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Model robustness improvement by absorption and reduced scattering spectra in short wave near infrared spectral region

Author:
He, Xueming, Fu, Xiaping, Rao, Xiuqin
Source:
Biosystems engineering 2018 v.176 pp. 114-124
ISSN:
1537-5110
Subject:
absorption, least squares, lipid content, liquids, milk, models, near-infrared spectroscopy, prediction, protein content, reflectance, reflectance spectroscopy, spectral analysis, transmittance
Abstract:
Model robustness has always been the research focus for near infrared spectroscopy technique. In this study, we compared the model performance of four different kinds of spectra (transmittance, reflectance, absorption (μa) and reduced scattering (μ′s)) for the prediction of fat and protein of milk. The protein content of milk in prediction set was totally out of range of fat calibration set. The same happens in the case of the fat content of milk in prediction set and protein calibration set. The aim of this present study was to investigate the effect of the difference of protein content on the robustness of four fat calibration models, and of the difference of fat content on the robustness of four protein calibration models. The single integrating sphere system was first calibrated by using 90 liquid phantoms. The detection accuracy of the calibrated system was externally validated by another 80 phantoms. It turned out that the system can measure the absorption and reduced scattering coefficients with relatively high precision in the range of 780–1073 nm, which gave mean relative errors less than 12% and 5% for μa and μ′s respectively. Partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) models were established, and the results showed that calibration model based on reduced scattering spectra could predict fat with the highest robustness for both regression methods, while the absorption spectra could predict protein content with the highest robustness by using SMLR. The separation of absorption and reduced scattering has great potential in improving the robustness of calibration models.
Agid:
6234547