• Asian-Australasian Journal of Animal Sciences
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• 제18권7호
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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.

• 한국초지조사료학회지
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• 제34권4호
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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.

• 한국식품과학회지
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• 제26권6호
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• pp.718-725
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• 1994

국내산 맵쌀 30종으로 쌀의 미질(米質)과 관련이 큰 수분, 단백질, 지방 및 아밀로오스 함량을 근적의 분광분석법에 의해 동시에 신속, 정확하게 비파괴 측청할 수 있는지의 가능성을 조사하였다. 기존의 습식분석법에 의한 수분, 단백질, 지방 및 아밀로오스 함량치 데이타와 근적외영역의 흡광도 데이터 사이에 중희귀분석을 행한 결과, 백미외 수분, 단백질, 지방, 아밀로오스 함량의 측청오차(SEP)는 각각 0.104, 0.196, 0398 및 1.427%이었고, 현미의 경우는 0.120, 1226, 0.153 및 1.923%이었다. 이상의 결과로서 근적외분광분석법을 응용함으로서 쌀의 수분과 단백질은 비파괴적으로 동시에 신속, 정확하게 측정 가능함을 알 수 있었으나, 지방과 아밀로오스의 측청정확도는 낮은편이었다.

• 아시안잔디학회지
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• 제23권1호
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• pp.77-90
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• 2009

Near Infrared Reflectance Spectroscopy(NIRS)는 짧은 시간 안에 식물의 다양한 영양소를 동시에 정확하고 빠르게 측정할 수 있다. 본 연구는 creeping bentgrass 'CY2' 엽의 여러 가지 기본 요소의 값을 예측하기 위해서 NIRS(근적의선 분광분석기)를 사용하여 측정하였다. 그 결과, 질소와 수분 그리고 탄수화물의 $r^2$은 각각 0.892, 0.925, 0.971이었다. 검량식에 대한 검증에서 $r^2$이 높은 상관관계를 나타냈으므로, 잔디에서 더 많은 연구를 위한 실용화 가능성을 확인 할 수 있었다.

• Journal of Crop Science and Biotechnology
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• 제10권3호
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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.

• 한국초지조사료학회지
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• 제34권3호
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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.

• 한국축산시설환경학회지
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• 제20권4호
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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.

• 한국작물학회지
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• 제61권1호
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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.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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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.

• 한국작물학회지
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• 제53권spc호
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• pp.108-114
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• 2008

녹차 품질평가의 한 요인이 되는 색도 평가 시 기존 평가 방법인 육안평가 혹은 색차 분석에 의존하고 있는 현행 분석방법을 신속, 간편하며 재현성이 높고, 녹차 품질관련 기타 성분과 동시분석이 가능한 녹차 색차 분석용 NIRS 검량식을 작성한 결과를 요약하면 다음과 같다. 1. 공시된 녹차 시료를 대상으로 색차계를 이용하여 색도 값(L, a, b)을 조사한 결과 검량식 작성용 시료는 L값이 평균 53.37($48.52{\sim}57.72$), a값이 평균 -7.55($-10.02{\sim}-4.63$), b 값이 평균 18.07($14.00{\sim}22.02$)을 나타내었고, 작성 검량식의 평가용으로 이용된 예견치 분석용 시료와 거의 동일한 범위를 나타내었다. 2. 녹차의 색차 분석용 NIRS 검량식을 검토한 결과 색차 중 명도에 해당하는 L 값은 원시 스펙트럼에 2차 미분(2nd derivative, 8 nm gap, 6 points smoothing, 1 point second smoothing)을 수행한 조건에서 $R^2$ = 0.936으로 가장 우수한 양상을 나타내었고, 적색에 해당되는 색차 a값과 황색에 해당하는 b값은 1차 미분(1st derivative, 4 nm gap, 4 points smoothing, 1 point second smoothing)조건에서 $R^2$가 각각 0.991 및 0.958로 가장 우수한 결과를 나타내었다. 3. 최적의 녹차 색차 분석용으로 작성된 각각의 NIRS 검량식을 미지시료에 적용하여 정확성을 평가한 결과 색도값 L, a 및 b의 결정계수는 각각 0.905, 0.986 및 0.931로 매우 높은 상관을 보였으며, 이들 검량식은 향후 NIRS를 이용한 녹차 관련 연구 및 녹차 산업현장에서 품질관리를 위한 효율적 분석방법으로 활용이 가능할 것으로 판단된다.