Calculation of gasoline classification models from chromatographic profiles
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Abstract
The quality control of gasoline requires the determination of its composition by high-resolution capillary gas chromatography; this analysis provides the total contents of paraffins, isoparaffins, aromatics, naphthenes and olefins (PIANO method). The objective of this work was to develop gasoline classification models taking the complete chromatographic profile as variables, which allowed extracting the chemical information related to the covariance of the different compounds present in the system. The profiles of 70 gasoline samples collected between 2016 and 2019 were processed, in which it was essential to minimize the shift in retention times and baseline that originates from the different measurements. Hierarchical cluster analysis (HCA) was used as a pattern recognition technique, which allowed defining by similarity two classes that group the samples of the year 2019 in a different category. Based on the results of this exploratory analysis, the Soft Independent Modeling of Class Analogy (SIMCA) classification method was applied, obtaining that the distance between the classes and their projections was adequate, without classification errors. These techniques are ideal tools to detect adulterations and changes in production processes.
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