘Kamhong’ cultivar could be identified from ‘Hwahong’ and ‘Fuji’ cultivars using the cluster model analysis. The test samples in calibration between ‘Kamhong’ and ‘Fuji’ cultivars could be completely identified. The test samples in calibration between ‘Kamhong’ and ‘Hwahong’ cultivars could be identified most of all. But, ‘Hwahong’ and ‘Fuji’ cultivars could not be quite classified each other. The apple skin influenced the identification process of apple cultivars. The samples without skin were more difficult to classify in calibration than the samples with skin. The physicochemical properties of apple cultivars showed like the result of identification in calibration using NIRS. Some physicochemical properties of ‘Kamhong’ cultivar were different from those of the other cultivars. Those of ‘Hwahong’ and ‘Fuji’ cultivars showed. similar to each other. The sucrose contents of ‘Kamhong’ cultivar were higher and the fructose contents and firmness of skin and flesh were lower than those of the others. The hypodermis layer of skin in ‘Kamhong’ cultivar was thinner than those of the others. In this studies, the identification of all apple cultivars by NIRS was not quite accurate because of the physicochemical properties which were different in the same cultivar, and inconsistent patterns by culivars in some properties. To solve these problems in NIRS application for apple cultivar identification, further study should be focused on the use of peculiar properties among the apple cultivars.

The calibration equations for Brix value determination of intact mango were developed using the NIR spectra in a short wavelength region from 700 to 1100 nm. Multiple linear regression (MLR) and partial least square regression (PLS) was used for the calibration. It was found that the best wavelength region for PLS calibration from 900 to 1000 nm was similar to the wavelength region selected by MLR from 906 nm to 996 nm. Both MLR and selected region PLS provided sufficiently accurate prediction equations for Brix determination of intact mango. For MLR, the prediction results were SEP = 0.45 Brix and Bias = -0.04 Brix while PLS prediction results were SEP : 0.46 Brix and Bias = -0.2 Brix. It was concluded that MLR and PLS would have similar abilities in making calibration equation for Brix determination of intact mango if the appropriate wavelengths or wavelength region were selected. The appropriate wavelength region for PLS regression could be assumed by using the wavelength region selected by MLR in place of random selection, The relationship between calibration results of MLR and PLS regression is discussed.

In this study, a newly constructed optical measurement system, whose main components were a parametric tunable laser and a near infrared photoelectric multiplier, was applied to detection of the information for the inside of Satsuma mandarin using time-of-flight near infrared spectroscopy (TOF-NIRS). The combined effects on the time resolved profile of sample diameter, sugar content, the wavelength of the laser beam, and the detection position of transmitted light were investigated in detail. The samples used were Satsuma mandarin (Citrus unshu $M_{ARC}$.) (location: Wakayama, Japan) having the diameters of 50-84 mm. The sugar content measured by a refractometer varied from 9.9 to 16.3 Brix%. Equator of sample was irradiated vertically with the pulsed laser, and transmitted output power was measured on the restricted position of the equator using the optical fiber cable. The sampling time and the number of averaging the output power were 100 ns and 100 times, respectively. The variation of the attenuance of peak maxima At, the time delay of peak maxima $\Delta$t and the variation of full width at half maximum Δw were strongly dependent on the detection position and the wavelength of the laser beam. At, $\Delta$t and $\Delta$w increased gradually as the sample diameter increased to be much absorbed and vigorously scattered. On the other hand, each optical parameter had a tendency to increase as the sugar content increased. Such behavior was remarkable when the transmitted light was detected at the side face of a sample. When we apply TOF-NIRS to detection of the information for the inside of fruit with high moisture content like Satsuma mandarin, it is very important to give attention to the difference in the scattered light within tissues and the semi-straightly propagated light. Furthermore, we tried to express the resulting phenomena by using a model samples composed of water, sucrose, and milk. The variation of the time resolved profile is strongly governed by the combination of the light absorption component, scattering medium, and refractive index.

In this study, a newly constructed optical measurement system, whose main components were a parametric tunable laser and a near infrared photoelectric multiplier, was applied to detection of the information for the inside of Satsuma mandarin using time-of-flight near infrared spectroscopy (TOF-NIRS). The combined effects on the time resolved profile of sample diameter, sugar content, the wavelength of the laser beam, and the detection position of transmitted light were investigated in detail. The samples used were Satsuma mandarin (Citrus unshu $M^{ARC}$.) (location: Wakayama, Japan) having the diameters of 50-84 mm. The sugar content measured by a refractometer varied from 9.9 to 16.3 Brix%. Equator of sample was irradiated vertically with the pulsed laser, and transmitted output power was measured on the restricted position of the equator using the optical fiber cable. The sampling time and the number of averaging the output power were 100 ns and 100 times, respectively. The variation of the attenuance of peak maxima At, the time delay of peak maxima t and the variation of full width at half maximum w were strongly dependent on the detection position and the wavelength of the laser beam. At, t and w increased gradually as the sample diameter increased to be much absorbed and vigorously scattered. On the other hand, each optical parameter had a tendency to increase as the sugar content increased. Such behavior was remarkable when the transmitted light was detected at the side face of a sample. When we apply TOF-NIRS to detection of the information for the inside of fruit with high moisture content like Satsuma mandarin, it is very important to give attention to the difference in the scattered light within tissues and the semi-straightly propagated light. Furthermore, we tried to express the resulting phenomena by using a model samples composed of water, sucrose, and milk. The variation of the time resolved profile is strongly governed by the combination of the light absorption component, scattering medium, and refractive index.

Near infrared (NIR) spectroscopy is a powerful technique for non-destructive analysis that can be obtained in a wide range of environments. Recently, NIR measurements have been utilized as probe for quantitative analysis in agricultural, industrial, and medical sciences. In addition, it is also possible to make practical application on NIR for molecular structural analysis. In this work, Fourier transform near infrared (FT-NIR) measurements were carried out to utilize extensively in the relative amounts of different secondary structures were employed, such as Iysozyme, concanavalin A, silk fibroin and so on. Several broad NIR bands due to the protein absorption were observed between 4000 and $5000\;^{-1}$. In order to obtain more structural information from these featureless bands, second derivative and Fourier-self-deconvolution procedures were performed. Significant band separation was observed near the feature at $4610\;^{-1}$ ,. Particularly the peak intensity at $4525\;^{-1}$ shows a characteristic change with thermal denaturation of fibroin. The structural information can be also obtained by mid-IR and CD spectral. Correlation of NIR spectra with protein structure is discussed.

The knowledge of the nutrition in animal production is difficult to assess due of difficulty to determine the amount and quality of feeds intake, mainly if forages are the most important compound in the diet. It must be able to know responses to the metabolic process in lactating dairy cows earring out feeds evaluation trials. These metabolism studies with cows, requires measurements of: I) The amount of all feeds consumed. II) Excretion of faeces. III) Excretion of urine. Taking as a basis these trials, forage and total intake, dietary digestibility and balances of nitrogen and energy can be calculated. However, these feeds evaluation experiments with animals are very time consuming and expensives. The faeces excreted by animals containt undigested residues of the diet consumed. For this reason, their analysis can be an successful tool to determine the amount and quality of feed intake and other important biological parameters. The aim of this work was to know if faeces analysis by NIR could be used to determine with enough accuracy some attributes of different lactating dairy cows diets, using a global equation, developed on an heterogeneus population. For this purpose a total of 79 faecal samples from eight grass, three grass silages and two maize silages metabolic trials, on six cows each one, were used to constitute the initial population. The calibration equations were developed to predict forage and total intake, organic matter digestibility, digestibility coefficient of crude energy and digestibility energy. The combination of several trials with different diets and animal conditions gave promising results.

NIR specroscopy is widely used today as a quantitative technique for predicting the chemical composition of various agricultural product. However there exist few application for seed quality assessment, especially for seed germinability. This study is to show the possibilities of a nondestructive estimation of germinability in radish (Raphanus sativus L) seeds. The experiment carried out on one radish cultivar (Chung Su Gung Jung, Nong Woo Bio Co., Ltd.) harvested in 1993. NIR(Foss Co.) spectral measurements were carried out on the seeds surface of flat side. The seeds after spectral measurements were planted on blotter individually and observed germination. The seeds were characterized to nongermination and germination group, which in turn grouped to normal and abnormal germination and then compared with the NIR spectra. The spectra from these seed groups were compared each other, The result suggested that NIR spectra could be applicable to determine radish seeds germinability.

Near-infrared spectroscopy is now being used in clinical diagnosis as a non-invasive monitor of tissue oxygenation state. However, due to lack of the optical pathlength information within tissues, it is still difficult to quantitate the hemoglobin concentration with present CW techniques. Time-resolved spectroscopy (TRS), which measures temporal profiles of emerging light from tissues, enables to estimate the pathlength distribution within tissues by converting time to distance. Consequently, quantitative measurement of tissue oxygenation is possible by analyzing the data with optical diffusion equation 1) or our Microscopic Beer-Lambert law2). Time-Resolved Spectroscopy System : TRS-1O3) Our TRS-10 system consists of a three-wavelength (759, 797, 833 nm) PLP as pulsed light source, a high speed PMT with high sensitivity and three signal-processing circuits for time-resolved measurement (CFD/TAC, A/D converter and histogram memory). Optical pulse train consisting of 759, 797 and 833nm is generated by PLP at 5㎒ repetition rate and irradiated a sample through a single optical fiber. The diffuse-reflected light from the sample is collected by a bundle fiber and then detected by the PMT for single photon measurement. After being amplified by a following fast amplifier, the electrical signals for each wavelength are picked out by CFD/TAC module. Then, a signal processing circuit integrated the TRS data for each wavelength individually. The simultaneous TRS measurement for three wavelengths achieved without any optical or mechanical switch. Experiment and Results Input and detection fibers of TRS-10 were attached at the human forehead with a fiber separation of 3cm. TRS measurements were continuously performed for about 20 minutes including 2 minutes hyper ventilation. It was observed that the total hemoglobin concentration was decreasing during the hyper ventilation and recovered until 2 minutes after hyper ventilation. On the other hand, the deoxy-hemoglobin concentration began to increase after hyper ventilation and had its peak at around 2 minute later, showing 502 drop from 75% to 60% due to inhibition of breathing by performing hyper ventilation. The results showed that this system might be able to quantitate the concentrations of oxy- and deoxy-hemoglobin in the human brain.

Near infrared, diffuse reflectance spectroscopy has shown significant potential for in vitro and in vivo assessment of metabolic status. However, the complexity of living samples can lead to ambiguous results. This presentation will focus on methods that provide controls for scattering and absorption estimation in tissue. For robust estimations, normalization procedures will be shown which can greatly improve interpretability of results. Normalization based on time, location and spectral property will be shown with data from models, tissue phantoms and in vivo measurements. In particular, interpretation of NIR spectra associated with major respiratory constituents will be examined. Measurement of constituents such as hemoglobin, myoglobin, tissue edema, and lactate will be shown. Results suggest that NIR may provide a valuable tool for physiological monitoring in critical care research and practice.

A chemoinfometrical method for evaluating the quantitative determination of crystallinity one polymorphs based on fourie-transformed near-infrared (FT-NIR) spectroscopy was established. A direct comparison of the data with the ones collected from using the and compared with the conventional powder X-ray diffraction method was performed. [Method] The pPure a and g forms of indomethacin (IMC) were prepared by reportedusing published methods. Six kinds of standard samples obtained by physically mixing of a and g forms. After the powder X-ray diffraction profiles of samples have been measured, the intensity values were normalized to against the intensity of silicon powder as the as an external standard. The calibration curves for quantification of crystal content were based upon the total relative intensity of four diffraction peaks from of the form g crystal. FT-NIR spectra of six calibration sample sets were recorded 5 times with the NIR spectrometer (BRAN+LUEBBE). Chemoinfometric analysis was performed on the NIR spectral data sets by applying the principal component regression (PCR). [Results] The relation between the actual and predicted polymorphic contents of form g IMC measured using by the X-ray diffraction method shows a good straight linen linear relation., and it has slope of 0.023, an intercept of 0.131 and a correlation coefficient of 0.986. PCR analyses wereis was performed based on normalized NIR spectra sets offer standard samples of known content of IMC g form. IMC. A calibration equation was determined to minimize the root mean square error of the predictionthe prediction. Figure 1 shows a plot of the calibration data obtained by NIR method between the actual and predicted contents of form g IMC. The predicted values were reproducible and had a smaller standard deviation. Figure 2 shows that the plot for the predicted transformation rate (%) of form a IMC to form g as measured by X-ray diffractomeoy against to those as measured by NIR method. The plot has a slope of 1.296, an intercept of 1,109, and a correlation coefficient of 0.992. The line represents a satisfactory correlation between the two predicted values of form g IMC content. Thus NIR spectroscopy is an effective method for the evaluation to the pharmaceutical products of quantitative of polymorph.

The ultra-high pressure technology fur the preservation of foods is under intense research to evaluate its potential as an alternative or complementary process to traditional methods of food preservation. Traditional processing methods usually need a large amount of energy, may cause unwanted reactions in the food, leading to cooked flavor and loss of vitamins, etc. The application of ultra-high hydrostatic pressure for food processing consists of subjecting the food to pressures in the range of 100-1000 ㎫. The ultra-high pressure inactivates the microorganisms and some enzymes, promotes the germination of spores and extends the shelf-life of the foods. This new technology follows the “minimal processing” concept minimizing the quality degradation, saving the vitamins, essential nutrients and flavors as well as utilizing less energy. We joined the research team at our University involved in the mentioned technology using an ultra-high pressure equipment, recording of the near infrared spectra and signal response of a chemosensor array (electronic nose) of their meat (beef and pork), vegetable and fruit samples exposed to different pressure. The results of our investigations achieved by evaluating the measured data using PCA and PQS methods will be presented.

Near Infrared spectroscopy (NIR) used on human skin measurement was explored in the past decade. Many publications in different journals and magazines discussed the feasibility of the NIR technique for cosmetic product property studies. Based upon the results of pioneers, we have pursued some work of the NIR instrument coupled with a probe module for skin measurement in vivo and vitro. In part I of this paper, the specific Near Infrared spectroscopy instrument stability, human subject conditions and other parameters, which could affect the measurements reproducibility are discussed. Second derivative NIR spectra and Principle Components Analysis (PCA) are utilised for data interpretation. In part II of this paper, the relationship of human skin moisture and ageing, the gender information and finally, the discovery of penetration depth of NIR incident light on skin are reported. A theoretical penetration depth calculation equation is proposed. In part III, the study results of a couple of commercial skin care products effect will be described. The skin lotions were applied on human skin (in vivo) in order to exam the NIR feasibility to monitor the changes of moisture level. The results are consistently positive. From our primary study, it can conclude that the NIR is potentially a very powerful instrument for skin condition diagnostics, either for cosmetic and/or for medication purposes.

Constituents of several .representative seaweeds, such as wakame Undaria pinnatifida; hijikia Hizikia fusifome; and kombu Laminaria japonica, were found to have fairly large reaction rates determined by quenching experiments of emission spectra in the near-infrared region (1max: 1270 nm) from singlet oxygen (102). Emission spectra of singlet oxygen generated from an aqueous solution of Rose Bengal under irradiation with a green laser (330 nm) were measured by a near-infrared (NIR) emission spectrometer constructed in our laboratory. The quenching experiments were as follows: Intensities of emission spectra were measured in the absence (I0) and in the presence of the seaweed constituents (I): Ratios of I0/I were plotted against every concentration of the quenchers (Stern-Volmer plots) which gives a straight line. The slope of each line gives a kqt value which gives a quenching constant kq value (an antioxidative constant against singlet oxygen) when the t value (half-life time of singlet oxygen in the solvent used) was given. The determined reaction rates are between 103-105 (g/l)-ls-1; the larger ones are as large as that of ascorbic acid, 8.4 ${\times}$ 104 (g/1)-ls-1. Most of these seaweed constituents also showed antioxidative activity against auto-oxidation and superoxide as well as their immunological enhancing activity. These results suggest a possibility that dietary fibers which are supposed to prevent the large-intestine cancer by their physical properties may prevent the cancer, at least in parts, by their chemical, antioxidative activity.

NIR has been extensively used to predict directly measurable properties of materials that are important to the appropriate industries. Commonly, NIR is used to perform fast, routine tests to improve control response as against the response time for the (normally chemical) base test. This paper discusses the use of NIR to measure indirect properties of materials. In these cases, the pure chemical or physical tests are either unable measure the appropriate parameters (eg GMO modification) or there are mitigating effects that are not properly addressed by the base tests. In particular, we looked at the digestible portion of amino acids within meat and bone meal. This is the desired response measurement by end-users of the product (intensive livestock producers) but is currently unable to be offered as a measurement by producers. The base test method is by controlled feeding trials. These are somewhat cumbersome, taking 2-3 months, involving several sets of animals, and considerable expense. A shortened test (feed trial based) would be of little use, as the precision blows out over short period feeding trials. For example, a rat ileal digestibility test requires around 2 months, and costs some $USD1000. This is clearly impractical test for a producer involved in continuous production, with a 1-2 day turn around. While the amino acid abundance is accessible chemically, the uptake of amino acids into usable material by mammalian species is not simply related to the measured abundance within the material. There are many co-related material properties that might help or hinder uptake, some chemical based (eg protein damage), some indirect (eg palatability), some physiological (intestinal tract response vs speed of throughput). We discuss the approaches taken to provide a suitable reference data set, and present the derived prediction and validation relationships.

The research described here was undertaken with the aim of monitoring, optimizing and ultimately controlling the production of heterofermentative microbes used as starters in the salami industry. The use of starter cultures in the fermented meats industry is a well-established technique used to shorten and standardize the ripening process, and to improve and control the organoleptic quality of the final product. Starter cultures are obtained by the submerged cultivation of suitable microorganisms in stirred, and sometimes aerated, fermenters where monitoring of key physiological parameters such as the concentration of biomass, substrates and metabolites suffers from the general lack of real-time measurement techniques applicable to aseptic processes. In this respect, the results of the present work are relevant to all submerged fermentation processes. Previous work on the application of on-line NIR spectroscopy to the lactic acid fermentation (Dosi et al. - Monreal NIR1995) had successfully used a system based on a measuring cell included in a circulation loop external to the fermenter. The fluid handling and sterility problems inherent in an external circulation system prompted us to explore the use of an in-line system where the NIR probe is immersed in the culture and is thus exposed to the hydrodynamic conditions of the stirred and aerated fluid. Aeration was expected to be a potential source of problems in view of the possible interference of air bubbles with the measurement device. The experimental set-up was based on an in-situ sterilizable NIR probe connected to the instrument by means of an optical fiber bundle. Preliminary work was carried out to identify and control potential interferences with the measurement, in particular the varying hydrodynamic conditions prevailing at the probe tip. We were successful in defining the operating conditions of the fermenter and the geometrical parameters of the probe (flow path, positioning, etc.) were the NIR readings were reliable and reproducible. The system thus defined was then used to construct and validate calibration curves for tile concentration of biomass, carbon source and major metabolites of two different microorganisms used as salami starters. Real-time measurement of such parameters coupled with the direct interfacing of the NIR instrument with the PC-based measurement and control system of the fermenter enabled the development of automated strategies for the interactive optimization of the starter production process.

Ingests and fecal contamination on a poultry carcass is a food safety hazard due to potential microbiological contamination. A visible/near-infrared (NIR) spectrometer was used to discriminate among pure ingesta and fecal material, breast skin contaminated with ingesta or fecal material and uncontaminated breast skin. Birds were fed isocaloric diets formulated with either maize, mile, or wheat and soybean meal for protein requirements. Following completion of the feeding period (14 days), the birds were humanely processed and eviscerated to obtain ingests from the crop or proventriculus and feces from the duodenum, ceca, and colon portion of the digestive tract. Pure feces and ingesta, breast skin, and contaminated breast skin were scanned from 400 to 2500 nm and analyzed from 400 to 900 nm. Principal component analysis (PCA) of reflectance spectra was used to discriminate between contaminates and uncontaminated breast skin. Results indicate that visible (400 to 760 nm) and NIR 760-900 nm spectra can detect contaminates. From PCA analysis, key wavelengths were identified for discrimination of uncontaminated skin from contaminates based the evaluation of loadings weights.

Imaging spectrometry or hyperspectral imaging is a recent development that makes possible quantitative and qualitative measurement for food quality and safety. This paper presents the research results that a hyperspectral imaging system can be used effectively for detecting fecal (from duodenum, cecum, and colon) and ingesta contamination on poultry carcasses from the different feed meals (wheat, mile, and corn with soybean) for poultry safety inspection. A hyperspectral imaging system has been developed and tested for the identification of fecal and ingesta surface contamination on poultry carcasses. Hypercube image data including both spectral and spatial domains between 430 and 900 nm were acquired from poultry carcasses with fecal and ingesta contamination. A transportable hyperspectral imaging system including fiber optically fabricated line lights, motorized lens control for line scans, and hypercube image data from contaminated carcasses with different feeds are presented. Calibration method of a hyperspectral imaging system is demonstrated using different lighting sources and reflectance panels. Principal Component and Minimum Noise Fraction transformations will be discussed to characterize hyperspectral images and further image processing algorithms such as image band ratio of dual-wavelength images and its histogram stretching with thresholding process will be demonstrated to identify fecal and ingesta materials on poultry carcasses. This algorithm could be further applied for real-time classification of fecal and ingesta contamination on poultry carcasses in the poultry processing line.

The implementation of NIR and chemometrics in the Pharmaceutical industries is still in strong progress, both regarding qualitative and quantitative applications and beneficial results are seen. Looking at the development so far, NIR will change the pharmaceutical industry even more in the future. This presentation will address the experiences and progress achieved regarding the application and implementation of quantitative methods for determination of content uniformity and assay of tablets with less than 10% w/w of active, using Near Infrared transmittance spectroscopy in combination with PLS. Also qualitative methods for identification of the same tablets by Near Infrared reflectance spectroscopy will be discussed. Four commercial tablet strengths are formulated and produced from two different compositions by direct compression. Three different strengths are dose proportional, i.e. fixed concentration by varying in size. The aim was to replace the conventional primary methods for analysing content uniformity, assay and identification by NIR. Studies were performed on comparing transmittance versus reflectance spectroscopy for both applications on the dose proportional tablets. The model for determination of content uniformity and assay was developed to cover both coated and uncoated tablets, whereas the qualitative model was developed to identify coated tablets only. The impact of the tablet formulation, tablet size and coating, resulted in individual models far each composition The best calibration was achieved using diffuse reflectance for the identification purposes and diffuse transmittance for the quantitative determination of the active content within the tablets. As NIR in combination with other techniques opens up the possibility of total quality management within the production, the transfer of the above-mentioned models from a laboratory based approach to an at-line approach at H.Lundbeck will be addressed too.

Near-Infrared Spectroscopy (NIRS) was investigated aiming at the identification of falsified drugs. The identification is based on comparison of the NIR spectrum of a sample with a typical spectra of an authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Two spectrophotometers (Brimrose - Luminar 2000 and 2030), based on acoustic-optical filter (AOTF) technology, sharing the same controlling computer, software (Brimrose - Snap 2.03) and the data acquisition electronics, were employed. The Luminar 2000 scans the range 850 1800 nm and was employed for transmitance/absorbance measurements of liquids with a transflectance optical bundle probe with total optical path of 5 mm and a circular area of 0.5 $\textrm{cm}^2$. Model 2030 scans the rage 1100 2400 nm and was employed for reflectance measurement of solids drugs. 300 spectra, acquired in about 20 s, were averaged for each sample. Chemometric treatment of the spectral data, modelling and classification were performed by using the Unscrambler 7.5 software (CAMO Norway). This package provides the Principal Component Analysis (PCA) and SIMCA algorithms, used for modelling and classification, respectively. Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to accomplish the diversity of physico-chemical characteristics found among commercial products. Parameters which could affect the spectra of a given drug (especially if presented as solid tablets) were investigated and the results showed that the first derivative can minimize spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The effect of ambient humidity and temperature were also investigated. The first factor needs to be controlled for model construction because the ambient humidity can cause spectral alterations that should cause the wrong classification of a real drug if the factor is not considered by the model.

Some two years ago our company undertook a project on manufacturing network rationalization to maximize competitiveness through continuous improvement in manufacturing efficiency. One key outcome was the recognition of the benefits that could be derived from timely application of new technology or novel use of existing technologies and even more importantly the need to develop company wide strategies to maximize the impact of such applications. As a direct result an exercise was undertaken to identify the ten most promising technologies from a list of literally hundreds seen as having the capability of making a rapid impact on the manufacturing initiative. One of the outcomes of this exercise was the identification of Near Infrared Spectroscopy as a pivotal technology for improving process understanding, performance, and control to deliver consistent product quality cost effectively with broad applicability across our product range. While NIR had been in use in targeted areas on some of our sites for some years our new challenge was to develop a strategy to extend NIRs application, initially over 17 manufacturing sites, while concurrently expanding the NIR skill base company wide to ensure that the return on initial investment could be further maximized as shared applications across the remaining sites as required. This presentation will provide an overview of how life cycle based user requirement specifications were developed covering: ㆍSpectrophotometers ㆍSample interfaces ㆍSoftware ㆍEquipment and Software qualification ㆍCalibration transfer ㆍ Ease of developing effective user interfaces and control for applications transferred to a production area ㆍUser training ㆍWorld wide support The presentation will also describe the process adopted for vendor selection to ensure maximum utilization of the existing company wide NIR skill base and its future development to expedite applications of the technology in development, quality control and production areas.

During the last years phytochemistry and phytopharmaceutical applications have developed rapidly and so there exists a high demand for faster and more efficient analysis techniques. Therefore we have established a near infrared transflectance spectroscopy (NIRS) method that allows a qualitative and quantitative determination of new polyphenolic pharmacological active leading compounds within a few seconds. As the NIR spectrometer has to be calibrated the compound of interest has at first to be characterized by using one or other a combination of chromatographic or electrophoretic separation techniques such as thin layer chromatography (TLC), high performance liquid chromatography (HPLC), capillary electrophoresis (CE), gas chromatography (GC) and capillary electrochromatography (CEC). Both structural elucidation and quantitative analysis of the phenolic compound is possible by direct coupling of the mentioned separation methods with a mass spectrometer (GC-MS, LC-MS/MS, CE-MS, CEC-MS) and a NMR spectrometer (LC-NMR). Furthermore the compound has to be isolated (NPLC, MPLC, prep. TLC, prep. HPLC) and its structure elucidated by spectroscopic techniques (UV, IR, HR-MS, NMR) and chemical synthesis. After that HPLC can be used to provide the reference data for the calibration step of the near infrared spectrometer. The NIRS calibration step is time consuming, which is compensated by short analysis times. After validation of the established NIRS method it is possible to determine the polyphenolic compound within seconds which allows to raise the efficiency in quality control and to reduce costs especially in the phytopharmaceutical industry.

In this study, portable near infrared (NIR) system was newly integrated with a photodiode array detector, which has no moving parts and this system has been successfully applied for evaluation of human skin moisture. The good correlation between NIR absorbance and absolute water content of separated hairless mouse skin was, in vitro, showed depending on the water content (7.42-84.94%) using this portable NIR system. Partial least squares (PLS) regression was used for the calibration with the 1100-1650 nm wavelength range. For the practical use for the evaluation of human skin based on moisture, PLS model for human skin moisture was, in vivo, developed using the portable NIR system based on the relative water content values of stratum corneum from the conventional capacitance method. The PLS model showed a good correlation. This study indicated that the portable NIR system could be a powerful tool for human skin moisture, which may be much more stable to environmental conditions such as temperature and humidity, compared to conventional methods. Furthermore, in order to confirm the performance of newly integrated portable NIR system, scanning type conventional NIR spectrometer was used in the same experiments and the results were compared.

The factors involved in development of electronic grading systems for commodities such as grains and seeds include determination of the factors that influence the end-product utilization of the commodities, and the degree to which these can be predicted by electronic methods. The possibility of exchanging existing methods of grading by electronic methods has to be considered. The respective merits of techniques such as Digital Imaging and Near-infrared (NIR) spectroscopy have to be considered. Digital Imaging is a computerized version of visual inspection and grading, whereas NIR spectroscopy has the potential for grading on the basis of composition and functionality, Selection and evaluation of NIR instruments is an important factor, as are sampling and sample presentation to electronic instruments, and particularly the engineering involved in sample presentation. Sample assembly, and software for calibration development are described in the presentation. Finally the impact and implications of introduction of electronic grading are discussed with particular attention to marketing of the commodities.

Documented quality control plays a vital role I the production of technical “Depth filter” sheets used in industries such as Beverage and pharmaceutical. Depth filter sheets which can be up to several millimeters thick are stacker in plate and frame filter systems. They are the core of stainless steel filter systems which can be up to several meters high. FT-NIR Spectroscopy has many potential applications in the whole production line of filter sheets. Raw materials such as different types of cellulose pads, white powdery fillers (e.g. Kieelgur, Perlite) or liquid chemicals such as wet-strength agents we, with the help of NIR, easy to identify. NIR can also determine physical parameters such as particle size, essential for the filtration behavior. FT-NIR can be used for the quality parameters of the final product. Moisture and permeability can be determined, and with the help of the speed of this NIR method it is possible to correct possible faults quickly in the production process. Waste production can be minimized which is good for both the product profitability and the environment. Further tests have shown that it is also possible to use NIR on-line in the production area, to check the concentrations and the homogeneity of the paper suspension consisting of cellulose fibres, fillers and additives.

Near infrared (NIR) spectroscopy has become a widely used method in food and beverage analysis because of its speed, accuracy and the simplicity of sample preparation. One of the basic requirements of NIR instruments is a wide dynamic range if weak, or small, absorption changes or concentrations are to be measured. Thus the instrument must be sufficiently luminous, and efficient, to enable measurements to be made in a reasonably short time, as for some applications (e.g. sorting) short response times are essential. Diode lasers function the same way as lasers but linewidths are not as narrow as typical lasers. In this work an array of seven laser diodes (in the range of 750-1100 nm) with energy outputs of around hundred milliwatts each were combined with a fast diode array spectrometer (400-1100 nm, 1024 pixels, integration time from 3 ms) as detector. Measurements in transmission mode were performed in solutions of sugars in aqueous solutions and in deuteriumoxide. The feasibility of non-destructive measurements in transmission mode was investigated for different fruits and vegetables.

The development and evolution of near infra-red spectroscopic (NIS) calibrations for high-moisture materials is an expensive proposition. Such investment is suspect unless the instrument, or instruments, on which calibrations were developed can be preserved intact or re-standardized as component replacements occurs. The objective of this paper is to detail the changes in performance of a six-year old instrument after maintenance in years five and six resulted in collection of spectral data that was increasingly removed from the calibration population. Calibrations for the analysis of mature sugarcane stalks, a high-moisture material, were developed successfully in 1995 using a broad sample population in terms of genetics, and spectral and temporal variation. The spectral library was further broadened in 1996. In 1997, 1999, 1999, and 2000, additional samples constituting 10% of the laboratories throughput were subjected to full component analyses using routine laboratory techniques. These samples were primarily random samples, but were complemented with samples that were significant for the spectral H statistic or for the component t statistic. In 1998, an additional calibration was developed for populations consisting of samples of either mature stalks (culms) or sucker culms. Substantial additional samples numbers were collected for this calibration in 1999 and 2000. Attempts to standardize the scanning spectrophotometer used for these calibrations with a second similar instrument in 1999 failed because the instruments were optically different, and standardization could not account for this. Maintenance adjustments were made to the remote reflectance probe of the original instrument in 1999, and replacement of its PbS detectors was done in 2000. Spectral data collected in 1999 and 2000 yielded spectral populations that were increasingly removed from the respective spectral populations on which the calibrations were developed. The mature stalk calibrations benefited marginally from evolutionary calib.

Information of body composition (fat and protein) in living animal is important to determine the nutrients requirement. Deuterium oxide (D2O) dilution techniques, as one of isotope dilution techniques have been useful for the prediction of body composition. However, the determination of D2O concentration is time consuming and complicated. Therefore this study was conducted to develop a new method to predict D2O concentration in plasma using near infrared spectroscopy technique (NIRS). Four dairy cows in early lactation were used. They were fed total mixed ration containing conr silage, timothy hay, and concentrates to make 17.0%CP and 14.0 MJDE/kgDM. Dosing D2O was at week 1,3 and 5 after parturition. After dosing D2O, the blood was collected from hour 0 to 72. Blood samples were then centrifuge at 3,000 rpm for 10 minutes to obtain plasma. D2O concentration was analyzed by gas chromatograph (deuterium oxide analyzable system, HK102, Shokotsusyou) after extracted from plasma by liophilization. Plasma sample was scanned by NIRS using Pacific Scientific (Neotec) model 6500 (Perstorp Analytical, Silver Spring, MD) in the range of wavelength from 1100 to 2500 nm. Calibration equation was developed using multiple linear regression. Sample from one animal (cow #550; n: 74) was used for developing the calibration while the rest three animals were used for validating the equation. The range, R and SEC of the calibration set samples were 135-925 ppm, 0.93 and 48.1 ppm, respectively. Validation of the calibration equation for three individual cows was done and the average of NIR predicted value of D2O at each collection time from three weeks injection showed a high correlation. The range, r and 53 of plasma from cow #474 were 322-840 ppm,0.93 and 53.1; cow #478 were 146-951 ppm,0.95 and 39.8; cow #942 were 313-885 ppm,0.95 and 37.2, respectively. Judgement of accuracy based on ratio of standard deviation and standard error in validation set samples (RPD) for cow #474, #478 and #942 were 2.2,4.3 and 3.4, respectively. The error in application due to the variation between individual was considered smaller than the bias from collection period, however, this prediction can be overcome with correction of standard zero-minute concentration of blood. The results of this preliminary study on the use of NIRS for determination of D2O in plasma showed very promising as shown by a convenient and satisfy accuracy. Further study on various physiological stage of animal should be done.

In multivariate analysis, absorbance spectrum is measured over a band of wavelengths. One does not often pay attention to the size of this wavelength band. However, it is desirable that spectrum is measured at only necessary wavelengths as long as the acceptable accuracy of prediction can be met. In this paper, the method of selecting an optimal band of wavelengths based on the loading vector analysis was proposed and applied for determining total protein in human serum using near-infrared transmission spectroscopy and PLSR. Loading vectors in the full spectrum PLSR were used as reference in selecting wavelengths, but only the first loading vector was used since it explains the spectrum best. Absorbance spectra of sera from 97 outpatients were measured at 1530∼1850 nm with an interval of 2 nm. Total protein concentrations of sera were ranged from 5.1 to 7.7 g/㎗. Spectra were measured by Cary 5E spectrophotometer (Varian, Australia). Serum in the 5 mm-pathlength cuvette was put in the sample beam and air in the reference beam. Full spectrum PLSR was applied to determine total protein from sera. Next, the wavelength region of 1672∼1754 nm was selected based on the first loading vector analysis. Standard Error of Cross Validation (SECV) of full spectrum (1530∼l850 nm) PLSR and selected wavelength PLSR (1672∼1754 nm) was respectively 0.28 and 0.27 g/㎗. The prediction accuracy between the two bands was equal. Wavelength selection based on loading vector in PLSR seemed to be simple and robust in comparison to other methods based on correlation plot, regression vector and genetic algorithm. As a reference of wavelength selection for PLSR, the loading vector has the advantage over the correlation plot since the former is based on multivariate model whereas the latter, on univariate model. Wavelength selection by the first loading vector analysis requires shorter computation time than that by genetic algorithm and needs not smoothing.

The aim of this study was to examine whether near infrared spectroscopy (NIRS) is an acceptable tool to determine cholesterol and collagen in human atherosclerotic plaque without destruction of the analyzed areas and without danger the endothelial cells - three preconditions for the development of a NIR-heart-catheter. The questions were: Can the cholesterol and collagen content of the arterial intima be estimated with acceptable precision in vitro by NIRS despite the matrix inhomogeneity of the plaques and their anatomic variability\ulcorner How deep can such NIR radiation penetrate into arterial tissue without danger for endothelial cells\ulcorner Is this penetration sufficient for information on the lipid and collagen accumulation\ulcorner Using NIRS, cholesterol and collagen can be determined with acceptable precision in model mixtures and human aortic specimens (r=0,896 to 0,957). The chemical reference method was HPLC. The energy dose was 71 mW/$cm^{-2}$ using a fiber optic strand with a length of 1.5m and an optical window of d=4mm. This dose appears to be not dangerous for endothelial cells, It will be attenuated to 50% by a arterial tissue of about 170-$200\mu\textrm{m}$ thickness. The results are also acceptable using a thin coronary catheter-like fiber optic strand (d=1mm).

Milk somatic cell count (SCC) is a recognized indicator of cow health and milk quality. The potential of near infrared (NIR) spectroscopy in the region from 1100 to 2500nm to measure SCC content of cow milk was investigated. A total of 196 milk samples from 7 Holstein cows were collected for 28 days, consecutively, and analyzed for fat, protein, lactose and SCC. Three of the cows were healthy, and the rest had mastitis periods during the experiment. NIR transflectance milk spectra were obtained by the InfraAlyzer 500 spectrophotometer in a wavelength range from 1100 to 2500 nm. The calibration for logSCC was performed using partial least square (PLS) regression and different spectral data pretreatment. The best accuracy of determination was found for equation, obtained using smoothed absorbance data and 10 PLS factors. The standard error of calibration was 0.361, calibration coefficient of multiple correlation 0.868, standard error of prediction for independent validation set of samples 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. The accuracy of logSCC determination by NIR spectroscopy would allow health screening of cows, and differentiation between healthy and mastitic milk samples. When the spectral information was studied it has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. In the case of mastitis, when the disease occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk.

Near infrared spectroscopy (NIRS) was employed to qualify and quantify on survival, the injury rate and apoptosis of the human breast cancer cell line MCF-7 cells. MCF-7 cells were cultured in RPMI medium supplemented with 10% FCS in a 95% air and 5% CO2 atmosphere at 37$^{\circ}C$. For the viable cells preparation, cells were de-touched by 0.1% of trypsin treatment and washed with RPMI supplemented with 10% FCS medium by centrifugation at 1000 rpm for 3min. For the dead cells preparation, cells were de-touched by a cell scraper. The cells were counted by a hemacytometer, and the viability was estimated by the exclusion method with frypan blue dye. Each viable and dead cells were suspended in PBS (phosphate bufferred saline) or milk at the cell density desired. For the quantitative determination of cell death by measuring the LDH (lactate dehydrogenase) activity liberated from cells with cell membrane injuries, LDH-Cytotoxic Test Wako (Wako, Pure Pharmaceutical Co. Ltd., Japan) was used. We found that NIRS measurement of MCF-7 cells at the density range could evaluate and monitor the different characteristics of living cells and dead cells. The spectral analysis was performed in two wavelength ranges and with 1,4, 10 mm pathlength. Different spectral data pretreatment and chemometrics methods were used. We applied SIMCA classificator on spectral data of living and dead cells and obtained good accuracy when identifying each class. Bigger variation in the spectra of living cells with different concentrations was observed when compared to the same concentrations of dead cells. PLS was used to measure the number of cells in PBS. The best model for measurement of dead cells, as well as living cells, was developed when raw spectra in the 600-1098 nm region and 4 mm pathlength were used. Smoothing and second derivative spectral data pretreatment gave worst results. The analysis of PLS loading explained this result with the scatter effect found in the raw spectra and increased with the number of cells. Calibration for cell count in the 1100-2500 nm region showed to be very inaccurate.

A method to discriminate human gallstones by nea. infrared(NIR) spectrometry using a soft independent modeling of class analogy (SIMCA) has been studied. The fifty NIR spectra of gallstones in the wavenumber range from 4500 to 10,000 cm$\^$-1/ were measured. The forty samples were classified to three classes, cholesterol stone, calcium bilirubinate stone and calcium carbonate stone according to the contents of major components in each gallstone. The training set which contained objects of the different known class was constructed using forty NIR spectra and the test set was made with ten different gallstone spectra. The number of important principal components(PCs) to describe the class was determined by cross validation in order to improve the decision criterion of the SIMCA for the training set. The score plots of the class training set whose objects belong to the other classes were inspected. The critical distance of each class was computed using both the Euclidean distance and the Mahalanobis distance at a proper level of significance(${\alpha}$). Two methods were compared with respect to classification and their robustness towards the number of PCs selected to describe different classes.

Previous works have shown the viability of NIRS technology for the prediction of fatty acids in Iberian pig fat, but although the resulting equations showed high precision, in the predictions of new samples important fluctuations were detected, greater with the time passed from calibration development to NIRS analysis. This fact makes the use of NIRS calibrations in routine analysis difficult. Moreover, this problem only appears in products like fat, that show spectrums with very defined absorption peaks at some wavelengths. This circumstance causes a high sensibility to small changes of the instrument, which are not perceived with the normal checks. To avoid these inconveniences, the software WinISI 1.04 has a mathematic algorithm that consist of create a “Repeatability File”. This file is used during calibration development to minimize the variation sources that can affect the NIRS predictions. The objective of the current work is the evaluation of the use of a repeatability file in quantitative NIRS analysis of Iberian pig fat. A total of 188 samples of Iberian pig fat, produced by COVAP, were used. NIR data were recorded using a FOSS NIRSystems 6500 I spectrophotometer equipped with a spinning module. Samples were analysed by folded transmission, using two sample cells of 0.1mm pathlength and gold surface. High accuracy calibration equations were obtained, without and with repeatability file, to determine the content of six fatty acids: miristic (SECV$\sub$without/=0.07% r$^2$$\sub$without/=0.76 and SECV$\sub$with/=0.08% r$^2$$\sub$with/=0.65), Palmitic (SECV$\sub$without/=0.28 r$^2$$\sub$without/=0.97 and SECV$\sub$with/=0.24% r$^2$$\sub$with/=0.98), palmitoleic (SECV$\sub$without/=0.08 r$^2$$\sub$without/=0.94 and SECV$\sub$with/=0.09% r$^2$$\sub$with/=0.92), Stearic (SECV$\sub$without/=0.27 r$^2$$\sub$without/=0.97 and SECV$\sub$with/=0.29% r$^2$$\sub$with/=0.96), oleic (SECV$\sub$without/=0.20 r$^2$$\sub$without/=0.99 and SECV$\sub$with/=0.20% r$^2$$\sub$with/=0.99) and linoleic (SECV$\sub$without/=0.16 r$^2$$\sub$without/=0.98 and SECV$\sub$with/=0.16% r$^2$$\sub$with/=0.98). The use of a repeatability file like a tool to reduce the variation sources that can disturbed the prediction accuracy was very effective. Although in calibration results the differences are negligible, the effect caused by the repeatability file is appreciated mainly when are predicted new samples that are not in the calibration set and whose spectrum were recorded a long time after the equation development. In this case, bias values corresponding to fatty acids predictions were lower when the repeatability file was used: miristic (bias$\sub$without/=-0.05 and bias$\sub$with/=-0.04), Palmitic (bias$\sub$without/=-0.42 and bias$\sub$with/=-0.11), Palmitoleic (bias$\sub$without/=-0.03 and bias$\sub$with/=0.03), Stearic (bias$\sub$without/=0.47 and bias$\sub$with/=0.28), oleic (bias$\sub$without/=0.14 and bias$\sub$with/=-0.04) and linoleic (bias$\sub$without/=0.25 and bias$\sub$with/=-0.20).

When a drug is prepared in a tablet, the active component represents only a small portion of the dosage form. The other components of the formulation include materials to assist in the dissolution, antioxidants, coloring agents and bulk fillers. The tablets are tested using approved testing methods usually involving separation and subsequent quantification of the active component. Tablets may also be tested by near-Infrared Reflectance spectrometry (NIRS). In the present study, based on NIRS and multivariate calibration methods, a novel and precise method is developed for direct determination of ascorbic acid in vitamin C tablet. Two different tablet formulations were powdered in three different sizes, 63-125 ${\mu}{\textrm}{m}$, and examined. Spectral region of 4750-4950 $cm^{-1}$ / was used and optimized for quantitative operations. Partial least squares (PLS) and multiple linear regression (MLR) methods were performed for this spectral region. The results of optimized PLS and MLR methods showed that reproducibility increase with decreasing grain size and standard error of calibration (SEP) of less than 1% w/w of ascorbic acid and a correlation coefficient of 0.998 can be achieved. The PLS method showed better results than MLR. Seven overdose and underdose samples (prepared in the laboratory to match marketed products) were tested by proposed and iodometric standard methods. A correlation between NIRS predicted ascorbic acid values and iodomet.ic values was calculated ($R^2$=0.9950). Finally, the direct analysis of individual intact tablets in their unit-dose packages (Blistering in aluminum and PVC foils) obtained from market were also carried out and a correlation coefficient of 0.9989 and SEP of 0.931% w/w of ascorbic acid were achieved.

The feasibility of NIR spectroscopy as a quick and nondestructive method for quality control of uniformity of coating thickness of pharmaceutical tablets was investigated. Near infrared spectra of a set of pharmaceutical tablets with varying coating thickness were measured with a diffuse reflectance fiber optic probe connected to a Broker IFS 28/N FT-NIR spectrometer. The challenging issues encountered in this study included: 1. The similarity of the formulation of the core and coating materials, 2. The lack of sufficient calibration samples and 3. The non-linear relationship between the NIR spectral intensity and coating: thickness. A peak at 7184 $cm^{-1}$ was identified that differed for the coating material and the core material when M spectra were collected at 2 $cm^{-1}$ resolution (0.4 nm at 7184 $cm^{-1}$). The study showed that the coating thickness can be analyzed by polynomial fitting of the peak area of the selected peak, while least squares calibration of the same data failed due to the lack of availability of sufficient calibration samples. Samples of coal powder and solid pieces of coal were analyzed by FT-NIR diffuse reflectance spectroscopy with the goal of predicting their ash content, percentage of volatile components, and energy content. The measurements were performed on a Broker Vector 22N spectrometer with a fiber optic probe. A partial least squares model was constructed for each of the parameters of interest for solid and powdered sample forms separately. Calibration models varied in size from 4 to 10 PLS ranks. Correlation coefficients for these models ranged from 86.6 to 95.0%, with root-mean-square errors of cross validation comparable to the corresponding reference measurement methods. The use of FT-NIR diffuse reflectance measurement techniques was found to be a significant improvement over existing measurement methodologies in terms of speed and ease of use, while maintaining the desired accuracy for all parameters and sample forms.(Figure Omitted).

This study developed effective assay method of pharmaceutical quality control was developed by near-infrared spectroscopy (NIRS). The calibration equation model of assay was developed by 2nd deriviative PLS(Partial Least Squares) regression method with NIRS over the wavelength range from 1100 to 1400nm using diazepam tablets (2mg, 5mg). Although diazepam tablets are made by 5-different manufacture, they have similar formulation. When the correlation was compared with values by NIRS and HPLC, the R-2s and standard error of calibration (SEC) for 2mg were 0.9300 and 0.98%, the R-2s and SEC for 5mg were 0.9165 and 0.63%. The validation of the calibration equation model yield that the R-2s and standard error of prediction (SEP) for 2mg were 0.9611 and 0.995%, the R-2s and SEP for 5mg were 0.9114 and 0.842%. The method was validated on assay method for diazepam tablets by the calibration equation.

For the quality control of tablets several parameters have to be checked. The most important one is the content of an active ingredient which has to match a narrow range around the designated content. The only useful measurement mode is transmission which provides information of the complete tablet. A measurement in diffuse reflectance would register only the surface which is useless especially in case of a coated tablet. In this work tablets for a clinical study (placebo/verum studies) with very low concentrations of the active ingredient were measured. The concentration range was 0 to 6 mg with a total weight of the tablets of 105 mg, leading to a highest concentration of the active component of 5.7% by weight. Especially the spectroscopic distinction between the placebo and the low dosage forms with 0.25 and 0.5 mg active agent requires an extraordinarily accurate sampling technique. Using the VECTOR 22/N-T in transmission mode allows the collection of the information from the complete tablets. A quantitative PLS-model with transmission spectra from the tablets described above shows that the active substance can be predicted with a RMSECV (root mean square error of cross validation) of 0.04% absolute for this special application. The results are compared with those of measurements in diffuse reflectance using different accessories.