Subscriber access provided by UNIV TORONTO
Article
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
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
Journal of Agricultural and Food Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 29
Journal of Agricultural and Food Chemistry
1 2
Understanding the relationship between red wine matrix, tannin activity and sensory properties.
3 4 5
Aude A. Watrelot,abϯ* Nadia K. Byrnes,bϯ Hildegarde Heymann,b James A. Kennedy.bc
6 7 8 9 10 11
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
12 13
ϯ
14
*corresponding author:
15
A. A. Watrelot
These authors contributed equally to this article
16
Department of Viticulture and Enology, University of California at Davis, One Shields Ave.,
17
Davis, CA 95616-5270, USA
18
Tel: 530-752-5054
19
Fax: 530-752-0382
20
E-mail:
[email protected] 1 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 2 of 29
21
Abstract
22
One major red wine mouthfeel characteristic, astringency, is derived from grape-
23
extracted tannins and is considered to be a result of interaction with salivary proteins and the
24
oral mucosa. To improve our understanding of the role that the enthalpy of interaction of
25
tannin with a hydrophobic surface (tannin activity) has in astringency perception, a
26
chromatographic method was used to determine the tannin concentration and activity of 34
27
Cabernet Sauvignon wines, as well as sensory analysis done on 13 of those wines. In addition,
28
astringency-relevant matrix parameters (pH, titratable acidity, ethanol, glucose, and fructose)
29
were measured across all wines. Tannin activity was not significantly correlated with any
30
matrix variables and the perception of drying and grippy was not correlated with tannin
31
concentration and activity. However, ethanol content was well related to mouthfeel attributes
32
and appeared to drive perceived drying. Although fructose and glucose content were well
33
correlated, they did not drive the perception of sweetness, that is explained by the well-known
34
mixture suppression effect.
35
Keywords
36 37
Tannins, red wines, concentration, activity, hydrophobic interaction, pH, ethanol, residual
38
sugar.
39 40
41
42 2 ACS Paragon Plus Environment
Page 3 of 29
Journal of Agricultural and Food Chemistry
43
Introduction
44
In red wine, condensed tannins are the main components responsible for astringency.1
45
They are composed primarily of proanthocyanidins extracted from grape skin and seed during
46
fermentation and maceration. Proanthocyanidins are polymeric flavan-3-ols linked via
47
interflavan bonds between C4-C8 and C4-C6 and their constitutive units are mostly (+)-
48
catechin,
49
astringency response from wine tannins is considered to be a result of tannin interaction with
50
salivary proteins through hydrophobic and hydrogen bond interactions, and subsequent
51
protein aggregation and precipitation.4–7 Tannin structure modification(s) as a result of wine
52
production influence(s) these non-covalent interactions. The mean degree of polymerization
53
(mDP) of tannins, that is the average number of constitutive units, in red wine can vary from
54
monomers to polymers (up to 30 in grape skins). In model solutions monomers are perceived
55
to be more bitter than astringent, but as the mDP increases so does astringency perception.8,9
56
In addition to the level of polymerization, the tannin subunit composition is considered to be
57
an important variable in determining astringency.10 (-)-Epicatechin and (+)-gallocatechin are
58
known to increase the perception of astringency, as well as the proportion of galloylation in
59
contrast to (-)-epigallocatechin.9,10 In addition, winemaking processes and overall wine age
60
have an effect on astringency.11 The perception of tannins in red wine has also been shown to
61
vary with matrix composition, as explained below.
(-)-epicatechin,
(-)-epicatechin-3-O-gallate
and
(-)-epigallocatechin.2,3
The
62
The main components of red wine, in addition to polyphenols, are polysaccharides,
63
residual sugars, alcohol, and organic acids. During the crushing and pressing of grapes, plant
64
cells are disrupted and polysaccharides of cell walls become available to bind to polyphenols,
65
creating interactions that can influence the astringency of the wine.12 Among these reactions
66
are the non-covalent interactions between pectins (mostly rhamnogalacturonan II) and
3 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
67
Page 4 of 29
tannins,13–15 as well as mannoproteins from yeast cell walls and tannins.16,17
68
During fermentation, glucose and fructose are converted into ethanol and carbon
69
dioxide. Residual sugars found in finished red wines (mostly fructose)18 are responsible for
70
the sweetness, but as expected, no direct relationships with astringency have been found.
71
Rinaldi et al.19 have shown that an increase in fructose concentration in tannin-salivary
72
protein complexes leads to a reduction in the precipitation of salivary proteins. Ethanol in
73
wine has been shown to enhance bitterness20 and reduce astringency.21,22 The effect of ethanol
74
on tannin-salivary proteins complexes is not well understood, but current literature suggests
75
that it decreases the strength of interaction between tannin and protein.23
76
Wine pH depends on the total amount of acid present, the ratio of malic acid to tartaric
77
acid and the quantity of potassium.18 The organic acid type has been shown to not effect
78
astringency,24 while an increase in pH has been shown to decrease salivary protein
79
precipitation19 and astringency.25 Variation in pH, and the effect of titratable acidity on
80
tannin-salivary protein interactions is not well studied and understood.
81
It is well known that astringency perception is due to associations between salivary
82
proteins (proline-rich proteins) and tannins through hydrophobic interactions.26,27 Based upon
83
the ability of tannins to form hydrophobic interactions with proteins, a high performance
84
liquid chromatography (HPLC) method was developed to determine the activity of red wine
85
tannins.28 Tannin activity is defined as the enthalpy of interaction between wine tannins and a
86
hydrophobic surface. Astringency has been shown, in sensory analysis, to vary with regard to
87
tannin concentration as well as other matrix components such as ethanol content, residual
88
sugar and acidity, as explained above; however, these variables are not adequate predictors of
89
overall astringency perception. In addition, it has been suggested that the activity of tannins is
90
also involved in the perception of mouthfeel characteristics.29
4 ACS Paragon Plus Environment
Page 5 of 29
Journal of Agricultural and Food Chemistry
91
The purpose of this study was to improve our understanding of the relationship
92
between wine tannin concentration and activity relative to other astringency-modifying matrix
93
components. An important assumption made in this study was that by narrowing the focus on
94
recently bottled red wines cv. Cabernet Sauvignon primarily from California, the relationship
95
between winemaker expectations with regard to mouthfeel and corresponding tannin
96
chemistry and other matrix parameters would be simplified. The study was focused on tannin
97
concentration and activity as well as matrix parameters across 34 wines to determine how all
98
parameters might be related to sensory perception.
99 100
Materials and methods
101
Chemicals
102
All chemical solvents used were HPLC-grade. Acetonitrile and ortho-phosphoric acid
103
were purchased from VWR International (Radnor, PA). (-)-epicatechin (purity ≥ 90%) was
104
purchased from Sigma-Aldrich (St Louis, MO). All water was purified using an Ultrapure
105
purification system (Evoqua Corporation, Alpharetta, GA).
106
Wine samples
107
The 34 wines were sourced from Cabernet Sauvignon cultivar in California,
108
Washington, and Australia. Amongst the 34 wines, 27 were from 2012, 5 from 2013, 1 from
109
2010 and 2011 (Table 1 in Supporting Information). All wines were bottled between January
110
and June 2014. All wines were filtered using a 13 mm PTFE syringe filters (0.45 µm, Grace
111
Davison Discovery Science, Deerfield, IL, USA) prior to analysis.
112
Matrix parameters
5 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 6 of 29
113
A Winescan FT120 (FOSS, Eden Prairie, Minnesota, USA) was used to determine the
114
pH, ethanol concentration, titratable acidity (TA), fructose, and glucose concentrations of
115
wines in triplicate sampling from the same bottle.
116
Tannin activity and concentration
117
The HPLC method for measuring the activity of wine tannin has been described
118
previously.28,29 Briefly, the HPLC method used a polystyrene divinylbenzene reversed-phase
119
column (PLRP-S, 2.1 × 50 mm, 100 Å, 3 µm, Agilent Technologies, Santa Clara, CA)
120
protected with a guard column (PRP-1, 3 × 8 mm, Hamilton Company, Reno, NV), with DAD
121
detection at 280 nm. The mobile phases consisted of 1.5 % (w/w) ortho-phosphoric acid in
122
water (180 mM, mobile phase A) and 20 % (v/v) mobile phase A in acetonitrile (mobile phase
123
B) with a flow rate of 0.3 mL/min. The linear gradient was as follows (time in min (%B)): 0
124
(14), 12.6 (34), 13.3 (34), 15.05 (70), 16.8 (70), 19.6 (14), and 28 (14).
125
To determine thermodynamic information, samples were run at four column
126
temperatures (25-40 °C, 5 °C increments), and temperatures were converted to Kelvin for
127
calculations. Chromatograms at 280 nm were baseline-subtracted using a water as a blank
128
injection and were integrated as previously described.28,29 Briefly, a baseline was drawn at 0
129
mAU and with the resulting area clipped at 5 and 28 min for total tannin (TanninT); partial
130
tannin (TanninP), that corresponded to polymers, was the peak area eluting between 16.8 and
131
28 min. For each chromatogram an alternative retention factor for the tannin (kalt) was
132
calculated as follows:
=
133
Tannin
Tannin − Tannin
The ln (kalt) is related to thermodynamic information as follows:
6 ACS Paragon Plus Environment
Page 7 of 29
Journal of Agricultural and Food Chemistry
( ) = −
ΔH° ΔS° + RT R
134
Where ∆H° and ∆S° are the enthalpy and the entropy (respectively) of the interaction,
135
R is the gas constant, T is the temperature of the experiment in Kelvin. The specific enthalpy
136
was calculated by the slope from the van’t Hoff plot (i.e. ln kalt versus the reciprocal of the
137
column temperature in Kelvin at each of the four temperatures).29 A purified grape skin tannin
138
isolate was used as an enthalpy of interaction control.
139
Tannin concentrations were determined by measuring total tannin peak area (TanninT)
140
at 280 nm and comparing this with an (-)-epicatechin quantitative standard. For comparison,
141
tannin concentrations were also determined by protein precipitation as previously described
142
by Harbertson et al. and Kennedy et al.30,31 In that case, the concentration of tannins was
143
expressed in (+)-catechin equivalents.
144
Sensory analysis
145
Panelists were recruited from UC Davis students, staff, university affiliates, and
146
community members to take part in a descriptive analysis panel on 13 of the Cabernet
147
Sauvignon wines (in bold in Supporting Information Table 1). Panelists were screened for age
148
(over 21), interest in wine, and availability to complete all the training and panel sessions.
149
During nine one-hour training sessions 12 panelists (5 men) sampled the study wines in
150
duplicate. In these sessions they generated terms describing the aroma/flavor, taste and
151
mouthfeel properties of the wines and came to a consensus about the references for aroma,
152
taste, and mouthfeel descriptors. During these training sessions panelists also came to
153
consensus regarding the definition of the mouthfeel attributes that were used in the final
154
evaluation. The definitions provided by Gawel and colleagues32 were used as a starting point
155
for these definitions. The final list (Table 2) contained 14 aroma/flavors, 4 tastes, and 5
7 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 8 of 29
156
mouthfeel terms. All aroma standards were presented to the panelists at the beginning of each
157
evaluation session. Taste and mouthfeel references were presented only at the first evaluation
158
session.
159
Panelists’ evaluation sessions were performed in isolated, temperature-controlled (20
160
°C) tasting booths. All wines and attribute references were presented under white light in
161
black, pear-shaped ISO glasses (ISO 1977) covered with clear plastic petri dishes. Prior to
162
assessing any of the wines, panelists were given a quiz on the aroma references. In a booth
163
identical to those used for evaluation, a full set of aroma references were presented in random
164
order with blinding codes and panelists were asked to match the blinded attribute references
165
to the appropriate attribute names. Each time the panelists completed the aroma quiz they
166
were presented with the same aroma references, though different blinding codes were used
167
and the presentation order was changed.
168
Panelists were presented with four or five wines during this portion of the evaluation
169
session, presented in randomized blocks and labeled with random three-digit codes. Each
170
sample consisted of 20 ml of wine presented at room temperature (20°C). Panelists evaluated
171
each wine monadically were asked during their 1-minute break between samples to cleanse
172
their palate with the filtered water (Arrowhead, Nestle, Stamford, CT) and unsalted top saltine
173
crackers (Nabisco, Mondelez Ltd, East Hanover, NJ) provided. All wine was expectorated.
174
Panelists rated the wine for each of the aroma attributes before putting the wine in their
175
mouths. Panelists held the wine in their mouth for 30 seconds, during which they rated the
176
taste and flavor attributes. After expectorating the wine, panelists rated the mouthfeel
177
attributes. Rating was performed using a computer and mouse on a 10-cm line scale ranging
178
from “none” on the left end of scale to “a lot” on the right end of the scale. This scale was
179
marked only with tick marks at the ends. Panelists evaluated all of the wine samples in
180
triplicate, completing in total nine evaluation sessions (13 wines × 3 replicates, 4 to 5 wines in 8 ACS Paragon Plus Environment
Page 9 of 29
Journal of Agricultural and Food Chemistry
181
each of 9 sessions). Data was collected and compiled using Fizz version 2.45A (Biosystèmes,
182
Couternon, France).
183
Statistical analysis
184
Exploratory data analysis of the chemical data was conducted using Principal
185
Components Analysis (PCA) on the correlation matrix of the averaged data set. Descriptive
186
Analysis (DA) data were analyzed by three-way multivariate analysis of variance
187
(MANOVA) with fixed-effect model for the wine, judge, and replication effects with all two-
188
way interactions. Following this, three-way univariate analyses of variance (ANOVAs) for
189
each attribute were used with a fixed-effect model for the wine, judge, and replication effects
190
with all two-way interactions. For those attributes that showed significant main effects of
191
wine as well as a significant wine-judge interaction, a pseudo-mixed model was used.33
192
Canonical variate analysis (CVA) was used to visualize the data, with 95% confidence circles
193
(Chatfield and Collins 1980).34 Partial Least Squares Regression (PLSR) was used to correlate
194
the chemical and the sensory data sets to one another. The data was standardized prior to
195
analysis and the leave-one-out cross-validation method was used.
196
All statistical analyses and graphs were prepared using RStudio,35 with the
197
SensoMineR36, car37, agricolae38, candisc39, and pls40 packages. All statistics were interpreted
198
using alpha = 0.05.
199
200
Results
201
Matrix variables, tannin concentration and activity
202
Summary statistics of all wine matrix parameters are shown in Table 1. Full chemical
203
data is shown in Supporting Information Table 1. The tannin concentration determined by 9 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 10 of 29
204
HPLC varied across the 34 wines from 2.75 to 6.16 g/L (mean ± standard error: 4.18 ± 0.76),
205
while tannin activity varied between -4.82 and -1.43 kJ/mol (-2.74 ± 0.69). Overall, the tannin
206
concentration values measured by the protein precipitation method varied from 0.35 to 1.34
207
g/L (0.66 ± 0.21) and were lower than when measured by HPLC, with an average of 4.18 g/L
208
by HPLC versus 0.66 g/L by protein precipitation (Table 1; Supporting Information Table 1).
209
The two methods were well correlated with a r of 0.84 (p = 0.00). Among the 34 Cabernet
210
Sauvignon wines, matrix variables varied as follows. The ethanol concentration varied from
211
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
212
(5.26 ± 0.40) and the pH varied from 3.61 to 4.02 (3.76 ± 0.11). The fructose concentration
213
varied from 1.00 to 3.85 g/L with an average of 2.16 (± 0.70) and the glucose concentration
214
was much lower, varying from not detectable to 1.75 g/L (0.24 ± 0.62). As observed in the
215
Supporting Information Table 1, the wine containing the highest ethanol concentration had the
216
highest tannin concentration, the lowest titratable acidity and one of the highest pH values.
217
The fructose and the glucose concentrations of this wine were slightly higher than the average
218
(3.16 g/L and 1.16 g/L, respectively). In contrast, the wine with the least ethanol
219
concentration did not contain extreme amounts of other matrix variables.
220
In order to understand the relationships between matrix parameters and tannin activity,
221
a principal component analysis (PCA) was performed across the 34 wines (Figure 1). In total,
222
66.8% of the variance was explained by the first two components (PC1 = 34.5%, PC2 =
223
32.3%). Regarding the loading plot, ethanol concentration was situated quite close to pH and
224
tannin activity but this last parameter was not significantly correlated with any matrix
225
variables. Fructose and glucose were positively correlated (r = 0.88, p = 0.00) and ethanol
226
concentration was negatively correlated with titratable acidity (r = -0.38, p = 0.03) and with
227
pH (r = 0.76, p = 0.00). Tannin concentration was significantly correlated with ethanol
228
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
Page 11 of 29
229
Journal of Agricultural and Food Chemistry
and pH (r = 0.56, p = 0.00).
230 231
Sensory analysis
232
Table 2 shows the attributes and attribute definitions and references that were
233
determined during the descriptive panel. Three-way MANOVA showed significant
234
differences between the wines. ANOVA of the attributes generated in descriptive analysis
235
showed that the outdoors aroma and flavor, vanilla oak aroma and flavor, fresh berry flavor,
236
green pepper flavor, tamarind flavor, sweetness, and the mouthfeel attributes grippy, drying,
237
and viscosity were significant for the wines. The means and least significant difference values
238
for these attributes are shown in Supporting Information Table 2.
239
CVA showed that three dimensions were most appropriate to represent the results
240
from descriptive analysis (Figure 2). The first two dimensions of CVA accounted for 78.7%
241
of the variance ratio (Figure 2a). The third dimension accounted for another 16.3% (Figure
242
2b). The first dimension was characterized on the positive side by fresh berry flavor and sweet
243
taste, and on the negative side by outdoors flavor and aroma and the mouthfeel attributes
244
grippy and drying. The second dimension was characterized by vanilla oak flavor and aroma
245
(negative) and green pepper flavor (positive). The third dimension was characterized by
246
tamarind flavor and the mouthfeel attributes drying and viscosity at the positive end.
247
There was a high level of overlap in the 95% confidence circles, as shown in Figure 2a
248
and 2b, indicating that the panelists did not perceive large differences between these wines.
249
Along the first dimension wines 3 and 13 were the most different, differentiated along this
250
axis by the mouthfeel characteristics of grippy and drying (high for wine 13) and tamarind
251
and fresh berry flavors (higher for wine 3). While wine 3 had the highest rating of sweet taste
252
and highest fructose concentration, these were not significantly higher than the other wines, 11 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 12 of 29
253
and the perceived sweetness in the wines was not correlated to either fructose or glucose
254
concentration in the wines. Along the second dimension, wines were distinguished by green
255
pepper flavor (wines 7 and 8) and vanilla oak flavor (wine 5), though none of these wines
256
were significantly different than the other wines for these attributes (Table 2). Wines 7 and 8
257
were the Australian wines, both from the same producer, which may account for the
258
similarities to one another and for the differences from the other wines in the study.
259
In Figure 2b, there was clear separation of the tamarind and fresh berry flavors along
260
the third dimension, which in Figure 2a appears to overlap. On this plot, it can be seen that
261
wine 3 was significantly higher in the fresh berry flavor than the other wines, while wines 9,
262
10, and 11 were characterized more by the tamarind flavor. Again however, there was not
263
much distinction between most of the wines along the third dimension. The wine that was the
264
most different from the rest of the wines in the study was wine 13. This wine was rated the
265
highest for both mouthfeel characteristics of grippy and drying. This was not unexpected, as
266
the tannin concentration in this wine was the highest of all of the wines, as was the tannin
267
activity. Interestingly however, the perception of drying and grippy was not correlated with
268
the tannin concentration or tannin activity. Instead, perception of drying showed a moderate
269
correlation with ethanol concentration, suggesting that the ethanol concentration was driving
270
the perception of the drying sensation. Overall, the mouthfeel characteristic drying was
271
significantly correlated with grippy (r = 0.66, p = 0.01) and viscosity (r = 0.61, p = 0.03),
272
however grippy and viscosity were not significantly correlated.
273 274
Regressing sensory on chemical variables
275
Figure 3 shows the output of PLSR of sensory attributes on the chemical variables.
276
Collectively, the first three model components account for roughly 80% of the total variance 12 ACS Paragon Plus Environment
Page 13 of 29
Journal of Agricultural and Food Chemistry
277
of the predictor matrix (chemical variables, Component 1: 40.82% , Component 2: 23.12%,
278
Component 3 – not shown: 17.00%). The model did not improve by adding additional
279
components. Overall, the chemical variables predicted the sensory variables moderately well,
280
however only the sensory variables viscosity, grippy, drying (all mouthfeel), outdoors flavor,
281
chemical aroma, and fresh berry flavor were sufficiently explained by the model.
282
Correlations between the sensory and chemical variables were observed. pH and
283
titratable acidity were related to chemical aroma (r = 0.79, p = 0.00 and r = -0.68, p = 0.01,
284
respectively). Titratable acidity was also related to the fresh berry flavor (r = 0.6, p = 0.03).
285
Outdoors aroma was correlated to fructose concentration (r = 0.59, p = 0.03) and tannin
286
activity was related to tamarind flavor (r = 0.57, p = 0.04). The only mouthfeel attribute to be
287
significantly related to any chemical variables was drying, which was related to ethanol
288
concentration (r = 0.60, p = 0.03).
289 290
Discussion
291
In an effort to reduce variability in the samples, we chose to focus on wines, primarily
292
from California of similar vintage. Doing so, we assumed that mouthfeel and taste aspects
293
(bitterness, astringency, sweetness, etc.) of perception would be similarly balanced. In the
294
present study, the relationships between tannin activity and other chemical components
295
related to mouthfeel were first investigated for 34 Cabernet Sauvignon wines produced in
296
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
299
tannins and a hydrophobic surface) did not involve precipitation/aggregation. The astringency
300
mouthfeel has been described as the non-covalent interactions between tannins and salivary 13 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 14 of 29
301
proteins through hydrogen bonds and hydrophobic interactions followed by aggregation and
302
complex precipitation, leading to the drying sensation.41 No significant correlation between
303
tannin activity, matrix variables and with any mouthfeel attributes was observed in this study
304
which could be explained by the chemical method used, where the tannin activity correspond
305
to the first step of interactions (hydrophobic interaction between tannins and a hydrophobic
306
surface) and not necessarily to the protein precipitation that is known to lead to the in-mouth
307
perceptions. Alternatively a more complex set of matrix interactions with tannins, insufficient
308
tannin activity variation or missing analytically-relevant information (e.g.: polysaccharides)
309
may have influenced these results.
310
Red wines contain many grape-derived compounds (e.g., polyphenols, organic acids,
311
sugars) as well as fermentation-derived compounds (e.g. ethanol) that contribute to the
312
mouthfeel characteristics of the wine.18 In contrast to the literature where the ethanol has
313
been previously related to an increase of the bitterness and burning sensation of wines20,42 and
314
to a decrease of astringency intensity,43,44 in this study, the ethanol concentration was well
315
correlated to the pH and to mouthfeel attributes such as grippy, drying and viscosity. In the
316
case of 13 Cabernet Sauvignon mostly from the 2012 vintage and from California, the ethanol
317
concentration was related to an increase in grippy, drying sensation and of the viscosity
318
perception. A principal purpose of wine production is to produce a “balanced” wine, meaning
319
that an increase in tannin concentration would be associated with an increase in ethanol for
320
attenuating the wine astringency that would result from tannin concentration alone. This was
321
shown in the present study, where a positive correlation between ethanol and tannin
322
concentration was observed. The pH has been found to be positively related to tannin
323
concentration and ethanol concentration as well as to mouthfeel attributes. This result is in
324
agreement with Kallithraka et al.24 who showed that a decrease in the pH increased the
325
maximum intensity and the total duration of astringency in model solutions and red wine as 14 ACS Paragon Plus Environment
Page 15 of 29
Journal of Agricultural and Food Chemistry
326
well as with Obreque-Slier et al.45 Similarly, an increase in wine pH has been shown to lead to
327
a decrease in salivary protein precipitation,19 even if in our sensory analysis, pH was not
328
clearly related to any taste or mouthfeel.
329
When present, fructose was found to be the most significant residual sugar in this set
330
of wines. Some researchers have found that an increase in fructose concentration decreased
331
the perceived astringency and decreased salivary protein precipitation,19,46 while others have
332
shown little effect on mouthfeel dryness.42 In our study, the fructose was correlated to glucose
333
in 34 wines and not to any other matrix variables, suggesting that for Cabernet Sauvignon
334
wines, residual sugars are not the main drivers of mouthfeel perception. Also in the
335
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,
337
the perception of sweetness was suppressed, accounting for the lack of correlation between
338
fructose or glucose and sweet taste in the wines.
339
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
Journal of Agricultural and Food Chemistry
Page 16 of 29
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
16 ACS Paragon Plus Environment
Page 17 of 29
371
Journal of Agricultural and Food Chemistry
1.
372 373
Wine Polyphenols. Aust. J. Grape Wine Res. 2001, 7 (1), 33–39. 2.
374 375
3.
4.
J.-M.;
Cheynier,
V.;
Brossaud,
F.;
Moutounet,
M.
Polymeric
Prinz, J. F.; Lucas, P. W. Saliva Tannin Interactions. J. Oral Rehabil. 2000, 27 (11), 991–994.
5.
380 381
Souquet,
Proanthocyanidins from Grape Skins. Phytochemistry 1996, 43 (2), 509–512.
378 379
Prieur, C.; Rigaud, J.; Cheynier, V.; Moutounet, M. Oligomeric and Polymeric Procyanidins from Grape Seeds. Phytochemistry 1994, 36 (3), 781–784.
376 377
Brossaud, F.; Cheynier, V.; Noble, A. C. Bitterness and Astringency of Grape and
Freitas, V. de; Mateus, N. Nephelometric Study of Salivary Protein–tannin Aggregates. J. Sci. Food Agric. 2002, 82 (1), 113–119.
6.
Noble, A. C. Astringency and Bitterness of Flavonoid Phenols. In Chemistry of Taste:
382
Mechanisms, Behaviors, and Mimics; Given, P., Paredes, D., Eds.; Amer Chemical
383
Soc: Washington, 2002; Vol. 825, pp 192–201.
384
7.
385
Bajec, M. R.; Pickering, G. J. Astringency: Mechanisms and Perception. Crit. Rev. Food Sci. Nutr. 2008, 48 (9), 858–875.
386
8.
Noble, A. Bitterness in Wine. Physiol. Behav. 1994, 56 (6), 1251–1255.
387
9.
Vidal, S.; Francis, L.; Guyot, S.; Marnet, N.; Kwiatkowski, M.; Gawel, R.; Cheynier,
388
V.; Waters, E. J. The Mouth-Feel Properties of Grape and Apple Proanthocyanidins in
389
a Wine-like Medium. J. Sci. Food Agric. 2003, 83 (6), 564–573.
390
10.
Quijada-Morín, N.; Regueiro, J.; Simal-Gándara, J.; Tomás, E.; Rivas-Gonzalo, J. C.;
391
Escribano-Bailón, M. T. Relationship between the Sensory-Determined Astringency
392
and the Flavanolic Composition of Red Wines. J. Agric. Food Chem. 2012, 60 (50),
393
12355–12361.
394 395
11.
Chira, K.; Pacella, N.; Jourdes, M.; Teissedre, P.-L. Chemical and Sensory Evaluation of Bordeaux Wines (Cabernet-Sauvignon and Merlot) and Correlation with Wine Age.
17 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
396 397
Page 18 of 29
Food Chem. 2011, 126 (4), 1971–1977. 12.
Quijada-Morín, N.; Williams, P.; Rivas-Gonzalo, J. C.; Doco, T.; Escribano-Bailón, M.
398
T. Polyphenolic, Polysaccharide and Oligosaccharide Composition of Tempranillo Red
399
Wines and Their Relationship with the Perceived Astringency. Food Chem. 2014, 154,
400
44–51.
401
13.
Carvalho, E.; Mateus, N.; Plet, B.; Pianet, I.; Dufourc, E.; De Freitas, V. Influence of
402
Wine Pectic Polysaccharides on the Interactions between Condensed Tannins and
403
Salivary Proteins. J Agric Food Chem 2006, 54 (23), 8936–8944.
404
14.
Watrelot, A. A.; Le Bourvellec, C.; Imberty, A.; Renard, C. M. G. C. Interactions
405
between Pectic Compounds and Procyanidins Are Influenced by Methylation Degree
406
and Chain Length. Biomacromolecules 2013, 14, 709–718.
407
15.
Watrelot, A. A.; Le Bourvellec, C.; Imberty, A.; Renard, C. M. G. C. Neutral Sugar
408
Side Chains of Pectins Limit Interactions with Procyanidins. Carbohydr. Polym. 2014,
409
99, 527–536.
410
16.
Escot, S.; Feuillat, M.; Dulau, L.; Charpentier, C. Release of Polysaccharides by Yeasts
411
and the Influence of Released Polysaccharides on Colour Stability and Wine
412
Astringency. Aust. J. Grape Wine Res. 2001, 7 (3), 153–159.
413
17.
Mazauric, J.-P.; Salmon, J.-M. Interactions between Yeast Lees and Wine Polyphenols
414
during Simulation of Wine Aging: I. Analysis of Remnant Polyphenolic Compounds in
415
the Resulting Wines. J. Agric. Food Chem. 2005, 53 (14), 5647–5653.
416
18.
Conde, C.; Silva, P.; Fontes, N.; Dias, A. C. P.; Tavares, R. M.; Sousa, M. J.; Agasse,
417
A.; Delrot, S.; Geros, H. Biochemical Changes throughout Grape Berry Development
418
and Fruit and Wine Quality. Food. 2007, 1, 1-22.
419 420
19.
Rinaldi, A.; Gambuti, A.; Moio, L. Precipitation of Salivary Proteins After the Interaction with Wine: The Effect of Ethanol, pH, Fructose, and Mannoproteins. J.
18 ACS Paragon Plus Environment
Page 19 of 29
Journal of Agricultural and Food Chemistry
421 422
Food Sci. 2012, 77 (4), C485–C490. 20.
423 424
Fischer, U.; Noble, A. The Effect of Ethanol, Catechin Concentration, and Ph on Sourness and Bitterness of Wine. Am. J. Enol. Vitic. 1994, 45 (1), 6–10.
21.
Vidal, S.; Courcoux, P.; Francis, L.; Kwiatkowski, M.; Gawel, R.; Williams, P.;
425
Waters, E.; Cheynier, V. Use of an Experimental Design Approach for Evaluation of
426
Key Wine Components on Mouth-Feel Perception. Food Qual. Prefer. 2004, 15 (3),
427
209–217.
428
22.
Casassa, L. F.; Larsen, R. C.; Beaver, C. W.; Mireles, M. S.; Keller, M.; Riley, W. R.;
429
Smithyman, R.; Harbertson, J. F. Sensory Impact of Extended Maceration and
430
Regulated Deficit Irrigation on Washington State Cabernet Sauvignon Wines. Am. J.
431
Enol. Vitic. 2013, 64 (4), 505–514.
432
23.
McRae, J. M.; Ziora, Z. M.; Kassara, S.; Cooper, M. A.; Smith, P. A. Ethanol
433
Concentration Influences the Mechanisms of Wine Tannin Interactions with Poly(l-
434
Proline) in Model Wine. J. Agric. Food Chem. 2015, 63 (17), 4345–4352.
435
24.
436 437
Affected by Malic and Lactic Acid. J. Food Sci. 1997, 62 (2), 416–420. 25.
438 439
Kallithraka, S.; Bakker, J.; Clifford, M. N. Red Wine and Model Wine Astringency as
Kallithraka, S.; Bakker, J.; Clifford, M. N. Effect of pH on Astringency in Model Solutions and Wines. J. Agric. Food Chem. 1997, 45 (6), 2211–2216.
26.
Pascal, C.; Poncet-Legrand, C.; Imberty, A.; Gautier, C.; Sarni-Manchado, P.;
440
Cheynier, V.; Vernhet, A. Interactions between a Non Glycosylated Human Proline-
441
Rich Protein and Flavan-3-Ols Are Affected by Protein Concentration and
442
Polyphenol/Protein Ratio. J. Agric. Food Chem. 2007, 55 (12), 4895–4901.
443
27.
Poncet-Legrand, C.; Edelmann, A.; Putaux, J.-L.; Cartalade, D.; Sarni-Manchado, P.;
444
Vernhet, A. Poly(l-Proline) Interactions with Flavan-3-ols Units: Influence of the
445
Molecular Structure and the Polyphenol/Protein Ratio. Food Hydrocoll. 2006, 20 (5),
19 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
446 447
687–697. 28.
448 449
Page 20 of 29
Barak, J. A.; Kennedy, J. A. HPLC Retention Thermodynamics of Grape and Wine Tannins. J. Agric. Food Chem. 2013, 61, 4270–4277.
29.
Revelette, M. R.; Barak, J. A.; Kennedy, J. A. High-Performance Liquid
450
Chromatography Determination of Red Wine Tannin Stickiness. J. Agric. Food Chem.
451
2014, 62, 6626–6631.
452
30.
Harbertson, J. F.; Kennedy, J. A.; Adams, D. O. Tannin in Skins and Seeds of Cabernet
453
Sauvignon, Syrah, and Pinot Noir Berries during Ripening. Am. J. Enol. Vitic. 2002, 53
454
(1), 54–59.
455
31.
Kennedy, J. A.; Ferrier, J.; Harbertson, J. F.; Gachons, C. P. des. Analysis of Tannins in
456
Red Wine Using Multiple Methods: Correlation with Perceived Astringency. Am. J.
457
Enol. Vitic. 2006, 57 (4), 481–485.
458
32.
Gawel, R.; Oberholster, A.; Leigh Francis, I. A “Mouth-Feel Wheel”:terminology for
459
Communicating the Mouth-Feel Characteristics of Red Wine. Aust. J. Grape Wine Res.
460
2000, 6, 203–207.
461
33.
Gay, C. Invitation to Comment. Food Qual. Prefer. 1998, 9 (3), 166.
462
34.
Chatfield, C.; Collins, A. J. Introduction to Multivariate Analysis; Springer US: Boston,
463 464
MA, 1980. 35.
465
R core Team. R: A Language and Environment for Statistical Computing.; R Foundation for Statistical Computing: Vienna, Austria, 2014.
466
36.
Husson, F.; Le, S.; Cadoret, M. Sensory Data Analysis with R; 2014.
467
37.
Fox, J.; Weisberg, S. An {R} Companion to Applied Regression, Second Edition.;
468
Thousand Oaks CA: Sage, 2016.
469
38.
de Mendiburu, F. Statistical Procedures for Agricultural Research; 2016.
470
39.
Friendly, M.; Fox, J. Visualizing Generalized Canonical Discriminant and Canonical
20 ACS Paragon Plus Environment
Page 21 of 29
Journal of Agricultural and Food Chemistry
471 472
Correlation Analysis; 2016. 40.
473 474
Mevik, B.-H.; Wehrens, R.; Liland, K. H. Partial Least Squares and Principal Component Regression; 2015.
41.
McRae, J. M.; Falconer, R. J.; Kennedy, J. A. Thermodynamics of Grape and Wine
475
Tannin Interaction with Polyproline: Implications for Red Wine Astringency. J. Agric.
476
Food Chem. 2010, 58 (23), 12510–12518.
477
42.
Villamor, R. R.; Evans, M. A.; Ross, C. F. Effects of Ethanol, Tannin, and Fructose
478
Concentrations on Sensory Properties of Model Red Wines. Am. J. Enol. Vitic. 2013,
479
64 (3), 342–348.
480
43.
Vidal, S.; Francis, L.; Noble, A.; Kwiatkowski, M.; Cheynier, V.; Waters, E. Taste and
481
Mouth-Feel Properties of Different Types of Tannin-like Polyphenolic Compounds and
482
Anthocyanins in Wine. Anal. Chim. Acta 2004, 513 (1), 57–65.
483
44.
Fontoin, H.; Saucier, C.; Teissedre, P.-L.; Glories, Y. Effect of pH, Ethanol and Acidity
484
on Astringency and Bitterness of Grape Seed Tannin Oligomers in Model Wine
485
Solution. Food Qual. Prefer. 2008, 19 (3), 286–291.
486
45.
Obreque-Slier, E.; Peña-Neira, Á.; López-Solís, R. Interactions of Enological Tannins
487
with the Protein Fraction of Saliva and Astringency Perception Are Affected by pH.
488
LWT - Food Sci. Technol. 2012, 45 (1), 88–93.
489
46.
Symoneaux, R.; Chollet, S.; Bauduin, R.; Le Quéré, J. M.; Baron, A. Impact of Apple
490
Procyanidins on Sensory Perception in Model Cider (Part 2): Degree of Polymerization
491
and Interactions with the Matrix Components. LWT - Food Sci. Technol. 2014, 57 (1),
492
28–34.
493
47.
Green, B. G.; Lim, J.; Osterhoff, F.; Blacher, K.; Nachtigal, D. Taste Mixture
494
Interactions: Suppression, Additivity, and the Predominance of Sweetness. Physiol.
495
Behav. 2010, 101 (5), 731–737.
21 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
496
48.
497 498
Kroeze, J. H. A.; Bartoshuk, L. M. Bitterness Suppression as Revealed by Split-Tongue Taste Stimulation in Humans. Physiol. Behav. 1985, 35 (5), 779–783.
49.
499 500
Page 22 of 29
McBurney, D. H.; Bartoshuk, L. M. Interactions between Stimuli with Different Taste Qualities. Physiol. Behav. 1973, 10 (6), 1101–1106.
50.
Ares, G.; Barreiro, C.; Deliza, R.; Gámbaro, A. Alternatives to Reduce the Bitterness,
501
Astringency and Characteristic Flavour of Antioxidant Extracts. Food Res. Int. 2009,
502
42 (7), 871–878.
503
51.
Jung, D.-M.; de Ropp, J. S.; Ebeler, S. E. Study of Interactions between Food Phenolics
504
and Aromatic Flavors Using One- and Two-Dimensional 1H NMR Spectroscopy. J.
505
Agric. Food Chem. 2000, 48 (2), 407–412.
506
52.
Goldner, M. C.; di Leo Lira, P.; van Baren, C.; Bandoni, A. Influence of Polyphenol
507
Levels on the Perception of Aroma in Vitis Vinifera Cv. Malbec Wine. South Afr. J.
508
Enol. Vitic. 2011, 32 (1), 21–27.
509
53.
510 511
Symoneaux, R.; Guichard, H.; Le Quere, JM.; Baron, A.; Chollet, S. Could cider aroma modify cider mouthfeel properties? Food Qual. Prefer. 2015, 45, 11-17.
54.
Sáenz-Navajas, M.-P.; Campo, E.; Avizcuri, J. M.; Valentin, D.; Fernández-Zurbano,
512
P.; Ferreira, V. Contribution of Non-Volatile and Aroma Fractions to in-Mouth Sensory
513
Properties of Red Wines: Wine Reconstitution Strategies and Sensory Sorting Task.
514
Anal. Chim. Acta 2012, 732, 64–72.
22 ACS Paragon Plus Environment
Page 23 of 29
Journal of Agricultural and Food Chemistry
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.
23 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 24 of 29
Figure 1.
Figure 2. a)
b) 24 ACS Paragon Plus Environment
Page 25 of 29
Journal of Agricultural and Food Chemistry
Figure 3.
25 ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Page 26 of 29
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
ACS Paragon Plus Environment
Page 27 of 29
Journal of Agricultural and Food Chemistry
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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
Warm/hot
Grippy
Drying
Viscosity
Page 28 of 29
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.
28 ACS Paragon Plus Environment
Page 29 of 29
Journal of Agricultural and Food Chemistry
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
29 ACS Paragon Plus Environment