• KOREAN JOURNAL OF CROP SCIENCE
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• v.41 no.1
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• pp.13-19
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• 1996

This study was conducted to establish the rapid determination method for major components of maturing covered barley grains, and to improve the efficiency of selection in barley breeding. Near Infrared Reflectance Spectroscopy (NIRS) is an established, economical and nondestructive technique applied widely to the food and feed industry. 34 barley lines were sampled at 5 day-interval from 25 to 35 days after heading. A standard regression analysis for the data obtained by analytical laboratory methods and NIRS method was carried out to get a useful calibration equation. The simple significant correlation between these two methods at 25 days after heading was recognized in starch and $\beta$-glucan contents. At 30 days after heading the data obtained by two methods showed significant correlation in starch, $\beta$-glucan and protein contents. Analyzed data and that from NIRS method at 35 days after heading was significantly correlated in starch and protein contents. It was concluded that the applicability of NIRS method for the components analysis in maturing barley grains was different depending on maturing stages and components.

• 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

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.4
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• pp.259-264
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• 2020

The aim of this study was to investigate the feasibility of discrimination 12 different cultivar of sorghum × sudangrass hybrid (Sorghum genus) seed through near infrared spectroscopy (NIRS). The amount of samples for develop to the best discriminant equation was 360. Whole samples were applied different three spectra range (visible, NIR and full range) within 680-2500 nm wavelength and the spectrastar 2500 Near near infrared was used to measure spectra. The calibration equation for discriminant analysis was developed partial least square (PLS) regression and discrimination equation (DE) analysis. The PLS discriminant analysis model for three spectra range developed with mathematic pretreatment 1,8,8,1 successfully discriminated 12 different sorghum genus. External validation indicated that all samples were discriminated correctly. The whole discriminant accuracy shown 82 ~ 100 % in NIR full range spectra. The results demonstrated the usefulness of NIRS combined with chemometrics as a rapid method for discrimination of sorghum × sudangrass hybrid cultivar through seed.

• 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.

• Proceedings of the Korean Society of Medical Physics Conference
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• 1999.11a
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• pp.342-344
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• 1999

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.spc1
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• pp.42-46
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• 2006

The purpose of this study was to assess whether mechanical sensory score by Toyo Midometer can be substituted by near-infrared spectroscopy (NIRS) method in whole-grain milled rice samples. Toyo values of collected commercial brand rice (n=127) had comparatively wide ranges from 62.9 to 84.2 (Mean=70.5; S.D.=4.0). Calibration equation was developed using 73 samples. Standard error of calibration (SEC) for sensory score equation and $R^2$ were 0.95, and 0.94, respectively, however, percentage of variation in the reference method values (1-VR) which give a true indication of equation performance was slightly lower (1-VR=0.81) than calibration equation. It was demonstrated that, even though NIRS has potential as a rapid tools to predict rice sensory score, the prediction of sensory score in rice by NIRS needs to be further investigation on a large number of sample with different varieties and growing locations.

• Journal of environmental and Sanitary engineering
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• v.22 no.4
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• pp.65-76
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• 2007

The possibility of rapid quantitative analysis of AV(Acid Value), IV(Iodine Value) and fatty acids in vegetable oils with NIRS(Near-infrared spectroscopy) was evaluated. A calibration equation calculated by MPLS regression technique was developed and correlation coefficient of determination for AV, IV, $C_{16:0},\;C_{18:0},\;C_{18:1},\;C_{18:2},\;C_{18:3},\;and\;C_{20:0}$ content were 0.9727, 0.997, 0.9805, 0.942, 0.9987, 0.9994, 0.9966, and 0.975 respectively. According to the data obtained from validation study, $R^2$ of contents of perilla, corn, soybean, rapaseed oils were 0.897, 0.993, 0.935, 0.707, 0.994, 0.996, 0.984, 0.798, SEP of contents of 0.185, 1.367, 0.899, 0.640, 1.498, 1.360, 0.476, 0.076 by MPLS. The results indicate that the NIRS procedure can potentially be used as a non-destructive analysis method for the rapid and simple measurement of AV, IV and fatty acids in vegetable oils.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1156-1156
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• 2001

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.