MoS2@Ag nanoflower-based SERS substrate coupled with transformer network for sensitive detection and intelligent classification of fluoroquinolone antibiotics in milk

A MoS 2 @Ag nanoflower SERS substrate with 1T/2H mixed-phase is synthesized. • The prepared substrate achieving a high SERS enhancement factor of 1.76 × 10 8 . • The Transformer network achieved 99.82% classification accuracy for four antibiotics. Fluoroquinolone antibiotics (FQs) residues in dairy products pose a persistent challenge to food safety. This work establishes an integrated analytical platform combining a three-dimensional molybdenum disulfide‑silver nanoflower (MoS 2 @Ag NFs) SERS substrate with a transformer network based deep learning model for the sensitive detection and intelligent classification of FQs in milk. The prepared MoS 2 @Ag NFs substrate with a 1T/2H mixed-phase MoS 2 and a hierarchical structure densely decorated with silver nanoparticles (Ag NPs), exhibits a high enhancement factor and achieves nanomolar-level detection limits for four representative FQs. To overcome the discrimination challenge posed by the highly similar spectral signatures of different FQs, a transformer network is employed to automatically extract decisive spectral features via its self-attention mechanism, which results in an exceptional classification accuracy exceeding 99.82%. The integrated platform enables sensitive and intelligent monitoring of FQs in milk, providing a promising strategy for on-site detection of antibiotic residues in complex food matrices. Download: Download high-res image (125KB) Download: Download full-size image