Key Laboratory of Drug Monitoring and Control, Drug Intelligence and Forensic Center, Ministry of Public Security, P.R.C., Beijing 100193, China.
China Pharmaceutical University, Nanjing Jiangsu 210009, China.
Forensic Sci Int. 2024 Apr;357:111974. doi: 10.1016/j.forsciint.2024.111974. Epub 2024 Mar 2.
Afghanistan and Myanmar are two overwhelming opium production places. In this study, rapid and efficient methods for distinguishing opium from Afghanistan and Myanmar were developed using infrared spectroscopy (IR) coupled with multiple machine learning (ML) methods for the first time. A total of 146 authentic opium samples were analyzed by mid-IR (MIR) and near-IR (NIR), within them 116 were used for model training and 30 were used for model validation. Six ML methods, including partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (OPLS-DA), k-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) were constructed and compared to get the best classification effect. For MIR data, the average of precision, recall and f1-score for all classification models were 1.0. For NIR data, the average of precision, recall and f1-score for different classification models ranged from 0.90 to 0.94. The comparison results of six ML models for MIR and NIR data showed that MIR was more suitable for opium geography classification. Compared with traditional chromatography and mass spectrometry profiling methods, the advantages of MIR are simple, rapid, cost-effective, and environmentally friendly. The developed IR chemical profiling methodology may find wide application in classification of opium from Afghanistan and Myanmar, and also to differentiate them from opium originating from other opium producing countries. This study presented new insights into the application of IR and ML to rapid drug profiling analysis.
阿富汗和缅甸是两个主要的鸦片产地。本研究首次采用中红外（MIR）和近红外（NIR）光谱结合多种机器学习（ML）方法，建立了一种快速、高效鉴别来自阿富汗和缅甸鸦片的方法。共分析了 146 个真实的鸦片样品，其中 116 个用于模型训练，30 个用于模型验证。构建并比较了包括偏最小二乘判别分析（PLS-DA）、正交偏最小二乘判别分析（OPLS-DA）、k-最近邻（KNN）、支持向量机（SVM）、随机森林（RF）和人工神经网络（ANNs）在内的 6 种 ML 方法，以获得最佳分类效果。对于 MIR 数据，所有分类模型的平均精度、召回率和 f1 分数均为 1.0。对于 NIR 数据，不同分类模型的平均精度、召回率和 f1 分数范围为 0.90 到 0.94。6 种 ML 模型对 MIR 和 NIR 数据的比较结果表明，MIR 更适合鸦片产地分类。与传统的色谱和质谱分析方法相比，MIR 的优势在于简单、快速、经济高效且环保。所建立的红外化学剖析方法可能会广泛应用于来自阿富汗和缅甸的鸦片分类，以及区分它们与来自其他鸦片生产国的鸦片。本研究为红外和 ML 在快速药物剖析分析中的应用提供了新的思路。
