Is there a direct relationship between oral astringency and human salivary protein binding?

For decades, it is believed that astringency is due to the polyphenol-induced complexation of proline-rich salivary proteins in the oral cavity. In order to compare for the first time the human sensory threshold concentrations and the salivary protein binding activity of a series of astringent stimuli, human saliva protein was incubated for 5 min at 37 °C in the presence of astringent food-derived compounds and, after micro-centrifugation, the amount of the target molecules in the supernatant was quantitatively determined by HPLC-UV/Vis. Significant protein binding was observed for (−)-epigallocatechin-3-gallate, (−)-gallocatechin-3-gallate, (+)-gallocatechin, and (−)-catechin-3-gallate, all of which containing at least one galloyl moiety in the molecule and exhibiting rather high sensory thresholds of more than 200 μmol/L. In comparison, (+)-catechin and procyanidin B2, both lacking in any galloyl function, showed only comparatively low binding activity and, most interestingly, quercetin-3-O-α-l-rhamnopyranosyl-(1 → 6)-β-d-glucopyranoside and 3-carboxymethyl-indole-1-N-β-d-glucopyranoside did not show any protein binding at all, although the later N- and O-glycosides exhibited extraordinarily low sensory threshold concentrations of less than 0.001 and 0.0003 μmol/L, respectively. The data give some first evidence that the quantity of the non-bound, “free” astringent stimulus in the saliva liquid might be more closely related to the sensory perception of astringency than the amount complexed or precipitated by proteins. It is therefore questionable as to whether oral perception of astringency is related to the complexation and/or precipitation of salivary proteins.     

Effective modelling of percolation at the landscape scale using data-based approaches

Process-based models have been extensively applied to assess the impact of landuse change on water quantity and quality at landscape scales. However, the routine application of those models suffers from large computational efforts, lack of transparency and the requirement of many input parameters. Data-based models such as Feed-Forward Multilayer Perceptrons (MLP) and Classification and Regression Trees (CART) may be used as effective models, i.e. simple approximations of complex process-based models. These data-based approaches can subsequently be applied for scenario analysis and as a transparent management tool provided climatic boundary conditions and the basic model assumptions of the process-based models do not change dramatically. In this study, we apply MLP, CART and Multiple Linear Regression (LR) to model the spatially distributed and spatially aggregated percolation in soils using weather, groundwater and soil data. The percolation data is obtained via numerical experiments with Hydrus1D. Thus, the complex process-based model is approximated using simpler data-based approaches. The MLP model explains most of the percolation variance in time and space without using any soil information. This reflects the effective dimensionality of the process-based model and suggests that percolation in the study area may be modelled much simpler than using Hydrus1D. The CART model shows that soil properties play a negligible role for percolation under wet climatic conditions. However, they become more important if the conditions turn drier. The LR method does not yield satisfactory predictions for the spatially distributed percolation however the spatially aggregated percolation is well approximated. This may indicate that the soils behave simpler (i.e. more linear) when percolation dynamics are upscaled.     

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