Departamento de Química Analítica, Nutrición y Bromatología, Facultad de Ciencias Químicas, Universidad de Salamanca, 37008 Salamanca, Spain.
Departamento de Química Analítica, Nutrición y Bromatología, Facultad de Ciencias Químicas, Universidad de Salamanca, 37008 Salamanca, Spain.
Talanta. 2018 May 1;181:373-379. doi: 10.1016/j.talanta.2018.01.032. Epub 2018 Feb 3.
In this work, a method for the quantitative and qualitative analysis of 11 polycyclic aromatic hydrocarbons (PAHs) in urine samples is reported. The method is based on the coupling of a programmed temperature vaporizer (PTV) with a quadrupole mass spectrometer (qMS), via a deactivated fused silica tubing. Before the PTV-qMS analysis, the samples were subjected to a liquid-liquid extraction (LLE). The method was rapid since no chromatographic separation was performed. The samples were introduced directly into the PTV, and the analytes were trapped in the Tenax-TA packed liner while the solvent was purged. After that, all the compounds reached the mass spectrometer, obtaining the fingerprint of the analysed samples. Urine samples free of PAHs and the same samples spiked with the compounds were analysed. The resulting profile signals were used to quantify the analytes using multivariate calibration, and to classify the samples according to the presence or absence of the PAHs. In the latter task, non-supervised and supervised pattern recognition techniques were employed. The calibration models worked satisfactorily and errors lower or equal to 15% were obtained, in most cases, when an external validation set was analysed. Regarding the classification of the samples, most of the supervised pattern recognition techniques provided excellent results (100% success), where all of the samples were classified correctly.
本工作报道了一种尿液样品中 11 种多环芳烃(PAHs)的定量和定性分析方法。该方法基于程序升温汽化器(PTV)与四极杆质谱仪(qMS)的耦合,通过一段去活的熔融石英管实现。在 PTV-qMS 分析之前,样品经过液液萃取(LLE)处理。由于未进行色谱分离,因此该方法非常快速。样品直接引入 PTV,溶剂被吹扫时,分析物被捕获在 Tenax-TA 填充的衬管中。之后,所有化合物都到达质谱仪,获得被分析样品的指纹图谱。分析了不含 PAHs 的尿液样品和相同样品中添加了这些化合物的样品。所得的图谱信号用于使用多元校准定量分析物,并根据 PAHs 的存在与否对样品进行分类。在后一项任务中,采用了无监督和有监督的模式识别技术。校准模型表现良好,在分析外部验证集时,大多数情况下得到的误差低于或等于 15%。关于样品的分类,大多数有监督的模式识别技术都提供了出色的结果(100%成功),其中所有样品都被正确分类。