Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach

Abstract

This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer. Gold-silver core-shell nanoparticles were synthesized and served as high-performance SERS substrates, which possess well-defined structures, uniform dispersion, and a core-shell composition with an average diameter of 21.44 ± 4.02nm, as characterized by TEM-EDS. SERSFormer employs sophisticated, task-specific data processing techniques and CNN embedders, powered by an architecture features weight-shared multi-head self-attention transformer encoder layers. The SERSFormer model demonstrated exceptional proficiency in qualitative analysis, successfully classifying six categories, including five pesticides (coumaphos, oxamyl, carbophenothion, thiabendazole, and phosmet) and a control group of spinach data, with 98.4% accuracy. For quantitative analysis, the model accurately predicted pesticide concentrations with a mean absolute error of 0.966, a mean squared error of 1.826, and an R2 score of 0.849. This novel approach, which combines SERS with machine learning and is supported by robust transformer models, showcases the potential for real-time pesticide detection to improve food safety in the agricultural and food industries.