Establishment of near-infrared rapid prediction model and comprehensive evaluation model for foxtail millet quality

Abstract

Foxtail millet (Setaria italica) is one of the earliest cereal crops in the world and is still an important food crop in Asia, Europe and Africa. However, there are few reports on rapid and comprehensive evaluations of foxtail millet quality. In this study, color, nutrient content, cooking quality and sensory quality of 45 varieties of foxtail millet from the main production areas of the country were determined. Near infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR) was developed for rapid and nondestructive detection of foxtail millet quality, where NIRS models for ash, amylopectin, protein, and VB1 and VB2 were desirable. In addition, correlation analysis, principal component analysis and cluster analysis were used for comprehensive evaluation of millet quality. The results showed that foxtail millet with low ash, protein, fat and fiber content and high amylopectin and carotenoid content has good cooking quality and was more preferred. This research will contribute to the quality evaluation and further processing of foxtail millet.