A machine learning-helped antifouling strategy for improving the accuracy of electrochemical sensors

The performance of electrochemical sensors is prone to signal degradation caused by biofouling in complex biological fluids. Overcoming these biofouling remains a critical hurdle for their large-scale commercial applications. In this work, the antifouling effect of three antifouling membranes were compared using indole-3-acetic acid (IAA) in lettuce as sample. The Polydopamine-poly (sulfobetaine methacrylate) (PDA-PSBMA) membrane demonstrated exceptional resistance to IAA oxidation products and other contaminants. To further enhance the accuracy of the sensors, six Machine Learning (ML) algorithms were employed to predict post-contamination standard curves of the sensor after multiple uses in real samples. The Random Forest (RF) model exhibited optimal predictive performance, enabling effective calibration of current data of IAA from lettuce samples at different growth stages (mature and vegetative). After the application of the antifouling membranes helped by the ML methods, the RSD values of the sensor in lettuce samples decreased from 21.93% to 4.16% (mature stage) and from 27.65% to 6.17% (vegetative stage). Our work provides an effective technical approach that ensures the accuracy of electrochemical sensor even after repeated use in complex biological samples, which represents a significant, data-driven enhancement over traditional antifouling strategies. This approach can be readily adapted by other researchers to extend its utility to a broader range of biological samples or analytes.