Untargeted analysis of chromatographic data for green and fermented rooibos: Problem with size effect removal

While analyzing chromatographic data, it is necessary to preprocess it properly before exploration and/or supervised modeling. To make chromatographic signals comparable, it is crucial to remove the scaling effect, caused by differences in overall sample concentrations. One of the efficient methods of signal scaling is Probabilistic Quotient Normalization (PQN) [1]. However, it can be applied only to data for which the majority of features do not vary systematically among the studied classes of signals. When studying the influence of the traditional “fermentation” (oxidation) process on the concentration of 56 individual peaks detected in rooibos plant material, this assumption is not fulfilled. In this case, the only possible solution is the analysis of pairwise log-ratios, which are not influenced by the scaling constant. To estimate significant features, i.e., peaks differentiating the studied classes of samples (green and fermented rooibos plant material), we propose the application of rPLR (robust pair-wise log-ratios) as proposed by Walach et al. [2]. It allows for fast computation and identification of the significant features in terms of original variables (peaks) which is problematic, while working with the unfolded pair-wise log ratios. As demonstrated, it can be applied to designed data sets and in the case of contaminated data, it allows proper conclusions.