数字农科院2.0

A novel approach for predicting aflatoxin B1 production using regression models and whole-cell biosensors in moldy maize and peanut kernels

文献类型： 外文期刊

作者： Lu Sun;Junning Ma;Yongping Jiang;Giorgia Purcaro;Yuanyuan Tian;Gang Wang;Weizhao Li;Bowen Tai;Fuguo Xing

作者机构：

关键词： Aspergillus flavus;CatBoost;Post-harvest storage;Volatile organic compound;Whole-cell biosensor array;XGBoost

期刊名称： Journal of Hazardous Materials

ISSN： 0304-3894

年卷期： 2025 年 498 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： 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.

分类号：

  • 相关文献

[1]Accurate and non-destructive monitoring of mold contamination in foodstuffs based on whole-cell biosensor array coupling with machine-learning prediction models. Junning Ma,Yue Guan,Fuguo Xing,Evgeni Eltzov,Yan Wang,Xu Li,Bowen Tai. 2023

[2]基于XGBoost算法的山东烟叶质量预测模型初探. 别瑞,周婷云,周显升,姜滨,周永,邱军,曹建敏. 2022

[3]基于无人机多源遥感数据和机器学习的高通量棉花估产研究. 冯美臣,苏悦,林涛,余汛,宋扬,金秀良. 2025

[4]Interaction between chitosan and natamycin maintain freshness quality and enzymatic activity of post-harvest Prunus salicina Lindl. cv. ‘Shazikongxinli’ fruit. Gangfeng Li,Linjie Luo,Shaobiao Feng,Huan Wang,Miao Zhu,Ruilin Li,Zhenzhen Lv. 2022

[5]Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses. Hong Zhang,韩超,Tom D. Breeze,Mengdan Li,Shibonage K. Mashilingi,Jun Hua,Wenbin Zhang,Xuebin Zhang,Shiwen Zhang,Jiandong An. 2022

[6]CatBoost algorithm for estimating maize above-ground biomass using unmanned aerial vehicle-based multi-source sensor data and SPAD values. Weiguang Zhai,Changchun Li,Shuaipeng Fei,Yanghua Liu,Fan Ding,Qian Cheng,Zhen Chen. 2023

[7]Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models. Mi Luo,Shoaib Ahmad Anees,Qiuyan Huang,Xin Qin,Zhihao Qin,Jianlong Fan,Guangping Han,Liguo Zhang,Helmi Zulhaidi Mohd Shafri. 2024

[8]Genetic Variation, Polyploidy, Hybridization Influencing the Aroma Profiles of Rosaceae Family. Xi Chen,Yu Zhang,Weihua Tang,Geng Zhang,Yuanhua Wang,Zhiming Yan. 2024

[9]Salicylic acid treatment inhibits cold-induced sweetening by regulating the transcription factors StAP2/ERF and StNAC2 in potato. Zhang, Jiadi,Lin, Qiong,Mao, Linli,Min, Ting,Guan, Wenqiang,Qi, Xin,Wang, Hongxun. 2024

[10]Stomatal conductance modeling for drip-irrigated kiwifruit in seasonal drought regions of South China: Evaluation of improved empirical models and interpretable machine learning approaches. Shunsheng Zheng,Ningbo Cui,Quanshan Liu,Shouzheng Jiang,Daozhi Gong,Xiaoxian Zhang. 2026

[11]Characterization of Physicochemical, Phenolic, and Volatile Profiles of Peach Wine Fermented by Different Saccharomyces and Non-Saccharomyces Yeast Strains. Zhang, Xiaoqing,Lv, Zhenzhen,Yang, Wenbo,Liu, Hui,Zhang, Qiang,Liu, Jiechao,Jiao, Zhonggao. 2025

[12]Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification. Sun, Ye,Zhao, Zhiyuan,Tong, Hailong,Sun, Baiming,Liu, Yanbiao,Ren, Nanqi,You, Shijie. 2023

[13]HPO-empowered machine learning with multiple environment variables enables spatial prediction of soil heavy metals in coastal delta farmland of China. Yingqiang Song,Dexi Zhan,Zhenxin He,Wenhui Li,Wenxu Duan,Zhongkang Yang,Miao Lu. 2023

[14]Monitoring Land Use Changes in the Yellow River Delta Using Multi-Temporal Remote Sensing Data and Machine Learning from 2000 to 2020. Yunyang Zhu,Linlin Lu,Zilu Li,Shiqing Wang,Yu Yao,Wenjin Wu,Rajiv Pandey,Aqil Tariq,Ke Luo,Qingting Li. 2024

[15]Tree-Structured Parzan Estimator–Machine Learning–Ordinary Kriging: An Integration Method for Soil Ammonia Spatial Prediction in the Typical Cropland of Chinese Yellow River Delta with Sentinel-2 Remote Sensing Image and Air Quality Data. Yingqiang Song,Mingzhu Ye,Zhao Zheng,Dexi Zhan,Wenxu Duan,Miao Lu,Zhenqi Song,Dengkuo Sun,Kaizhong Yao,Ziqi Ding. 2023

[16]Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage. Xiaohui Yang,Feng Gao,Hongwei Yuan,Xiuqing Cao. 2024

[17]Comparative Analysis of Machine Learning Algorithms for Object-Based Crop Classification Using Multispectral Imagery. Madjebi Collela Be,Antsa Sarobidy Randrianantenaina,James E. Kanneh,Yingchun Han,Yaping Lei,Xiaoyu Zhi,Shiwu Xiong,Yahui Jiao,Shilong Shang,Yunzhen Ma,Beifang Yang,Lin Tao,Yabing Li. 2025

[18]Interpretable salinization estimation model for Dongying City based on integrated multi-dimensional spectral indices with XGBoost-driven transformations. Jicun Yang,Bing Guo,Miao Lu,Baomin Han,Rui Zhang. 2025

[19]Spatial variability of soil geochemical elements using a novel ecological factor-incorporated interpretable machine learning model in China's coastal saline-alkali farmlands. Jiazheng Li,Yunting Liu,Xiaohang Yang,Miao Lu,Yingqiang Song. 2025

[20]Multi-indicator adaptive prediction model for mutton based on hyperspectral imaging technology. Chen, Yuzhong,Zheng, Xiaochun,Li, Tengfei,Wang, Weiting,Ma, Yanli,Hu, Peng,Cao, Jianfang,Du, Pengfei,Abd El-Aty, A. M.. 2025

作者其他论文 更多>>