机器学习驱动的中药银杏叶SERS多维度鉴定OA

This study establishes a multidimensional discrimination strategy for ginkgo folium based on surface-enhanced Raman spectroscopy(SERS)combined with machine learning.A systematic investigation was conducted across four dimensions(geographical origin,drying process,storage quality,and pesticide residues),enabling intelligent classification and quality assessment of ginkgo folium.The results showed that the SERS spectra of ginkgo folium extracts exhibited characteristic peaks of flavonoids(quercetin,kaempferol,isorhamnetin)and ginkgolides.Based on SERS combined with partial least squares discriminant analysis(PLS-DA),100%classification accuracy was achieved for ginkgo folium samples from five origins and processed by four drying methods,indicating that both origin differences and drying processes significantly affect their chemical composition.Furthermore,SERS combined with partial least squares regression(PLS)accurately predicted storage time(training set,R²=0.991;cross-validation set,R²=0.841)and enabled highly sensitive quantitative detection of pesticide residues,including glyphosate and thiram.In summary,SERS combined with machine learning provides a rapid,highly sensitive,and reliable analyti-cal strategy for origin tracing,process monitoring,storage evaluation,and pesticide residue detection of ginkgo folium and other traditional Chinese medicinal materials.This approach opens a novel technical pathway for quality control and safety supervision in traditional Chinese medicines field.