New comprehensive procedure for enhanced dataanalysis and interpretation of Real-Time Measurements: A unique feature for real time avour generation

info:eu-repo/semantics/publishedVersio

Process control in flavour generation : NIR-MVA as a tool to monitor key odorants formation

info:eu-repo/semantics/publishedVersio

(Un)targeted analysis to unravel critical interactions between sugars and phenolics in Strecker aldehydes formation in beverages

info:eu-repo/semantics/publishedVersio

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Impact of phenolic compounds in strecker aldehyde formation in wine model systems: target and untargeted analysis

The Strecker degradation of phenylalanine has been studied in a phenolic compound/phenylalanine wine model system. Six phenolic compounds (3,4-dihydroxybenzoic acid, gallic acid, caffeic acid, ferulic acid, catechin, and epicatechin) were compared in the formation of phenylacetaldehyde when in the presence of glucose or methylglyoxal (MG). The addition of glucose reduced the formation of Strecker aldehyde, independently of the phenolic compound. The addition of MG, on the other hand, increased phenylacetaldehyde formation for hydroxybenzoic acids and decreased phenylacetaldehyde formation for flavan-3-ols, confirming their capacity to trap the dicarbonyl compound. As a target phenolic compound, catechin was chosen to perform kinetic studies to further understand the reaction intermediates involved in the mechanism of phenylacetaldehyde formation, in particular, catechin o-quinone and catechin–MG adduct. The addition of glucose and MG increased the consumption of catechin, while a reduction in the respective o-quinone was observed, suggesting that these substrates have an impact in other reactions involving catechin. In that regard, for the first time, it was demonstrated that the catechin–MG adduct was capable of oxidizing and forming a new o-quinone, contributing to wine instability promoted by oxidation reactions.info:eu-repo/semantics/publishedVersio

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. VIPS from OPLS-DA and mean decrease accuracy from random forest were used as feature selection parameters. Fifty-one molecules correlated with 5 classes, from which 23 were selected has having higher sensitivities (AUC > 0.85). For the first time to our knowledge hydroxy esters ethyl-2-hydroxy-3-methylbutanal and ethyl-2-hydroxy-4-methylpentanal were found to be correlated with oxidation markers and consequently to be discriminant of the wine oxidative status.</p