Machine Learning-Driven 3D Plasmonic Cavity-in-Cavity Surface-Enhanced Raman Scattering Platform with Triple Synergistic Enhancement Toward Label-Free Detection of Antibiotics in Milk

The surface-enhanced Raman scattering (SERS) technique with ultrahigh sensitivity has gained attention to meet the increasing demands for food safety analysis. The integration of machine learning and SERS facilitates the practical applicability of sensing devices. In this study, a machine learning-driven 3D plasmonic cavity-in-cavity (CIC) SERS platform is proposed for sensitive and quantitative detection of antibiotics. The platform is prepared by transferring truncated concave nanocubes (NCs) to an obconical-shaped template surface. Owing to the triple synergistic enhancement effect, the highly ordered 3D CIC arrays improve the simulated electromagnetic field intensity and experimental SERS activity, demonstrating a 33.1-fold enhancement compared to a typical system consisting of Au NCs deposited on a flat substrate. The integration of machine learning and Raman spectroscopy eliminates subjective judgments on the concentration of detectors using a single feature peak and achieves accurate identification. The machine learning-driven CIC SERS platform is capable of detecting ampicillin traces in milk with a detection limit of 0.1 ppm, facilitating quantitative analysis of different concentrations of ampicillin. Therefore, the proposed platform has potential applications in food safety monitoring, health care, and environmental sampling.

Automated summary

This study proposes a machine learning-driven 3D plasmonic cavity-in-cavity SERS platform for sensitive and quantitative detection of antibiotics. The application of this platform enables quantitative analysis of antibiotics at different concentrations.