Abstract

Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC–MS) and gas chromatography with flame-ionization detection (GC–FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R²). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R² of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R² of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources.

中文翻译：

---

光谱技术与化学计量学相结合进行可卡因粉末分析的比较

摘要

光谱技术与化学计量学相结合是分析缉获的药物粉末的有前途的工具。在这项研究中，比较了三种光谱技术[中红外（MIR），拉曼和近红外（NIR）]的性能。总共分析了364种缉获的粉末，其中包括276种可卡因粉末（浓度范围为4至99 w％）和88种无可卡因粉末。每个光谱数据集都建立了一个分类模型（使用支持向量机[SVM]判别分析）和一个量化模型（使用SVM回归），以便将可卡因粉末与其他粉末区分开来，并对可卡因阳性粉末中的可卡因进行定量。将模型的性能与气相色谱-质谱联用（GC-MS）和气相色谱-火焰电离检测（GC-FID）进行了比较。使用了不同的评估标准：假阴性数（FNs），假阳性数（FPs），准确性，交叉验证的均方根误差（RMSECV）和测定系数（R²）。由于在拉曼光谱中观察到荧光背景，因此从分类数据集中排除了十种有色粉末。对于分类，使用MIR光谱可获得最佳准确性（99.7％）。使用拉曼光谱和近红外光谱，准确度分别为99.5％和98.9％。为了进行定量，使用NIR光谱可获得最佳结果。可卡因含量的测定是3.79％的RMSECV和0.97的R ²。MIR和拉曼预测可卡因浓度的性能低于NIR，RMSECV分别为6.76％和6.79％，且均具有R ²为0.90。三种光谱技术可用于可卡因的分类和定量，但检测到了一些性能差异。用MIR光谱获得最好的分类。然而，为了定量，MIR和拉曼的RMSECV是NIR的两倍。光谱技术与化学计量学相结合可以减少确认分析（例如基于色谱法）的工作量，从而节省时间和资源。