Voltammetric classification of ciders with PLS-DA

Voltammograms recorded on the glassy carbon electrode (GCE) may be a chemical fingerprints of food samples, enabled distinguishing the origin of the considered products. In this work the objects of the study was 5 Polish ciders of various brands. For each sample 10 scans were recorded by DPV in the potential range between -0.2 and 1.0 V in Britton-Robinson buffer at pH 2.0. The signals preprocessing realized by baseline correction with 4-th degree polynomial and normalization (in 0 to 1 interval), performed to reduce problems with insufficient signal's repeatability associated with mechanical renovation of the electrode surface before each measurement. The PLS-DA classification models were built using the training set and then validated using the samples absent in the learning process. The final multi-class model with optimized complexity enables classification of the ciders with 100% sensitivity and specificity, with the exception of one cider, where specificity was 95% (for validation set).