Journal Article

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

Huang, Wenqian, Yang, Guang, An, Ting, Long, Yuan, Xiang, Daqian, Fan, Yaoyao, Tian, Xi

2024

Overview

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.

• Publisher: Springer US,Springer Nature B.V
• Subject: Accuracy / Analytical Chemistry / Aspergillus flavus / Chemistry / Chemistry and Materials Science / Chemistry/Food Science / Chromatography / Classification / Corn / Data analysis / fluorescence / Food contamination / Food contamination & poisoning / Food safety / Food Science / Fungi / Humidity / Hyperspectral imaging / Kernels / Laboratories / Lasers / Legumes / Microbiology / Mold / Peanuts / species identification / Spectrum analysis