Abstract
A new approach for the discrimination of the adulteration process of ethanol fuel with water is reported using a copper interdigitated electrode and chemometrical tools. The sensor was constructed using copper sheets with non-chemical modification of the electrode surface. The discrimination process was performed using capacitance values recorded at different frequencies (1000Hz to 0.1MHz) as the input data for non-supervised pattern recognition methods (PCA: principal component analysis and HCA: hierarchical cluster analysis). The relative standard deviation for the capacitance signals obtained from ten independent interdigitated sensors was below 5.0%. The ability of the device to differentiate non-adulterated ethanol samples from those adulterated with water was demonstrated. In all analysed cases, there was good separation between the different samples in the score plots and the dendrograms obtained from PCA and hierarchical cluster analyses, respectively. Furthermore, the water content was quantified using a PCA approach. The results were consistent with those obtained using the Karl–Fischer method at a 95% confidence level, as measured using Student's t-test.
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