Due to the diversity and fast evolution of new psychoactive substances (NPS), both public health and safety are threatened around the world. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR), which serves as a simple and rapid technique for targeted NPS screening, is challenging with the rapid structural modifications of NPS. To achieve the fast non-targeted screening of NPS, six machine learning (ML) models were constructed to classify eight categories of NPS, including synthetic cannabinoids, synthetic cathinones, phenethylamines, fentanyl analogues, tryptamines, phencyclidine types, benzodiazepines, and “other substances” based on the 1099 IR spectra data items of 362 types of NPS collected by one desktop ATR-FTIR and two portable FTIR spectrometers. All these six ML classification models, including k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), extra trees (ET), voting, and artificial neural networks (ANNs) were trained through cross validation, and f1-scores of 0.87–1.00 were achieved. In addition, hierarchical cluster analysis (HCA) was performed on 100 synthetic cannabinoids with the most complex structural variation to investigate the structure-spectral property relationship, which leads to a summary of eight synthetic cannabinoid sub-categories with different “linked groups”. ML models were also constructed to classify eight synthetic cannabinoid sub-categories. For the first time, this study developed six ML models, which were suitable for both desktop and portable spectrometers, to classify eight categories of NPS and eight synthetic cannabinoids sub-categories. These models can be applied for the fast, accurate, cost-effective, and on-site non-targeted screening of newly emerging NPS with no reference data available.

由于新型精神活性物质（NPS）的多样性和快速发展，世界各地的公共健康和安全都受到威胁。衰减全反射傅里叶变换红外光谱 (ATR-FTIR) 是一种简单快速的靶向 NPS 筛选技术，但它在 NPS 的快速结构修饰方面面临着挑战。为了实现NPS的快速非靶向筛查，构建了6种机器学习（ML）模型，对8类NPS进行分类，包括合成大麻素、合成卡西酮、苯乙胺、芬太尼类似物、色胺、苯环己哌啶类、苯二氮卓和“其他物质” ”基于一台台式 ATR-FTIR 和两台便携式 FTIR 光谱仪收集的 362 种 NPS 的 1099 个红外光谱数据项。所有这六种机器学习分类模型，包括 k 最近邻 (KNN)、支持向量机 (SVM)、随机森林 (RF)、额外树 (ET)、投票和人工神经网络 (ANN) 均通过交叉验证进行训练， f1 分数为 0.87-1.00。此外，对100种结构变异最复杂的合成大麻素进行层次聚类分析（HCA），以研究结构-光谱性质关系，从而总结出具有不同“关联基团”的八个合成大麻素子类别。还构建了机器学习模型来对八个合成大麻素子类别进行分类。这项研究首次开发了六种适用于台式和便携式光谱仪的机器学习模型，对八类 NPS 和八种合成大麻素子类别进行分类。这些模型可用于在没有参考数据的情况下对新兴的NPS进行快速、准确、经济、现场非针对性的筛查。