采用近红外光谱和偏最小二乘法(PLS)建立了涵盖7种掺杂物、 纯度范围为10%~100%的甲基苯丙胺定量分析模型。 甲基苯丙胺缴获样品中检出频率较高的掺杂物为二甲基砜、 异丙基苄胺、 蔗糖、 环己胺、 明矾、 吡拉西坦、 麻黄碱这7种。 为保证模型尽可能地覆盖实际缴获样品的掺杂物种类和纯度区间, 采用高纯度甲基苯丙胺和掺杂物混合制样的方式进行了中、 低纯度建模样品的制备。 甲基苯丙胺和不同掺杂物的特征吸收峰出现在不同波段, 选取全波段建立PLS定量模型。 对不同光谱预处理方法进行了考察, 结果表明标准正态变量校正+一阶导数(SNV+1D)的交叉验证均方差(RMSECV)值最小, 效果最佳。 鉴于掺杂物种类较多, 建立了两个PLS定量模型, 模型1的PLS因子数为8、 测定系数( R²)为99.9、 RMSECV为0.8%、 预测均方差(RMSEP)为2.0%, 适用于未进行掺杂的高纯度甲基苯丙胺样品以及掺杂物为二甲基砜、 异丙基苄胺、 蔗糖、 环己胺的甲基苯丙胺样品的定量分析; 模型2的PLS因子数为5、 R²为99.9、 RMSECV为0.8%、 RMSEP为1.7%, 适用于掺杂物为明矾、 麻黄碱、 吡拉西坦的甲基苯丙胺样品的定量分析。 两个定量模型的重复性均≤2.1%, 重现性均≤4.0%。 对72份验证样品进行了测定, 液相色谱法和近红外光谱法测定的平均纯度值分别为74.3%和72.9%, t统计值为3.0, 大于显著性水平0.05, 表明这两个方法的测定结果无显著性差异。 选用马氏距离法和光谱残差法作为光谱异常值的筛查方法, 当待测样品与建模样品的马氏距离值≤2且光谱残差值≤3时, 定量结果可靠; 反之, 定量结果不可靠, 此时需要采用其他方法进行定量分析。 本方法制样简单、 测试速度快、 定量结果准确、 精度高, 适合于缴获毒品样品中甲基苯丙胺的快速定量分析。 该研究中涉及的制样方法和建模方法同样适用于其他毒品。

In this study, a near infrared partial least squares (NIR-PLS) quantitative model, which involved seven adulterants and with methamphetamine purity ranging from 10% to 100%, was established for the first time. Seven adulterants of dimethyl sulfone, isopropyl benzylamine, sucrose, cyclohexylamine, aluminum potassium sulfate, piracetam and ephedrine were most frequently detected in seized methamphetamine samples. High purity methamphetamine and adulterants were mixed to prepare the model samples to make sure the established quantitative model can cover the common adulterant species and purity range of actual seized samples. The characteristic absorption peaks of methamphetamine and adulterants occur in different spectrum range, so the whole spectrum range was used for the PLS modeling. The standard normal variate transformation+first-order derivative (SNV+1D) was proved to be the best spectral pretreatment method. Two separate PLS quantitative models were established to improve the accuracy of the models. The PLS factor, coefficient of determination ( R²), root mean square error of cross validation (RMSECV), and root mean square error of prediction (RMSEP) for model 1 was 8, 99.9, 0.8%, and 2.0%, respectively. Model 1 is suitable for high purity methamphetamine samples without adulterant and methamphetamine samples adulterated with dimethyl sulfone, isopropyl benzylamine, sucrose, and cyclohexylamine. The PLS factor, R², RMSECV, and RMSEP for model 2 was 5, 99.9, 0.8%, and 1.7%, respectively. Model 2 was suitable for methamphetamine samples adulterated with aluminum potassium sulfate, ephedrine, and piracetam. The repeatability and reproducibility for both models were less than 2.1% and 4.0%, respectively. Seventy-two seized methamphetamine samples with purity ranging from 13.9% to 99.4% were used to validate the accuracy of the two models. The average purity determined by liquid chromatography and near infrared spectroscopy was 74.3% and 72.9%, respectively. The t-statistics values were 3.0, which was higher than the significant level of 0.05, so it showed that there was no significant difference between the two methods. Mahalanobis distance and spectral residual were selected as the outlier identification methods. When the Mahalanobis distance value is less than 2, and the spectral residual value is less than 3, the quantitative result is reliable. On the contrary, the quantitative result is unreliable, and the other method is needed for quantitative analysis. The established NIR-PLS method is simple in sample preparation, fast in testing, accurate in quantitative results and high in accuracy. It is suitable for rapid quantitative analysis of methamphetamine in seized samples. The sampling and modeling methods involved in this study are also applicable to other drugs.