Multi-scale convolution neural network with residual modules for determination of drugs in human hair using surface-enhanced Raman spectroscopy with a gold nanorod film self-assembled by inverted evaporation

Abstract

Detection of illegal drug users is crucial in controlling drug-related crimes, reducing drug prevalence, and protecting human lives to ensure social stability. In this study, surface-enhanced Raman spectroscopy (SERS) and deep learning networks were employed to determine methamphetamine, ketamine, and morphine in human hair. Drugs were obtained from hair through alkaline hydrolysis and liquid–liquid extraction, and gold nanorods were employed to prepare the self-assembled film as the SERS substrate by inverted evaporation. The film showed good uniformity and excellent sensitivity, with a relative standard deviation of 15.6% and a detection limit of at least 10-10 M in the SERS detection of crystal violet. The spectra of methamphetamine, ketamine, and morphine at 0.05–1.0, 0.1–2.0, and 0.1–2.0 ng/mg were obtained, and the three drugs could be detected. Inception, a multi-scale feature extraction network, was combined with residual modules (Inception–ResNet) to develop the identification models of drugs, and the effect of spectral input form as a vector or matrix was explored. Inception–ResNet with input form of matrix outweighed other methods with 100.00%, 100.00%, and 99.23% accuracies in the training, validation, and prediction sets, respectively. In brief, SERS and Inception–ResNet with the spectra in matrix form provide an efficient and accurate determination of drugs in human hair, enabling the retrospective evaluation of drug use, and the method will be anticipated to detect excitant, poison, and toxic chemicals in human hair.