• Asian-Australasian Journal of Animal Sciences
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• v.18 no.7
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• pp.1036-1040
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• 2005

Near-infrared reflectance spectroscopic (NIRS) calibrations were developed for accurate and fast prediction of whole broiler chicken carcass composition. The Feed and Forage Foss systems Model 5000 Reflectance Transport Model 5000 with near-infrared reflectance spectroscopy (NIRS)-WinISI II windows software was used for this purpose. One equation was developed for the prediction of each carcass component. One hundred and fifty freeze dried broiler whole carcass samples were ground in a Cyclotech 1,093 sample mill and analyzed for dry matter, protein, fat, calcium and phosphate. Samples were divided into two sets: a calibration set from which equations were derived and a prediction set used to validate these equations. The chemical analysis values (mean${\pm}$SD) were calculated based on dry matter basis as follows: dry matter: 33.41${\pm}$2.78 (range: 26.41-43.47), protein: 54.04${\pm}$6.63 (range: 36.20-76.09), fat 35.44${\pm}$8.34 (range: 7.50-55.03), calcium 2.55${\pm}$0.65 (range: 0.99-4.41), phosphorus 1.38${\pm}$0.26 (range: 0.60-2.28). One hundred and three samples were used to calibrate the equations and prediction values. The software used was modified to obtain partial least square regression statistics, as it is the most suitable for natural products analysis. The coefficients of determination ($R^2$) and the standard errors of prediction were 0.82 and 1.83 for the dry matter, 0.96 and 1.98 for protein, 0.99 and 1.07 for fat, 0.90 and 0.30 for calcium and 0.91 and 0.11 for phosphorus, respectively. The present study indicated that NIRS can be calibrated to predict the whole broiler carcass chemical composition, including minerals in a rapid, accurate, and cost effective manner. It neither requires skilled operators nor generates hazardous waste. These findings may have practical importance to improve instrumental procedures for quick evaluation of broiler carcass composition.

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.4
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• pp.277-282
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• 2014

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination ($R^2$) and the root mean squared error of prediction (RMSEP). The results showed the moisture content ($R^2_{val}=0.97$, RMSEP=0.109), crude protein content ($R^2_{val}=0.94$, RMSEP=0.212), neutral detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.763), acid detergent fiber content ($R^2_{val}=0.96$, RMSEP=0.142), gross energy ($R^2_{val}=0.82$, RMSEP=23.249), in vitro dry matter digestibility ($R^2_{val}=0.68$, RMSEP=1.69), and metabolizable energy (approximately $R^2_{val}$ >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.

• Korean Journal of Food Science and Technology
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• v.26 no.6
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• pp.718-725
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• 1994

The applicability of near infrared reflectance spectroscopy (NIRS) to determine moisture, protein, fat and amylose content of domestic rice was studied. The standard error of prediction (SEP) of moisture, protein, fat and amylose in polished rice was 0.014, 0.196, 0.098 and 1.427%, and those SEP of brown rice was 0.12, 1.226, 0.153 and 1.923%, respectively. It is concluded that the NIRS method allowed to detect the content of moisture and protein in rice samples with fair precision comparing conventional analysis, but the accuracy for determining amylose and fat was not acceptable.

• Asian Journal of Turfgrass Science
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• v.23 no.1
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• pp.77-90
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• 2009

Near Infrared Reflectance Spectroscopy(NIRS) is a quick, accurate, and non-destructive method to measure multiple nutrient components in plant leaves. This study was to acquire a liner regression equation by evaluating the nutrient contents of 'CY2' creeping bentgrass rapidly and accurately using NIRS. In particular, nitrogen fertility is a primary element to keep maintaining good quality of turfgrass. Nitrogen, moisture, carbohydrate, and starch were assessed and analyzed from 'CY2' creeping bentgrass clippings. A linear regression equation was obtained from accessing NIRS values from NIR spectrophotometer(NIR system, Model XDS, XM-1100 series, FOSS, Sweden) programmed with WinISI III project manager v1.50e and ISIscan(R) (Infrasoft International) and calibrated with laboratory values via chemical analysis from an authorized institute. The equation was formulated as MPLS(modified partial least squares) analyzing laboratory values and mathematically pre-treated spectra. The accuracy of the acquired equation was confirmed with SEP(standard error of prediction), which indicated as correlation coefficient($r^2$) and prediction error of sample unacquainted, followed by the verification of model equation of real values and these monitoring results. As results of monitoring, $r^2$ of nitrogen, moisture, and carbohydrate in 'CY2' creeping bentgrass was 0.840, 0.904, and 0.944, respectively. SEP was 0.066, 1.868, and 0.601, respectively. After outlier treatment, $r^2$ was 0.892, 0.925, and 0.971, while SEP was 0.052, 1.577, and 0.394, respectively, which totally showed a high correlation. However, $r^2$ of starch was 0.464, which appeared a low correlation. Thereof, the verified equation appearing higher $r^2$ of nitrogen, moisture, and carbohydrate showed its higher accuracy of prediction model, which finally could be put into practical use for turf management system.

• Journal of Crop Science and Biotechnology
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• v.10 no.3
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• pp.185-192
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• 2007

Near-infrared spectroscopy(NIRS) was used to develop a rapid and efficient method to determine lignan glucosides in intact seeds of sesame(Sesamum indicum L.) germplasm accessions in Korea. A total of 93 samples(about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for lignan glucosides contents were measured by high performance liquid chromatography. Calibration equations for sesaminol triglucoside, sesaminol($1{\rightarrow}2$) diglucoside, sesamolinol diglucoside, sesaminol($1{\rightarrow}6$) diglucoside, and total amount of lignan glucosides were developed using modified partial least square regression with internal cross validation(n=63), which exhibited lower SECV(standard errors of cross-validation), higher $R^2$(coefficient of determination in calibration), and higher 1-VR(ratio of unexplained variance divided by variance) values. Prediction of an external validation set(n=30) showed a 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, as factors used to evaluate the accuracy of equations. The models for each glucoside content had relatively higher values of SD/SEP(C) and $r^2$(more than 2.0 and 0.80, respectively), thereby characterizing those equations as having good quantitative information, while those of sesaminol($1{\rightarrow}2$) diglucoside showing a minor quantity had the lowest SD/SEP(C) and $r^2$ values(1.7 and 0.74, respectively), indicating a poor correlation between reference values and NIRS estimated values. The results indicated that NIRS could be used to rapidly determine lignan glucosides content in sesame seeds in the breeding programs for high quality sesame varieties.

• Journal of The Korean Society of Grassland and Forage Science
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• v.34 no.3
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• pp.209-213
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• 2014

This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical and fermentation parameters of whole crop winter rye silages. A representative population of 216 fresh winter rye silages was used as database for studying the possibilities of NIRS to predict chemical composition and fermentation parameters. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh winter rye silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH as well as lactic acid content with correlation coefficients of cross-validation ($R^2cv$) of 0.96, 0.86, 0.79, 0.85, 0.82 and 0.78 respectively and standard error of cross-validation (SECV) of 1.89, 2.02, 2.79, 1.14, 1.47 and 0.46 % DM respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical parameters of winter rye silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of Animal Environmental Science
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• v.20 no.4
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• pp.183-188
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• 2014

Near-infrared reflectance spectroscopy (NIRS) was used to study the potential of adulteration of imported forage. Hay samples were prepared two set ; calibration set and validation one. The former were mixed 12 sets from 100% to 50% with Yangcho (Chinese leymus, leymus chinensis Trin.) and the latter were adulterated with 6 set of 8% to 38% in 5% interval. Mixed materials with Yangcho were rice straw, reed and alfalfa. Stand error of prediction (SEP) in calibration equation for alfalfa, reed and rice straw were 0.97, 0.97 and 0.99 also 0.54, 0.86 and 1.26%. Multiple correlation coefficient ($R^2$) for alfalfa, reed and rice straw were 0.99, 0.97 and 0.99. SEP in the same samples were 1.88, 2.15 and 1.49, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.61 no.1
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• pp.64-69
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• 2016

This study was conducted to develop a fast and efficient screening method to determine the quantity of fatty acid in peanut oil for high oleate breeding program. A total of 329 peanut samples were used in this study, 227 of which were considered in the calibration equation development and 102 were utilized for validation, using near infrared reflectance spectroscopy (NIRS). The NIRS equations for all the seven fatty acids had low standard error of calibration (SEC) values, while high R2 values of 0.983 and 0.991 were obtained for oleic and linoleic acids, respectively in the calibration equation. Furthermore, the predicted means of the two main fatty acids in the calibration equation were very similar to the means based on gas chromatography (GC) analysis, ranging from 36.7 to 77.1% for oleic acid and 7.1 to 42.7% for linoleic acid. Based on the standard error of prediction (SEP), bias values, and $R^2$ statistics, the NIRS fatty acid equations were accurately predicted the concentrations of oleic and linoleic acids of the validation sample set. These results suggest that NIRS equations of oleic and linoleic acid can be used as a rapid mass screening method for fatty acid content analysis in peanut breeding program.

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

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