Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology

Abstract

To meet rapid and non-destructive identification of selenium-enriched agricultural products selenium-enriched millet and ordinary millet were taken as objects. Image regions of interest (ROI) were selected to extract the spectral average value based on hyperspectral imaging technology. Reducing noise by the Savitzky-Golay (SG) smoothing algorithm, variables were used as inputs that were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), CARS-SPA, UVE-SPA, and UVE-CARS, while sample variables were used as outputs to build support vector machine (SVM) models. The results showed that the accuracy of CARS-SPA-SVM was 100% in the training set and 99.58% in the test set equivalent to that of CARS-SVM and UVE-CARS-SVM, which was higher than that of SPA-SVM, UVE-SPA-SVM, and UVE-SVM. Therefore, the method of CARS-SPA had superiority, and CARS-SPA-SVM was suitable to identify selenium-enriched millet. Finally, 454.57 nm, 484.98 nm, 885.34 nm, and 937.1 nm, which were obtained by wavelength extraction algorithms, were considered as the sensitive wavelengths of selenium information. This study provided a reference for the identification of selenium-enriched agricultural products.

Simultaneous Detection of Amylose and Amylopectin Content of Foxtail Millet Flour with Hyperspectral Imaging

“…In the past, scholars generally used hyperspectral imaging for the classification of foxtail millet varieties and the detection of nutritional components [40][41][42]. This study combined hyperspectral imaging with chemometrics to set different rates of sheep manure application and collected 358 samples of foxtail millet flour for the detection of amylose and amylopectin content.…”