Complex numbers-partial least-squares applied to the treatment of electrochemical impedance spectroscopy data.

This work investigated the application of partial least-squares regression of complex numbers on multivariate data obtained by electrochemical impedance spectroscopy (EIS). The use of complex numbers-PLS was evaluated in the individual determination of two well-known redox probes: ferrocyanide and hydroquinone. The predictive ability of complex numbers-PLS was evaluated for EIS spectra obtained at different applied potentials and perturbation amplitudes, and was also compared to that obtained with PLS applied to EIS data presented as real numbers - only the real or imaginary part of the complex impedance, or the absolute impedance or the phase angle. It is shown that complex numbers-PLS is more efficient (better prediction models) when more complex electrochemical systems (hydroquinone) are probed. Excellent predictions were obtained for the determination of hydroquinone and catechol in the direct analysis of spiked tap water samples with EIS and complex numbers-PLS.

A simple voltammetric electronic tongue for the analysis of coffee adulterations.

This work presents a simple and low-cost analytical approach to detect adulterations in ground roasted coffee by using voltammetry and chemometrics. The voltammogram of a coffee extract (prepared as simulating a home-made coffee cup) obtained with a single working electrode is submitted to pattern recognition analysis preceded by variable selection to detect the addition of coffee husks and sticks (adulterated/unadulterated), or evaluate the shelf-life condition (expired/unexpired). Two pattern recognition methods were tested: linear discriminant analysis (LDA) with variable selection by successive projections algorithm (SPA), or genetic algorithm (GA); and partial least squares discriminant analysis (PLS-DA). Both LDA models presented satisfactory results. The voltammograms were also evaluated for the quantitative determination of the percentage of impurities in ground roasted coffees. PLS and multivariate linear regression (MLR) preceded by variable selection with SPA or GA were evaluated. An excellent predictive power (RMSEP = 0.05%) was obtained with MLR aided by GA.