Determination of the complexing capacity of synthetic and real wine for Zn using Absence of Gradients and Nerstian Equilibrium Stripping technique

The complexing capacity of synthetic (0.011 M tartrate in 13.5% ethanol) and real wine (Raimat Abadia) in titrations with added total Zn concentrations up to 0.03 M has been determined following the free Zn concentrations with AGNES (absence of gradients and Nernstian equilibrium stripping) technique. A correction to find the preconcentration factor or gain (Y1) really applied at each one of the ionic strengths reached due to Zn additions along the titration has been applied. The standard implementation of AGNES to real wine led to the observation of two anomalous behaviors: (a) an increasingly negative current in the deposition stage (labeled as “HER” effect) and (b) a minimum in the currents of the stripping stage plot (labeled as the “dip” effect). A practical strategy to apply AGNES avoiding the dip effect has been developed to quantify properly free Zn concentrations. The van den Berg–Ružic–Lee linearization method (assuming the existence of just 1:1 complexes) has been adapted to consider the dilution effect and the ionic strength changes. Aggregated stability constants and total ligand concentrations have been calculated from synthetic and wine titration data. The found complexing capacity in the studied wine (cT,L = 0.0179 ± 0.0007 M) indicates the contribution of ligands other than tartrate (which is confirmed to be the main one).

The effects of milk as a food matrix for polyphenols on the excretion profile of cocoa (-)-Epicatechin metabolites in healthy humans subjects

The effect of different food matrices on the metabolism and excretion of polyphenols is uncertain. The objective of the study was to evaluate the possible effect of milk on the excretion of (2)-epicatechin metabolites from cocoa powder after its ingestion with and without milk. Twenty-one volunteers received the following three test meals each in a randomised cross-over design with a 1-week interval between meals: (1) 250 ml whole milk as a control; (2) 40 g cocoa powder dissolved in 250 ml whole milk (CC-M); (3) 40 g cocoa powder dissolved in 250 ml water (CC-W). Urine was collected before consumption and during the 0-6, 6-12 and 12-24 h periods after consumption. (2)-Epicatechin metabolite excretion was measured using liquid chromatography-MS. One (2)-epicatechin glucuronide and three (2)-epicatechin sulfates were detected in urine excreted after the intake of the two cocoa beverages (CC-M and CC-W). The results show that milk does not significantly affect the total amount of metabolites excreted in urine. However, differences in metabolite excretion profiles were observed; there were changes in the glucuronide and sulfate excretion rates, and the sulfation position between the period of excretion and the matrix. The matrix in which polyphenols are consumed can affect their metabolism and excretion, and this may affect their biological activity. Thus, more studies are needed to evaluate the effect of these different metabolite profiles on the body.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.