Machine learning-driven Ag/SiO2/Cu/rice leaf SERS platform for intelligent identification of pharmacodynamic substances

The combination of surface-enhanced Raman scattering (SERS) and machine learning algorithm provides an effective means for the identification of pharmacodynamic substances. This paper reported a biomimetic synthesis route of Ag-30/SiO 2 -4/Cu-20/rice leaf (Ag-30/SiO 2 -4/Cu-20/RL) SERS platform with multiple synergistic electromagnetic enhanced performance. The localized surface plasmon resonance (LSPR) effect was strengthened as the SiO 2 nanolayer generated between Ag and Cu. This SERS platform demonstrated high sensitivity, with a low limit of detection (LOD) of 1 × 10 −10  M for 4-Aminophenylthiophenol (4-ATP) and an enhancement factor (EF) of 3.86 × 10 6 . More importantly, the principal component analysis (PCA) was adopted to analyze the SERS data of three different Traditional Chinese medicine (TCM) pharmacodynamic substances (Orientin, Atractylenolide III and Prim-o-glucosylcimifugin). The K-nearest neighbor (KNN) and Naïve Bayes (NB) achieved classification accuracy of 0.9474 and 0.9649, respectively. The platform provides guidance for the accurate identification of TCM pharmacodynamic substances.