Potato flour content determination of potato–wheat flour mixture based on hyperspectral imaging

Summary Potato–wheat flour mixture (PWFM) has important significance for the development of potato staple food products. However, conventional chemical methods are unable to detect the content of potato flour. Hyperspectral imaging (HSI) technology combined with chemometrics was investigated to predict the feasibility of potato flour content in PWFM. In this study, seven pretreatment algorithms were performed to process the raw spectral data. The results showed that the standard normalised variables (SNV) to establish a partial least square regression (PLSR) model had the most predictive accuracy with the determination coefficient of prediction () of 0.9729. The characteristic wavelengths of raw spectral information were extracted using three algorithms to reduce the model complexity. Eventually, the extracted characteristic wavelengths of potato flour content combine with SNV to establish PLSR, principal component regression (PCR) and support vector regression (SVR) models. The results revealed that the optimal model was SNV‐competitive adaptive reweighted sampling (CARS)‐PLSR with the values of of 0.9853. These results showed that HSI determination of potato flour content in PWFM was feasible and provided a non‐destructive method.