Study on Rice Origin and Quality Identification Based on Fluorescence Spectral Features

Yixin Qiu, Yong Tan, Yingying Zhou, Zhipeng Li, Zhuang Miao, Changming Li, Xitian Mei, Chunyu Liu () and Xing Teng ()
Additional contact information
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

Agriculture, 2024, vol. 14, issue 10, 1-17

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.

Keywords: fluorescence spectroscopy; spectral fusion; identification of origin; rice (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/10/1763/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/10/1763/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:10:p:1763-:d:1492940

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().