The grapevine (Vitis vinifera L.), used for the production of wine, table grapes, and raisins, is one of the most widely grown fruit species worldwide. According to the Interna-tional Organization of Vine and Wine statistics, grape pro-duction for fresh consumption has been increasing. Grapes, a nonclimacteric fruit, are highly perishable without appro-priate postharvest storage. Observed quality losses mainly arise from weight loss, color changes, alterations in flavor, and accelerated softening (Mencarelli et al. 1994, Ergun et al. 2008).
Consumer acceptability of table grapes, as of all fruits in general, is highly dependent on maturity level, and harvest-ing at the correct time is essential. Visual attributes and chemical-physical characteristics are involved in the sensory evaluation of table grapes (Zeppa et al. 1999) and raisins (Angulo et al. 2007). Many instrumental measurements are correlated with consumer preferences and can be used as predictors of consumer acceptability (Abbott 1999). Among these, soluble solids concentration (SSC), acid content, SSC/acid ratio (Jayasena and Cameron 2008), and skin color are
important quality parameters in table grapes (Dokoozlian et al. 1995), although nutritive and functional properties in-creasingly influence consumer choice (Valero et al. 2006). In this context, grapes are also important dietary sources of phenols (catechins, flavonol glycosides, phenolic acid, antho-cyanins), although resveratrol concentration in table grapes is generally low.
For fresh consumption, texture is an important factor de-termining the quality of table grapes. Berry firmness is con-sidered a measurement of its freshness (Vargas et al. 2001). Sensory attributes such as skin friability, skin thickness, and flesh firmness have been proposed to characterize commer-cial table grape cultivars (Cliff et al. 1996). Given their high correlations with the mechanical parameters determined by texture analysis, these sensory descriptors can be also ac-quired by laboratory instruments under controlled conditions (Sato et al. 1997, Le Moigne et al. 2008), making them more objective and stable than sensory tests, which may fluctuate depending on the test-operators.
Among mechanical properties, particular attention has focused on pulp. Crispness is the most desirable texture for table use, and cultivars with a crisp flesh texture are impor-tant genetic materials for table-grape breeding. A puncture test conducted with a f lat probe was used to measure the crispness of the pulp as small deformation at first major peak (DFP ≤ 2.5 mm) and large maximum force (MF ≥ 0.9N) in the force-deformation curve of the test (Sato and Yamada 2003). Recently, the texture profile analysis (TPA) test has been proposed to study the shelf life of table grape and the hardness, springiness, cohesiveness, and chewiness param-eters to define the pulp texture characteristics (Deng et al. 2005).
Comparative Study of Texture Properties, Color Characteristics, and Chemical Composition
of Ten White Table-Grape Varieties
Luca Rolle,1 Simone Giacosa,1 Vincenzo Gerbi,1 and Vittorino Novello2*
Abstract: The texture, color, and chemical characteristics of 10 white table-grape varieties were compared: Delizia del Vaprio, Matilde, Moscato di Terracina, Pansé precoce, Pizzutello bianco, Regina, Regina dei vigneti, Sublima seedless, Sultanina (or Thompson Seedless), and Vincere. Consumer acceptability of table grapes depends on such factors as visual attributes, chemical constituents, nutritive values, mechanical properties, and, obviously, sensory attributes, which are affected by the cultivar. Notable differences among the studied varieties were found in CIRG (color index of red grapes), solid soluble content, malic acid, hydroxycinnamic acid, and the amount of total poly-phenols. However, the mechanical parameters of berries, as determined by texture profile analysis, and berry skin characteristics, evaluated by puncture testing, were the most important properties for the differentiation of varieties. In particular, PCA analysis indicated that hardness, gumminess, chewiness, break skin force, break skin energy, and Young’s modulus of elasticity of skin were the best indices with which to characterize the varieties. These results may help viticulture and postharvest professionals recognize the behavior of each variety and thus better respond to the consumer market.
Nevertheless, there are few descriptions of the physical-mechanical parameters of berry skins of table grapes and of whole berries in the literature, and only few reports are available on varietal differences of the mechanical proper-ties of grape texture. The aim of this study is therefore to characterize and to differentiate 10 white table-grape variet-ies growing in Italy, investigating their mechanical behavior, color characteristics, and chemical composition, including phenolic content.
Materials and MethodsGrapes and sampling. The study was carried out in 2008
on 10 white table-grape cultivars (Vitis vinifera L.): Delizia del Vaprio, Matilde, Moscato di Terracina, Pansé precoce, Pizzutello bianco, Regina, Regina dei vigneti, Sublima seed-less, Sultanina (Thompson Seedless), and Vincere. All were present in the Turin collection-experimental University vine-yard located at 300 m a.s.l. in Alessandria province (Pied-mont, Italy). All grape varieties had similar characteristics. Ten-year-old vines, grafted onto 196-17 Castel rootstock, were planted 1.2 x 1.8 m apart, vertical shoot-positioned, and cane pruned. Technological parameters of ripeness were monitored at a regular interval of 5 days from 19 Aug–23 Sept 2008. Grapes of each variety were collected when three consecutive samplings (10 days) showed no increase in sugar content. At harvest, each sample consisted of 25 bunches picked random-ly from 10 different vines. After transfer to the laboratory, a set of 500 berries was randomly sampled from different parts of the bunches. The weight of each sample of 30 berries (BW) and of their skins (SkW) was determined using a technical balance (Gibertini E1700, Modena, Italy). The measurements of grape mechanical properties were made on subsamples of 30 berries for each texture test (Maury et al. 2009), taken from the 500 berry samples. Other subsamples of 30 berries from each of the varieties were used for color determination. Thirty berries, three replicates of 10 berries, were taken for acid and phenolic composition analysis. The must of the re-maining berries was used for total soluble solids content, pH, and titratable acidity determinations.
Texture analysis. The texture tests, puncture test, com-pression test, and texture profile analysis (TPA) were per-formed on the same day as picking to avoid changes. Before the test, the berries, arranged as a single layer, were ther-mally conditioned for 1 hour at 20°C, in a thermostatically controlled chamber. For the appraisal of grape mechanical properties, different tests were carried out.
The measurements were performed using a Universal Testing Machine TAxT2i Texture Analyzer (Stable Micro Systems, Godalming, Surrey, UK) equipped with a HDP/90 platform and a 5 kg load cell. All acquisitions were carried out at 400 hz using Texture Expert Exceed software ver. 2.54 (Stable Micro Systems) working in the MS Windows envi-ronment.
Skin hardness was evaluated by puncture testing using a SMS P/2 needle probe (Stable Micro Systems) and a speed test of 1 mm s-1. The berries were punctured in the lateral face and three parameters were measured: berry skin break
force (N, as Fsk), berry skin break energy (N, as Wsk), and the Young’s modulus of elasticity of the skin (N/mm, as Esk)(Letaief et al. 2008b).
For the measurement of berry skin thickness (µm, as Spsk), a piece of skin of ~0.25 cm2 was removed from the lateral side of all berries with a razor blade. Care was taken when removing the pulp from the skin and when positioning the skin sample on the HDP/90 platform to prevent folds. After instrumental probe position calibration, the skin thickness was calculated as the distance between the point corresponding to the probe contact with the berry skin (trigger) and the plat-form base during a compression test. Further, the insertion of an instrumental trigger threshold of 0.05 N enabled the plane surface of the probe to adhere completely to the skin sample before acquisition began. It allowed a reduction or even elimination of the “tail” effect due to the postponement of the contact point (Letaief et al. 2008a, Rio Segade et al. 2008).
For the TPA test, each whole berry was compressed in the equatorial position by an SMS P/35 flat probe (Stable Micro Systems) under a deformation of the berry of 25% with a waiting time between the two bites of 2 sec using 1 mm s-1 as speed test (Letaief et al. 2008a). Typical texture parameters were determined and calculated by the software: hardness (N, as P1), cohesiveness (adimensional, as (A2+A2W)/(A1+A1W)), gumminess (N, as hardness* cohesiveness), springiness (mm, as d2), chewiness (mJ, as gumminess*springiness), and resil-ience (adimensional, as (A1w/A1)) (Deng et al. 2005, Letaief et al. 2008a, Rio Segade et al. 2011). Berry width was calculated as the distance between the whole berry trigger point and the platform base.
Color measurement and indexes. Before color analysis, the bloom was removed from the skin using paper tissue. Three measurements were made around the equatorial belt of each berry. The color values, L*, a*, b*, C (chroma value), and h (hue angle) were determined (CIE 1986) with a CR-410 Chroma Meter (Minolta Corp., Osaka, Japan) reflectance spectrophotometer. Standard illuminant D65 was used as re-ference. Two indexes for each variety were calculated: CIRG (color index of red grapes) = (180-H) / (L*+C) and CIRG2 = (180-H) / (L*xC) proposed as colorimetric index for table grapes (Carreno et al. 1995).
Chemical analyses. Reagents and standards. Solvents of HPLC-gradient grade and all other chemicals of analyti-cal reagent grade were purchased from Sigma (Milan, Italy). Phenol standards (caffeic acid, (+)-catechin) were supplied from Extrasynthèse (Genay, France).
Technological parameters of ripeness. Total soluble solids concentration (Brix, as SSC) was measured using an Atago 0–32 Brix temperature compensating refractometer (Atago Co., Tokyo, Japan), and pH was determined by potentiom-etry using a Crison electrode (Carpi, Italy). Titratable acidity (TA), expressed as g L-1 tartaric acid, was estimated using the official European Union method. Citric, tartaric, and malic acid (g kg berries-1) were analyzed using an HPLC system (P100-AS3000; Thermo Electron Corporation, Waltham, MA) equipped with a UV detector (UV3000) set to 210 nm. The analyses were performed isocratically at 0.8 mL/min flow and
65°C column temperature, with a 300 x 7.8 mm i.d. Aminex HPX-87H cation exchange column and a Cation H+ Micro-guard cartridge (Bio-Rad Laboratories, Hercules, CA), using 0.0013 mol/L H2SO4 as mobile phase (Schneider et al. 1987). Data treatment was carried out using the ChromQuest chro-matography data system (ThermoQuest, Inc, San Jose, CA).
Phenol extraction and analysis. Berry skins were removed manually from the pulp using a laboratory spatula, dried with absorbent paper, and quickly immersed in 25 mL hydroal-coholic buffer at pH 3.2, containing 2 g/L Na2S2O5 and 12% ethanol. The pulp was collected in a beaker containing 50 mg Na2S2O5.
Skin and pulp were subsequently homogenized with an Ultraturrax T25 (IKA Labortechnik, Staufen, Germany) and centrifuged for 5 min at 3000 x g at 20°C. The supernatant was then used for analysis. Spectrophotometric methods were used to evaluate the total polyphenols (TP) (mg(+)-catechin kg-1 grape, as TP) in berry skin and pulp and total hydroxy-cinnamic acids (HCA) (mg caffeic acid kg-1 grape, as HCA) in the pulp (Di Stefano and Cravero 1991). The relative standard deviation (RSD) based on repeated analysis (n = 12) of the same sample was 1.58% for TP and 1.82% for HCA.
Statistical analysis. Analysis of variance was applied to all the variables studied. The mean values obtained in the different categories were compared by one-way ANOVA and significant differences among means at p < 0.01 were determined by Tukey’s test. Analysis of variance and prin-cipal component analysis (PCA) were performed using the statistical software package SPSS (version 17.0; SPSS Inc., Chicago, IL).
Results and DiscussionOn the basis of the OIV descriptor list for grape varieties
and Vitis species (OIV 2009), ampelographic characteristics are traditionally used to describe and differentiate grapevine cultivars. On the other hand, agronomical and chemical-phys-ical parameters are generally used as the main criteria for evaluation of their productive aptitudes. However, many of these characteristics are strongly influenced by environmental conditions (Sato et al. 2000, Le Moigne et al. 2008). There-fore, to minimize the influence of growing locations on the measured parameters, this comparative study used only table grapes that were grown in the same vineyard. Furthermore, in order not to induce modifications of the chemical-physical fruit characteristics in a diversified manner among the culti-vars, for all varieties plant-growth regulators were not used, although their use is common practice in table grape produc-tion (Sato et al. 2004, Peppi et al. 2006).
Texture properties. Typical curves obtained by several texture tests on table grapes shown (Figure 1). The timescale on the x-axis can be converted into deformation knowing the test speed. In accordance with data reported in the literature, all table-grape force-time curves showed the same trends as those observed for winegrapes (Maury et al. 2009, Letaief et al. 2008a). The skin mechanical values of the table grapes (Table 1) did not indicate differences when compared to the hardness and thickness data, acquired in earlier work using
the same methods on winegrapes (Letaief et al. 2008a, Rio Segade et al. 2008). In contrast, there were many differences among the table grape. The hardest berry skin was associated with Delizia del Vaprio with average values of 0.560 N for Fsk and 0.303 mJ for Wsk. The lower values in both parameters were found in Sultanina grapes skin: 0.411 N (Fsk) and 0.142 mJ (Wsk). Despite the high variability of Spsk data, there were many differences in skin thickness among the table grapes. This characteristic may influence the texture desirability of grapes, and, in those varieties with thick skins, if not associ-ated with a high skin friability, would limit their commercial acceptance (Cliff et al. 1996), in particular for those varieties also characterized by a high SkW/BW ratio. On the other hand, skin thickness and toughness are factors that contribute to the resistance of table grape to fungal pathogens (Rosen-quist and Morrison 1988) and to injury during harvest, pack-ing, transport, and storage (Kök and Çelik 2004). Regina (266 µm), Sublima seedless (264 µm), and Pizzutello Bianco (260 µm) showed higher Spsk values. Only Regina showed evidence of a high hardness and no correlations were found between
Figure 1 Typical force-time (deformation) curves corresponding to texture profile analysis (A), to puncture test for the assessment of berry skin hard-ness (B), and to compression test for berry skin thickness evaluation (C) of table grapes. (Fsk, berry skin break force; Wsk, berry skin break energy; Esk, Young’s modulus of skin; Spsk, berry skin thickness.)
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Fsk and Spsk parameters, in accordance with an earlier report (Rolle et al. 2008).
On the basis of these results, the skin mechanical prop-erties (particularly Fsk and Spsk) of table grapes were good parameters for differentiating varieties. Hence, they might be used to advantage as varietal markers, since these parameters are also little influenced by the ripening stage of the grape, as has been demonstrated for winegrapes (Torchio et al. 2010).
Texture characteristics determined with the TPA test allow the different cultivars to be discriminated. In fact, signifi-cant varietal differences were found (Table 2). Based on the texture properties of each berry, the varieties can be classi-fied into several groups. In this double compression test, the influence of the pulp and skin properties on berriy mechani-cal characteristics is aggregated (Grotte et al. 2001). Sublima seedless (11.707 N) had the hardest berries while Pansè pre-coce (6.867 N) had the softest. Sultanina was characterized by berries with low gumminess (3.668 N) and springiness (2.197 mm). Higher values of chewiness were found in Vincere ber-ries (26.017 mJ). However, cohesiveness, strength of internal bonds comprising the berry body, and resilience, a dimen-
sionless parameter that represents how well a berry fights to regain its original position after the first compression, are the optimal texture parameters to characterize table-grape variet-ies because they are independent of fruit size. The varieties showed differences in berry weight and in berry width val-ues, although the texture behavior can only be partly imputed to berry size. Cohesiveness is inversely correlated with the sensory quality descriptors elasticity, touch resistance, and firmness (Le Moigne et al. 2008). Matilde (0.499) and Sub-lima seedless (0.500) varieties had lower values of cohesive-ness, while Regina (0.617) and Regina dei vigneti (0.592) had higher values. Resilience was also higher in Regina (0.342) and lower in Sublima seedless (0.273) and Moscato di Ter-racina (0.274).
With respect to skin texture, the instrumental TPA param-eters of the whole berry evolve during ripening (Vargas et al. 2001) and the change continues fairly intensely throughout postharvest in relation to the various techniques of preser-vation and packaging (Mencarelli et al. 1994). In particular, the cohesiveness value increases during grape ripeness (Le Moigne et al. 2008) and decreases in the postharvest period
Table 1 Skin weight and mechanical properties of the berry skins determined by puncture and compression tests of the white table grapes studied; average value ± standard deviation (n = 30).
p value < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
aFsk, berry skin break force; Wsk, berry skin break energy; Esk, Young’s modulus of skin; Spsk, berry skin thickness; SkW, skin weight; BW, berry weight.*Different letters within the same column mean significant differences according to Tukey test (p ≤ 0.01).
Table 2 Berry weight, berry width, and texture profile analysis parameters of white table grapes studied; average value ± standard deviation (n = 30).
p value < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
*Different letters within the same column mean significant differences according to Tukey test (p ≤ 0.01).
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(Deng et al. 2005). The softening of the grape during matura-tion is a result of significant changes in the cell-wall constitu-ent composition, particularly at the cellular level of the pulp. This change during ripening has been measured instrumental-ly with a compression test and expressed using the rheological parameter fermeté (g mm-1; Force Max/respective displace-ment) (Robin et al. 1997). Therefore, both this parameter and cohesiveness can be used as grape ripeness predictors (Rio Segade et al. 2011).
Although some mechanical properties between table grape and winegrape showed similar values and behaviors during ripening, analysis by texture profile parameters hardness, co-hesiveness, gumminess, and chewiness enables discrimination of the two types of grapes (Letaief et al. 2008a). The flesh texture of table grapes is harder than that of winegrapes, as demonstrated previously by puncture testing of the pulp (Sato and Yamada 2003). Although histological studies during grape pericarp development are reported in the literature (Hardie et al. 1996), further specific work is required to explain the relationship between mechanical behavior and tissue charac-teristics.
Color parameters. Table-grape CIELAB parameters and the calculated colorimetric indexes highlight the relevant dif-
ferences among the studied varieties (Table 3). L*, a*, b*, hue angle (H), and chroma (C) values are in agreement with data reported in the literature for grapes with visual color “yellow” (Carreno et al.1995). The CIRG index differed between 1.20 for Sultanina and 1.61 for Moscato di Terracina. These two varieties have also the lowest (0.10) and highest (0.18) value of CIRG2 index, respectively.
In particular, for white table grapes, the use of the color indexes allowed us to distinguish varieties that are classified in the same group “green-yellow,” based on the OIV code 225 color, as reported in the OIV descriptor list for table vine varieties and Vitis species (OIV 2009). Further, it would be of considerable commercial interest to describe the color of a table grape by means of a standardized and optimized index (a single numerical value), as external color is a characteristic strongly influencing consumer acceptability (Cliff et al. 1996, Zeppa et al. 1999).
Chemical analyses. The soluble solids concentration rep-resented as Brix ranged from 14.9 for Matilde to 22.0 for Delizia del Vaprio. Insufficient sugar was found in Vincere (12.6), although, on the basis of the low acid value present, the ripening process can be considered complete (Table 4). In accordance with recent research (Jayasena and Cameron
Table 3 CIE L*, a*, b* parameters, and color indexes of white table grapes; average value ± standard deviation (n = 30).
*Different letters within the same column mean significant differences according to Tukey test (p ≤ 0.01).
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2008), consumer acceptance increased with the increase in Brix from 16 to 20, whereas grapes with Brix values >20 never achieved a higher consumer acceptance than those with only 20 Brix. Delizia del Vaprio was the only cultivar that showed a sugar content >20 Brix, while three cultivars were characterized by SSC values <16 Brix: Matilde (14.9), Sublima seedless (15.5), and Moscato di Terracina (15.7). Ac-cording to OIV resolution VITI 1/2008 (OIV 2008), white table grapes are considered to be ripened with SSC ≥ 16 Brix. Moscato di Terracina reached almost 16 Brix. For SSC < 16 Brix and > 12.5 Brix, table grapes are considered ripe when the SSC (expressed as g L-1)/TA (expressed as g L-1 tartaric acid) ratio is >20 (OIV 2008); both Matilde and Sublima seed-less had SSC/TA ratios >20.
In our comparative study, TA ranged from 4.5 g L-1 for Pizzuttello bianco to 9.3 g L-1 for Regina and Moscato di Terracina, not considering the higher value of Sultanina grapes (14.5). No significant correlation (r2 = 0.22; p = 0.17) was found between SSC and TA but rather between TA and pH (r2 = 0.77; p = 0.001). Citric acid concentration showed smaller differences among the varieties. Only Sultanina was differentiated by citric acid, with 0.26 g kg berries-1. Malic acid ranged from 0.70 g kg berries-1 for Vincere to 4.15 g kg berries-1 for Moscato di Terracina. Lower variability was found in the tartaric acid values, which ranged from 1.21 g kg berries-1 for Regina dei vigneti to 2.39 g kg berries-1 for Sultanina.
Sensory testing on Crimson seedless (Jayasena and Cam-eron 2008) demonstrated that acidity showed a negative cor-relation with the degree of consumer satisfaction and that SSC/TA ratio is a better predictor of the sensory attributes sweetness, sourness, and flavor than Brix or acidity alone. For the studied varieties, this index ranged from 1.68 to 2.92, not considering the higher value (4.24) of Pizzuttello bianco and the lower value (1.34) of Sultanina. Interestingly, these two varieties are characterized by the same level of sugars.
However, although SSC, TA, and sugar-acid balance in the table grape are important quality criteria of consumer accep-tance, it is not possible to define a universally valid table-grape quality standard because consumer behavior varies from country to country. This was clearly demonstrated for
Table 5 Total polyphenols (TP) and hydroxycinnamic acid (HCA) of white table grapes; average value ± standard deviation (n = 3).
TP skin (mg (+)-catechin kg-1 berries)
TP flesh (mg (+)-catechin kg-1 berries)
HCA flesh (mg caffeic acid kg-1 berries)
Delizia del Vaprio 612 ± 54c * 105 ± 9c 39 ± 2de
Matilde 877 ± 127bc 197 ± 24ab 85 ± 7a
Moscato di Terracina 663 ± 50c 119 ± 17c 65 ± 5b
Pansè precoce 2052 ± 508a 127 ± 30c 45 ± 1cd
Pizzutello bianco 1293 ± 410b 214 ± 11a 38 ± 4ef
Regina 1012 ± 17bc 170 ± 10b 49 ± 4c
Regina dei vigneti 1038 ± 85bc 122 ± 7c 40 ± 2de
Sublima seedless 623 ± 63c 124 ± 17c 31 ± 3f
Sultanina 1180 ± 100bc 128 ± 11c 34 ± 1ef
Vincere 665 ± 114c 133 ± 5c 37 ± 2ef
p value 0.0002 <0.0001 <0.0001
*Different letters within the same column mean significant differences according to Tukey test (p ≤ 0.01).
Redglobe table grapes where consumers in China were found to be more sensitive to acidity than to SSC/TA ratio (Crisosto and Crisosto 2002).
Figure 2 Two-dimensional plot of the two first principal components in PCA of the cultivars studied: DE, Delizia del Vaprio; MA, Matilde; MT, Moscato di Terracina; PP, Pansè Precoce; PB, Pizzuttello bianco; RE, Regina; RV, Regina dei vigneti; SU, Sublima; ST, Sultanina; VI, Vincere. Fsk, berry skin break force; Wsk, berry skin break energy; Esk, Young’s modulus of skin.
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Table 6 Loadings of selected parameters in the first three principal components.
There were many differences in the phenolic composition of berry skin and flesh of the white table (Table 5). In berry skin, total polyphenol (TP) values ranged from 612 to 2052 mg (+)-catechin kg-1 of grape for Delizia del Vaprio and Pansè precoce, respectively. However, there were fewer differences in TP of pulp within the cultivars. Pizzutello bianco was char-acterized by the highest TP in flesh (214 mg (+)-catechin kg-1 of grape). In general, the varieties with higher TP in the skin were distinguished by lower TP in the flesh. The TP skin/TP flesh ratios were higher in the varieties Pansè precoce (16.15), Sultanina (9.21), and Regina dei vigneti (8.50). Lower ratios were found in Matilde (4.45). The pulp of this variety contained the highest hydroxycinnamic acid (HCA): 85 mg caffeic acid kg-1 berries and Moscato di Terracina contained 65 mg caffeic acid kg-1 berries. All other varieties had HCA values closer to 60, in agreement with those reported in the literature (Artés-Hernández et al. 2007).
Finally, principal components analysis (PCA) was per-formed to better understand the differences among grapes according to cultivar and chemical, physical, and mechanical parameters (Figure 2). From the loadings of selected vari-ables (Table 6), PC1 (41.20 % total variance) was most highly correlated with TPA parameters, with gumminess as the best representative. Skin mechanical properties, in particular Fsk and Wsk, dominated in PC2 (33.05 % total variance), and col-or index CIRG correlated with PC3 (15.06 % total variance).
ConclusionThe different characteristics of the 10 table-grape cultivars
studied can be explained on the basis of texture behavior, color indexes and sugars, acids, and phenolic composition. There were notable differences among the varieties in each of the considered parameters, confirming their importance in the characterization of the variety and in the assessment of potential consumer acceptability. In particular, the cultivars demonstrated different reactions to the stress applied; thus, berry skin and pulp mechanical properties can be appropriate to explain varietal differences and to allow their differentia-tion. In agreement with the PCA results, the TPA parameters (hardness, gumminess, chewiness) and berry skin charac-teristics (break skin force, break skin energy, modulus of elasticy) were the indices best able to fulfill this aim.
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