Author
Listed:
- Yixin Qiu
(School of Physics, Changchun University of Science and Technology, Changchun 130022, China)
- Yong Tan
(School of Physics, Changchun University of Science and Technology, Changchun 130022, China)
- Yingying Zhou
(School of Physics, Changchun University of Science and Technology, Changchun 130022, China)
- Zhipeng Li
(School of Physics, Changchun University of Science and Technology, Changchun 130022, China)
- Zhuang Miao
(School of Physics, Changchun University of Science and Technology, Changchun 130022, China)
- Changming Li
(School of Physics, Changchun University of Science and Technology, Changchun 130022, China)
- Xitian Mei
(School of Physics, Changchun University of Science and Technology, Changchun 130022, China)
- Chunyu Liu
(School of Physics, Changchun University of Science and Technology, Changchun 130022, China)
- Xing Teng
(Jilin Academy of Agricultural Sciences (Northeast Agricultural Research Center of China), Changchun 130000, China)
Abstract
The origin of agricultural products significantly influences their quality and safety. Fluorescence spectroscopy was used to analyse Japonica rice 830, grown in different areas of Jilin Province, by examining rice seed, brown rice, and rice flour from 12 origins. Fluorescence spectra were pre-processed through normalisation and smoothing to remove noise. These processed spectra were input into decision trees, support vector machines (SVMs), K-nearest neighbour (KNN), and neural network models for classification. The analysis revealed that the combined four models achieved an average classification accuracy of 98.05% with a computation time of 180 s, while the reduced-scale models improved accuracy to 98.36% and reduced computation time to 11.25 s. Additionally, prediction models using standard rice starch content values across different states achieved R² values over 0.8. This method provides a rapid, precise approach for assessing rice quality and origin, demonstrating significant potential for application in rice analysis.
Suggested Citation
Yixin Qiu & Yong Tan & Yingying Zhou & Zhipeng Li & Zhuang Miao & Changming Li & Xitian Mei & Chunyu Liu & Xing Teng, 2024.
"Study on Rice Origin and Quality Identification Based on Fluorescence Spectral Features,"
Agriculture, MDPI, vol. 14(10), pages 1-17, October.
Handle:
RePEc:gam:jagris:v:14:y:2024:i:10:p:1763-:d:1492940
Download full text from publisher
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:gam:jagris:v:14:y:2024:i:10:p:1763-:d:1492940. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.