This paper deals with the application of a voltammetric electronic tongue (ET) towards beers classification. For this purpose, samples were analyzed using cyclic voltammetry without performing any sample pretreatment, albeit its dilution with distilled water. The voltammetric signals were first preprocessed employing Fast Fourier Transform (FFT). Then, using the obtained coefficients, responses were evaluated using three different clustering techniques: Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA). In this case, the ET has demonstrated a good capability to correctly discriminate and classify the different beer samples according to its type (Lager, Stout and IPA) and manufacture process (commercial and craft). © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
|Publication status||Published - 1 Jul 2013|
- Electronic tongue
- Fast Fourier transform
- Linear discriminant analysis
- Voltammetric sensors