Carbon nuclear magnetic resonance spectroscopic fingerprinting of commercial gasoline: pattern-recognition analyses for screening quality control purposes.
In this work, the combination of carbon nuclear magnetic resonance ((13)C NMR) fingerprinting with pattern-recognition analyses provides an original and alternative approach to screening commercial gasoline quality. Soft Independent Modelling of Class Analogy (SIMCA) was performed on spectroscopic fingerprints to classify representative commercial gasoline samples, which were selected by Hierarchical Cluster Analyses (HCA) over several months in retails services of gas stations, into previously quality-defined classes. Following optimized (13)C NMR-SIMCA algorithm, sensitivity values were obtained in the training set (99.0%), with leave-one-out cross-validation, and external prediction set (92.0%). Governmental laboratories could employ this method as a rapid screening analysis to discourage adulteration practices.
Flumignan
Danilo Luiz
DL
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. danilo@iq.unesp.br
Boralle
Nivaldo
N
Oliveira
José Eduardo de
JE
D005742:Gasoline / Q000032:analysis*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D011786:Quality Control
D005742:Gasoline