Phenylacetaldehyde real-time release kinetics in wine like model solutions.

The present work shows key possibilities in modelling the kinetics of phenylacetaldehyde formation as a function of sugar, phenolic compounds, metals and sulphur dioxide. The release kinetics were measured online by proton transfer reaction-mass spectrometry (PTR-MS). Phenylacetaldehyde formation was fitted using Weibull models and an activation energy of 73 kJ/mol estimated.

Discrimination of white wine ageing based on untarget peak picking approach with multi-class target coupled with machine learning algorithms.

The complexity of the chemical reactions occurring during white wine storage, such as oxidation turns the capacity of prediction and consequently the capacity to avoid it extremely difficult. This study proposes an untarget methodology based on machine learning algorithms capable to classify wines according to their "oxidative-status". Instead of the most common approach in statistics using one class for classification, in this work eight classes were selected based on target oxidation markers for the extraction of relevant compounds.

Response surface methodology: A tool to minimize aldehydes formation and oxygen consumption in wine model system.

A response surface methodology was applied to study the effect of precursors on o-quinone and phenylacetaldehyde formation in wine model systems stored at 40 °C during 24 h. The results confirmed that glucose plays an important role in reducing aldehyde formation by inhibiting the formation of o-quinone. The regression equations showed that oxygen consumption followed a 2nd polynomial equation whereas phenylacetaldehyde and o-quinone were best fit with a polynomial function containing quadratic terms.

Strecker Aldehyde Formation in Wine: New Insights into the Role of Gallic Acid, Glucose, and Metals in Phenylacetaldehyde Formation.

Strecker degradation (SD) leading to the formation of phenylacetaldehyde (PA) was studied in wine systems. New insights were gained by using two full factorial designs focusing on the effects of (1) pH and (2) temperature. In each design of experiments (DoE) three factors, glucose, gallic acid, and metals at two levels (present or absence), were varied while phenylalanine was kept constant.

Untangling the chemistry of port wine aging with the use of GC-FID, multivariate statistics, and network reconstruction.

Chromatography separates the different components of complex mixtures and generates a fingerprint representing the chemical composition of the sample. The resulting data structure depends on the characteristics of the detector used, univariate for devices such as a flame ionization detector (FID) or multivariate for mass spectroscopy (MS). This study addresses the potential use of a univariate signal for a nontargeted approach to (i) classify samples according to a given process or perturbation, (ii) evaluate the feasibility of developing a screening procedure to select candidates related to the process, and (iii) provide insight into the chemical mechanisms that are affected by the perturbation.