A Direct Fuel Cell–Chemometric Platform for Quantitative Analysis and Exploratory Differentiation of Pharmaceutical and Biomedical Compounds

A simple, low-cost analytical platform based on a commercial direct methanol–ethanol fuel cell (D(M–E)FC) combined with chemometric data analysis is investigated for the quantitative analysis and exploratory differentiation of pharmaceutical and biomedical compounds containing at least one hydroxyl group with alcoholic or partially alcoholic character. Quantitative measurements were based on conventional calibration curves obtained from the steady-state current response, whereas compound differentiation exploited the dynamic current discharge profiles. Principal Component Analysis (PCA) and the multiblock Common Components and Specific Weights Analysis (ComDim) algorithm were employed to explore and integrate the information contained in the discharge curves and in their derived kinetic descriptors. Five representative compounds—chloramphenicol, imipenem, atropine, cortisone, and ethanol—were investigated. Steady-state calibration showed satisfactory linear responses for chloramphenicol, imipenem, and ethanol (R2 = 0.9961, 0.9868, and 0.9888, respectively), whereas poorer linear behaviour was observed for atropine and cortisone (R2 = 0.5076 and 0.7956), highlighting the analyte-dependent quantitative performance of the platform. Exploratory analysis of the averaged dynamic profiles revealed compound-dependent response patterns, with most of the relevant variance concentrated in the early transient. Restricting the acquisition to the first 120 s preserved the main structure observed for the complete profiles while substantially reducing the measurement time. Overall, the results indicate that dynamic fuel-cell responses contain analyte-dependent multivariate information that can be exploited for the exploratory differentiation of structurally diverse compounds, providing a proof of concept for combining commercial catalytic fuel cells with chemometric analysis.