Semi-quantitative analysis of gentian violet by surface-enhanced Raman spectroscopy using silver colloids

Surface-enhanced Raman spectroscopy for rapid identification and quantification of Flibanserin in different kinds of wine

Wine has always been a popular carrier for psychedelic drugs, with the rapid identification and quantification of psychedelic drugs in wine being the focus of regulating illegal behavior. In this study, surface-enhanced Raman spectroscopy (SERS) is used for the rapid detection of Flibanserin in liquor, beer and grape wine. First, the theoretical Raman spectrum with characteristic Flibanserin peaks was calculated and identified, and the limit of detection of 1 μg mL-1 for Flibanserin in liquor was determined. The curve equation was obtained by fitting using the least squares method, and the correlation coefficient was 0.995. The recovery range of the Flibanserin liquor solution ranged from 93.70% to 108.32%, and the relative standard deviation (RSD) range was 2.77% to 7.81%. Identification and quantification of Flibanserin in liquor, beer and grape wine were done by principal component analysis (PCA) and support vector machine (SVM). Machine learning algorithms were used to reduce the workload and the possibility of manual misjudgements. The classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 100.00%, 95.80% and 92.00%, respectively. The quantitative classification accuracies of the Flibanserin liquor, beer and grape wine spectra were 92.30%, 91.70% and 92.00%, respectively. The machine learning algorithms were used to verify the advantages and feasibility of this method. This study fully demonstrates the huge application potential of combining SERS technology and machine learning in the rapid on-site detection of psychedelic drugs.

Interactions between the antifungal drug myclobutanil and gold and silver nanoparticles in Penicillium digitatum investigated by surface-enhanced Raman scattering

Surface-enhanced Raman scattering (SERS) of an antifungal reagent, myclobutanil (MCB), was performed on Au and Ag nanoparticles (NPs) to estimate the drug-release behaviors in fungal cells. A density functional theory (DFT) calculation was introduced to predict a favorable binding site of MCB to either the Ag or Au atom. Myclobutanil was presumed to bind more strongly to Au than to Ag in their most stable, optimized geometries of the N4 atom in its 1,2,4-triazole unit binding to the metal atom. Strong intensities were observed in the Ag SERS spectra only at acidic pH values, whereas the most prominent peaks in the Au SERS spectra of MCB matched quite well with those of 1,2,4-triazole regardless of pH conditions. The Raman spectral intensities of the MCB-assembled Ag and Au NPs decreased after treatment with either potato dextrose agar (PDA) or glutathione (GSH). Darkfield microscopy and confocal SERS were performed to analyze the MCB-assembled metal NPs inside Penicillium digitatum fungal cells. The results suggested that MCB was released from the metal NPs in the intracellular GSH in the fungi because we observed only fungal cell peaks.