Origin identification of foxtail millet (Setaria italica) by using green spectral imaging coupled with chemometrics

Abstract

Foxtail millet is an important edible crop in China and its demand continues to increase with the growth of the population. There are differences in the quality of millet from different origins, leading to differences in price. Therefore, an effective and rapid technique is needed to monitor the authenticity of millet origin. This study evaluates the feasibility of hyperspectral imaging (HSI) in identifying the origin of millet. A total of 120 millet samples from four regions in Inner Mongolia of China were analyzed and the hyperspectral data in the wavelength of 900–1700 nm were collected. The support vector machine (SVM) model built on full-band spectral data achieved acceptable results with a discriminative accuracy of 87.50% for the prediction set. Compared with the full-band spectra, the performance of the SVM model built on the basis of data dimensionality reduction was improved. Among them, the principal component analysis (PCA)-SVM model achieved the best discriminative performance with an accuracy of 95.00% for the prediction set. This study demonstrates the feasibility of NIR-HSI for origin traceability of millet, which provides a high-throughput, rapid and non-destructive quality control means to ensure the commodity flow of millet.