Abstract
Staphylococcal enterotoxin B (SEB) holds critical importance in disease diagnosis, food safety, and public health due to its high toxicity and potent pathogenicity. Traditional immunoassay methods for detecting SEB often exhibit insufficient accuracy and robustness. This study leverages machine learning technology to integrate the quantitative measurement advantages of electrochemical methods with the strong specificity of immunoassays, achieving high-precision coupled electrochemical immunodetection of SEB. Firstly, an electrochemical immunosensing system was developed to capture the target analyte SEB by immobilizing specific antibodies on the electrode surface. Cyclic voltammetry (CV) was utilized to accurately characterize the immune response process. Secondly, feature selection methodologies within machine learning are utilized to identify eight key parameters from CV curves that are highly related to SEB concentration. This enhancement significantly improves both the accuracy and interpretability of SEB measurement data. Lastly, a multivariate linear regression algorithm is employed to effectively train and fit the extracted feature data. This approach successfully mitigates noise introduced by variations in electrode batches, experimental conditions, and operational techniques-thereby enabling robust quantitative measurements of SEB concentration with high precision. The entire detection process requires only 20 μL sample and is accomplished in just two minutes. This method can detect antigen concentrations at both ng/mL and μg/mL levels, with a detection limit of 1 ng/mL. The [Formula: see text] score for predicting SEB antigen concentration is approximately 0.999, accompanied by a mean absolute percentage error (MAPE) of 6.09% This approach achieves high precision, robustness, and specificity in SEB detection, offering extensive detection range, rapid response time, and cost-effectiveness, presenting new opportunities for identifying various pathogenic toxins.