Analysis of Wine Components in Cynthiana and Syrah Wines

Walker, Teresa L.; Morris, J. R.; Threlfall, Renee T.; Main, Gary L.

doi:10.1021/jf026132o

Cited by 32 publications

(24 citation statements)

References 16 publications

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“…Most methods are not applicable to wine due to the alcohol content or low resolution 19. Frequently, isocratic elutions are described using an acidified aqueous solvent and separation time is no longer than 20 min 20. It is also common to find in the literature that organic acid analysis includes a sample pretreatment which increases analysis time and affects the reliability of the results.…”

Section: Introductionmentioning

confidence: 99%

HPLC‐DAD methodology for the quantification of organic acids, furans and polyphenols by direct injection of wine samples

Pereira

Câmara

Cacho

et al. 2010

J of Separation Science

120

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This article proposes a simple and sensitive HPLC method with photo-diode array detection for the analysis of organic acids, monomeric polyphenols and furanic compounds in wine samples by direct injection. The chromatographic separation of 8 organic acids, 2 furans and 22 phenolic compounds was carried out with a buffered solution (pH 2.70) and acetonitrile as mobile phases and a difunctionally bonded C18 stationary phase, Atlantis dC18 (250 Â 4.6 mm, 5 mm) column. The elution was performed in 12 min for the organic acids and in 60 min for the phenolic compounds, including phenolic acids, stilbenes and flavonoids. Target compounds were detected at 210 nm (organic acids, flavan-3-ols and benzoic acids), 254 nm (ellagic acid), 280 nm (furans and cinnamic acid), 315 nm (hydroxycinnamic acids and trans-resveratrol) and 360 nm (flavonoids). The RSD for the repeatability test (n 5 5) of peak area and retention times were below 3.1 and 0.3%, respectively, for phenolics and below 1.0 and 0.2% for organic acids. The RSDs expressing the reproducibility of the method were higher than for the repeatability results but all below 9.0%. Method accuracy was evaluated by the recovery results, with averaged values between 80 and 104% for polyphenols and 97-105% for organic acids. The calibration curves, obtained by triplicate injection of standard solutions, showed good linearity with regression coefficients higher than 0.9982 for polyphenols and 0.9997 for organic acids. The LOD was in the range of 0.07-0.49 mg/L for polyphenols (cinnamic and gallic acids, respectively) and 0.001-0.046 g/L for organic acids (oxalic and lactic acids, respectively). The method was successfully used to measure and assess the polyphenolic fingerprint and organic acids profile of red, white, rosé and fortified wines.

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Section: Introductionmentioning

confidence: 99%

HPLC‐DAD methodology for the quantification of organic acids, furans and polyphenols by direct injection of wine samples

Pereira

Câmara

Cacho

et al. 2010

J of Separation Science

120

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“…The grape must is composed of a wide variety of compounds such as water (75-80%), sugars (15-25% made up of glucose and fructose) (Walker et al 2003), organic acids (tartaric, malic, and citric acid, mainly), mineral salts, nitrogen substances (amino acids and ammonia), phenolic compounds (anthocyanins and tannins), proteins, and vitamins (biotin, thiamine B1, pantothenic acid, etc) (Peynaud 1984).…”

mentioning

confidence: 99%

Bioconversion of l-phenylalanine into 2-phenylethanol by Kluyveromyces marxianus in grape must cultures

Garavaglia

Flôres

Pizzolato

et al. 2007

World J Microbiol Biotechnol

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A 2 3 factorial design was employed to find the best conditions of pH, L-phenylalanine concentration and temperature for the production of 2-phenylethanol by Kluyveromyces marxianus CBS 6556. The cultivation was carried out on grape must, which contains a great amount of nitrogen compounds. Central composite design (CCD) was used for the analysis of treatment combinations. Results showed a second-degree polynomial regression model with good agreement of experimental data, with R 2 = 0.92015 (p < 0.05). The maximum production of 2-phenylethanol was found at pH 7.0, temperature of 37°C, and a concentration of 3.0 g of L-phenylalanine L -1 . Further experiments in bioreactors showed that oxygen concentration is also important to 2-phenylethanol production, with best results obtained at oxygen mass transfer rates of 2.0 h -1 .

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“…The juice was filtered through a 0.45-mm nylon filter (VWR International, Radnor, PA) and analyzed using HPLC. Glucose, fructose, tartaric acid, isocitric acid, and malic acid of the juice were measured using previously established HPLC procedures (Segantini et al, 2018;Walker et al, 2003). The HPLC was equipped with a Bio-Rad HPLC Organic Acid Analysis Aminex HPX-87H ion exclusion column (300 · 7.8 mm), a Bio-Rad HPLC fast acid analysis column (100 · 7.8 mm), and a Bio-Rad HPLC column for fermentation monitoring (150 · 7.8 mm) in series (Bio-Rad, Hercules, CA).…”

Section: Physiochemical Analysismentioning

confidence: 99%

Physiochemical and Descriptive Sensory Analysis of Arkansas Muscadine Grapes

Felts¹,

Threlfall²,

Clark³

et al. 2018

horts

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Understanding how consumer perception is related to physiochemical attributes assists in the identification of harvest and marketability parameters for muscadine grapes (Vitis rotundifolia Michx.). Three muscadine cultivars (Ison, Nesbitt, and Summit) and three advanced breeding selections (AM-9, AM-74, and AM-83) were harvested from vines at the University of Arkansas System Division of Agriculture Fruit Research Station in Clarksville, AR. The physiochemical (physical and composition) and sensory attributes (descriptive) of the genotypes were evaluated at harvest. Significant differences between genotypes were observed for berry weight (9.25-14.38 g), soluble solids (12.73% to 15.40%), pH (2.88-3.33), titratable acidity (0.54% to 1.01%), soluble solids/ titratable acidity ratio (13.12-28.49), skin firmness [0.85-1.48 Newtons/millimeters (N · mm L1 )], and flesh firmness (0.89-2.14 N). Total sugars (6.17-9.75 g/100 g) and total organic acid (0.50-0.84 g/100 g) levels were not significantly different for these genotypes. A trained descriptive sensory panel (n = 8) evaluated the fruit attributes for aroma (n = 9), external appearance (n = 7), internal appearance (n = 3), basic tastes (n = 3), aromatics (n = 10), feeling factors (n = 2), and texture (n = 7). The descriptive sensory panel detected differences among genotypes for external appearance, internal appearance, and basic taste attributes, more specifically with desirable attributes rather than unfavorable. However, the panelists found no differences among genotypes for texture attributes. Of the physiochemical attributes, total sugars had the most significant correlations with the descriptive sensory attributes, followed by soluble solids/titratable acidity ratio. Total sugars were correlated to 12 attributes (three aromas, two exterior appearances, two basic tastes, four aromatics, and one feeling factor) and soluble solids/titratable acidity was correlated to five attributes (one aroma, one basic taste, two aromatics, and one feeling factor). A lexicon of terms for descriptive sensory attributes for fresh-market muscadine grapes was established. This lexicon can be used for other research and breeding efforts, as well as establishing the relationship between the physiochemical and descriptive sensory attributes.

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Analysis of Wine Components in Cynthiana and Syrah Wines

Cited by 32 publications

References 16 publications

HPLC‐DAD methodology for the quantification of organic acids, furans and polyphenols by direct injection of wine samples

HPLC‐DAD methodology for the quantification of organic acids, furans and polyphenols by direct injection of wine samples

Bioconversion of l-phenylalanine into 2-phenylethanol by Kluyveromyces marxianus in grape must cultures

Physiochemical and Descriptive Sensory Analysis of Arkansas Muscadine Grapes

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