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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. [Display omitted] • IR and six ML methods were firstly used for the geographical classification of opium. • 146 Myanmar and Afghanistan authentic opium were analyzed by IR. • The developed IR classification method is simple, rapid, and cost-effective. • All of the 30 test samples are correctly classified by the classification models. [ABSTRACT FROM AUTHOR]

This study presents a new strategy to discriminate between opium samples obtained from different geographical regions. Nuclear magnetic resonance (NMR) profiling and chemometrics were applied to geographical classification of opium originating from Myanmar and Afghanistan, which are two major opium producing countries in the world. A total of 50 Myanmar and 46 Afghanistan authentic opium samples were analyzed by 1 H-NMR, and the chemical profiles were characterized. Different sample preparation procedures, data processing methods, and chemometrics were compared to obtain the best classification effect. It was found that drying and the addition of buffer solutions were unnecessary for classification purposes; thus, the gum opium samples were extracted directly with CD 3 OD, which shortened sample preparation time. A full discrimination between the two geographical origins was achieved by 1 H-NMR profiling and orthogonal partial least squares discriminant analysis. All 30 opium samples were classified correctly by the developed orthogonal partial least squares discriminant analysis model. Compared with traditional chromatography and mass spectrometry profiling methods, the 1 H-NMR profiling method was faster (with instrument analysis time of less than 3 min) and reproducible. This study provides new insights into the applying of NMR profiling and chemometrics to rapid drug profiling analysis., (© 2023 John Wiley & Sons, Ltd.)