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

Authors :
Yixin Qiu
Yong Tan
Yingying Zhou
Zhipeng Li
Zhuang Miao
Changming Li
Xitian Mei
Chunyu Liu
Xing Teng
Source :
Agriculture, Vol 14, Iss 10, p 1763 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

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.

Details

Language :
English
ISSN :
20770472
Volume :
14
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Agriculture
Publication Type :
Academic Journal
Accession number :
edsdoj.faadf2db082540ae8b63b0f7249f69b3
Document Type :
article
Full Text :
https://doi.org/10.3390/agriculture14101763