文献类型： 外文期刊
作者： Qiu, Yixin 1 ; Tan, Yong 1 ; Zhou, Yingying 1 ; Li, Zhipeng 1 ; Miao, Zhuang 1 ; Li, Changming 1 ; Mei, Xitian 1 ; Liu, Chunyu 1 ; Teng, Xing 2 ;
作者机构： 1.Changchun Univ Sci & Technol, Sch Phys, Changchun 130022, Peoples R China
2.Northeast Agr Res Ctr China, Jilin Acad Agr Sci, Changchun 130000, Peoples R China
关键词： fluorescence spectroscopy; spectral fusion; identification of origin; rice
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）
ISSN：
年卷期： 2024 年 14 卷 10 期
页码：
收录情况： SCI
摘要： 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 R-2 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.
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