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dc.contributor.authorde Paula Pedroza, Ricardo Henrique
dc.date.accessioned2019-10-02T11:41:53Z
dc.date.available2019-10-02T11:41:53Z
dc.date.issued2019-09-28
dc.date.submitted2019-09-27T22:00:06Z
dc.identifier.urihttps://hdl.handle.net/1956/20896
dc.description.abstractGasoline quality control is essential for SI engines performance and to reduce environmental impacts by generation of undesirable pollutants. Methods established by the American Society for Test and Materials (ASTM) are the most employed for determining physicochemical quality parameters of motor gasoline, however, these methods present some disadvantages such as time-consuming analysis and need of large amount of sample. For this purpose, near-infrared (NIR) and Raman spectroscopies could be promising alternatives, since they are nondestructive techniques which require little or no sample preparation, a small amount of sample, short analysis time, and also present the possibility of simultaneous determination of many parameters. Although, the use of chemometric tools is often needed in order to extract maximum of useful information from the NIR and Raman spectra related to the parameter being studied. In this work, the qualitative classification of commercial gasoline samples related to their ethanol contents and antiknock indexes was reached by using principal component analysis (PCA) and soft independent modelling of class analogy (SIMCA) models. The values for the misclassification error obtained for the classification of these parameters by both NIR and Raman spectroscopies were less than 3.0%. The multivariate calibra