• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.56 no.1
• /
• pp.88-93
• /
• 2011

This experiment was carried out to find suitable sample type for the more accurate prediction and non-destructive way in the application of near infrared reflectance spectroscopy (NIRS) technique for estimation the protein, total amino acids, and total isoflavone of soybean by comparing three different sample types, single seed, whole seeds, and milled seeds powder. The coefficient of determination in calibration ($R^2$) and coefficient of determination in cross-validation (1-VR) for three components analyzed using NIRS revealed that milled powder sample type yielded the highest, followed by single seed, and the whole seeds as the lowest. The coefficient of determination in calibration for single seed was moderately low($R^2$ 0.70-0.84), while the calibration equation developed with NIRS data scanned with whole seeds showed the lowest accuracy and reliability compared with other sample groups. The scatter plot for NIRS data versus the reference data of whole seeds showed the widest data cloud, in contrary with the milled powder type which showed flatter data cloud. By comparison of NIRS results for total isoflavone, total amino acids, and protein of soybean seeds with three sample types, the powder sample could be estimated for the most accurate prediction. However, based from the results, the use of single bean samples, without grinding the seeds and in consideration with NIRS application for more nondestructive and faster prediction, is proven to be a promising strategy for soybean component estimation using NIRS.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.41 no.3
• /
• pp.354-361
• /
• 1996

Near Infrared Reflectance Spectroscopy(NIRS) is accepted as today's most versatile method for rapid chemical analysis. The technique offers rapid multicomponent analysis. This study was conducted to improve the efficiency of quality analysis in covered barley grain, and to search for the effects of chemical components variation in covered barley grain on NIRS. Among the three groups with different range in the contents, each equation for starch contents increased standard error of prediction(SEP) and increased correlation coefficient from 0.872 to 0.883. According as, $\beta$-glucan and protein contents decreased SEP and increased correlation coefficient by expanded chemical components variation. Effective equation for ash contents analysis was obtained from group 3. Among the covered barley chemical components, starch and ash contents were required to conduct futher studies in term of accuracy and variation of contents. It was concluded that NIRS method would be applicable for the rapid determination of $\beta$-glucan and protein contents in covered barley grains.

• Journal of the Korean Society of Tobacco Science
• /
• v.22 no.1
• /
• pp.76-83
• /
• 2000

This study was carried out to predict blending ratio of cut tobacco(CT), expanded stem(ES), and expanded cut tobacco(ECT) in cigarettes. CT, ES, and ECT samples from A brand were, ground and blended with reference to A blending ratio, and scanned by near infrared spectroscopy(NIRSystem Co., Model 6500). Calibration equations were developed and then determined blending ratio by NIRS. The standard error of calibration(SEC) and performance(SEP) of C factory samples between NIRS and known blending ratio were 0.97%, 1.93% for CT, 0.50%, 1.12 % for ES and 0.68%, 1.10% for ECT, respectively. The SEP of CT, ES and ECT of Band D factory samples determined by C factory calibration equation were more inaccurate than those of C factory samples determined by C factory calibration equations. These results were caused by the difference of CT, ES and ECT spectra followed by each factory. The SEP of CT, ES and ECT of Band D factories determined by calibration equations derived from each factory samples were more accurate than those of determined by calibration equation derived from C factory samples. Each factory SEP of CT, ES and ECT determined by calibration equation derived from all calibration samples(B+C+D factory) was similar to that determined by calibration equation derived from each factory samples. To improve the analytical inaccuracy caused by spectra difference, we need to apply a specific calibration equation for each factory sample. Data in development of specific calibrations between sample and NIRS spectra might supply a method for rapid determination of blending ratio of CT, ES, and ECT.

• Journal of Crop Science and Biotechnology
• /
• v.10 no.1
• /
• pp.13-18
• /
• 2007

Near-infrared spectroscopy(NIRS) was used as a rapid and nondestructive method to determine the fatty acid composition in intact seed samples of rapeseed(Brassica napus L.). A total of 349 samples(about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for fatty acid composition were measured by gas-liquid chromatography. Calibration equations for individual fatty acids were developed using the regression method of modified partial least-squares with internal cross validation(n=249). The equations had low SECV(standard errors of cross-validation), and high $R^2$(coefficient of determination in calibration) values(>0.8) except for palmitic and eicosenoic acid. Prediction of an external validation set(n=100) showed significant correlation between reference values and NIRS estimated values based on the SEP(standard error of prediction), $r^2$(coefficient of determination in prediction), and the ratio of standard deviation(SD) of reference data to SEP. The models developed in this study had relatively higher values(> 3.0 and 0.9, respectively) of SD/SEP(C) and $r^2$ for oleic, linoleic, and erucic acid, characterizing those equations as having good quantitative information. The results indicated that NIRS could be used to rapidly determine the fatty acid composition in rapeseed seeds in the breeding programs for high quality rapeseed oil.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.57 no.1
• /
• pp.29-34
• /
• 2012

The objective of this study was to rapidly evaluate fatty acids in a collection of millet (Panicum miliaceum subsp. miliaceum) of different origins so that this information could be disseminated to breeders to advance germplasm use and breeding. To develop the calibration equations for rapid and nondestructive evaluation of fatty acid content, near-infrared reflectance spectroscopy (NIRs) spectra (1104-2494 nm) of samples ground into flour ($n$=100) were obtained using a dispersive spectrometer. A modified partial least-squares model was developed to predict each component. For foxtail millet germplasm, our models returned coefficients of determination ($R^2$) of 0.89, 0.89, 0.89, and 0.92 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively. The prediction of the external validation set (n=10) showed significant correlation between references values and NIRs values ($r^2$=0.64, 0.90, 0.79, and 0.89 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively). Standard deviation/standard errors of cross-validation (SD/SECV) values were close to 3 (2.62, 2.40, 1.85, and 2.23 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively). These results indicate that these NIRs equations are functional for the mass screening and rapid quantification of the oleic and total fatty acids characterizing millet germplasm. Among the samples, IT153514 showed an especially high content of fatty acids ($48.14mg\;g^{-1}$), whereas IT123909 had a very low content ($34.44mg\;g^{-1}$).

• Korean Journal of Breeding Science
• /
• v.42 no.5
• /
• pp.448-454
• /
• 2010

The objective of this study was to rapidly evaluate fatty acids in a collection of foxtail millet (Setaria italica (L.) P. Beauv) of different origins so that this information could be disseminated to breeders to advance germplasm use and breeding. To develop the calibration equations for rapid and nondestructive evaluation of fatty acid content, near-infrared reflectance spectroscopy (NIRs) spectra (1104-2494 nm) of samples ground into flour (n=100) were obtained using a dispersive spectrometer. A modified partial least-squares model was developed to predict each component. For foxtail millet germplasm, our models returned coefficients of determination ($R^2$) of 0.91, 0.89, 0.98 and 0.98 for strearic acid, oleic acid, linoleic acid, and total fatty acids, respectively. The prediction of the external validation set (n=10) showed significant correlation between references values and NIRs values ($r^2=0.97$, 0.91, 0.99 for oleic, linoleic, and total fatty acids, respectively). Standard deviation/standard error of cross-validation (SD/SECV) values were greater than 3 (3.11, 5.45, and 7.50 for oleic, linoleic, and total fatty acids, respectively). These results indicate that these NIRs equations are functional for the mass screening and rapid quantification of the oleic, linolenic, and total fatty acids characterizing foxtail millet germplasm. Among the samples, IT153491 showed an especially high content of fatty acids ($84.06mg\;g^{-1}$), whereas IT188096 had a very low content ($29.92mg\;g^{-1}$).

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1246-1246
• /
• 2001

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.

• Journal of the Korean Society of Tobacco Science
• /
• v.21 no.1
• /
• pp.95-101
• /
• 1999

This study was carried out to analyze chemical components in burley tobacco using near infrared spectroscopy(NIRS). Samples were collected in '96 and '97 crop year. Calibration equations were developed by modified partial least square. The standard error performance(SEP) of '96 crop year samples between NIRS and standard laboratory analysis were 0.25% for nicotine, 0.18% for total nitrogen, 0.59% for crude ash, 0.32% for ether extracts, and 0.14% for chlorine, respectively. The analytical results of '97 crop year samples were similar to those of '96 crop year samples. The analytical result of '97 crop year samples analyzed by '96 calibration equation was more inaccurate than that of '96 crop year samples. The SEP of '96 or '97 crop year samples applying calibration equation derived from '96 plus '97 crop year samples was similar to that of '96 or '97 crop year samples analyzed by '96 or '97 calibration equation, respectively. The SEP of '97 crop year samples analyzed by calibration equation derived from '96 plus '97 crop year samples was more accurate than that of '97 crop year samples analyzed by '96 calibration equation. To improve the analytical inaccuracy caused by the difference of crop year between calibration and prediction samples, we need to include the prediction sample spectra which were different from calibration sample spectra in recalibration sample spectra, and then develop recalibration equation. The NIRS can apply to analyze burley leaf tobacco, leaf process or tobacco manufacturing process which were required the rapid analytical result.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.53 no.spc
• /
• pp.108-114
• /
• 2008

Near infrared spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. The applicability of near infrared reflectance spectroscopic method was tested to determine the color value (L, a, b) of green tea. A total of 162 green tea calibration samples and 82 validation samples were used for NIRS equation development and validation, respectively. In the developed NIRS equation for analysis of the color value (L, a, b), the most accurate equation for L value was obtained at 2, 8, 6, 1 (2nd derivative, 8 nm gap, 6 points smoothing, and 1pointsecond smoothing), and for a, and b value were obtained at 1, 4, 4, 1 (1st derivative, 4 nm gap, 4points smoothing, and 1 point second smoothing) math treatment condition with SNVD (Standard Normal Variate and Detrend) scatter correction method and entire spectrum ($400{\sim}2,500\;nm$) by using MPLS (Modified Partial Least Squares) regression. Validation results of these NIRS equations showed very low bias (L: 0.005%, a: 0.003%, b: -0.013%) and standard error of prediction (SEP, L: 0.361%, a: 0.141%, b: 0.306%) as well as high coefficient of determination ($R^2$, L: 0.905, a: 0.986, b: 0.931). Therefore, these NIRS equations can be applicable and reliable for determination of color value (L, a, b) of green tea, and NIRS method could be used as a mass screening technique for breeding programs and quality control in the green tea industry.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.50 no.6
• /
• pp.394-399
• /
• 2005

Near Infrared Spectroscopy (NIRS) has been used as a tool for the rapid, accurate and nondestructive assay of the fresh rice leaf in nitrogen content. NIRS used in this study was visible and near infrared spectroscopy type instrument, Foss model 6500. To obtain a useful calibration equation, standard regression between the data was analyzed by chemical analysis and by NIRS method. Accuracy of calibration equation for nitrogen content on fresh leaf of rice were 0.879, 0.858 and 0.819, respectively. Accuracy of calibration equation after outlier treatment increased as 0.017, 0.02 and 0.061 improved each with 0.896, 0.878 and 0.880, respectively. Calibration equation combined using merge function after accuracy of calibration equation more increased by 0.911. Difference analysis value between calibration equation and lab value by kjeldahl showed $0.001\%$. With this as same result is the possibility of closing the deterioration of the sample in order to omit a construction and pulverization process it is judged with the fact that the nitrogen content measurement of the fresh rice leaf which the possibility of reducing an hour and an expense is by a near infrared spectroscopy technique will be possible.