Detection of adulteration in hydrated ethyl alcohol fuel using infrared spectroscopy and supervised pattern recognition methods.

This paper proposes an analytical method to detect adulteration of hydrated ethyl alcohol fuel based on near infrared (NIR) and middle infrared (MIR) spectroscopies associated with supervised pattern recognition methods. For this purpose, linear discriminant analysis (LDA) was employed to build a classification model on the basis of a reduced subset of wavenumbers. For variable selection, three techniques are considered, namely the successive projection algorithm (SPA), the genetic algorithm (GA) and a stepwise formulation (SW). For comparison, models based on partial least squares discriminant analysis (PLS-DA) were also employed using full-spectrum. The method was validated in a case study involving the classification of 181 hydrated ethyl alcohol fuel samples, which were divided into three different classes: (1) authentic samples; (2) samples adulterated with water and (3) samples contaminated with methanol. LDA/GA and PLS-DA models were found to be the best methods for classifying the spectral data obtained in NIR region, which achieved a correct prediction rate of 100% in the test set, while the LDA/SPA and LDA/SW were correctly classified at 84.4% and 97.8%, respectively. For MIR data, all models (PLS-DA and LDA coupled with the SW, SPA and GA) employed in this study correctly classified all samples in the test set.