Enhanced detection of Aspergillus flavus in peanut kernels using a multi-scale attention transformer (MSAT): Advancements in food safety and contamination analysis

文献类型： 外文期刊

第一作者： Guo, Zhen

作者： Guo, Zhen;Zhang, Jing;Dong, Haowei;Li, Shiling;Shao, Xijun;Huang, Jingcheng;Yin, Xiang;Guo, Yemin;Sun, Xia;Guo, Zhen;Dong, Haowei;Li, Shiling;Shao, Xijun;Huang, Jingcheng;Guo, Yemin;Sun, Xia;Guo, Zhen;Dong, Haowei;Li, Shiling;Shao, Xijun;Huang, Jingcheng;Guo, Yemin;Sun, Xia;Wang, Haifang;Zhang, Qi;Darwish, Ibrahim

作者机构：

关键词： Multi-scale attention; Transformer; Peanut kernels; Hyperspectral imaging; Aspergillus flavus

期刊名称：INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY （ 影响因子：5.2； 五年影响因子：5.3 ）

ISSN： 0168-1605

年卷期： 2024 年 423 卷

页码：

收录情况： SCI

摘要： In this study, a multi-scale attention transformer (MSAT) was coupled with hyperspectral imaging for classifying peanut kernels contaminated with diverse Aspergillus flavus fungi. The results underscored that the MSAT significantly outperformed classic deep learning models, due to its sophisticated multi-scale attention mechanism which enhanced its classification capabilities. The multi-scale attention mechanism was utilized by employing several multi-head attention layers to focus on both fine-scale and broad-scale features. It also integrated a series of scale processing layers to capture features at different resolutions and incorporated a self-attention mechanism to integrate information across different levels. The MSAT model achieved outstanding performance in different classification tasks, particularly in distinguishing healthy peanut kernels from those contaminated with aflatoxigenic fungi, with test accuracy achieving 98.42 +/- 0.22%. However, it faced challenges in differentiating peanut kernels contaminated with aflatoxigenic fungi from those with non-aflatoxigenic contamination. Visualization of attention weights explicitly revealed that the MSAT model's multi-scale attention mechanism progressively refined its focus from broad spatial-spectral features to more specialized signatures. Overall, the MSAT model's advanced processing capabilities marked a notable advancement in the field of food quality safety, offering a robust and reliable tool for the rapid and accurate detection of Aspergillus flavus contaminations in food.

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