Recognition of adulteration of Italian wines by thin-film multisensor array and artificial neural networks

The paper reports the use of a chemoresistive multisensor array for recognition of some adulterated Italian wines (two white, four red, two rosè) added with methanol, ethanol or other same-colour wine. A multisensor array constituted by four thin-film semiconducting metal oxide sensors, surface-activated by Pt, Au, Pd, Bi metal catalysts, has been used to generate the chemical pattern of the volatile compounds present in the wine samples. The responses of the multisensor array towards wines tested by headspace sampling have been evaluated. Multivariate analysis including principal component analysis (PCA) as well as back-propagation method trained artificial neural networks (ANNs) have been applied to analytical data generated from the multisensor array to identify both the adulteration of wines and to determine the added content of adulterant agent of methanol or ethanol up to 10 vol.%. The cross-validated ANNs provide the highest achieved percentage of correct classification of 93% and the highest achieved correlation coefficient of 0.997 and 0.921 for predicted-versus-true concentration of methanol and ethanol adulterant agent, respectively.