Understanding the Relationship between Red Wine Matrix, Tannin

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Understanding the relationship between red wine matrix, tannin activity and sensory properties. Aude Annie Watrelot, Nadia K. Byrnes, Hildegarde Heymann, and James A. Kennedy J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b03767 • Publication Date (Web): 02 Nov 2016 Downloaded from http://pubs.acs.org on November 5, 2016

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Journal of Agricultural and Food Chemistry

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Understanding the relationship between red wine matrix, tannin activity and sensory properties.

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Aude A. Watrelot,abϯ* Nadia K. Byrnes,bϯ Hildegarde Heymann,b James A. Kennedy.bc

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a

Department of Viticulture and Enology, California State University, 2360 East

Barstow Avenue, MS VR89, Fresno, CA 93740-8003, USA b

Department of Viticulture and Enology, University of California at Davis, One

Shields Ave., Davis, CA 95616-5270, USA c

Constellation Brands, Inc., 12667 Road 24, Madera, California, USA

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ϯ

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*corresponding author:

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A. A. Watrelot

These authors contributed equally to this article

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Department of Viticulture and Enology, University of California at Davis, One Shields Ave.,

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Davis, CA 95616-5270, USA

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Tel: 530-752-5054

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Fax: 530-752-0382

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E-mail: [email protected] 1 ACS Paragon Plus Environment


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Abstract

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One major red wine mouthfeel characteristic, astringency, is derived from grape-

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extracted tannins and is considered to be a result of interaction with salivary proteins and the

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oral mucosa. To improve our understanding of the role that the enthalpy of interaction of

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tannin with a hydrophobic surface (tannin activity) has in astringency perception, a

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chromatographic method was used to determine the tannin concentration and activity of 34

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Cabernet Sauvignon wines, as well as sensory analysis done on 13 of those wines. In addition,

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astringency-relevant matrix parameters (pH, titratable acidity, ethanol, glucose, and fructose)

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were measured across all wines. Tannin activity was not significantly correlated with any

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matrix variables and the perception of drying and grippy was not correlated with tannin

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concentration and activity. However, ethanol content was well related to mouthfeel attributes

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and appeared to drive perceived drying. Although fructose and glucose content were well

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correlated, they did not drive the perception of sweetness, that is explained by the well-known

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mixture suppression effect.

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Keywords

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Tannins, red wines, concentration, activity, hydrophobic interaction, pH, ethanol, residual

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sugar.

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Introduction

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In red wine, condensed tannins are the main components responsible for astringency.1

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They are composed primarily of proanthocyanidins extracted from grape skin and seed during

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fermentation and maceration. Proanthocyanidins are polymeric flavan-3-ols linked via

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interflavan bonds between C4-C8 and C4-C6 and their constitutive units are mostly (+)-

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catechin,

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astringency response from wine tannins is considered to be a result of tannin interaction with

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salivary proteins through hydrophobic and hydrogen bond interactions, and subsequent

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protein aggregation and precipitation.4–7 Tannin structure modification(s) as a result of wine

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production influence(s) these non-covalent interactions. The mean degree of polymerization

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(mDP) of tannins, that is the average number of constitutive units, in red wine can vary from

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monomers to polymers (up to 30 in grape skins). In model solutions monomers are perceived

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to be more bitter than astringent, but as the mDP increases so does astringency perception.8,9

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In addition to the level of polymerization, the tannin subunit composition is considered to be

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an important variable in determining astringency.10 (-)-Epicatechin and (+)-gallocatechin are

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known to increase the perception of astringency, as well as the proportion of galloylation in

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contrast to (-)-epigallocatechin.9,10 In addition, winemaking processes and overall wine age

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have an effect on astringency.11 The perception of tannins in red wine has also been shown to

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vary with matrix composition, as explained below.

(-)-epicatechin,

(-)-epicatechin-3-O-gallate

and

(-)-epigallocatechin.2,3

The

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The main components of red wine, in addition to polyphenols, are polysaccharides,

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residual sugars, alcohol, and organic acids. During the crushing and pressing of grapes, plant

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cells are disrupted and polysaccharides of cell walls become available to bind to polyphenols,

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creating interactions that can influence the astringency of the wine.12 Among these reactions

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are the non-covalent interactions between pectins (mostly rhamnogalacturonan II) and

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tannins,13–15 as well as mannoproteins from yeast cell walls and tannins.16,17

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During fermentation, glucose and fructose are converted into ethanol and carbon

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dioxide. Residual sugars found in finished red wines (mostly fructose)18 are responsible for

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the sweetness, but as expected, no direct relationships with astringency have been found.

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Rinaldi et al.19 have shown that an increase in fructose concentration in tannin-salivary

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protein complexes leads to a reduction in the precipitation of salivary proteins. Ethanol in

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wine has been shown to enhance bitterness20 and reduce astringency.21,22 The effect of ethanol

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on tannin-salivary proteins complexes is not well understood, but current literature suggests

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that it decreases the strength of interaction between tannin and protein.23

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Wine pH depends on the total amount of acid present, the ratio of malic acid to tartaric

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acid and the quantity of potassium.18 The organic acid type has been shown to not effect

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astringency,24 while an increase in pH has been shown to decrease salivary protein

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precipitation19 and astringency.25 Variation in pH, and the effect of titratable acidity on

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tannin-salivary protein interactions is not well studied and understood.

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It is well known that astringency perception is due to associations between salivary

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proteins (proline-rich proteins) and tannins through hydrophobic interactions.26,27 Based upon

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the ability of tannins to form hydrophobic interactions with proteins, a high performance

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liquid chromatography (HPLC) method was developed to determine the activity of red wine

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tannins.28 Tannin activity is defined as the enthalpy of interaction between wine tannins and a

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hydrophobic surface. Astringency has been shown, in sensory analysis, to vary with regard to

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tannin concentration as well as other matrix components such as ethanol content, residual

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sugar and acidity, as explained above; however, these variables are not adequate predictors of

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overall astringency perception. In addition, it has been suggested that the activity of tannins is

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also involved in the perception of mouthfeel characteristics.29


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The purpose of this study was to improve our understanding of the relationship

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between wine tannin concentration and activity relative to other astringency-modifying matrix

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components. An important assumption made in this study was that by narrowing the focus on

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recently bottled red wines cv. Cabernet Sauvignon primarily from California, the relationship

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between winemaker expectations with regard to mouthfeel and corresponding tannin

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chemistry and other matrix parameters would be simplified. The study was focused on tannin

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concentration and activity as well as matrix parameters across 34 wines to determine how all

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parameters might be related to sensory perception.

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Materials and methods

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Chemicals

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All chemical solvents used were HPLC-grade. Acetonitrile and ortho-phosphoric acid

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were purchased from VWR International (Radnor, PA). (-)-epicatechin (purity ≥ 90%) was

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purchased from Sigma-Aldrich (St Louis, MO). All water was purified using an Ultrapure

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purification system (Evoqua Corporation, Alpharetta, GA).

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Wine samples

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The 34 wines were sourced from Cabernet Sauvignon cultivar in California,

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Washington, and Australia. Amongst the 34 wines, 27 were from 2012, 5 from 2013, 1 from

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2010 and 2011 (Table 1 in Supporting Information). All wines were bottled between January

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and June 2014. All wines were filtered using a 13 mm PTFE syringe filters (0.45 µm, Grace

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Davison Discovery Science, Deerfield, IL, USA) prior to analysis.

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Matrix parameters



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A Winescan FT120 (FOSS, Eden Prairie, Minnesota, USA) was used to determine the

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pH, ethanol concentration, titratable acidity (TA), fructose, and glucose concentrations of

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wines in triplicate sampling from the same bottle.

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Tannin activity and concentration

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The HPLC method for measuring the activity of wine tannin has been described

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previously.28,29 Briefly, the HPLC method used a polystyrene divinylbenzene reversed-phase

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column (PLRP-S, 2.1 × 50 mm, 100 Å, 3 µm, Agilent Technologies, Santa Clara, CA)

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protected with a guard column (PRP-1, 3 × 8 mm, Hamilton Company, Reno, NV), with DAD

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detection at 280 nm. The mobile phases consisted of 1.5 % (w/w) ortho-phosphoric acid in

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water (180 mM, mobile phase A) and 20 % (v/v) mobile phase A in acetonitrile (mobile phase

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B) with a flow rate of 0.3 mL/min. The linear gradient was as follows (time in min (%B)): 0

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(14), 12.6 (34), 13.3 (34), 15.05 (70), 16.8 (70), 19.6 (14), and 28 (14).

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To determine thermodynamic information, samples were run at four column

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temperatures (25-40 °C, 5 °C increments), and temperatures were converted to Kelvin for

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calculations. Chromatograms at 280 nm were baseline-subtracted using a water as a blank

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injection and were integrated as previously described.28,29 Briefly, a baseline was drawn at 0

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mAU and with the resulting area clipped at 5 and 28 min for total tannin (TanninT); partial

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tannin (TanninP), that corresponded to polymers, was the peak area eluting between 16.8 and

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28 min. For each chromatogram an alternative retention factor for the tannin (kalt) was

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calculated as follows:

=

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Tannin

Tannin − Tannin

The ln (kalt) is related to thermodynamic information as follows:


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( ) = −

ΔH° ΔS° + RT R

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Where ∆H° and ∆S° are the enthalpy and the entropy (respectively) of the interaction,

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R is the gas constant, T is the temperature of the experiment in Kelvin. The specific enthalpy

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was calculated by the slope from the van’t Hoff plot (i.e. ln kalt versus the reciprocal of the

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column temperature in Kelvin at each of the four temperatures).29 A purified grape skin tannin

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isolate was used as an enthalpy of interaction control.

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Tannin concentrations were determined by measuring total tannin peak area (TanninT)

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at 280 nm and comparing this with an (-)-epicatechin quantitative standard. For comparison,

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tannin concentrations were also determined by protein precipitation as previously described

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by Harbertson et al. and Kennedy et al.30,31 In that case, the concentration of tannins was

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expressed in (+)-catechin equivalents.

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Sensory analysis

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Panelists were recruited from UC Davis students, staff, university affiliates, and

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community members to take part in a descriptive analysis panel on 13 of the Cabernet

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Sauvignon wines (in bold in Supporting Information Table 1). Panelists were screened for age

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(over 21), interest in wine, and availability to complete all the training and panel sessions.

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During nine one-hour training sessions 12 panelists (5 men) sampled the study wines in

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duplicate. In these sessions they generated terms describing the aroma/flavor, taste and

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mouthfeel properties of the wines and came to a consensus about the references for aroma,

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taste, and mouthfeel descriptors. During these training sessions panelists also came to

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consensus regarding the definition of the mouthfeel attributes that were used in the final

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evaluation. The definitions provided by Gawel and colleagues32 were used as a starting point

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for these definitions. The final list (Table 2) contained 14 aroma/flavors, 4 tastes, and 5



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mouthfeel terms. All aroma standards were presented to the panelists at the beginning of each

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evaluation session. Taste and mouthfeel references were presented only at the first evaluation

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session.

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Panelists’ evaluation sessions were performed in isolated, temperature-controlled (20

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°C) tasting booths. All wines and attribute references were presented under white light in

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black, pear-shaped ISO glasses (ISO 1977) covered with clear plastic petri dishes. Prior to

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assessing any of the wines, panelists were given a quiz on the aroma references. In a booth

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identical to those used for evaluation, a full set of aroma references were presented in random

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order with blinding codes and panelists were asked to match the blinded attribute references

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to the appropriate attribute names. Each time the panelists completed the aroma quiz they

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were presented with the same aroma references, though different blinding codes were used

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and the presentation order was changed.

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Panelists were presented with four or five wines during this portion of the evaluation

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session, presented in randomized blocks and labeled with random three-digit codes. Each

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sample consisted of 20 ml of wine presented at room temperature (20°C). Panelists evaluated

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each wine monadically were asked during their 1-minute break between samples to cleanse

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their palate with the filtered water (Arrowhead, Nestle, Stamford, CT) and unsalted top saltine

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crackers (Nabisco, Mondelez Ltd, East Hanover, NJ) provided. All wine was expectorated.

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Panelists rated the wine for each of the aroma attributes before putting the wine in their

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mouths. Panelists held the wine in their mouth for 30 seconds, during which they rated the

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taste and flavor attributes. After expectorating the wine, panelists rated the mouthfeel

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attributes. Rating was performed using a computer and mouse on a 10-cm line scale ranging

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from “none” on the left end of scale to “a lot” on the right end of the scale. This scale was

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marked only with tick marks at the ends. Panelists evaluated all of the wine samples in

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triplicate, completing in total nine evaluation sessions (13 wines × 3 replicates, 4 to 5 wines in 8 ACS Paragon Plus Environment

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each of 9 sessions). Data was collected and compiled using Fizz version 2.45A (Biosystèmes,

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Couternon, France).

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Statistical analysis

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Exploratory data analysis of the chemical data was conducted using Principal

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Components Analysis (PCA) on the correlation matrix of the averaged data set. Descriptive

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Analysis (DA) data were analyzed by three-way multivariate analysis of variance

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(MANOVA) with fixed-effect model for the wine, judge, and replication effects with all two-

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way interactions. Following this, three-way univariate analyses of variance (ANOVAs) for

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each attribute were used with a fixed-effect model for the wine, judge, and replication effects

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with all two-way interactions. For those attributes that showed significant main effects of

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wine as well as a significant wine-judge interaction, a pseudo-mixed model was used.33

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Canonical variate analysis (CVA) was used to visualize the data, with 95% confidence circles

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(Chatfield and Collins 1980).34 Partial Least Squares Regression (PLSR) was used to correlate

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the chemical and the sensory data sets to one another. The data was standardized prior to

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analysis and the leave-one-out cross-validation method was used.

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All statistical analyses and graphs were prepared using RStudio,35 with the

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SensoMineR36, car37, agricolae38, candisc39, and pls40 packages. All statistics were interpreted

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using alpha = 0.05.

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Results

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Matrix variables, tannin concentration and activity

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Summary statistics of all wine matrix parameters are shown in Table 1. Full chemical

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data is shown in Supporting Information Table 1. The tannin concentration determined by 9 ACS Paragon Plus Environment


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HPLC varied across the 34 wines from 2.75 to 6.16 g/L (mean ± standard error: 4.18 ± 0.76),

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while tannin activity varied between -4.82 and -1.43 kJ/mol (-2.74 ± 0.69). Overall, the tannin

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concentration values measured by the protein precipitation method varied from 0.35 to 1.34

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g/L (0.66 ± 0.21) and were lower than when measured by HPLC, with an average of 4.18 g/L

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by HPLC versus 0.66 g/L by protein precipitation (Table 1; Supporting Information Table 1).

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The two methods were well correlated with a r of 0.84 (p = 0.00). Among the 34 Cabernet

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Sauvignon wines, matrix variables varied as follows. The ethanol concentration varied from

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13.04 to 17.05 v/v %, with an average of 14.44 (± 0.81). The TA varied from 4.48 to 6.22 g/L

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(5.26 ± 0.40) and the pH varied from 3.61 to 4.02 (3.76 ± 0.11). The fructose concentration

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varied from 1.00 to 3.85 g/L with an average of 2.16 (± 0.70) and the glucose concentration

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was much lower, varying from not detectable to 1.75 g/L (0.24 ± 0.62). As observed in the

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Supporting Information Table 1, the wine containing the highest ethanol concentration had the

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highest tannin concentration, the lowest titratable acidity and one of the highest pH values.

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The fructose and the glucose concentrations of this wine were slightly higher than the average

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(3.16 g/L and 1.16 g/L, respectively). In contrast, the wine with the least ethanol

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concentration did not contain extreme amounts of other matrix variables.

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In order to understand the relationships between matrix parameters and tannin activity,

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a principal component analysis (PCA) was performed across the 34 wines (Figure 1). In total,

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66.8% of the variance was explained by the first two components (PC1 = 34.5%, PC2 =

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32.3%). Regarding the loading plot, ethanol concentration was situated quite close to pH and

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tannin activity but this last parameter was not significantly correlated with any matrix

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variables. Fructose and glucose were positively correlated (r = 0.88, p = 0.00) and ethanol

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concentration was negatively correlated with titratable acidity (r = -0.38, p = 0.03) and with

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pH (r = 0.76, p = 0.00). Tannin concentration was significantly correlated with ethanol

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concentration (r = 0.39, p = 0.02), fructose (r = 0.46, p = 0.01), glucose (r = 0.40, p = 0.02), 10 ACS Paragon Plus Environment

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and pH (r = 0.56, p = 0.00).

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Sensory analysis

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Table 2 shows the attributes and attribute definitions and references that were

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determined during the descriptive panel. Three-way MANOVA showed significant

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differences between the wines. ANOVA of the attributes generated in descriptive analysis

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showed that the outdoors aroma and flavor, vanilla oak aroma and flavor, fresh berry flavor,

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green pepper flavor, tamarind flavor, sweetness, and the mouthfeel attributes grippy, drying,

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and viscosity were significant for the wines. The means and least significant difference values

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for these attributes are shown in Supporting Information Table 2.

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CVA showed that three dimensions were most appropriate to represent the results

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from descriptive analysis (Figure 2). The first two dimensions of CVA accounted for 78.7%

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of the variance ratio (Figure 2a). The third dimension accounted for another 16.3% (Figure

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2b). The first dimension was characterized on the positive side by fresh berry flavor and sweet

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taste, and on the negative side by outdoors flavor and aroma and the mouthfeel attributes

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grippy and drying. The second dimension was characterized by vanilla oak flavor and aroma

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(negative) and green pepper flavor (positive). The third dimension was characterized by

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tamarind flavor and the mouthfeel attributes drying and viscosity at the positive end.

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There was a high level of overlap in the 95% confidence circles, as shown in Figure 2a

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and 2b, indicating that the panelists did not perceive large differences between these wines.

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Along the first dimension wines 3 and 13 were the most different, differentiated along this

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axis by the mouthfeel characteristics of grippy and drying (high for wine 13) and tamarind

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and fresh berry flavors (higher for wine 3). While wine 3 had the highest rating of sweet taste

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and highest fructose concentration, these were not significantly higher than the other wines, 11 ACS Paragon Plus Environment


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and the perceived sweetness in the wines was not correlated to either fructose or glucose

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concentration in the wines. Along the second dimension, wines were distinguished by green

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pepper flavor (wines 7 and 8) and vanilla oak flavor (wine 5), though none of these wines

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were significantly different than the other wines for these attributes (Table 2). Wines 7 and 8

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were the Australian wines, both from the same producer, which may account for the

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similarities to one another and for the differences from the other wines in the study.

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In Figure 2b, there was clear separation of the tamarind and fresh berry flavors along

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the third dimension, which in Figure 2a appears to overlap. On this plot, it can be seen that

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wine 3 was significantly higher in the fresh berry flavor than the other wines, while wines 9,

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10, and 11 were characterized more by the tamarind flavor. Again however, there was not

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much distinction between most of the wines along the third dimension. The wine that was the

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most different from the rest of the wines in the study was wine 13. This wine was rated the

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highest for both mouthfeel characteristics of grippy and drying. This was not unexpected, as

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the tannin concentration in this wine was the highest of all of the wines, as was the tannin

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activity. Interestingly however, the perception of drying and grippy was not correlated with

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the tannin concentration or tannin activity. Instead, perception of drying showed a moderate

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correlation with ethanol concentration, suggesting that the ethanol concentration was driving

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the perception of the drying sensation. Overall, the mouthfeel characteristic drying was

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significantly correlated with grippy (r = 0.66, p = 0.01) and viscosity (r = 0.61, p = 0.03),

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however grippy and viscosity were not significantly correlated.

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Regressing sensory on chemical variables

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Figure 3 shows the output of PLSR of sensory attributes on the chemical variables.

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Collectively, the first three model components account for roughly 80% of the total variance 12 ACS Paragon Plus Environment

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of the predictor matrix (chemical variables, Component 1: 40.82% , Component 2: 23.12%,

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Component 3 – not shown: 17.00%). The model did not improve by adding additional

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components. Overall, the chemical variables predicted the sensory variables moderately well,

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however only the sensory variables viscosity, grippy, drying (all mouthfeel), outdoors flavor,

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chemical aroma, and fresh berry flavor were sufficiently explained by the model.

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Correlations between the sensory and chemical variables were observed. pH and

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titratable acidity were related to chemical aroma (r = 0.79, p = 0.00 and r = -0.68, p = 0.01,

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respectively). Titratable acidity was also related to the fresh berry flavor (r = 0.6, p = 0.03).

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Outdoors aroma was correlated to fructose concentration (r = 0.59, p = 0.03) and tannin

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activity was related to tamarind flavor (r = 0.57, p = 0.04). The only mouthfeel attribute to be

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significantly related to any chemical variables was drying, which was related to ethanol

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concentration (r = 0.60, p = 0.03).

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Discussion

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In an effort to reduce variability in the samples, we chose to focus on wines, primarily

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from California of similar vintage. Doing so, we assumed that mouthfeel and taste aspects

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(bitterness, astringency, sweetness, etc.) of perception would be similarly balanced. In the

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present study, the relationships between tannin activity and other chemical components

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related to mouthfeel were first investigated for 34 Cabernet Sauvignon wines produced in

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various regions. Subsequently, 13 of the wines were characterized using sensory descriptive

297

analysis. In the methodology used here, the dynamic system (gradient by HPLC) and the use

298

of a hydrophobic surface to determine tannin activity (non-covalent interactions between

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tannins and a hydrophobic surface) did not involve precipitation/aggregation. The astringency

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mouthfeel has been described as the non-covalent interactions between tannins and salivary 13 ACS Paragon Plus Environment


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proteins through hydrogen bonds and hydrophobic interactions followed by aggregation and

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complex precipitation, leading to the drying sensation.41 No significant correlation between

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tannin activity, matrix variables and with any mouthfeel attributes was observed in this study

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which could be explained by the chemical method used, where the tannin activity correspond

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to the first step of interactions (hydrophobic interaction between tannins and a hydrophobic

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surface) and not necessarily to the protein precipitation that is known to lead to the in-mouth

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perceptions. Alternatively a more complex set of matrix interactions with tannins, insufficient

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tannin activity variation or missing analytically-relevant information (e.g.: polysaccharides)

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may have influenced these results.

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Red wines contain many grape-derived compounds (e.g., polyphenols, organic acids,

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sugars) as well as fermentation-derived compounds (e.g. ethanol) that contribute to the

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mouthfeel characteristics of the wine.18 In contrast to the literature where the ethanol has

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been previously related to an increase of the bitterness and burning sensation of wines20,42 and

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to a decrease of astringency intensity,43,44 in this study, the ethanol concentration was well

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correlated to the pH and to mouthfeel attributes such as grippy, drying and viscosity. In the

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case of 13 Cabernet Sauvignon mostly from the 2012 vintage and from California, the ethanol

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concentration was related to an increase in grippy, drying sensation and of the viscosity

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perception. A principal purpose of wine production is to produce a “balanced” wine, meaning

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that an increase in tannin concentration would be associated with an increase in ethanol for

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attenuating the wine astringency that would result from tannin concentration alone. This was

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shown in the present study, where a positive correlation between ethanol and tannin

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concentration was observed. The pH has been found to be positively related to tannin

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concentration and ethanol concentration as well as to mouthfeel attributes. This result is in

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agreement with Kallithraka et al.24 who showed that a decrease in the pH increased the

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maximum intensity and the total duration of astringency in model solutions and red wine as 14 ACS Paragon Plus Environment

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well as with Obreque-Slier et al.45 Similarly, an increase in wine pH has been shown to lead to

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a decrease in salivary protein precipitation,19 even if in our sensory analysis, pH was not

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clearly related to any taste or mouthfeel.

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When present, fructose was found to be the most significant residual sugar in this set

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of wines. Some researchers have found that an increase in fructose concentration decreased

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the perceived astringency and decreased salivary protein precipitation,19,46 while others have

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shown little effect on mouthfeel dryness.42 In our study, the fructose was correlated to glucose

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in 34 wines and not to any other matrix variables, suggesting that for Cabernet Sauvignon

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wines, residual sugars are not the main drivers of mouthfeel perception. Also in the

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descriptive analysis on 13 wines, it is possible, that due to well-known mixture suppression

336

effects, which could arise from the perception of bitterness47–49 or astringency50 in the wines,

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the perception of sweetness was suppressed, accounting for the lack of correlation between

338

fructose or glucose and sweet taste in the wines.

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The lack of a relationship between the tannin concentration and activity and sensory

340

perception of astringency observed in this study could be due to a set of wines that were too

341

perceptually similar for participants to distinguish between. It is also possible that interactions

342

between aroma, flavor, and astringent compounds in the wines altered the perception of

343

astringency in these wines. Literature suggests that aromatic compounds and phenolic

344

compounds in wines and other foods and beverages may interact to suppress the perception of

345

each sensation.51-53 It is also possible that there are more complex matrix effects impacting the

346

perception of astringency in these wines. As documented by Ares and colleagues, the

347

presence of astringent compounds can suppress the perception of sweetness in wines.

348

Additionally, the presence of certain aromas, such as fruity or berry aromas, can enhance the

349

perceptually sweetness of a food or beverage without any increase in the concentration of

350

sweet tastant.54 It is possible that the interaction between the cherry and fresh fruit/berry 15 ACS Paragon Plus Environment


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351

aromas and flavors, as well as the perceived sweetness, present in these wines directly or

352

indirectly impacted the perception of astringent qualities in this sample set of wines.

353

Additional work in the area of mixture suppression within the wine matrix is needed to fully

354

clarify the impact that the various components and tannin activity have on the perception of

355

astringency in red wines.

356

Acknowledgements

357 358

The authors thank all of the wineries that contributed wines to this study.

359

Funding sources

360 361

We thank the American Vineyard Foundation (AVF) for project funding (UGMVE # 2015-

362

1691).

363

364

Supporting Information

365

Tannin concentration and activity data and matrix variables values (pH, ethanol, titratable

366

acidity, fructose and glucose) of 34 red wines (cv. Cabernet Sauvignon). Means and least

367

significant difference values for the descriptive analysis attributes of red wines. This material

368

is available free of charge via the Internet at http://pubs.acs.org.

369 370

References


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Figure captions.

Figure 1. PCA loadings plot for chemical variables on 34 Cabernet Sauvignon wines. Figure 2. CVA biplots for sensory analysis on 13 Cabernet Sauvignon wines. Wines, coded CS1-CS13, are shown in the left pane of the figure. In the right pane, only significant attributes are displayed (alpha = 0.05). “ar” indicates the attribute is an aroma attribute, “fl” indicates that the attribute is a flavor attribute, and “mf” indicates that the attribute is a mouthfeel attribute. CV1 versus CV2 displayed in a) and CV1 versus CV3 displayed in b). Figure 3. PLSR correlation plots for the first and second model components on 13 Cabernet Sauvignon wines. Predictors (chemical variables) are shown in black italic font, while the predicted variables (sensory attributes) are shown in red font.



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Figure 1.

Figure 2. a)

b) 24 ACS Paragon Plus Environment

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Figure 3.



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Table captions. Table 1. Summary statistics for the 34 Cabernet Sauvignon wines used in this study. Full chemical data available in Supporting Information Table 1. TA is titratable acidity. Table 2. Attributes and attribute definitions and references generated during descriptive analysis on 13 wines.

Table 1.

Tannin Concentration (HPLC ; g/L)

Tannin Concentration (Protein precipitation ; g/L)

Tannin activity (kJ/mol)

Ethanol (v/v %)

TA (g/L)

pH

Fructose (g/L)

Glucose (g/L)

Minimum - Maximum

2.75 –6.16

0.35 – 1.34

- 1.43 – -4.82

13.04 – 17.05

4.48 – 6.22

3.61 – 4.02

1.00 – 3.85

-0.60 – 1.75

Mean ± standard error

4.18 ± 0. 76

0.66 ± 0.20

-2.74 ± 0.69

14.44 ± 0.81

5.26 ± 0.40

3.76 ± 0.11

2.16 ± 0.70

0.24 ± 0.62

26

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Table 2. Group

Attribute Alcohol Cedar Cherry

Chocolate Citrus Cooked, dried fruit

Fresh fruit/berry Aroma/flavor Green pepper

Outdoors

Spiced Stemmy

Tamarind Vanilla oak Bitter Savory Taste

Sour Sweet Tingling/pricking

Mouthfeel

Reference/Definition 1mL vodka (Ketel One) 2mL Cedar liquid (cedar sheets extracted in alcohol) 2g frozen dark sweet cherries (Woodstock), 2g canned Bing cherries (Bada Bing), 2g canned cherry pie filling (Duncan Hines), 2g powdered cherry candy (Wonka) 4g 100% unsweetened cocoa powder (Ghirardelli Chocolate Company) 0.05g orange rind (Nugget Markets, Davis, CA), 1.75g grapefruit rind (Nugget Markets, Davis, CA), 1g blackberry preserves (Safeway Inc.), 1g currant jelly (The J. M. Smucker Co.), 1mL prune juice (Sunsweet), 1g dried cherries (Safeway Inc.), 1g freeze-dried raspberries (Just Raspberries) 1g blueberry (Nugget Markets, Davis, CA), 1g blackberry (Nugget Markets, Davis, CA), 1g strawberry (Nugget Markets, Davis, CA), 1g raspberry (Nugget Markets, Davis, CA) 0.5g serrano pepper (Nugget Markets, Davis, CA), 0.5g jalapeno pepper (Nugget Markets, Davis, CA), 1g green bell pepper Nugget Markets, Davis, CA) 0.25g leather shoe lace (Kiwi), 1g grated Burr oak branch, 0.1g tobacco (Malboro) 0.5g dirt from 771 Pole Line Rd, 0.5g dirt and leaf matter from Mace Ranch Park 0.5g pumpkin pie spice (McCormick & Co., Inc.), 0.1g ground ginger (McCormick & Co., Inc.), 0.25g freshly ground pepper (McCormick & Co., Inc.), 2g table grape stems(Nugget Markets, Davis, CA) 2g tamarind (Melissa’s/World Variety Produce), 0.1g tobacco (Malboro) 5 High Vanilla oak stave (EvOak, Oak Solutions, Napa, CA) 1.5g/L caffeine (Sigma-Aldrich) in filtered water 1.6g/L MSG (monosodium glutamate, Accent Flavor Enhancer) in filtered water 1.25g/L L-(+)-tartaric acid, FCC, FG (SigmaAldrich) in filtered water 20g sucrose (C&H) in filtered water 15mL club soda (Canada Dry) Definition: Tingling is low on the scale while pricking is a higher intensity. Light, diffuse pins 27

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Warm/hot

Grippy

Drying

Viscosity

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and needles sensation on the tongue (tingling). Deeper, more localized needle prick sensation on the tongue (pricking). 20% alcohol (Ketel One). Definition: Warm is lower intensity than hot. This scale represents the sensation of alcohol in the mouth 5g/L VF tannin (Biotan, tanin proanthocyanidique, Laffort) in filtered water. Definition: Lack of slip of tongue with mouth surfaces. Requires movement to be felt. 5g/L grape tannins (Biotan, tanin proanthocyanidique, Laffort) in filtered water. Definition: Feeling of water leaving the mouth. Does not require movement to be felt and is perceived on all surfaces of the mouth. 15 mL nonfat milk (Lucerne) Definition: The thinness/thickness of a solution when moved in the mouth.


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Table of Content Graphic.

For Table of Contents Only. Understanding the relationship between red wine matrix, tannin activity and sensory properties. Aude A. Watrelot, Nadia K. Byrnes, Hildegarde Heymann, James A. Kennedy


Understanding the Relationship between Red Wine Matrix, Tannin

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