Label-Free identification of fentanyl analogues using surface-enhanced Raman scattering and machine learning algorithm

Abstract

The identification and analysis of drug abuse is crucial for controlling and combating this global public health problem. In this regard, surface-enhanced Raman spectroscopy (SERS) is a highly sensitive detection technique that is fast, non-destructive, and widely used in various fields. In this study, Ag@Au nanowires (Ag@Au NWs) were synthesized using the seed growth method. The SERS performance of Ag@Au NWs was evaluated using R6G as a probe molecule, and the minimum detection concentration of R6G was found to be 10−9 M. Similarly, the minimum detection concentration of fentanyl was observed to be 10−7 M. Furthermore, the study also involved performing routine Raman detection on 10 fentanyl analogs that were structurally similar. Additionally, density functional theory (DFT) calculations were carried out to distinguish between these substances. The Raman spectra calculated by DFT provided vibrational frequencies and normal mode assignments for each species. By comparing normal Raman spectra, SERS and DFT calculated spectrogram data, we assigned characteristic peaks of 10 substances. Finally, principal component analysis was used to reduce the dimension of the data, and support vector machine were further used to classification of SERS spectral data of the above 10 substances, with an accuracy of 96.4%.