Drug classification based on X-ray spectroscopy combined with machine learning

TL;DR

This work tackles the need for fast, accurate detection of drug analogs by leveraging X-ray absorption spectroscopy fused with machine learning. It introduces a CPSVM pipeline that uses a LeNet-5–style CNN to extract $84$-dimensional features from spectra, followed by an SVM with an RBF kernel whose parameters $C$ and $\sigma$ are optimized via IPSO. Compared to a baseline SVM with random parameters and an IPSO-SVM without feature extraction, CPSVM achieves high accuracy ($\approx 99.14\%$) with substantially reduced runtime, illustrating improved efficiency without sacrificing performance. The approach offers a practical, non-destructive method for real-time drug classification with potential deployment in field settings such as customs and airports.

Abstract

The proliferation of new types of drugs necessitates the urgent development of faster and more accurate detection methods. Traditional detection methods have high requirements for instruments and environments, making the operation complex. X-ray absorption spectroscopy, a non-destructive detection technique, offers advantages such as ease of operation, penetrative observation, and strong substance differentiation capabilities, making it well-suited for application in the field of drug detection and identification. In this study, we constructed a classification model using Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Particle Swarm Optimization (PSO) to classify and identify drugs based on their X-ray spectral profiles. In the experiments, we selected 14 chemical reagents with chemical formulas similar to drugs as samples. We utilized CNN to extract features from the spectral data of these 14 chemical reagents and used the extracted features to train an SVM model. We also utilized PSO to optimize two critical initial parameters of the SVM. The experimental results demonstrate that this model achieved higher classification accuracy compared to two other common methods, with a prediction accuracy of 99.14%. Additionally, the model exhibited fast execution speed, mitigating the drawback of a drastic increase in running time and efficiency reduction that may result from the direct fusion of PSO and SVM. Therefore, the combined approach of X-ray absorption spectroscopy with CNN, PSO, and SVM provides a rapid, highly accurate, and reliable classification and identification method for the field of drug detection, holding promising prospects for widespread application.

Figures (9)

• Figure 1: Schematic diagram of X-ray spectral detection system
• Figure 2: The spectra of 14 samples
• Figure 3: The architecture of CNN model
• Figure 4: The accuracy and loss value during model training
• Figure 5: The Model Framework Diagram of CPSVM