Author
Listed:
- Fu Zhang
(College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Zhenjiang, China
Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, 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)
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.
Suggested Citation
Fu Zhang & Xiahua Cui & Chaochen Zhang & Weihua Cao & Xinyue Wang & Sanling Fu & Shuai Teng, 2022.
"Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology,"
Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 40(6), pages 445-455.
Handle:
RePEc:caa:jnlcjf:v:40:y:2022:i:6:id:129-2022-cjfs
DOI: 10.17221/129/2022-CJFS
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlcjf:v:40:y:2022:i:6:id:129-2022-cjfs. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.