27 July 2021 Original Citation:
Effect of viticulture practices on concentration of polyphenolic compounds and total antioxidant capacity of Southern Italy red wines
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DOI:10.1016/j.foodchem.2013.11.142 Terms of use:
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6Food Chemistry
, 2014, DOI 10.1016/j.foodchem.2013.11.142,
7Antonio Coletta, Silvia Berto, Pasquale Crupi, Maria Carla Cravero, Pasquale
8Tamborra, Donato Antonacci, Pier Giuseppe Daniele, Enrico Prenesti, 152,
9Elsevier, 2014, pagg.467-474
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14 15 16 17 18 19 20 21Effect of viticulture practices on concentration of polyphenolic
22compounds and Total Antioxidant Capacity of Southern Italy red
23wines
2425
Antonio COLETTAc, Silvia BERTOa, Pasquale CRUPIc, Maria Carla CRAVEROb, Pasquale 26
TAMBORRAc, Donato ANTONACCIc, Pier Giuseppe DANIELEa and Enrico PRENESTIa* 27
28
(a)
Università di Torino, Dipartimento di Chimica, Via Pietro Giuria 7, I-10125, Torino, Italy 29
(b)
Consiglio per la Ricerca e la sperimentazione in Agricoltura – Centro di ricerca per l’Enologia 30
(CRA-ENO), Via Pietro Micca 35, 14100, Asti, Italy 31
(c)
Consiglio per la Ricerca e la sperimentazione in Agricoltura - Unità di ricerca per l’uva da tavola 32
e la vitivinicoltura in ambiente mediterraneo (CRA-UTV), Via Casamassima 148, 70010 Turi (BA), 33
Italy 34
35
36
* Corresponding author: Enrico Prenesti 37
Università di Torino, Dipartimento di Chimica, Via Pietro Giuria 7, I-10125, Torino, Italy 38
e-mail address: enrico.prenesti@unito.it
39 phone: +39 011 670 5261 40 fax: +39 011 670 5242 41 42
ABSTRACT
43
44
This study aims to assess the effect of three wine grape varieties, three training systems and 45
two bud loads on the Total Antioxidant Capacity (TAC) and polyphenolic composition of 46
Southern Italy red wines produced, during two vintages. Primitivo, Malvasia nera of Brindisi-47
Lecce and Montepulciano as grape varieties, single Guyot (SG), single spur pruned low 48
cordon (SLC) and single spur pruned high wire cordon (HSLC) as training systems, 8 and 12 49
buds/plant as bud loads were compared. Significant differences in polyphenolic families are 50
shown by the grape varieties and modifying the vine growing practices. Moreover, results 51
demonstrated that varieties influence the TAC (indicating the Malvasia as the more effective 52
one), that SLC leads to the lowest level of TAC and, that 8 buds/plant increases it. The 53
relationship between antioxidant indexes and the concentration of single polyphenolic 54
families was evaluated finding the highest correlation degrees with total polyphenols and 55
proanthocyanidins family. 56
57
Keywords: Total Antioxidant Capacity, TEAC, BRAI, red grape, wine, grape variety,
58
polyphenolic compounds, training system, bud load. 59
60
61
1.
Introduction
63
64
Polyphenolic compounds, as anthocyanins, flavonols, or flavan-3-ols, play an important role 65
in determining the quality of wine, particularly contributing to the sensory characteristics as 66
color and astringency (Arnold, Noble & Singleton, 1980). Moreover, they are recognized to 67
be the main responsible of the beneficial health effects of wine, since they can directly protect 68
the cardiovascular system against the effect of the free radicals produced by human aerobic 69
metabolism (Virgili & Contestabile, 2000; Halpern, Dahlgren, Laakso, Seppanen-Laakso, 70
Dahlgren & McAnulty, 1998). 71
Apart from technological tools, such as winemaking process (Ricardo-da-Silva, Rosec, 72
Bourzeix, Mourgues & Moutounet, 1992; Spranger et al., 2004) or aging and storage 73
conditions of wine (Pérez-Magariño & González-San José, 2004; Sun et al., 2011; Tsanova-74
Savova, Dimov & Ribarova, 2002), polyphenolic compounds content in wine can be mainly 75
influenced by grape variety and berry ripening degree (Pérez-Magariño et al., 2004; Ricardo-76
da-Silva et al., 1992), together with vine growing methods and related training systems 77
(Jackson & Lombard, 1993; Pérez-Lamela, García-Falcón, Simal-Gándara, Orriols-78
Fernández, 2007; Pérez-Magariño et al., 2004; Peterlunger, Celotti, Da Dalt, Stefanelli, 79
Gollino & Zironi, 2002). The effect of the grape ripening degree on wine color and flavanols 80
and anthocyanins level has been assessed (Pérez-Magariño et al., 2004) as well as the 81
importance of training system on Pinot Noir grape and related wine composition. Particularly, 82
anthocyanins content was studied (Peterlunger et al., 2002). However, few researches have 83
been carried out concerning the relationships among the polyphenolic compounds 84
concentration, the Total Antioxidant Capacity (henceforth: TAC) of wine and the viticulture 85
practices. Therefore, this study is aimed to evaluate the relationship between the vine growing 86
practices − namely, training system and bud load level − and both the polyphenolic 87
composition and the TAC of three Southern Italy red wines, Primitivo, Malvasia nera of 88
Brindisi-Lecce (henceforth: Malvasia B/L) and Montepulciano, in two consecutive vintages 89
(2005 and 2006). The effect of the viticultural practices was evaluated also on yield 90
components and on grape quality. 91
The polyphenolic compounds were determined by specific spectrophotometric analysis, 92
which are very widespread, due to their simplicity and low cost, to compare the wine 93
polyphenolic changes related to environmental conditions or specific chemical or agrarian 94
treatments or aging (Tabart, Kevers, Pincemail, Defraigne & Dommes, 2010; Baiano, 95
Terracone, Gambacorta & La Notte, 2009; Ough & Amerine, 1988). As for the redox 96
capacity, the TAC was evaluated by way of BRAI (Briggs-Rauscher Antioxidant Index) and 97
TEAC (Trolox Equivalent Antioxidant Capacity) methods. The first one uses the reaction of 98
polyphenolic molecules with the small hydroperoxyl radical HOO (Prenesti, Toso & Berto, 99
2005), whereas TEAC is based on the reduction of a bulky cationic radical, ABTS+ (2,2’-100
azinobis-(3-ethylbenzthiazoline-6-sulfate)) (Prior, Wu & Schaich, 2005). 101
102
2. Materials and methods
103 104
2.1 Viticultural practices
105 106 2.1.1 Plant material 107The research was carried out in 2005 and 2006 in a vineyard located in the same farm on a flat country 108
(60 m above sea level), in the rural area of Brindisi, Apulia region, Italy (Latitude 40°37'43"32 N; 109
Longitude 17°56'15"36). 110
The “1103 Paulsen” rootstock (Vitis berlandieri × V. Rupestris) was planted in 2002 and, one year 111
later, V. vinifera L. Primitivo (Zinfandel), a medium - ripening red wine grape, (3th decay of August - 112
1st decay of September) Malvasia nera of Brindisi-Lecce (Malvasia Br/Le), and Montepulciano, two 113
medium-late ripening red wine grape (2nd-3th decay of September) were grafted on it. The plant 114
density was 6.250 vines ha-1 and the vine spacing was 2.0 m between rows and 0.8 m within each row. 115
The rows were oriented NE-SW. The site was characterized by clay-sandy soil (sand 49.7%, clay 116
16.4% and silt 34%) deep and very homogeneous, with a 0 - 1% slope. The organic matter content was 117
1.3%. 118
Two bud-loads - 8 buds/plant (8b/p) and 12 buds/plant (12 b/p) - and three training systems - single 119
Guyot (SG), single spur pruned low cordon (SLC) and single spur pruned high wire cordon (HSLC), 120
with cordon wire height at 150 cm - were imposed. The vines were cane pruned in SG and spur pruned 121
in both, SLC and HSLC. Varieties, bud loads and training systems were arranged as a split plot design 122
with three replications. The variety represented the main plots (Primitivo, Malvasia B/L, 123
Montepulciano; three randomized levels); training system (SG, SLC and HSLC; three randomized 124
levels) represented the subplots; and bud load (8b/p and 12b/p; two randomized levels) represented the 125
sub-subplots. 126
Each of the three replications consisted of 36 rows of 180 vines. The main plot (variety) included 12 127
rows of 240 vines each, whereas the subplot (training system) was made by 4 rows of 120 vines each 128
and sub-subplot (bud-load) was 4 rows of 60 vines each. Only the central 50 vines of the middle two 129
rows for each subplot were used to collect the experimental data. In the area a Mediterranean climate 130
prevails, with an annual rainfall of about 650 mm, so water irrigation is necessary mainly from June to 131
September. Irrigation is required because rain generally falls in the dormant phase of the growing 132
season during which the water soil profile is not sufficient to supply the vineyard evapotranspiration. 133
Vineyards were irrigated according to a controlled water deficit (CWD), which counterbalanced about 134
24% of crop evapotranspiration. Starting 10 days after the end of veraison (at 100% of berry coloring) 135
and until harvest, all vineyards were irrigated 4 times and the interval between irrigation cycle was 136
approximately 15 days. A volume of 150, 200, 150, 100 m3 ha-1 of water in the scheduled irrigation 137
was applied on 12th and 31st July, 15th August, and 10th September in 2005 whereas on 20th July, 5th 138
and 20th August, and 15th September on 2006. Primitivo on 19th and 25th September was harvested in 139
2005 and 2006, respectively. Both, Malvasia and Montepulciano, on 7th and 12th October were 140
harvested in 2005 and 2006, respectively. Grapes were harvested at commercial maturity and during 141
each harvest time, grapes coming from the training systems and the bud loads were harvested together. 142
143
2.1.2 Plant water status 144
From the middle of July to the harvest time the midday stem water potential (ψs) was measured in the 145
two years of study (2005 and 2006) at the same stages (pre and post irrigations), and mean values are 146
reported in Table 1. The measures were performed on two mature and non-transpiring leaves per vine, 147
that had been bagged with plastic sheets inserted into aluminum foil at least 1 h before measurement. 148
The bagging used to prevent the leaf transpiration, equaling the leaf water potential to the stem water 149
potential (Begg & Turner, 1970). Leaves were then detached and their ψs was measured immediately 150
in the field by a model 600-pressure chamber instrument (PMS Instrument Company, Albany, Oregon, 151
USA). The measurements of stem ψ were collected during the steady period of the water potential 152
diurnal curve (from 11.00 am to 14.00 pm). 153
154
2.1.3 Yield and fruit components. 155
Yield (kg vine-1) was determined at harvest averaging five vines yield per sub-subplot at harvesting. 156
The mean cluster weight was calculated on 30 clusters (10 clusters per replicate, sampled from 10 157
different vines). The average berry weight was determined on 150 berries (50 berries per replicate). 158
then the berries were hand-crushed, and the juice was collected and centrifuged at 1556 g for 5 min at 159
20°C, to measure soluble solids by a portable refractometer (Atago PR32, Norfolk, Virginia, U.S.A.). 160
Titratable acidity (expressed as g/L of tartaric acid) was analyzed by titration according to the EEC 161
Regulation 2676/90. The juice pH was also measured. In winter, the pruning weight of five vines for 162
each replicate was determined and the Ravaz Index (Kliewer & Dokoozlian, 2005; Ravaz, 1903) and 163
the yield – pruning weight ratio was calculated. 164
165
2.1.4 Sampling and winemaking 166
Grapes were harvested and processed separately in the same wine cellar. For each treatment, about 50 167
kg of grapes were destemmed/crushed and collected in stainless steel 50 L tanks. Before starting the 168
fermentation, the crushed grapes were supplied with potassium metabisulphite, 6 g/100 kg (equivalent 169
to 30 mg/kg of total SO2) and dry yeasts, Activeflore C F33 (Laffort, Floirac, France), 20 g/100 kg. 170
During the alcoholic fermentation, two daily gentle punching down of the cap of the pulp into the 171
fermenting juice were made. After running-off and light pressure of the marcs, the wine was 172
inoculated (25 g/100 L) by a commercial strain of Oenococcus oeni, Uvaferm alpha (Lallemand 173
oenologie, France), and placed at 20 °C. After the malolactic fermentation, the wine was decanted and 174
a second aliquot of potassium metabisulphite (equivalent to 30 mg/kg of total SO2) was added. Finally, 175
after a second decantation performed in March-April the wine was bottled in 1 liter glass bottles with a 176
crown cap and stored in dark condition at room temperature. For each vintage, the analyzes were 177
conducted within a year, starting from the sixth month after bottling. 178 179
2.2 Analytical procedures
180 181 182 2.2.1 Chemicals 183Folin-Ciocalteu reagent, hydrogen peroxide (30 % w/w), malonic acid (purity grade >99%), 184
gallic acid (purity grade >98%), sodium pyrosulfite (purity grade 98%) and ethanol 185
(analytical grade 99.8%) were purchased from Fluka (ordered by Sigma Aldrich, Milan, 186
Italy), and Na2CO3 (purity >99%) was from Merk (Darmstadt, Germany). Ethanol 96%, 187
methanol 99%, hydrochloric acid 37%, sulfuric acid 95%-97%, o-vanillin (purity 99%), (+)-188
catechin monohydrate (purity 98.5%), malvidin-3-glucoside chloride (purity >90%), cyanidin 189
chloride (purity >95%), iron(II) sulfate heptahydrate (purity >99.0%), ABTS, 2,2’-azinobis-190
(3-ethylbenzthiazoline-sulfonic acid), diammonium salt (purity grade >98%) and Trolox, 6-191
hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (purity grade >98%) were Sigma-192
Aldrich products (Milan, Italy). Potassium periodate (purity grade 99.7-100.4%) and starch 193
were from Riedel-de Haën (ordered by Sigma Aldrich, Milan, Italy). Manganese sulfate 194
monohydrate was from Carlo Erba Reagents (Milan, Italy). 195
196
2.2.2 Spectrophotometric measurements of polyphenolic compounds
197
Phenolic compounds in wine were determined by means of a Jasco V-550 Uv-vis double-198
beam spectrophotometer (quartz, optical path length 1.000 cm; JASCO International Co., 199
Ltd., Tokyo, Japan). 200
The Folin–Ciocalteu method (Ough et al., 1988) was used for the analysis of the total 201
polyphenolic compounds (TPP) here expressed as mg/L of gallic acid equivalent. The 202
determination of the total flavonoids (TF), total anthocyanins (TA), flavonoids without 203
anthocyanins (FNA) and proanthocyanidins (PrA) were realized following the methods of Di 204
Stefano, Cravero and Gentilini (1989). The content of TF and FNA is expressed as (+)-205
catechin equivalents (mg/L), the TA are expressed as malvidin-3-glucoside equivalents 206
(mg/L) and the PrA as cyanidin chloride (mg/L). The flavan-3-ols (F) were quantified as 207
described by Baiano et al. (2009)and are expressed as (+)-catechin equivalents (mg/L). Other 208
experimental details are reported in the Supplementary Material file. 209
210
2.2.3 BRAI measurement
The Briggs-Rauscher (BR) oscillating reaction was used to measure the total antioxidant 212
ability of each wine; the BR test environment is prepared mixing the reagents as fully 213
described (Prenesti et al., 2005 and ref. therein). The oscillating regime was 214
potentiometrically monitored using a combined platinum electrode (Metrohm mod. 215
6.0402.100 (LE)) and a Metrohm automatic computer-assisted potentiometric apparatus 216
(Basic Titrino 794). The reaction temperature was controlled (25°C) by means of a thermo 217
cryostat (model DI-G Haake). When antioxidant free-radical scavengers (i.e. wine samples) 218
were added to an oscillating BR mixture, a break of the oscillating regime occurred, and the 219
corresponding so-called “inhibition time” (tinhib) varied linearly with their concentration. 220
The values of tinhib of reference standard solution (gallic acid) and wine samples were plotted 221
versus the mass of gallic acid, for the calibration, or the volume of wine injected in the BR 222
mixture, respectively, obtaining linear trends. The experimental points were fitted with 223
straight-lines equations. BRAI (Briggs-Rauscher Antioxidant Index) was expressed as the 224
ratio of the slopes of the straight-line equations obtained for each sample, slopesample = 225
tinhib./mLsample, and for the standard molecule, slopestandard = tinhib./mggallic acid. The ratio of slopes 226
thus obtained, mggallic acid/mLsample, is finally referred to 100 ml of beverage (Prenesti et al., 227 2005). 228 229 230 231 2.2.4 TEAC measurement 232
As previously described (De Beer, Joubert, Gelderblom & Manley, 2003), the ABTS+
radical 233
solution was prepared, every six days, by ABTS oxidation with peroxodisulfate ion whilst the 234
Trolox stock solution was prepared in ethanol. Two mL of ABTS+ solution and 20 µL sample 235
or Trolox standard solution were added into the spectrophotometric cell. As the reducing 236
compounds, contained in both the standard and wines, turn the ABTS+
solution from intense 237
blue to colorless, the absorbance decrease at 734 nm after 4 minutes of reaction at room 238
temperature was recorded. The values of A734 were plotted versus the mass of Trolox, for the 239
calibration, or the volume of wine, obtaining linear trends. The experimental points were 240
fitted with straight-lines equations. TEAC was expressed as the ratio of the slopes of the 241
straight-line equations obtained for each sample, slopesample = A734/µLsample, and for the 242
standard molecule, slopestandard = A734./µgTrolox (Greco, 2013). The ratio of slopes thus obtained, 243
µgTrolox/µLsample, is finally referred to 100 mL of beverage. This analytical procedure allows to 244
compare TEAC values obtained from different wine dilutions and to check the linearity of the 245 analytical response. 246 247
2.3 Statistical analysis
248Analysis of variance (ANOVA) was made using a three-way model to separate the effect of 249
variety, training system and bud-load and their significant interactions. F test was used to 250
compare the averages within the bud-load factor. The Fisher LSD multiple range test was 251
used to compare the means within variety and training system and to compare the significant 252
interactive effects. The correlations and the regression analyses between the TAC estimated 253
according to the two methods and the polyphenolic compounds, as well as ANOVA and 254
Fisher LSD test, were performed using the software Statistica ver. 6.1 (Statsoft, Tulsa, UK). 255
256
3. Results and discussion
257
258
3.1 Yield components and fruit composition
259Yield components and qualitative parameters of grapes, as affected by variety, training system and 261
bud-load, and mediated along the two vintages (2005 and 2006), are reported in Table 2. A significant 262
influence of the variety was found; Montepulciano and Primitivo grapes showed high level of total 263
acidity and yield component together with the consequent lower soluble solids content. Conversely, 264
Malvasia Br/Le was characterized by the highest concentration of sugars and lower total acidity. 265
Considering that Malvasia vine had water stress index (ψs = -1.14) higher than that of Primitivo and 266
Montepulciano (ψs = -1.24 and -1.29, respectively), this finding seems in agreement with Reynolds, 267
Lowrey, Tomek, Hakimi & De Savigny (2007), who observed an increment of sugar content in grapes 268
of an Ontario Chardonnay vineyard characterized by the decrease of ψs during its growth cycle. 269
Furthermore, Malvasia variety showed the lowest Ravaz Index value to highlight less capability than 270
Primitivo and Montepulciano in producing the maximum yield per canopy weight. 271
Several studies about the training system effect on yield and composition of grapes have been 272
conducted with opposite results depending on the agronomic and pedoclimatic conditions. No 273
differences on yield and soluble solids in grapes of Barbera trained on various trellis systems were 274
found (Bernizzoni, Gatti, Civardi & Poni, 2009); conversely, the training system significantly 275
modified the yield, vine size, and canopy density of Pinot Noir and Traminette, although it only 276
slightly influenced the berry composition, such as soluble solids, pH, titratable acidity and 277
monoterpenes (Peterlunger et al., 2002; Bordelon, Skinkis & Howard, 2008). It is worth noting that in 278
this study a very significant (P < 0.01) impact of the training system factor on yield, bunch weight, 279
pruning weight and Ravaz Index was revealed. Single spur pruned low cordon (SLC) allowed to 280
produce the highest quantity of grape and the highest bunch weight without exceeding in canopy 281
growth, respect to single armed Guyot (SG). The small canopy development registered in high trellis 282
(HSLC) with the highest yield/pruning weight ratio is to be pointed out; this behavior should be 283
probably related to the canopy architecture and to the height of the cluster zone (which could have 284
favored, as sink, the bunches to the canopy) rather than to the high level of ψs (-0.95 and -1.39) 285
registered in the first and second period of the growth cycle, respectively. 286
Finally, no significant influence on any measured parameters was determined by the bud-load factor. 287
288
3.2 Relation between polyphenolic compounds and viticultural practices
289290
The influence of variety, training system and bud-load, together with their interactions, on the 291
polyphenolic compounds content are reported in Tables 3. Very significant differences in polyphenolic 292
families are shown by the variety and training system factors: the highest amounts of compounds was 293
generally quantified in Malvasia Br/Le and Montepulciano or HSLC and SG trellis system. In 294
agreement with the literature data (Esteban, Villanueva & Lissarague., 2001; Crupi et al., 2012), a 295
genetic dependence of total anthocyanins was evident in this research, because the highest levels of 296
these compounds were found in Malvasia Br/Le (361 ± 48) and Montepulciano (382 ± 56), in the two 297
vintages (2005, 2006), irrespective to ψs values (Table 1), training system or bud-load, as confirmed 298
by the absence of significant interactions among the three factors (Table 3). 299
On the contrary, the water stress and viticultural practices seemed to play a more decisive role on the 300
other polyphenolic compounds. 301
Indeed, the highest values of TPP (2987 ± 155), TF (2286 ± 135), FNA (1788 ± 135), F (1453 ± 137) 302
and PrA (2947 ± 157) were registered in Malvasia trained on the HSLC system, which were 303
characterized by mild water stress condition (ψs = -1.30) in the late ripening period. According to the 304
literature (Dokoozlian & Kliewer, 1996; Figueiredo-González, Cancho-Grande, Boso, Santiago, 305
Martínez & Simal-Gándara, 2013), mild ψs values can cause a lower canopy density, with a 306
consequent cluster exposition to sunlight, resulting in an improvement of total polyphenols 307
concentration. 308
Excepting for total polyphenols, no significant effect by the bud-load factor was observed; on the 309
contrary, a positive interaction between variety and bud-load was observed, with polyphenolic 310
compounds concentration higher in 8 b/p than in 12 b/p, both in Primitivo and Montepulciano (Table 311
3). 312
313
3.3 Relation between TAC and viticultural practices
314315
Table 3 reports the effect of variety, training system and bud-load on the TAC of wines. 316
Significant differences in the TAC among varieties, training systems and bud load were 317
found, except in the case of the BRAI index in the training system factor. This is probably 318
caused by the high dispersion of the BRAI values due to the difference between the results 319
from the two bud-load levels of the same training system. Both the antioxidant capacity 320
parameters result sensitive to the bud-load, whereas the bud-load variance component did not 321
determine any significant effect on single polyphenolic families. This apparent discrepancy 322
can be explained observing that for the single family the 8 b/p gives higher values for the 323
most of samples but these increments are small and, very probably, with no statistical 324
significance. If all polyphenolic compounds (TPP parameter) are taken into account, as in the 325
case of antioxidant indexes, the small increments become significant, moreover it could exists 326
a synergic effect between polyphenolic molecules, as described in previous studies 327
(Kirakosyan et al., 2010). The more marked differences in varieties were highlighted. The 328
varieties showed two different levels of TAC with BRAI test and three different levels with 329
the TEAC one. Therefore, a dependence of the amount and the antioxidant capacity of 330
polyphenolic compounds from the training system is stated, but its extent is influenced by the 331
variety. This is in accordance with observation of Jackson and Lombard (1993): the quality of 332
wines produced from grape of Cabernet Sauvignon cultivated with different training systems 333
is similar, whereas the color and the aroma of Pinot Noir depend upon the vine growing 334
practices. In particular, for the Pinot Noir differences in polyphenolic compounds content 335
were observed varying the training system and an increased content of these molecules with 336
the light exposure of berries. Different results were also obtained for Pinot Noir in the Friuli 337
(a region in the North-East of Italy) hills (Peterlunger et al., 2002). In this case, the influence 338
of the training system on the composition of grapes and on wine quality resulted minimal, 339
nevertheless the relevance of the light exposure was confirmed. 340
The interactions between the factors, reported in Figure 2 and in Table S1 of the Supplementary 341
Material file, show that there is a sensitivity to bud load, in particular for Primitivo, but there is no 342
interaction between variety and training system on both BRAI or TEAC values. 343
The wines derived from SG and HSLC training systems show higher values of TAC with 344
respect to the wines obtained from SLC method. The relation between each antioxidant index 345
and various concentration values were evaluated by the Pearson test (Table 4). Particularly, a 346
strong correlation between TEAC and TPP, or PrA, or F was found, showing r of 0.957, 0.938 347
and 0.896 respectively. Conversely, BRAI index showed a weaker significant correlation with 348
the same polyphenol families, never exceeding 0.763 which was the highest Pearson’ 349
coefficient value registered between BRAI and PrA. These results could be linked to the 350
difference in the reaction mechanism involved in BRAI and TEAC methods, as reported in 351
the Introduction. Figure 1 presents the results for the fit of the regression model where each 352
index refers to the most related polyphenolic families and to the total polyphenol content. 353
These results confirm the direct relation between the TAC and the total amount of 354
polyphenolic compounds and, in particular, the TAC seems to be more strictly related to PrA 355 family. 356 357
4. Conclusions
358 359The results obtained evidenced that: 360
i) the grape varieties, the training system and the bud load can affect the polyphenolic 361
compounds concentration of the wine and, therefore, the TAC, 362
ii) the grape varieties show different sensitivity to the type of viticultural practice. 363
The training systems and pruning level that provide higher polyphenolic compounds contents and, 364
consequently, higher TAC values were identified. Moreover, training system induces significant 365
differences also in all the polyphenolic families changing the quantitative ratios between them, which 366
are strictly related to the sensory characteristics of wine. These information could be taken into 367
account in both viticultural and oenological fields in order to affect the concentration, the type of 368
polyphenolic substances and the related redox behavior. 369
370
Acknowledgements
371
The authors thank the Italian Ministry of Agriculture, Food and Forestal Policy for financial support 372
“Progetto per il miglioramento qualitativo delle produzioni vitivinicole e dell’uva da tavola nel 373
mezzogiorno d’Italia” (Vitivin-Valut). 374
Moreover, a specific thank goes to Mr. Sabino Roccotelli and Mr. Vincenzo Catalano for their very 375
efficient collaborative work. 376
377
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FIGURES
a 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 TPP (mg /L) 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 T E A C ( m g t ro lo x / 1 0 0 m l) y = 5,138 + 0,1623*x; p = 0.000 r2 = 0,9162 0,95 Conf.Int b 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 PrA (mg/L) 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 T E A C ( m g t ro lo x / 1 0 0 m l) y = 106,0929 + 0,1271*x; p = 0.000 r2 = 0,8800 0,95 Conf.Int c 700 800 900 1000 1100 1200 1300 1400 1500 1600 F (mg /L) 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 T E A C ( m g t ro lo x / 1 0 0 m l) y = 88,2095 + 0,3007*x; p = 0,000; r2 = 0,8038; 0,95 Conf.Int d 180 200 220 240 260 280 300 320 340 360 380 400 420 440 TA (mg /L) 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 T E A C ( m g t ro lo x / 1 0 0 m l) y = 216,8551 + 0,6482*x; p = 0,005; r2 = 0,3913 0,95 Conf.Int e 1400 1600 1800 2000 2200 2400 2600 2800 TF (mg /L) 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 T E A C ( m g t ro lo x / 1 0 0 m l) y = 130,2503 + 0,1529*x; p = 0,005; r2 = 0,3986; 0,95 Conf.Int f 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 TPP (mg/L) 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200 B R A I ( m g g a ll ic a c id / 1 0 0 m l) y = -324,0652 + 1,2091*x; r2 = 0,582 p = 0,000; 0,95 Conf.Int g 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 PrA (mg/L) 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200 B R A I ( m g g a ll ic a c id / 1 0 0 m l) y = 368,5239 + 0,9696*x; r2 = 0,586; p = 0,000 0,95 Conf.Int h 700 800 900 1000 1100 1200 1300 1400 1500 1600 F (mg/L) 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200 B R A I ( m g g a ll ic a c id / 1 0 0 m l) y = 288,4359 + 2,2461*x; r2 = 0,513; p = 0,000; 0,95 Conf.Int I 180 200 220 240 260 280 300 320 340 360 380 400 420 440 TA (mg/L) 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000 4200 B R A I ( m g g a ll ic a c id / 1 0 0 m l) y = 526,0828 + 6,9653*x; r2 = 0,517; p = 0,000; 0,95 Conf.IntFigure 1 Variations of TAC in relation to TPP (a), PrA (b), F (c), TA (d), TF (e) concentration for
a HSLC SLC SG Trellis system 2200 2400 2600 2800 3000 3200 3400 3600 B R A I ( m g g a ll ic a c id / 1 0 0 m l)
Bud load 12 buds/plant 8 buds/plant
b
Primitivo Montepulciano Malvasia Br/Le
Variety 1600 1800 2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 B R A I ( m g g a ll ic a c id / 1 0 0 m l)
Bud load 12 buds/plant 8 buds/plant c
Primitivo Montepulciano Malvasia Br/Le
Variety 280 300 320 340 360 380 400 420 440 460 480 500 520 540 T E A C ( m g t ro lo x / 1 0 0 m l)
Bud load 12 buds/plant 8 buds/plant
d Variety: Primitivo T re ll is s y s te m : H S L C S L C S G 1000 1500 2000 2500 3000 3500 4000 4500 5000 B R A I (m g g a ll ic a c id / 1 0 0 m l) Variety: Montepulciano T re ll is s y s te m : H S L C S L C S G
Variety: Malvasia Br/Le
T re ll is s y s te m : H S L C S L C S G
Bud load 12 buds/plant 8 buds/plant e
Variety: Primitivo T re ll is s y s te m : H S L C S L C S G 200 250 300 350 400 450 500 550 600 T E A C ( m g t ro lo x / 1 0 0 m l) Variety: Montepulciano T re ll is s y s te m : H S L C S L C S G
Variety: Malvasia Br/Le
T re ll is s y s te m : H S L C S L C S G
Bud load 12 buds/plant 8 buds/plant
Figure 2 Significant interactions between bud load and trellis system on BRAI (a), between
bud load and variety on BRAI (b) and TEAC (c), between bud load, variety and trellis system on BRAI (d) and TEAC (e). Vertical bars denote 0.95 confidence intervals.
TABLES
Table 1 Midday stem (ψs) water potential values of vines during the 2005 and 2006 vintages,
as affected by variety, training system and bud-load. For each stage the two years average value is shown. Irrigation stage 12th July - 5th August ( 1st - 2nd irrigation) 15th August - 15th September (3rd- 4th irrigation)
Pre – irrigation 6 - days
after irrigation Pre – irrigation
6 - days after irrigation Variety Primitivo -1.11a ±0.16b -0.84 ±0.14 -1.61 ±0.24 -1.24 ±0.21 Malvasia Br/Le -0.98 ±0.18 -0.78 ±0.14 -1.31 ±0.25 -1.14 ±0.21 Montepulciano -0.97 ±0.11 -0.85 ±0.10 -1.52 ±0.22 -1.29 ±0.10 Training systemc HSLC -0.95 ±0.20 -0.89 ±0.21 -1.36 ±0.27 -1.25 ±0.24 SLC -1.13 ±0.08 -0.96 ±0.15 -1.68 ±0.26 -1.32 ±0.20 SG -1.07 ±0.13 -0.81 ±0.12 -1.43±0.12 -1.27 ±0.27 Bud load 12 buds/plant -1.18 ±0.24 -0.90 ±0.28 -1.53 ±0.24 -1.40 ±0.18 8 buds/plant -1.08 ±0.18 -0.75 ±0.27 -1.51 ±0.22 -1.38 ±0.23 a
Means of three replicates;
b
Standard deviation at p ≤ 0.05
c
Table 2 Influence of variety, training system and bud-load on the yield components and the main qualitative characteristics of grapes. Factors Yield (kg/vine) s.d. Bunch weight (g) s.d. Berry weight (g) s.d. Soluble solids (Brix) s.d. pH s.d. Titratable aciditya s.d. Pruning weight (kg) s.d. Ravaz Index s.d. Variety Primitivo 1.67b ac 0.56d 106.96 27.07 0.86 0.15 21.63 b 1.78 3.35 b 0.12 6.94 a 0.86 0.49 ab 0.18 3.71 ab 2.02 Malvasia Br/Le 1.13 b 0.53 89.91 59.09 0.82 0.15 24.63 a 1.21 3.56 a 0.13 4.71 c 0.36 0.51 a 0.21 2.54 b 1.46 Montepulciano 1.54 ab 0.46 111.51 30.52 0.90 0.11 21.00 b 0.84 3.24 c 0.12 5.71 b 0.34 0.37 b 0.018 5.56 a 4.26 Significance ** n.s. n.s. * ** *** * ** Traning systeme HSLC 1.39 ab 0.40 75.80 b 42.81 0.85 0.15 21.73 2.01 3.38 0.19 5.97 1.05 0.31 b 0.13 5.60 a 4.15 SLC 1.78 a 0.57 130.26 a 37.89 0.91 0.15 22.65 2.28 3.41 0.18 5.87 1.28 0.55 a 0.21 3.82 ab 2.35 SG 1.18 b 0.55 102.34 ab 24.33 0.81 0.11 22.88 1.82 3.37 0.19 5.52 0.86 0.52 a 0.18 2.40 b 1.09 Significance ** *** n.s n.s n.s n.s. *** *** Bud-load 12 buds/plant 1.58 0.62 99.24 24.40 0.84 0.15 21.90 2.24 3.38 0.19 5.57 0.87 0.50 0.22 3.75 2.23 8 buds/plant 1.32 0.48 106.36 54.12 0.87 0.12 22.94 1.77 3.39 0.17 6.01 1.23 0.42 0.19 4.14 3.76 Significance n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. a
expressed as g/L of tartaric acid
b
Means of three replicates;
c
Different letters in the same line are significantly different at 5% level (Tukey HSD test);
d
Standard deviation at p ≤ 0.05;
e
HSLC = single spur pruned high wire cordon; SLC = single spur pruned low cordon; SG = single Guyot. * = P<0.05, ** = P<0.01, *** = P<0.001, n.s. = not significant.
Table 3. Total polyphenols (TPP), total flavonoids (TF), flavonoids without anthocyanins (FNA), anthocyanins (TA), flavans (F),
proanthocyanidins (PrA), BRAI and TEAC antioxidant indexes in wines as affected by variety, training system and bud-load.
a
Values followed by the same letters within columns did not differ significantly at P < 0.05, using the Fisher LSD multiple range test. Not significant interactions are not listed.
b
HSLC: single spur pruned high wire cordon; SLC: single spur pruned low cordon SG: single armed Guyot;
c
For the interactions of BRAI and TEAC see Figure 1 and Supplementary Material file. * = P<0.05, ** = P<0.01, *** = P<0.001, n.s. = not significant.
Factors TPP s.d. TF s.d. FNA s.d. TA s.d F s.d. PrA s.d. BRAI s.d. TEAC s.d.
Variety
Primitivo 2273 ba ±325 1931 b ±381 1526 ab ±311 278 b ±69 996 b ±174 2067 b ±340 2305 b ±401 368 c ±48
Malvasia Br/Le 2958 a ±168 2204 a ±157 1678 a ±162 361a ±48 1331 a ±169 2991 a ±208 3272 a ±537 496 a ±26
Montepulciano 2765 a ±168 1896 b ±153 1302 b ±155 382 a ±56 1158 ab ±179 2770 a ±275 3119 a ±410 448 b ±43 Significance *** ** *** **. ** *** *** *** Traning systemb HSLC 2758 a ±430 2019 a ±254 1542 a ±242 302 b ±70 1261 a ±229 2685 a ±434 2929 ±671 455 a ±68 SLC 2507 b ±433 1879 b ±271 1371 b ±213 348 ab ±77 1072 b ±215 2359 b ±539 2825 ±694 408 b ±75 SG 2731 ab ±256 2134 a ±284 1594 a ±299 371 a ±56 1153 ab ±181 2784 a ±380 2941 ±515 450 a ±48 Significance ** * * * n.s. *** n.s. ** Bud load 12 buds/plant 2566 ±424 1985 ±293 1471 ±254 335 ±129 1110 ±232 2601 ±547 2795 ±740 425 ±75 8 buds/plant 2764 ±335 2036 ±281 1533 ±280 345 ±126 1214 ±196 2618 ±419 3003 ±458 450 ±56 Significance ** n.s. n.s. n.s. n.s. n.s. * * Interactionsc Variety × T. system * ** * n.s. * n.s.
Variety × Bud load n.s. * n.s. n.s. * *
T. system × Bud load n.s. n.s. n.s. n.s. n.s. n.s.
Table 4 Correlation analysis of TEAC and BRAI indexes with TPP (Total polyphenols); TF (Total
flavonoids); FNA (Flavonoids without anthocyanins); F (Flavan-3-ols); PrA (Proanthocyanidins); TA (Total anthocyanins).
TPP TF FNA TA F PrA
TEAC 0.957195 0.631335 0.452540 0.625571 0.896576 0.938092
BRAI 0.763103 0.461356 0.197713 0.719223 0.716563 0.765939
