• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.2
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• pp.154-160
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• 2017

This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible-NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The $R^2$ value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.

• Journal of the Korean Applied Science and Technology
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• v.16 no.2
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• pp.155-161
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• 1999

Red pepper powder (Capsicum annum L.) is an important seasoning as a kimchi ingredient in korea and most korean consumer tend to eat the korean red pepper powder as the better than other oriental country such as China. Near infrared reflectance spectroscopy (NIRS) was applied for discrimination according to geographical origin (Korea, China) of red pepper powder. The objective of this study is to determine if NIR technique could be used to discriminate between the korean red pepper powder and non-korean red pepper powder according to seed content and maxing ratio in red pepper powder by using the new method. Rapid, precise and nondestructive analysis method for determination of the geographical origin of red pepper powder by near infrared spectroscopy and chemometrics were performed. It has been observed discriminant analysis with PLS is adequate to determinate the geographical origin of red pepper powder. It tend to difficult the discrimination of geographical origin according to increase the seed content of red pepper powder. The accuracy of discrimination in mixed red pepper powder was range from 95.2% to 100%.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1263-1263
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• 2001

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.

• Korean Journal of Food Science and Technology
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• v.38 no.2
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• pp.165-168
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• 2006

Fourier transform-near infrared spectroscopy (FT-NIRS) was evaluated for determination of total dietary fiber (TDF) content of brown rice. Enzymatic-gravimetric method was suitable to obtain reference values for calibration of NIR at 1,000-2,500 nm range. Standard error of laboratory procedure ranged 0.17 to 0.72%. Partial least square (PLS) regression was used to develop the calibration equations. Regression was performed automatically using NIRCal chemometric software. Accuracy of prediction model for TDF content was certified for regression coefficient (r), standard error of estimation (SEE) and standard error of prediction (SEP), showing 0.9780, 0.0636, and 0.0642, respectively. This prediction model can be used for determination of TDF in brown rice and would be useful for real-time analysis in food industry.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.45-52
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• 2006

This study was carried out to explore the accuracy of Near Infrared Reflectance Spectroscopy (NIRS) fer the prediction of digestibility and energy value of corn silages. The spectral data were regressed against a range of digestibility and energy parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with first and second order derivatization, with scatter correction procedure(SNV-Detrend) to reduce the effect of extraneous noise. Calibration models for NIRS measurements gave multivariate correlation coefficients of determination$(R^2)$ and standard errors of cross validation of 0.92(SECV 1.73), 0.91(SECV 1.13) and 0.93(SECV 1.74) for in vitro dry matter digestibility(IVDMD), in vitro true digestibility(IVTD), and cellulase dry matter digestibility(CDMD), respectively. The standard error of prediction(SEP) and the multiple correlation coefficient of validation$(R^2v)$ on the validation set(n=39) was used in comparing the prediction accuracy. The SEP value was 0.30(TDN), 0.01(NEL), and 0.01(ME). The relative ability of NIRS to predict digestibility and energy value was very good for CDMD, total digestible nutrients(TDN), net energy fer lactation(NEL) and metabolizable energy(ME). This paper shows the potential of NIRS to predict the digestibility and energy value of con silage as a routine method in feeding programmes and for giving advice to farmers.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.6
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• pp.716-722
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• 1995

Near Infrared Reflectance Spectroscopy (NIRS) has been used as a tool for the rapid, accurate and nondestructive assay of small grain and forage quality analysis. The objective of this study was to establish the rapid, easy and accurate analysis method for major components of covered barley using NIRS system. NIRS used in this study was filter type instrument, Neotec 102. To obtain a useful calibration equation, standard regression between the data was analyzed by chemical analysis and by NIRS method. Standard errors of prediction (SEP) and simple correlations for unknown samples were calculated using obtained equation. SEPs for starch, $\beta$-glucan, protein and ash contents were 2.75%, 0.64%, 0.26% and 0.19%, respectively. The simple correlations for starch, $\beta$-glucan, protein and ash contents were 0.932, 0.588, 0.984 and 0.867, respectively. It was concluded that the NIRS method would be applicabl for the rapid determination of starch, protein and ash contents in barley grains.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1513-1513
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• 2001

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.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.100-100
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• 2020

Wheat is mainly composed of carbohydrate but it contains a moderate amount of protein, which gives a very useful characteristics to flour food such as the unique elasticity and stickiness of the dough. We developed a calibration equation for analyzing crude protein content using Near Infrared Spectroscopy to quick analyze the crude protein content of wheat germplasm stored in the National Agrobiodiversity Center, RDA, Korea. The 1,798 wheat germplasms were used to draw up the calibration formula. The crude protein's interval distribution of 1,798 wheat germplasms used for the calibration was 7.04-20.84%, the average content was 13.2%, and standard deviation was 2.6%. The germplasms distribution was composed of a suitable group for the preparation of the calibration formula because the content distribution was a normal, excluding the 13.0-15.5% content section. In order to verify the applicability of the NIRS prediction model, we measured the crude protein content of the 300 wheat germplasms that were not used for the calibration using both Kjeldahl analysis and NIR spectrum. The analysis value calculated using each method were statistically processed, and the test results and statistical indicators of the predictive model were compared. As a result, The R² value of the optimized NIRS prediction model was 0.997, and the Standard error of Calibration value(SEC) was 0.132, and slope value was 1.000. With prediction model selection, compared to Kjeldahl method, R² values were 0.994(Kjeldahl), 0.998(NIRS), and the SEC value were 0.191 and 0.132, respectively, comparing the statistical indices of the forecast model. And slope value were 1.013, 1.000, respectively. The analysis of crude protein content by the NIRS predictive model developed by each statistical index showing similar figures is judged to show a high degree of correlation with the Kjeldahl analysis. The proven calibration equation will be used to measure the crude protein content of wheat germplasms held by the National Agrobiodiversity Center, and by dividing the wheat germplasms by their use according to the crude protein content, it will provide useful information to relevant researchers.

• Near Infrared Analysis
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• v.2 no.1
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• pp.59-66
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• 2001

Nowadays, medical diagnostics is efficiently supported by clinical chemistry and near infrared spectroscopy is becoming a new dimension, which has shown high potential to provide valuable information for diagnosis. The investigation was carried out to study the influence of mammary gland inflammation, called mastitis, on cow´s milk spectra and milk composition measured by near infrared spectroscopy (NIRS). Milk somatic cell counts (SCC) in milk were used as a measure of mammary gland inflammation. Naturally occurred variations with milk composition within lactation and in the process of milking were included in the experimental design of this study. Time series of unhomogenized, raw milk spectral data were collected from 3 cow along morning and evening milking, for 5 consecutive months, within their second lactation. In the time of the trial, the investigated cows had periods with mammary gland inflammation. Transmittance spectra of 258 milk samples were obtained by NIRSystem 6500 spectrophotometer in 1100-2400 nm region. Calibration equations for the examined milk components were developed by PLS regression using 3 different sets of samples: samples with low somatic cell count (SCC), samples with high SCC and combined data set. The NIR calibration and prediction of individual cow´s milk fat, protein, and lactose were highly influenced by the presence of mil samples from animals with mammary gland inflammation in the data set. The best accuracy of prediction (i.e. the lower SEP and the higher correlation coefficient) for fat, protein and lactose was obtained for equations, developed when using only “healthy” samples, with low SCC. The standard error of prediction increased and correlation coefficient decreased significantly when equations for low SCC milk were used to predict examined components in “mastitis” samples with high SCC, and vice versa. Combined data set that included samples from healthy and mastitis animals could be used to build up regression models for screening. Further use of separate model for healthy samples improved milk composition measurement. Regression vectors for NIR mild protein measurement obtained for “healthy” and “mastitic” group were compared and revealed differences in 1390-1450 nm, 1500-1740 nm and 1900-2200 nm regions and thus illustrated post-secretory breakdown of milk proteins by hydrolytic enzymes that occurred with mastitis. For the first time it has been found that monitoring the spectral differences in water bands at 1440 nm and 1912 nm could provide valuable information for inflammation diagnosis.