Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples.

Abstract

The accurate determination of mycotoxins in food samples is crucial to guarantee food safety and minimize their toxic effects on human and animal health. This study proposed the use of a support vector regression (SVR) predictive model improved by two metaheuristic algorithms used for optimization namely, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) to predict chromatographic retention time of various food mycotoxin groups. The dataset was collected from secondary sources and used to train and validate the SVR-HHO and SVR-PSO models. The performance of the models was assessed via mean square error, correlation coefficient, and Nash-Sutcliffe efficiency. The SVR-HHO model outperformed existing methods by 4-7% in both the two learning (training and testing) phases respectively. By using metaheuristic optimization, parameter adjustment became more effective, avoiding trapping in local minima and improving model generalization. These results demonstrate how machine learning and metaheuristics may be combined to accurately forecast mycotoxin levels, providing a useful tool for regulatory compliance and food safety monitoring. The SVR-HHO framework is perfect for commercial quality assurance, regulatory testing, and extensive food safety programs because it provides exceptional accuracy and resilience in predicting mycotoxin retention times. In contrast to conventional models, SVR-HHO effectively manages intricate nonlinear interactions, guaranteeing accurate mycotoxin identification and improving food safety while lowering hazards to human and animal health.