LAUSR

Abstract Quality and authenticity monitoring are activities of growing necessity worldwide. However, methods of rapid evaluation and low cost need to be further explored to intensify and optimize the monitoring process. Hence, the present study proposes a procedure for discrimination of lager beers (premium and standard American lager beers) using commercially available screen-printed electrodes (SPEs) (carbon (SPCE), gold (SPGE), and carbon nanotube (SPEs-CNT)) and chemometric classification methods (soft independent modeling of class analogy (SIMCA) and partial least squares regression discriminant analysis (PLS-DA)). Overall, PLS-DA models obtained, on average, 88% for sensitivity, specificity, and precision in the validation dataset. The SIMCA models, on the other hand, showed, on average, 72% of sensitivity, 82% specificity and 80% of precision. The electronic tongue’s final configuration of an SPCE coupled with PLS-DA showed a predictive power of 94%, using only one SPCE with the capacity of analyze up to 35 beers per SPEs.