{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:27:47Z","timestamp":1790742467577,"version":"4.1.0"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Accelerated industrialization has caused complex mixed toxicant pollution, where synergistic or antagonistic interactions render conventional detection methods inadequate. Herein, we develop an integrated framework by pioneering the integration of microbial electrochemical systems (MECs) with machine learning (ML) for quantifying formaldehyde, tetracycline, Ag+, and Cu2+ in multi-component, multi-ratio, and multi-concentration mixtures. MECs generated dynamic current\u2013time (I\u2013t) signals responsive to toxicant stress, though signal overlap from mixed toxicants hindered direct quantification. Guided by toxicokinetics and electrochemical mechanisms, we developed a novel mechanism-driven feature engineering strategy with exclusively original indicators, which extracted 22 multidimensional features capturing instantaneous characteristics, kinetic patterns, and microbial stress-adaptive responses to resolve signal ambiguity, and provided biologically meaningful, high-information feature inputs that effectively bridge electrochemical response signals and ML modeling. Comparative analysis of four ML models (SVM, KNN, PLS, and RF) showed RF outperformed others, achieving R2 &gt; 0.9 for all toxicants (formaldehyde: 0.959; tetracycline: 0.934; Ag+: 0.936; Cu2+: 0.957) with minimized MAE and RMSE. Microbial community analysis identified Geobacter anodireducens (71.5%, electroactive for heavy metals) and Comamonas testosteroni (12.9%, organic degrader) as key functional taxa, supported by KEGG enzyme abundance data. This work overcomes traditional MEC limitations via innovative feature engineering and pioneering ML integration, providing a rapid, low-cost, and high-accuracy tool for environmental mixed toxicant monitoring.<\/jats:p>","DOI":"10.3390\/bios16030144","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T08:51:57Z","timestamp":1772441517000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine Learning-Enhanced MEC Sensors with Feature Engineering for Quantitative Analysis of Multi-Component Toxicants"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiaguo","family":"Yan","sequence":"first","affiliation":[{"name":"PT COSL INDO, China Oilfield Services Limited (COSL), Jakarta 12930, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renxin","family":"Liang","sequence":"additional","affiliation":[{"name":"Division of Oilfield Chemicals, China Oilfield Services Limited (COSL), Tianjin 300459, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqing","family":"Yan","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3522-5627","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Pollution Processes and Environmental Criteria, Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, No. 38 Tongyan Road, Jinnan, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135547","DOI":"10.1016\/j.jhazmat.2024.135547","article-title":"Which pollutants and sources should be prioritized for control in multi-pollutants complex contaminated areas?","volume":"478","author":"Ouyang","year":"2024","journal-title":"J. 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