Flumignan Danilo Luiz, Tininis Aristeu G, Ferreira Fabrício de O, de Oliveira José Eduardo
Centro de Monitoramento e Pesquisa da Qualidade de Combustíveis, Petróleo e Derivados, Departamento de Química Orgânica, Instituto de Química de Araraquara, Universidade Estadual Paulista Júlio de Mesquita Filho, Araraquara, SP, Brazil.
Anal Chim Acta. 2007 Jul 9;595(1-2):128-35. doi: 10.1016/j.aca.2007.02.049. Epub 2007 Feb 24.
A total of 2400 samples of commercial Brazilian C gasoline were collected over a 6-month period from different gas stations in the São Paulo state, Brazil, and analysed with respect to 12 physicochemical parameters according to regulation 309 of the Brazilian Government Petroleum, Natural Gas and Biofuels Agency (ANP). The percentages (v/v) of hydrocarbons (olefins, aromatics and saturated) were also determined. Hierarchical cluster analysis (HCA) was employed to select 150 representative samples that exhibited least similarity on the basis of their physicochemical parameters and hydrocarbon compositions. The chromatographic profiles of the selected samples were measured by gas chromatography with flame ionisation detection and analysed using soft independent modelling of class analogy (SIMCA) method in order to create a classification scheme to identify conform gasolines according to ANP 309 regulation. Following the optimisation of the SIMCA algorithm, it was possible to classify correctly 96% of the commercial gasoline samples present in the training set of 100. In order to check the quality of the model, an external group of 50 gasoline samples (the prediction set) were analysed and the developed SIMCA model classified 94% of these correctly. The developed chemometric method is recommended for screening commercial gasoline quality and detection of potential adulteration.
在6个月的时间里，从巴西圣保罗州的不同加油站收集了总共2400份巴西商用C汽油样品，并根据巴西政府石油、天然气和生物燃料局（ANP）的第309条规定，对12项物理化学参数进行了分析。还测定了碳氢化合物（烯烃、芳烃和饱和烃）的体积百分比（v/v）。采用层次聚类分析（HCA），根据其物理化学参数和碳氢化合物组成，选择了150个相似度最低的代表性样品。通过带有火焰离子化检测的气相色谱法测量所选样品的色谱图，并使用类类比软独立建模（SIMCA）方法进行分析，以便创建一个分类方案，根据ANP 309规定识别合格汽油。在优化SIMCA算法后，能够正确分类训练集中100个商用汽油样品中的96%。为了检查模型的质量，对一组50个汽油样品（预测集）进行了分析，所开发的SIMCA模型正确分类了其中的94%。推荐使用所开发的化学计量学方法来筛选商用汽油质量并检测潜在掺假情况。
