[en] Aspergillus flavus is a major cause of post-harvest losses in maize and peanuts through aflatoxin B1 (AFB1) contamination, highlighting the urgent need for sensitive and scalable early detection strategies. In this study, we developed a transcriptome-guided whole-cell biosensor array by integrating eight infection-induced promoters, identified from E. coli transcriptomic responses to volatile organic compounds, into calcium alginate-immobilized bioreporters coupled with machine learning regression models. Time-resolved bioluminescence signals were used to train ensemble regressors for the first time, including XGBoost, CatBoost, and RandomForest, for quantitative prediction of infection stages and AFB1 levels. XGBoost consistently achieved superior performance with R² values of 0.94 and 0.98 in internal validation for maize infection staging and AFB1 quantification and maintained strong generalization in external validation with independent A. flavus isolates (R² = 0.92 and 0.91). Comparable results were observed in peanuts, where XGBoost achieved R² = 0.94 and 0.97 internally and 0.92 and 0.86 externally, confirming robustness across different substrates and fungal strains. Direct comparison with our previous biosensors constructed from 14 general stress-responsive promoters revealed that the novel biosensors based on 8 new transcriptome-guided promoters yielded markedly higher predictive accuracy, particularly under external validation conditions. Feature importance analysis revealed that early host responses, including transcriptional regulation and biofilm formation, served as key predictive features, thereby providing mechanistic interpretability not attainable with conventional optical or chemical assays. Together, these findings establish a biologically informed, non-invasive, and cost-efficient biosensing platform that integrates promoter-level transcriptomic insights with ensemble learning, offering a versatile approach for real-time aflatoxin risk assessment and scalable food safety monitoring across diverse agroecosystems.
Disciplines :
Biotechnology
Author, co-author :
Sun, Lu ; Université de Liège - ULiège > TERRA Research Centre
Ma, Junning; Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, PR China
Jiang, Yongping; Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, PR China
Purcaro, Giorgia ; Université de Liège - ULiège > Département GxABT > Chemistry for Sustainable Food and Environmental Systems (CSFES)
Tian, Yuanyuan; Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, PR China
Wang, Gang; Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, PR China
Li, Weizhao; Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, PR China
Tai, Bowen; Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, PR China
Xing, Fuguo ; Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences /Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process, Ministry of Agriculture and Rural Affairs, Beijing 100193, PR China. Electronic address: xingfuguo@caas.cn
Language :
English
Title :
A novel approach for predicting aflatoxin B1 production using regression models and whole-cell biosensors in moldy maize and peanut kernels.
NSCF - National Natural Science Foundation of China
Funding text :
This research was supported by the Research and Development of Common Key Technologies and Equipment for Grain Postharvest Loss Reduction (CAAS-ZDRW202414, CAAS-ASTIP-G2025-IFST-09), and National Natural Science Foundation of China (32372458). The authors are grateful for the support provided by the foundation.
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