Organic Chemistry Department, Institute of Chemistry, Center for Monitoring and Research of the Quality of Fuels, Biofuels, Crude Oil and Derivatives, São Paulo State University, R. Prof. Francisco Degni s/n, Quitandinha, 14800-900 Araraquara, São Paulo, Brazil.
Talanta. 2010 Jun 30;82(1):99-105. doi: 10.1016/j.talanta.2010.04.002. Epub 2010 Apr 10.
The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the combination of Hydrogen Nuclear Magnetic Resonance ((1)H NMR) spectroscopic fingerprintings with pattern-recognition multivariate Soft Independent Modeling of Class Analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a Monitoring Program for Quality Control of Automotive Fuels. SIMCA was performed on spectroscopic fingerprints to classify the quality of representative commercial gasoline samples selected by Hierarchical Cluster Analysis (HCA) and collected over a 6-month period from different gas stations in the São Paulo state, Brazil. Following optimized the (1)H NMR-SIMCA algorithm, it was possible to correctly classify 92.0% of commercial gasoline samples, which is considered acceptable. The chemometric method is recommended for routine applications in Quality-Control Monitoring Programs, since its measurements are fast and can be easily automated. Also, police laboratories could employ this method for rapid screening analysis to discourage adulteration practices.
有机溶剂对汽油的掺假识别并非易事，因为构成溶剂的化合物已经存在于汽油成分中。在这项工作中，氢核磁共振 ((1)H NMR) 光谱指纹图谱与模式识别多元软独立建模类比 (SIMCA) 化学计量学分析相结合，为巴西商业汽油质量的监测计划提供了一种新颖且可替代的筛选方法。SIMCA 对光谱指纹图谱进行分类，以通过层次聚类分析 (HCA) 选择有代表性的商业汽油样品，并在 6 个月内从巴西圣保罗州的不同加油站收集。在优化 (1)H NMR-SIMCA 算法后，能够正确分类 92.0%的商业汽油样品，这被认为是可以接受的。该化学计量方法建议用于质量控制监测计划的常规应用，因为其测量速度快且易于自动化。此外，警察实验室也可以采用这种方法进行快速筛选分析，以阻止掺假行为。
