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Unraveling almonds deterioration using whole-cell biosensor coupled with machine learning approaches and SHAP interpretation

文献类型： 外文期刊

作者： Li, Qianqian 1 ; Chen, Shengfan 1 ; Han, Jinhua 1 ; Li, Bei 2 ; Wu, Lijun 3 ; Li, Jianxun 1 ;

作者机构： 1.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Inst Food Sci & Technol, Key Lab Agroprod Qual & Safety Control Storage & T, Beijing 100093, Peoples R China

2.Hainan Inst Food Control, Key Lab Trop Fruits & Vegetables Qual & Safety Sta, Hainan 570314, Peoples R China

3.China Tobacco Yunnan Ind Co Ltd, Kunming, Peoples R China

关键词： Almonds; In situ; whole-cell biosensor; Machine learning; SHAP values

期刊名称：FOOD CHEMISTRY （ 影响因子：9.8； 五年影响因子：9.7 ）

ISSN： 0308-8146

年卷期： 2025 年 484 卷

页码：

收录情况： SCI

摘要： As almonds are prone to oxidation during storage, it is essential to construct a real-time method to monitor the quality of almonds efficiently. In this study, the in situ detection was developed using whole-cell biosensor combined with machine learning algorithms. Mantel test between volatile compounds and promoters was conducted to provide theoretical support for luminescence response of whole-cell biosensor. SHAP algorithm was implemented to visualize machine learning models for global and local explanations. As a result, six biosensors of pspA, uvrA, katG, ropS, grpE, and leuA were explored to fabricate whole-cell biosensor. The LDA, LR, and PLS-DA exhibited relatively lower prediction accuracy, while SVM, and RF outperformed the above linear models with the accuracy of 97.5 % and 100 %. Moreover, the whole-cell biosensor array combined with RF algorithm offers a favorable strategy for almond deterioration. This study provides an in situ, efficient, environment-friendly approach for quality assurance in almonds and other food products.

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