The objective of this study was to quantify the chemical content of multiple products using one single calibration model. This study involved seven tuber and root powders from arrowroot, Canna edulis , cassava, taro, as well as purple, yellow, and white sweet potato, for partial least square (PLS) regression to predict polysaccharide contents (i.e., amylose, starch, and cellulose). The developed PLS models showed acceptable results, with R c 2 of 0.9, 0.95, and 0.85 and SEC of 2.7%, 3.33%, and 3.22%, for amylose, starch, and cellulose, respectively. The models also successfully predicted polysaccharide contents with R p 2 of 0.89, 0.95, and 0.79; SEP of 2.83%, 3.33%, and 3.55%; and RPD of 3.02, 4.47, and 2.18 for amylose, starch, and cellulose, respectively. These results showed the potential of Fourier transform near-infrared spectroscopy to quantify the chemical composition of multiple products instead of using one individual model.

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Understanding the physicochemical properties of flour in food preparation is important to determine the appropriate food utilization and processing. Some crops are processed into flour to improve their shelf life and extend their applications in food preparation. This study employed shortwave infrared hyperspectral imaging (SWIR HSI) coupled with multivariate analyses to determine wheat's protein, starch, amylose, glucose, and moisture compositions and several tuber flours. Tubers used were arrowroot, Canna edulis, modified cassava flour, taro, and sweet potato (purple, yellow, and white color). Hyperspectral images of all flour samples were captured using the SWIR HSI system at the wavelength range of 895-2504 nm in reflectance mode. The extracted spectral data were then processed and analyzed using partial least square regression (PLSR). Normalization (mean, max, and range), multiple scatter correction, standard normal variate, first and second Savitzky-Golay derivatives, and smoothing spectral pre-processing were applied to reduce scattering noise resulting from the HSI system. The PLSR models predicted the chemical concentrations of all samples with coefficients of determination of 0.85-0.97, 0.83-0.96, and 0.85-0.96 for calibration, validation, and prediction, respectively. Moreover, the models resulted in standard errors of 0.61-27.26, 0.63-27.71, and 0.63-29.14 for calibration, validation, and prediction. The concentration and distribution of protein, starch, amylose, glucose, and moisture in the flour samples were visualized by chemical imaging. This paper confirmed the potential of HSI for the rapid interpretation of chemical contents in different flour samples.