Development of a predictive model to determine potato flour content in potato-wheat blended powders using near-infrared spectroscopy

Potato is a good source of dietary energy and several micronutrients, and the development of staple foods using potato-wheat blended powder has received much attention recently in China. A rapid and accurate method for determining the potato flour content in potato staple foods would be valuable to market regulation efforts. We developed a predictive model for the potato flour content in potato-wheat blended powders based on near-infrared spectroscopy (NIRS) analysis. The correction of the near-infrared optical path was carried out to eliminate optical path differences using multiplicative scatter correction (MSC) and smoothing of the spectra using the Savitzky-Golay smoothing first-orderderivative (S-G-1stD).The prediction model was developed based on partial least squares (PLS) combined with cross-validation(CV) within the full spectrum(850–1100 nm). The results showed that the optimal main factors of potato flour content in potato-wheat blended powder were 9, and the coefficient of determination of calibration (R2c) and standard error of cross-validation (SECV) of the prediction model reached 0.9997 and 0.51, respectively, indicating a good correlation. The repeatability standard deviation (SDr) and repeatability coefficient of variation of cross-validation (CVr) in validated samples were 0.246 and 0.967, respectively, which indicated that the prediction model had good repeatability. The bias-corrected standard error of prediction (SEP) and correlation coefficient of validation (R2P) were 0.69 and 0.9995, respectively, demonstrating good accuracy and stability. The results of this study demonstrated that this prediction model based on NIRS could determine the potato flour content in potato-wheat blended powders accurately and quickly