This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2 cv) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, R2 cv, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy (R2 cv 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

본 연구는 춘파용 사초의 사료가치를 신속하고 정확하게 측정할 수 있는 습식분석의 대안을 모색하기 위하여 수행하였다. 근적외선분광 분석법을 이용한 사초의 분석 가능성을 타진하기 위해 2009년에 생산된 사초 175점을 시료로 사용하였다. 시료는 이탈리안 라이그라스와 보리, 그리고 완두를 혼파한 것으로 NIR System으로 400~2,400nm 사이의 파장을 얻었다. 그리고 수분, 조단백질, 조회분, NDF, ADF를 분석한 다음, 파장과 습식분석치를 이

Farmers need timely information on the nutritional status of their animals and the nutritive value of pastures and supplementary feeds if they are to apply successfully this existing nutritional information. Near infrared reflectance(NIR) spectroscopy has

A statistical analysis for 3651 genetic resources collected from China (1,542), Japan (1,409), Korea (413), and India (287) was conducted using normal distribution, variability index value (VIV), analysis of variation (ANOVA) and Ducan’s multiple range test (DMRT) based on a data obtained from NIRS analysis. In normal distribution, the average protein content was 8.0%, whereas waxy type amylose and common rice amylose were found to be 8.7% and 22.7%, respectively. The protein contents ranged from 5.4 to 10.6% at the level of 95%. The waxy amylose and common rice amylose ranged from 5.9 to 11.5%, and from 16.9 to 28.5% at 95% confidence level, respectively. The VIV was 0.59 for protein, 0.64 for low amylose, and 0.81 for high amylose contents. The average amylose contents were 18.85% in Japanese, 19.99% in Korean, 20.27% in Chinese, and 25.46% in Indian resources, while the average protein contents were found to be 7.23% in Korean, 7.73% in Japanese, 8.01% in Chinese, and 8.17% in Indian resources. The ANOVA of amylose and protein content showed significant differences at the level of 0.01. The F-test for amylose content was 158.34, and for protein content 53.95 compared to critical value 3.78. The DMRT of amylose and protein content showed significant difference (p<0.01) between resources of different countries. Japanese resources had the lowest level of amylose contents, whereas, the lowest level of protein content was found in Korean resources compared to other origins. Indian resources showed the highest level of amylose and protein contents. It is recommended these results should be helpful to future breeding experiments.

This study was conducted to characterize the amylose and protein contents of 4,948 rice landrace germplasm using the NIRS model developed in the previous study. The average amylose content of the germplasm was 20.39% and ranged between 3.97 and 37.13%. The amylose contents in the standard rice were 4.99, 18.63 and 20.55% in Sinseonchal, Chucheong and Goami, respectively. The average protein content was 8.17% and ranged from 5.20 to 17.45%. Protein contents in Sinseonchal, Chucheong and Goami were 6.824, 6.869 and 7.839%, respectively. A total of 62% germplasm were distributed between 20.06% and 27.02% in amylose content. Germplasm of 81.60% represented protein content of 6.78-9.75%. The distinguishable ranges of amylose contents according to origin were 16.58-20.06% in Korea, 20.06-23.25% in Japan, 23.25-27.02% in North Korea, and 27.02-37.13% in China. In the protein content, approximately 30% of Chinese resources ranged from 9.75 to 17.45%, whereas less than 10% were detected in other origin accessions. Fifty resources were selected with low and high amylose ranging from 3.97-6.66% and 30.41-37.13%, respectively. Similarly, fifty resources were selected with low and high protein ranging from 5.20-6.09% and 13.21-17.45%, respectively. Landraces with higher protein could be adapted to practical utilization of food sources.

The objective of this research was to develop Near-Infrared Reflectance Spectroscopy (NIRS) model for amylose and protein contents analysis of large accessions of rice germplasm. A total of 511 accessions of rice germplasm were obtained from National Agrobiodiversity Center to make calibration equation. The accessions were measured by NIRS for both brown and milled brown rice which was additionally assayed by iodine and Kjeldahl method for amylose and crude protein contents. The range of amylose and protein content in milled brown rice were 6.15-32.25% and 4.72-14.81%, respectively. The correlation coefficient (R 2 ), standard error of calibration (SEC) and slope of brown rice were 0.906, 1.741, 0.995 in amylose and 0.941, 0.276, 1.011 in protein, respectively, whereas R 2 , SEC and slope of milled brown rice values were 0.956, 1.159, 1.001 in amylose and 0.982, 0.164, 1.003 in protein, respectively. Validation results of this NIRS equation showed a high coefficient determination in prediction for amylose (0.962) and protein (0.986), and also low standard error in prediction (SEP) for amylose (2.349) and protein (0.415). These results suggest that NIRS equation model should be practically applied for determination of amylose and crude protein contents in large accessions of rice germplasm.

This study was investigated to develop mass evaluation system for the contents of crude protein, oil and fatty acid in soybean germplasm using NIRS. NIRS equations were created with 345 soybeans, multiple correlation coefficients of crude protein, oil, palmitic, stearic, oleic, linoleic and linolenic acid between data obtained from NIRS and quantitative analysis were 0.983, 0.969, 0.592, 0.514, 0.978, 0.961 and 0.957, respectively. Equation statistics indicated that contents of crude protein, oil and unsaturated fatty acid except palmitic and stearic acid in soybean seed were suitable for determination by NIRS. Those NIRS equations were applied to examine crude protein, oil and unsaturated fatty acid of 854 soybean landraces from Korea. The average contents and ranges of crude protein and oil were 39.2% with a range of 33.7-47.0% and 15.0% with a range of 9.8-20.3%, individually. In addition, those of oleic, linoleic and linolenic acid were 21.4% with a range of 12.1-30.2%, 55.6% with a 47.8-62.3% and 8.1% with a range of 5.9-10.7% respectively. We conducted quantitative analysis to reconfirm with IT154552 (45.1%) and IT023955(46.9%) above 45% of crude protein, the results were similar from NIRS (45.2%, 47.0%). NIRS data for protein from this study made no difference with lab data, which would be useful for mass evaluation. There was negative correlation (-0.203) between crude protein and oil, positive correlation (0.379) between crude oil and oleic acid, and significantly negative correlation (-0.879) between oleic and linoleic acid.

Brown rice grain pigments of black rice have a higher content of bioactive substances such as anti-mutagenic substance than the non-pigmented rice grain. The major anthocyanin pigment contained in black rice was cyanidin-3-glucoside. This study was conducted to establish a rapid analysis method for determining cyanidin-3-glucoside contents in flour and whole rice seeds of black rice using VIS/NIRS technique. A total of 60 black rice samples were used for VIS/NIRS equation model development and validation. The value of coefficient of determination of external validation (r 2 ) and standard error of performance (SEP) in whole rice seed sample were 0.653 and 97.2, respectively. Therefore, the value of it seemed to be difficult to analyze cyanidin-3-glucoside content in whole rice seed samples using VIS/NIRS. However, in rice flour sample, the best accurate equation model was obtained from the partial least square regression (PLS) method. The value of r 2 , SEP and bias were 22.5, 0.922 and -1.45 in the calibration transformed to the N-point smooth of log 1/R signal, 5 factors, respectively. Therefore, the results of our study clearly demonstrate that the VIS/NIRS method would be applicable only for rapid determination of cyanidin-3-glucose content in black rice flour samples.

This experiment was carried out to find suitable sample type for a more accurate prediction and non-destructive way in the application of NIRS technique for estimation amino acid composition of soybean by comparing three different sample types, single seed, whole seeds, and milled seeds’ powder. The coefficient of determination in calibration and 1-VR of cross-validation for 17 amino acids analyzed by NIR using milled powder were highest, followed by single seed, and then whole seeds were the lowest. The R2 coefficients of determination in calibration for single bean perimeters were higher than those of whole beans and showed higher R2 coefficient than 0.8 with the exception of seven amino acids. On the other hand, calibration equation development for only glutamic acid analyzed NIRS data scanned with whole seeds showed higher R2 coefficient than 0.8. Eleven different amino acids, such as aspartic acid, threonine, serine, glutamine, glycine, alanine, valine, isoleucine, leucine, arginine and proline, of soybean seeds in the powder had higher R2 coefficient of determination in calibration than 0.8 and could be estimated for the most accurate prediction. However, judging from the results of single bean samples, in consideration on NIR application for more nondestructive and faster prediction, these amino acids using single bean sample could be estimated without grinding the seeds.

In order to find out an alternative way of analysis of food waste compost, the Near Infrared Reflectance Spectroscopy(NIRS) was used for the compost assessment because the technics has been known as non-detructive, cost-effective and rapid method. One hundred thirty six compost samples were collected from Incheon food waste compost factory at Namdong Indurial Complex. The samples were analyzed for nitrogen, organic matter (OM), ash, P, and K using Kjedahl, ignition method, and acid extraction with spectrophotometer, respectively. The samples were scanned using FOSS NIRSystem of Model 6500 scanning monochromator with wavelength from 400~2,400㎚ at 2nm interval. Modified partial Least Squares(MPLS) was applied to develop the most reliable calibration model between NIR spectra and sample components such as nitrogen, ash, OM, P, and K. The regression was validated using validation set(n=30). Multiple correlation coefficient(R²) and standard error of prediction(SEP) for nitrogen, ash, organic matter, OM/N ratio, P and K were 0.87, 0.06, 0.72, 1.07, 0.68, 1.05, 0.89, 0.31, 0.77, 0.06, and 0.64, 0.07, respectively. The results of this experiment indicates that NIRS is reliable analytical method to assess some components of feed waste compost, also suggests that feasibility of NIRS can be justified in case of various sample collection around the year.