Targeted metabolomics of anthocyanin derivatives during prolonged wine aging: Evolution, color contribution and aging prediction

Zhang, Xin‐Ke; Lan, Yi-Bin; Huang, Yue; Zhao, Xu; Duan, Chang‐Qing

doi:10.1016/j.foodchem.2020.127795

Cited by 73 publications

(53 citation statements)

References 33 publications

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“…Moreover, a loss of violet hue (increase in a* and b*) and a raise of tawny tonality (larger b* and hue) were detected. Apart from the luminosity parameter L*, for which there is no agreement in literature on its changes during wine evolution [23], the other trends are widely discussed in the literature and are typical of red wine evolution [24]. A different contribution of each class of anthocyanin derivatives (pinotins, flavanyl-pyranoanthocyanins, vitisin A, vitisin B, direct anthocyanin-flavan-3-ol condensation products, anthocyanin ethyl-linked flavan-3-ols) in various aging states is responsible for variation detected in CIELAB parameters, as recently shown in a study in which a regression model was used to evaluate the contribution of several anthocyanin derivatives to the chromatic attributes of the wines over time [23].…”

Section: End Of Alcoholic Fermentation 32 L Mm L Mw H Mm H Mwmentioning

confidence: 96%

How the Management of pH during Winemaking Affects Acetaldehyde, Polymeric Pigments and Color Evolution of Red Wine

et al. 2022

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Due to climate change and the consequent rise in grape pH, there is often the necessity of acidifying musts or wines during winemaking. In this study, the effect of early (on musts, during fermentation) and late (on wines, after the end of the fermentation) acidification was evaluated. The experimental design consisted of the preparation of seven wines from the same batch of grapes fermented in a first tank at the original pH of 3.2 and two other tanks in which the pH was adjusted to 3.5 (3.5W) and 3.9 (3.9W). On the third day of fermentation, and one week after the end of the fermentation–maceration process, aliquots of both 3.5W and 3.9W were treated to lower pH to thus obtain four more wines. After one year of aging, wines treated so as to reach a 3.2 pH significantly differed from the control wine in terms of contents of acetaldehyde, tannins reactive towards proteins and polymeric pigments. Differences were more conspicuous when acidification was carried out after the end of the fermentation–maceration process. Data highlight that the timing of acidification has a significant effect on polymerization reactions typically occurring during wine aging.

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Section: End Of Alcoholic Fermentation 32 L Mm L Mw H Mm H Mwmentioning

confidence: 96%

How the Management of pH during Winemaking Affects Acetaldehyde, Polymeric Pigments and Color Evolution of Red Wine

et al. 2022

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“…Fayek et al (2021), Huang et al (2021), and X. Zhang et al (2021) introduced the application of metabolomics in the study of metabolic characteristics and quality prediction during crop production, including citrus, wine, and olives.…”

Section: Introductionmentioning

confidence: 99%

Effects of different nitrogen application rates on the quality and metabolomics of cigar tobacco

Yang

et al. 2022

Agronomy Journal

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Nitrogen (N) is an essential nutrient element in plants that participates in physiological and biochemical regulation. However, the effects of different N applications on cigar tobacco (Nicotiana tabacum L.) growth are not well known. In this study, the differences in agronomical characteristics and nutritional quality of cigar tobacco leaves at the mature and curing stages under different exogenous N applications were explored. The dry matter accumulation of cigar tobacco leaves increased with increasing N application rates. In the two stages, the nutritional quality of cigar tobacco leaves was similar, but the concentrations of N and Cl were increased. To understand the effect of exogenous N on cigar tobacco metabolism, untargeted metabolomics was applied. Six significantly differential metabolites, including phenylalanine, phosphoserine, glutamate, oxoproline, succinylhomoserine, and homoserine, were identified, and the main metabolic pathways around the tricarboxylic acid cycle were identified. These results provide a better understanding of the effects of exogenous N application on the physiological, biochemical, and metabolic processes of cigar tobacco and provide a reference for fertilization control in cigar tobacco production.

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“…For example, in an Irish cohort, triglycerides and glucose in fasting serum samples were clustered using k-means to identify individual dietary habits [15]. In another study, k-means clustered metabolic profiles from liquid chromatography-tandem mass spectrometry (LC-MS/MS) data were used from red wine [16].…”

Section: Introductionmentioning

confidence: 99%

The usefulness of sparse k-means in metabolomics data: An example from breast cancer data

Goudo

Sugimoto

Hiwa

et al. 2022

Preprint

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In processing metabolomics data, multidimensional quantitative data from thousands of metabolites are often sparse, that is, only a small fraction of metabolites are relevant to the phenotype of interest. Clustering is therefore used to discover subtypes from omics data. Sparse processing, which selects important metabolites from the total omics data, is an effective clustering technique. This study investigated the effectiveness of sparse k-means for metabolomics data. Specifically, sparse k-means was used to cluster blood lipid metabolite data of breast cancer patients in two studies: (1) before and after menopause, and (2) pre- and postoperative chemotherapy. In both cases, sparse k-means showed comparable discrimination accuracy with fewer metabolites than k-means. Furthermore, when the L1 norm values were varied, no significant changes were observed. The mean silhouette coefficients of sparse k-means and k-means were (1) 0.38 pm 0.14 (S.D.) and 0.17 pm 0.01, (2) 0.38 pm 0.07 and 0.17 pm 0.01, indicating that feature selection using sparse k-means can improve clustering results. In addition, metabolite selection using sparse k-means was consistent regardless of the test data or the constrained value of the L1 norm, indicating robustness.

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Targeted metabolomics of anthocyanin derivatives during prolonged wine aging: Evolution, color contribution and aging prediction

Cited by 73 publications

References 33 publications

How the Management of pH during Winemaking Affects Acetaldehyde, Polymeric Pigments and Color Evolution of Red Wine

How the Management of pH during Winemaking Affects Acetaldehyde, Polymeric Pigments and Color Evolution of Red Wine

Effects of different nitrogen application rates on the quality and metabolomics of cigar tobacco

The usefulness of sparse k-means in metabolomics data: An example from breast cancer data

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