文献类型： 外文期刊

作者： Song Shao-zhong ¹ ; Fu Shao-yan ² ; Liu Yuan-yuan ² ; Qi Chun-yan ³ ; Li Jing-peng ⁴ ; Gao Xun ² ;

作者机构： 1.Jilin Normal Univ Engn & Technol, Sch Data Sci & Artificial Intelligence, Changchun 130052, Peoples R China

2.Changchun Univ Sci & Technol, Sch Phys, Changchun 130022, Peoples R China

3.Jilin Acad Agr Sci, Rice Res Inst, Changchun 130033, Peoples R China

4.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China

关键词： Laser-induced breakdown spectroscopy; Machine learning algorithms; Identification of rice production areas; Identification accuracy

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.7； 五年影响因子：0.6 ）

ISSN： 1000-0593

年卷期： 2024 年 44 卷 6 期

页码：

收录情况： SCI

摘要： Rice is the primary grain crop in China, and the quality of rice is closely related to the external environment, such as soil characteristics, climate, sunshine time, and irrigation water. The high-quality rice-origin area has certain regional limitations. Therefore,the rice can be seen as an apparent geographical marker. There are often some counterfeits or branded famous high-quality rice in the market, which can damage the rice brand, reduce the rice quality guarantee of consumers, and disturb the market stability, so rapid identification technology of rice origin is needed. The rice origin identification models of five sources in Jilin Province (Daan, Gongzhuling, Qianguo, Songyuan and Taoerhe) are done by laser-induced breakdown spectroscopy and machine learning algorithms. The principal component analysis (PCA) algorithm, combined with four machine learning algorithms, Bagged Trees, Weighted KNN, Quadratic SVM, and Coaster Gaussian SVM, has been established. A total of 450 groups of LIBS data are selected. The spectral data of rice LIBS are pretreated with Savitzky-Golay smoothing (SG smoothing) is used for noise reduction and normalisation. The principal component analysis uses the rice LIBS data, which shows that the rice origins had an excellent cluster distribution of clustering spaces. Still, there is spatial overlap in some rice origins. Utilising5x cross-validation, the identification accuracy of rice origins can reachmore than 91.8% by adopting PCA-Bagged Trees, PCA-Weighted KNN, PCA-Quadratic SVM and PCA-Coarse Gaussian SVM, and the recognition accuracy of PCA-Quadratic SVM model is as high as 97.3%. The results show that the combination of LIBS technology and machine learning algorithms can identify rice origin with high precision and high efficiency.