SERS and machine learning based effective feature extraction for detection and identification of amphetamine analogs

Jing Cai; Yulun Wu; Haohao Bai; Yingsheng He; Yazhou Qin

doi:10.1016/j.heliyon.2023.e23109

SERS and machine learning based effective feature extraction for detection and identification of amphetamine analogs

Heliyon. 2023 Nov 30;9(12):e23109. doi: 10.1016/j.heliyon.2023.e23109. eCollection 2023 Dec.

Authors

Jing Cai¹, Yulun Wu¹, Haohao Bai¹, Yingsheng He², Yazhou Qin¹

Affiliations

¹ Key Laboratory of Drug Prevention and Control Technology of Zhejiang Province, Zhejiang Police College, 555 Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang Province, PR China.
² Key Laboratory of Drug Control and Monitoring, National Anti-Drug Laboratory Zhejiang Regional Center, 555 Binwen Road, Binjiang District, Hangzhou, 310053, Zhejiang Province, PR China.

Abstract

Surface-enhanced Raman spectroscopy (SERS) is extensively researched in diverse disciplines due to its sensitivity and non-destructive nature. It is particularly considered a potential and promising technology for rapid on-site screening in drug detection. In this investigation, a technique was developed for fabricating nanocrystals of Ag@Au SNCs. Ag@Au SNCs, as the basic material of SERS, can detect amphetamine at concentrations as low as 1 μg/mL. The Ag@Au SNCs exhibits a strong surface plasmon resonance effect, which amplifies molecular signals. The SERS spectra of ten substances, including amphetamine and its analogs, showed a strong peak signal. To establish a qualitative distinction, we examined the Raman spectra and conducted density functional theory (DFT) calculations on the ten aforementioned species. The DFT calculation enabled us to determine the vibrational frequency and assign normal modes, thereby facilitating the qualitative differentiation of amphetamines and its analogs. Furthermore, the SERS spectrum of the ten mentioned substances was analysed using the support vector machine learning algorithm, which yielded a discrimination accuracy of 98.0 %.

Keywords: DFT; Machine learning; Methamphetamines; Raman; SERS.