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

Fu Zhang, Xiahua Cui, Chaochen Zhang, Weihua Cao, Xinyue Wang, Sanling Fu and Shuai Teng
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Fu Zhang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
Xiahua Cui: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
Chaochen Zhang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
Weihua Cao: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
Xinyue Wang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
Sanling Fu: College of Physical Engineering, Henan University of Science and Technology, Luoyang, China
Shuai Teng: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China

Czech Journal of Food Sciences, 2022, vol. 40, issue 6, 445-455

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.

Keywords: millet; selenium element; spectroscopy; secondary variable screening; support vector machine (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjf:v:40:y:2022:i:6:id:129-2022-cjfs

DOI: 10.17221/129/2022-CJFS

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