Quantitative analysis and visualization of moisture and anthocyanins content in purple sweet potato by Vis–NIR hyperspectral imaging

Abstract

Total anthocyanin (TA) and moisture content (MC) are critical indexes of processed purple sweet potatoes (PSPs). The current study examined the feasibility of hyperspectral imaging to investigate how MC and TA contents of PSPs change during processing under two different drying methods namely convective hot-air drying (CHD) and microwave drying (MD). Models based on spectral data and the integration of spectral and one or three gray-level co-occurrence matrix features were established with partial least square regression. The best prediction outcome based on Spectra + HOMOGENEITY + CONTRAST + ENTROPY combinations were ( = 0.862, 0.847; RMSEP = 0.079, 0.303), respectively, for MC and TA for the CHD samples. Similarly, for the MD samples, the best prediction outcomes were ( = 0.867, 0.859; RMSEP = 0.088, 0.241), respectively, for MC and TA. Image algorithms were also developed to generate distribution of MC and TA in some representative samples. Practical applications Anthocyanins have been reported as having the potential to lower blood pressure, improve eyesight, reduce cancer cell multiplication, impede tumor formation, and prevent diabetes. Studies have, however, shown that cooking modes including boiling, baking, and steaming decreased the contents of anthocyanins. This study assessed the impact of convective hot-air and microwave drying (MD) process of purple sweet potato (PSP) on total anthocyanin (TA) content as a function of moisture loss. Hyperspectral Imaging (HSI) was useful in estimating not only the contents of TA and moisture, but also in generating a visual map of their distribution pattern in the processed PSPs. The TA content of PSPs processed by MD was higher than that by convective hot-air drying, but the distribution uniformity of TA in microwave dried PSPs was worse than that in convective hot-air dried samples. Finally, to guarantee consistency in drying processes of food products, the HSI could be used to obtain visual images of their chemical parameters.