Paper
4 December 2024 Rapid screening and classification of common drugs using Raman spectroscopy based on the hybrid and cascade-SVM model
Xueling Li, Qi Li, Jing Yu, Ke Li, Haiyang Zhang, Zhengdong Zhang
Author Affiliations +
Proceedings Volume 13283, Conference on Spectral Technology and Applications (CSTA 2024); 1328336 (2024) https://doi.org/10.1117/12.3037041
Event: Conference on Spectral Technology and Applications (CSTA 2024), 2024, Dalian, China
Abstract
In the field of drug detection, achieving rapid and accurate screening and classification of drugs and non-drugs is crucial. To meet this demand, this study proposes a new strategy that combines Raman spectroscopy technology with machine learning algorithms. Using a dataset of methamphetamine and heroin samples for model training, a Hybrid and Cascade-SVM model was constructed. This model consists of two OneClassSVM models and a standard SVM model. The unique hybrid cascade architecture design helps to enhance the model's discrimination accuracy. Although the model is trained only on the drug dataset, its performance is comprehensively evaluated during the validation and testing phases using a dataset that includes both drugs and non-drugs. The two OneClassSVM models first screen out drug samples from the mixed data, and the SVM model subsequently performs binary classification on these screened samples. Experimental results show that the Hybrid and Cascade-SVM model demonstrates good performance in drug identification and classification. This method provides an efficient solution for rapid on-site drug detection, optimizes the data processing workflow, and offers new insights for detecting drugs in complex matrices.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xueling Li, Qi Li, Jing Yu, Ke Li, Haiyang Zhang, and Zhengdong Zhang "Rapid screening and classification of common drugs using Raman spectroscopy based on the hybrid and cascade-SVM model", Proc. SPIE 13283, Conference on Spectral Technology and Applications (CSTA 2024), 1328336 (4 December 2024); https://doi.org/10.1117/12.3037041
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KEYWORDS
Data modeling

Statistical modeling

Education and training

Raman spectroscopy

Solids

Cross validation

Mathematical optimization

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