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Identification of Peanut Kernels Infected with Multiple Aspergillus flavus Fungi Using Line-Scan Raman Hyperspectral Imaging

文献类型： 外文期刊

作者： Yang, Guang 1 ; Tian, Xi 2 ; Fan, Yaoyao 2 ; Xiang, Daqian 1 ; An, Ting 2 ; Huang, Wenqian 2 ; Long, Yuan 2 ;

作者机构： 1.Jiangsu Univ, Sch Agr Engn, Zhenjiang 212013, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

3.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

4.Minist Agr, Key Lab Agri Informat, Beijing 100097, Peoples R China

5.Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China

关键词： Line-scan Raman imaging; Aspergillus flavus; Peanut kernels; Feature variable selection; Support vector machine

期刊名称：FOOD ANALYTICAL METHODS （ 影响因子：2.9； 五年影响因子：3.0 ）

ISSN： 1936-9751

年卷期： 2023 年

页码：

收录情况： SCI

摘要： The mold contamination caused by Aspergillus flavus poses a serious threat to food safety. In this study, three artificially inoculating strains of Aspergillus flavus (A. flavus 142,801, A. flavus 142,803, A. flavus 336,156) were used to infect two healthy peanut varieties (variety A: GS1210, variety B: fengyingluohan) kernels. These healthy and Aspergillus flavus-infected peanut kernels were identified and differentiated by using a line-scan Raman hyperspectral imaging system. Firstly, the average spectra of healthy and infected peanuts were extracted, followed by preprocessing using Savitzky-Golay smoothing and airPLS for fluorescence background removal. Finally, four feature variable selection methods were used to optimize the models. In the binary classification model (healthy vs. A. flavus), the SVM method yielded the best modeling results, with accuracy above 99%. The best accuracy achieved in the three-classification model for mold on variety A peanut was 88.9%, and for variety B, it was 92.4%. In the model for mold on a mixture of both varieties, the highest accuracy reached was 74.8%. The results show that line-scan Raman hyperspectral imaging technology is practical in identifying healthy and Aspergillus flavus-infected peanut kernels. Moreover, this technique has great potential in identifying different Aspergillus flavus of a single peanut variety and provides a feasible method for fungal species identification.

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