Principal Component Regression Analysis of the Relation Between CIELAB Color and Monomeric Anthocyanins in Young Cabernet Sauvignon Wines

Center for Viticulture and Enology, College of Food Science & Nutritional Engineering, China Agricultural University, Haidian District, Beijing, PR China.
Molecules (Impact Factor: 2.42). 02/2008; 13(11):2859-70. DOI: 10.3390/molecules13112859
Source: PubMed


Color is one of the key characteristics used to evaluate the sensory quality of red wine, and anthocyanins are the main contributors to color. Monomeric anthocyanins and CIELAB color values were investigated by HPLC-MS and spectrophotometry during fermentation of Cabernet Sauvignon red wine, and principal component regression (PCR), a statistical tool, was used to establish a linkage between the detected anthocyanins and wine coloring. The results showed that 14 monomeric anthocyanins could be identified in wine samples, and all of these anthocyanins were negatively correlated with the L*, b* and H*ab values, but positively correlated with a* and C*ab values. On an equal concentration basis for each detected anthocyanin, cyanidin-3-O-glucoside (Cy3-glu) had the most influence on CIELAB color value, while malvidin 3-O-glucoside (Mv3-glu) had the least. The color values of various monomeric anthocyanins were influenced by their structures, substituents on the B-ring, acyl groups on the glucoside and the molecular steric structure. This work develops a statistical method for evaluating correlation between wine color and monomeric anthocyanins, and also provides a basis for elucidating the effect of intramolecular copigmentation on wine coloring.

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    • "An Acquity UPLC system coupled with a SYNAPT Q-TOF mass spectrometer (Waters, Milford, Conn., U.S.A.) was used for the identification of anthocyanins. The analysis followed the previously published studies with some modifications (Han and others 2008; He and others 2010; Alberts and others 2012). A reversedphase C18 column (BEH C18, 100 mm × 2.1 mm i.d., 1.7 μm, Waters, Milford, Massachusetts, U.S.A) was used to separate an- thocyanins. "
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    ABSTRACT: Monomeric anthocyanin contributions to young red wine color were investigated using partial least square regression (PLSR) and aqueous alcohol solutions in this study. Results showed that the correlation between the anthocyanin concentration and the solution color fitted in a quadratic regression rather than linear or cubic regression. Malvidin-3-O-glucoside was estimated to show the highest contribution to young red wine color according to its concentration in wine, whereas peonidin-3-O-glucoside in its concentration contributed the least. The PLSR suggested that delphinidin-3-O-glucoside and peonidin-3-O-glucoside under the same concentration resulted in a stronger color of young red wine compared with malvidin-3-O-glucoside. These estimates were further confirmed by their color in aqueous alcohol solutions. These results suggested that delphinidin-3-O-glucoside and peonidin-3-O-glucoside were primary anthocyanins to enhance young red wine color by increasing their concentrations. This study could provide an alternative approach to improve young red wine color by adjusting anthocyanin composition and concentration.
    Journal of Food Science 11/2015; In press. DOI:10.1111/1750-3841.13155 · 1.70 Impact Factor
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    • "The analyses of anthocyanins in the enzyme assays and in the grape skin extracts were carried out according to the method reported by Han et al. (2008) with little alteration. An Agilent 1100 series LC-MSD trap VL ( "
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    ABSTRACT: Generally, red Vitis vinifera grapes only contain monoglucosidic anthocyanins, whereas most non-vinifera red grapes of the Vitis genus have both monoglucosidic and bis-glucosidic anthocyanins, the latter of which are believed to be more hydrophilic and more stable. Although previous studies have established the biosynthetic mechanism for formation of monoglucosidic anthocyanins, less attention has been paid to that of bis-glucosidic anthocyanins. In the present research, the full-length cDNA of UDP-glucose: anthocyanin 5-O-glucosyltransferase from Vitis amurensis Rupr. cv. 'Zuoshanyi' grape (Va5GT) was cloned. After acquisition and purification of recombinant Va5GT, its enzymatic parameters were systematically analyzed in vitro. Recombinant Va5GT used malvidin-3-O-glucoside as its optimum glycosidic acceptor when UDP-glucose was used as the glycosidic donor. Va5GT-GFP was found to be located in the cytoplasm by analyzing its subcellular localization with a laser-scanning confocal fluorescence microscope, and this result was coincident with its metabolic function of modifying anthocyanins in grape cells. Furthermore, the relationship between the transcriptional expression of Va5GT and the accumulation of anthocyanidin bis-glucosides during berry development suggested that Va5GT is a key enzyme in the biosynthesis of bis-glucosidic anthocyanins in V. amurensis grape berries. Copyright © 2015. Published by Elsevier Ltd.
    Phytochemistry 07/2015; 117:363-372. DOI:10.1016/j.phytochem.2015.06.023 · 2.55 Impact Factor
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    • "In contrast to optical remote sensing devices (satellite or airborne sensors), colorimetric devices do not suffer the shadow effect caused by topography terrain and/or clouds (Schott 1997), the main disadvantage of these approaches. Previous studies have successfully used reflectance color measurements expressed by means of the CIELAB color coordinates (CIE Publication 15–2; CIE 1986) to study the phytopigment content of several food types (e.g., Mínguez- Mosquera et al. 1991, 2005; Han et al. 2008; Moyano et al. 2008) and building stone biofilms, mainly consisting of filamentous cyanobacteria (e.g., Prieto et al. 2002, 2004; Sanmartín et al. 2010a, b). Nevertheless, to our knowledge, such measurements have not been used to study phytopigments in sediments as a proxy for the measurement of algae content. "
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    ABSTRACT: PurposeThe quantification of phytopigments in riverbed sediments deserves further attention because it provides information about eutrophic levels, and therefore about sediment and water quality. Due to the current interest in the study of eutrophication processes, there is a need for the development of a rapid, simple, cost-effective, and nondestructive method of quantifying phytopigment content. We describe one method based on color measurements and without the need for extraction and chemical assay. Materials and methodsSediment cores were collected along the watercourse of the Anllóns River (northwest Spain). The reflectance color measurements were analyzed with the CIELAB color parameters, using Cartesian (L*a*b*) and cylindrical (L*C*abhab) coordinates. Phytopigments (chlorophyll a, chlorophyll b, phaeopigments and total carotenoids) were extracted with dimethylsulphoxide and were determined spectrophotometrically. Results and discussionThe CIELAB color parameters were significantly correlated with phytopigment content (involving logarithmic, inverse, and simple data). The linear regression equations were used to predict chlorophyll a, chlorophyll b, and carotenoid contents, as well as the total pigment contents from parameter a* (redness–greenness of the color) and parameter C*ab (chroma of the color), and values of adjusted R 2 were close to 0.9. The closest relation between phytopigments and color parameters corresponded to chlorophyll a, which may be estimated by means of Chla = 46.4–70.6 log10 (a*) and Chla = −15.7 + 238.1 (1/C*ab) as predictive equations. The phaeophytinization quotient is somewhat questionable due to the low adjusted R 2 values obtained. ConclusionsThis work demonstrates that it is possible to determine phytopigment contents in riverbed sediments by means of a nondestructive colorimetric method employing the CIELAB color parameters. In addition, the abundance of phytopigments in the sediment core showed no clear trend either along the longitudinal axis of the river or in relation to sediment depth. KeywordsBenthic algae–Chlorophyll–CIELAB color measurements–River sediments–Settled phytoplankton
    Journal of Soils and Sediments 07/2011; 11(5):841-851. DOI:10.1007/s11368-011-0358-z · 2.14 Impact Factor
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