In this article. drift correction algorithms were used in order to remove l
inear drift in multivariate spaces of two data sets obtained by an electron
ic tongue based on voltammetry. The electronic tongue consisted of various
metal electrodes (Au, Ir, Pt, kh) combined with pattern recognition tools,
such as principal component analysis. The first data set contained differen
t types of liquid, from well defined to more complex solutions. The second
data set contained different black and green teas. Component correction (CC
) was compared to a simple additive correction. In CC. the drift direction
of measured reference solutions in a multivariate space was subtracted from
other types of solution. In additive correction. responses from reference
samples were subtracted from other samples. CC showed similar or better per
formance in reducing drift compared to additive correction for the two data
sets. The additive correction method was dependent on the fact that the di
fferences in between samples of a reference solution were similar to the ch
anges in between samples of other liquids, which was not the case with CC.