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

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.