Phenolic composition of rooibos changes during simulated fermentation: Effect of endogenous enzymes and fermentation temperature on reaction kinetics.

Phenolic compounds of Aspalathus linearis (rooibos) are susceptible to oxidation during "fermentation", a process characterized by the formation of a red-brown leaf color. The role of enzymes in this process is not yet understood. An experiment with dried green rooibos plant material pre-treated at 170 °C for 30 min to denature and "inactivate" endogenous enzymes was conducted to confirm the role of oxidative enzymes. The phenolic composition of "enzyme inactivated" plant material was not significantly (p ≥ .05) affected by simulated fermentation, compared to control samples, as determined using piece-wise multivariate analysis of variance for successive time intervals. This proves that rooibos enzymes participate in the oxidation of phenolic compounds during fermentation of the plant material. A kinetic modeling approach was subsequently used to establish reaction kinetic parameters for selected rooibos phenolic compounds. The degradation of aspalathin and nothofagin and formation of eriodictyol glucosides during simulated fermentation at four temperatures from 37 to 50 °C were best described by the fractional conversion model based on first-order kinetics (r2 > 0.98), which allows for non-zero equilibrium concentrations. The extent of degradation for other compounds was too low to enable kinetic modeling. Reaction rates for the degradation/formation of phenolic compounds during fermentation followed the Arrhenius law. Less phenolic degradation (higher equilibrium concentration), but a higher reaction rate constant, was observed at higher temperatures, which could possibly be attributed to inactivation of enzymes.

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.