Electronic tongue based on a single impedimetric sensor and complex numbers-supervised pattern recognition

Abstract

This work investigated the application of supervised pattern recognition of complex number data obtained by electrochemical impedance spectroscopy (EIS) in order to obtain qualitative/discriminatory electroanalytical methods based on electronic tongues/noses. As a case study, electrochemical impedance spectra of aqueous extracts of coffee samples were obtained with a single label-free sensor and submitted to complex numbers-supervised pattern recognition to detect the adulteration of ground roasted coffee by the addition of coffee husks and sticks. Partial least-squares discriminant analysis (PLS-DA) and soft independent modeling by class analogy (SIMCA), two of the most employed multivariate classification techniques, were evaluated to classify coffee samples as adulterated or unadulterated by the direct analysis of complex impedance spectra. They are techniques whose mathematical logic can be easily employed in the treatment of complex numbers with very little or even no modification, by using the Hermitian transpose instead of the common nonconjugate transpose. The predictive ability of complex numbers–supervised pattern recognition on the identification of adulterated ground roasted coffees was compared to that obtained with impedance data presented as real numbers – real (Z’), imaginary (Z”), absolute impedance (|Z|), and phase angle (ϕ). A discussion about the most suitable classification algorithm for the type of data employed was held. The highest predictive ability was obtained by applying complex numbers-PLS-DA, with 100 % and 96 % of the adulterated and unadulterated test samples correctly identified, respectively, with no need for reducing complex spectra to real numbers.