ABSTRACT:The main goal of this study was used the hyperspectral imaging (HSI) technology in the range of 901-2251 nm was investigated to examine the starch content of potato flour noodles. Different spectral pre-treatment methods were used to improve the stability of the partial least squares regression (PLSR) and principal component regression (PCR) models. Compared the effects of other models, the result shows that PLSR is the best model. Based on the established SNV-CARS-PLSR model, the calibration performance of the dataset with R2c of 0.9490 and RMSEC of 0.1337%, and the validation performance of the dataset with R2p of 0.8946 and RMSEP of 0.1811%. The changes of starch contents of potato flour noodles during comprehensive comparison selected the optimal models were successfully visualized.
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