Sciensano, Scientific Direction Chemical and Physical Health Risks, Service of Medicines and Health Products, J. Wytsmanstraat 14, B-1050 Brussels, Belgium.
VUB, Faculty of Sciences and Bio-Engineering, Department Chemistry, Analytical, Environmental and Geo-Chemistry, Pleinlaan 2, B-1050 Brussels, Belgium.
Molecules. 2024 Mar 1;29(5):1116. doi: 10.3390/molecules29051116.
The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed.
在受保护的用户中心环境中使用之前，对海洛因样本进行分析可能是减少伤害背景下的一项补充服务。在这种情况下，红外光谱与多元校准相结合可能是一项有价值的资产，因此，从用户那里直接收集了 125 个海洛因样本，并使用经典色谱技术进行了分析。此外，还对所有样本进行了中红外光谱采集，以便在偏最小二乘（PLS）建模中使用，从而根据实际样本获得定性和定量模型。该方法表明，基于所收集的光谱数据和 PLS 建模，有可能识别和定量样本中的海洛因。这些模型能够正确识别外部测试集 96%的样本中的海洛因，精度、特异性和敏感性值分别为 100.0、75.0 和 95.5%。对于回归，得到的预测均方根误差（RMSEP）为 0.04，表明具有良好的预测性能。此外，在质谱筛选过程中，遇到了 10 种不同的掺杂物和杂质。使用光谱数据来模拟每种物质的存在，得到了其中 7 种物质的表现良好的模型。所有模型都显示出有希望的正确分类率（在 92%到 96%之间），并且具有良好的敏感性、特异性和精度值。对于可待因和吗啡，模型不太理想，可能是由于这些杂质的浓度较低，这是由于乙酰化的结果。对于美沙酮，该方法失败了。
