Rapid recognition of different sources of methamphetamine drugs based on hand-held near infrared spectroscopy and multi-layer-extreme learning machine algorithms
Journal of Near Infrared Spectroscopy(2022)
摘要
The rapid recognition of the sources of the drugs can provide valuable clues and provide the basis for determining the nature of a drug case. Here, a novel recognition method was put forward to identify the source of methamphetamine drugs rapidly and non-destructively by using a hand-held near infrared (NIR) spectrometer and a multi-layer-extreme learning machine (ML-ELM) algorithm. The accuracy, precision, sensitivity, and F-score were higher with the proposed ML-ELM algorithm than in traditional linear discriminant analysis (LDA), extreme learning machine (ELM) classification, and partial least squares (PLS) regression algorithms. The prediction accuracy of ML-ELM algorithm is 25.0%, 15.3% and 18.1% higher than that of LDA, ELM and PLS regression, respectively. The ML-ELM models for recognizing the different sources of methamphetamine drugs had the best generalization ability and prediction results. The experimental results indicated that the combination of hand-held NIR technology and ML-ELM algorithm can recognize the different sources of methamphetamine drugs rapidly, accurately, and non-destructively.
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关键词
Hand-held near infrared spectroscopy,multi-layer-extreme learning machine,methamphetamine drugs,source
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