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- W4311549325 abstract "This study examined the potential of hyperspectral techniques for the rapid detection of characteristic indicators of yak meat freshness during the oxidation of yak meat. TVB-N values were determined by significance analysis as the characteristic index of yak meat freshness. Reflectance spectral information of yak meat samples (400-1000 nm) was collected by hyperspectral technology. The raw spectral information was processed by 5 methods and then principal component regression (PCR), support vector machine regression (SVR) and partial least squares regression (PLSR) were used to build regression models. The results indicated that the full-wavelength based on PCR, SVR, and PLSR models were shown greater performance in the prediction of TVB-N content. In order to improve the computational efficiency of the model, 9 and 11 characteristic wavelengths were selected from 128 wavelengths by successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. The CARS-PLSR model exhibited excellent predictive power and model stability." @default.
- W4311549325 created "2022-12-27" @default.
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- W4311549325 date "2023-03-01" @default.
- W4311549325 modified "2023-10-16" @default.
- W4311549325 title "Non-destructive prediction of yak meat freshness indicator by hyperspectral techniques in the oxidation process" @default.
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- W4311549325 doi "https://doi.org/10.1016/j.fochx.2022.100541" @default.
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