Prediction of sweetpotato starch physiochemical quality and pasting properties using near-infrared reflectance spectroscopy

Abstract

A rapid predictive method based on near-infrared reflectance (NIR) spectroscopy (NIRS) was developed to measure sweetpotato starch physiochemical quality and pasting properties. The starch samples were scanned by NIRS and analyzed for quality properties by reference methods, respectively. Results of statistical modeling indicated that NIRS was reasonably accurate in predicting amylose content (AC), amylose percent (AP), total starch content (TSC), protein content (PRC), phosphorus content (PHC), solubility (SOL), swelling power (SP), average granule diameter (AGD), big granule percent (BGP), small granule percent (SGP), crystallinity (CRY), peak viscosity (PKV), hot paste viscosity (HPV), setback (SB), and pasting temperature ( P temp) with high coefficients of determination (RSQ = 0.85–0.92) and relatively low standard errors of prediction. The results showed that NIR analysis was sufficiently accurate and effective for rapid evaluation of starch physicochemical properties in sweetpotato. The NIR-based protocol developed in this study can be used for screening large number of starch samples in food enterprises and sweetpotato breeding programs.