Deep learning-assisted SERS for detection of propoxate and isopropoxate in E-cigarettes

Abstract

The illicit use of new psychoactive substances in e-cigarettes has posed severe threats to human health and social security, urgently necessitating the development of targeted rapid and highly sensitive detection methods. In this study, we developed a highly sensitive detection approach for propoxate and isopropoxate, commonly illegally added drugs in e-cigarettes, by integrating deep learning-assisted SERS technology. First, the characteristic spectral peaks of the two isomeric compounds were identified through conventional Raman and SERS analysis of reference standards. Furthermore, DFT calculations were employed to interpret the vibrational modes in the Raman spectra corresponding to their molecular structures. Subsequently, a sample pre-treatment method was developed for spiked e-cigarette samples, enabling trace-level SERS detection of both substances. Finally, an innovative dual-branch deep learning network integrating time-domain and frequency-domain features was developed for high-precision classification and identification of two structurally similar substances, achieving an identification accuracy of 99.73%. This study provides a reference for the detection of structurally similar compounds.