Ohtani, S.
(Department of Food and Bioresources Science, Agricultural, Food and Environmental Sciences Research Center of Osaka Prefecture)
,
Wang, T.
(Veterinary Department, Jiangsu Animal Husbandry and Veterinary College)
,
Nishimura, K.
(Department of Food and Bioresources Science, Agricultural, Food and Environmental Sciences Research Center of Osaka Prefecture)
,
Irie, M.
(Department of Food and Bioresources Science, Agricultural, Food and Environmental Sciences Research Center of Osaka Prefecture)
We have evaluated the application of spectroscopy using an insertion-type fiber-optic probe and a sensor at wavelengths from 400 to 1,100 nm to the measurement of milk fat content on dairy farms. The internal reflectance ratios of 183 milk samples were determined with a fiber-optic spectrophotometer...
We have evaluated the application of spectroscopy using an insertion-type fiber-optic probe and a sensor at wavelengths from 400 to 1,100 nm to the measurement of milk fat content on dairy farms. The internal reflectance ratios of 183 milk samples were determined with a fiber-optic spectrophotometer at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$. Partial least squares (PLS) regression was used to develop calibration models for the milk fat. The best accuracy of determination was found for an equation that was obtained using smoothed internal reflectance data and three PLS factors at 20$^{\circ}C$. The correlation coefficients between predicted and reference milk fat at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$ were r=0.753, r=0.796 and r=0.783, respectively. The predictive explained variances ($Q^2$) of the final model, moreover, were more than 0.550 at all temperatures, and the regression coefficients of determination ($R^2$) were more than 0.6 (60%). Our results indicate that milk has different internal reflectance measured in the range of visible and near infrared wavelengths (400 to 1,100 nm), depending on its fat content.
We have evaluated the application of spectroscopy using an insertion-type fiber-optic probe and a sensor at wavelengths from 400 to 1,100 nm to the measurement of milk fat content on dairy farms. The internal reflectance ratios of 183 milk samples were determined with a fiber-optic spectrophotometer at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$. Partial least squares (PLS) regression was used to develop calibration models for the milk fat. The best accuracy of determination was found for an equation that was obtained using smoothed internal reflectance data and three PLS factors at 20$^{\circ}C$. The correlation coefficients between predicted and reference milk fat at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$ were r=0.753, r=0.796 and r=0.783, respectively. The predictive explained variances ($Q^2$) of the final model, moreover, were more than 0.550 at all temperatures, and the regression coefficients of determination ($R^2$) were more than 0.6 (60%). Our results indicate that milk has different internal reflectance measured in the range of visible and near infrared wavelengths (400 to 1,100 nm), depending on its fat content.
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데이터처리
The differences in internal reflectance ratio at different temperatures were compared statistically by MANOVA followed by Tukey-Kramer’s multiple range test.
The differences in internal reflectance ratio at different temperatures were compared statistically by MANOVA followed by Tukey-Kramer’s multiple range test.
이론/모형
The log-transformed reflectance ratio data were smoothed over 25 point intervals, and transformed to second derivatives with the Salvitzki-Golay second-order polynominal filter (Salvitzki-Golay). A milk fat calibration model was developed at each temperature by partial least squares (PLS) regression with the Chemish version 3.55, which was provided by the Computer Aided Chemistry Forum, University of Tokyo (Chemish). PLS factors were determined from the spectra and the corresponding reference data including protein data by a cross-validation method using the leave-one-out method.
One hundred-ml samples were taken during morning and evening milkings with a milk meter (MC6B®, Orion, Suzaka, Japan), kept at 4°C, and aliquots of the samples were analyzed for milk fat and protein content by the reference method (AOAC official methods 989.05, 972.16 FG).
A total of 183 milk samples from 92 Holstein cows were collected twice a week. One hundred-ml samples were taken during morning and evening milkings with a milk meter (MC6B®, Orion, Suzaka, Japan), kept at 4°C, and aliquots of the samples were analyzed for milk fat and protein content by the reference method (AOAC official methods 989.05, 972.16 FG). The milk fat content of the samples analyzed as reference varied from 2.
55, which was provided by the Computer Aided Chemistry Forum, University of Tokyo (Chemish). PLS factors were determined from the spectra and the corresponding reference data including protein data by a cross-validation method using the leave-one-out method. The PLS model was evaluated by the predictive explained variances (Q2), which was accepted as a measure of accuracy of determination and was calculated as follows.
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