Interactive effects of the rootstock and the deficit irrigation technique on wine composition, nutraceutical potential, aromatic profile, and sensory attributes under semiarid and water limiting conditions

Agricultural Water Management 225 (2019) 105733

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Interactive effects of the rootstock and the deficit irrigation technique on wine composition, nutraceutical potential, aromatic profile, and sensory attributes under semiarid and water limiting conditions

T

Pascual Romeroa, , Pablo Botíaa, Francisco M. del Amorc, Rocío Gil-Muñozb, Pilar Floresd, Josefa María Navarroa ⁎

a

Equipo de Riego y Fisiología del Estrés, Departamento de Bioeconomía, Agua y Medio Ambiente, Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA), c/ Mayor s/n, 30150, La Alberca, Murcia, Spain Equipo de Enología y Viticultura, Departamento de Desarrollo Rural, Enología y Agricultura Sostenible, Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA), c/ Mayor s/n, 30150, La Alberca, Murcia, Spain c Equipo de Horticultura, Departamento de Producción Vegetal y Agrotecnología, IMIDA, Spain d Equipo de Sostenibilidad y Calidad Hortofrutícola, Departamento de Desarrollo Rural, Enología y Agricultura Sostenible, IMIDA, Spain b

ARTICLE INFO

ABSTRACT

Keywords: Aromatic compounds Partial root zone drying irrigation Polyphenolics Regulated deficit irrigation Rootstocks Sensory attributes Wine composition

Field-grown mature Monastrell grapevines grafted on five different rootstocks (140Ru, 1103 P, 41B, 110R, 16149C) were subjected to regulated deficit irrigation (RDI) and partial root zone irrigation (PRI) in a semiarid region in SE Spain (D.O. Bullas, Region of Murcia). The main goal was to analyse the effects of the rootstock (R), irrigation method (IM), and their interaction (R x IM) on the final wine composition, volatile aromatic profile, and wine sensory attributes. The application of low annual water volumes (85–90 mm year−1) to low vigorous rootstocks (161-49C, 110R) was reflected in wines with higher contents of polyphenolics and alcohol, a higher wine quality index (QIwine), enhanced levels of health-promoting bioactive compounds (flavonols, malvidins), and better organoleptic perception compared to other rootstocks. These wines also had lower concentration of aromatic compounds (alcohols and esters). The 140Ru wines, although having a lower polyphenolic concentration and worse color, were among those rated most highly and preferred by the tasters. These wines had a high content of lactic acid and amino acids, higher tartaric/malic and anthocyanins/tannins ratios and a low concentration of aromatic compounds. In contrast, 1103 P and 41B wines had lower polyphenolic content-nutraceutical value, lower QIwine, tartaric/malic and anthocyanins/tannins ratios, more aromatic compounds, abundant green-vegetable/astringent notes, and more defect-causing compounds. In addition, these wines were also the worst rated in the sensory analysis. Significant positive correlations between the polyphenolic content and alcoholic degree and the score in the wine sensory analysis indicated that the greater the polyphenolic and alcohol contents in the wine, the better valued and more preferred by the tasters it was. PRI method improved wine quality and organoleptic perception for low vigor rootstocks (especially 161-49C), compared to RDI. These wines showed darker color, higher sugar content and nutraceutical potential as well as better sensory perception compared to other rootstock-IM combinations. In contrast, for high vigor rootstocks (1103 P, 140Ru), RDI was more beneficial for wine composition, global quality, and sensory perception. Overall, the PRI method also increased the presence of some volatile unpleasant alcohols in the wines. We recommend the use of low vigor rootstocks and DI techniques with small water volumes to improve Monastrell wine quality, and as a measure to adapt vineyards to climate change under semiarid and water limiting conditions.

1. Introduction According to the climate projections for the middle of this century (2040–2070), it will be in semiarid regions of southern Europe (e.g., SE Spain) where viticulture will need to make the greatest adaptation to ⁎

climate change (CC), with greater costs to maintain quality and productivity, since they are going to experience more severe water stress and impacts of greater magnitude than other winegrowing areas (Guiot and Cramer, 2016; Resco et al., 2016). It is also expected that in these regions there will be a greater loss of optimal areas for vine cultivation

Corresponding author. E-mail address: [email protected] (P. Romero).

https://doi.org/10.1016/j.agwat.2019.105733 Received 29 November 2018; Received in revised form 26 July 2019; Accepted 31 July 2019 Available online 07 September 2019 0378-3774/ © 2019 Elsevier B.V. All rights reserved.

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projected yield losses, but in some of the warmer and drier areas yields still declined significantly even with irrigation, which was attributed to the synergistic effects of thermal and water stress (Fraga et al., 2018). To face the risks associated with CC and to achieve environmentally sustainable viticulture, the substitution of existing plant material by different rootstocks, varieties of vine, or clones of the same variety selected for their better adaptation to and tolerance of the new climatic conditions - has also been proposed as another effective measure of adaptation to CC in the medium/long term (Fraga et al., 2013; Berdeja et al., 2015). This vine genetic diversity should be taken advantage of to increase the diversity of vineyards and overcome the threats of CC (Wolkovich et al., 2018). In a recent five-year study, we evaluated the interactive effects of the irrigation method (RDI and PRI) and rootstock (invigorating and non-invigorating) on vine performance and berry quality. We concluded that the application of low water volumes (85–90 mm year−1) with well-designed DI strategies for Monastrell vines grafted on low vigor rootstocks resulted in better berry quality and nutraceutical potential than the use of more invigorating rootstocks, under semiarid conditions in SE Spain (Romero et al., 2018). Such a synergic approach can serve as an adaptation measure in the face of CC, to improve vine performance and enhance Monastrell vineyard sustainability under semiarid and water limiting conditions. The main goal of this study was to analyse whether the physiological changes and altered final berry quality conditioned by the interactive effects of the rootstock and irrigation method, observed in a previous study (Romero et al., 2018), are reflected in the final wine composition, global wine quality, aromatic profile, sensory attributes, and organoleptic properties. To achieve this, we analysed, over three years, the technological, phenolic, metabolomics-nutraceutical, and aromatic composition of the wines from the different rootstock x irrigation method combinations (R x IM) studied. In addition, a sensory analysis of the wines by panels of expert tasters was carried out in order to relate the final wine chemical composition and quality characteristics to the sensory attributes, taster preferences, and scores of the wines.

(Tóth and Végváry, 2016). In these warm and more vulnerable areas of the Mediterranean region it will be necessary to implement deep changes in vineyard management - combining different adaptation measures, especially those related to water management and availability (Iglesias and Garrote, 2015) - even in those areas considered optimal for vineyards in the future (Tóth and Végváry, 2016). Recent analysis of the economic impact of global warming on wine production point in the same direction; although with some uncertainties, it indicated that in certain regions of the European countries closest to the equator, such as Spain and France, CC will have adverse effects on the wine market, compared to regions located farther north, at higher latitudes (Ashenfelter and Storchmann, 2016). Thus, in the last 30 years, a significant advance in vine phenology and a clear change in the composition of the grape have been observed in many viticultural regions, which can be partly attributed to CC. In general, grapes now contain more sugar and less organic acids (mainly malic) and have a higher pH. In addition, changes have also been observed in some aromatic components (Van Leeuwen and Destrac-Irvine, 2017). The negative effects on wine quality associated with CC include inhibition of anthocyanin synthesis, loss of grape color and acidity, increasing pH, alcoholic degree, and volatilization of aromatic compounds (producing grapes with a low aromatic content), and an increased risk of organoleptic degradation and their aging potential and deterioration of the wine (Resco et al., 2016; Pons et al., 2017). Besides, recent studies predict in the near and distant future a significant advance for all phenological stages (e.g. shorter ripening period and advanced harvest up to four weeks) which could affect the quality and suitability of winegrapes production in many areas, if appropriate adaptation strategies are not taken (Alikadic et al., 2019; Molitor and Junk, 2019). The predicted increase in the evapotranspiration and water needs of the vine as a result of CC will make necessary the application of irrigation water to maintain the sustainability of vineyards and to prevent severe water stress in many winegrowing Mediterranean regions, such as southern Spain (Resco et al., 2016). This future scenario, with more recurrent drought phenomena and heatwaves, will make it more necessary to apply efficient deficit irrigation (DI) strategies and techniques as an adaptation to CC, because the water resources will be increasingly limiting. In this respect, we have verified that DI techniques, such as regulated deficit irrigation (RDI) and partial root-zone drying irrigation (PRI), using moderate annual volumes of water, maintain high yields and improve the long-term water use efficiency (WUE) and berry and wine quality in Monastrell wine grapes under semiarid conditions (Romero et al., 2016a, 2016b). However, to maintain the longterm sustainability of the vineyards, until the year 2050 and beyond, it will be necessary to apply additional adaptation measures, besides irrigation (Fraga et al., 2018). In this respect, a recent study in Portugal, where the application of efficient DI as a measure to adapt a vineyard to CC was simulated in the medium term (2041–2070), concluded that irrigation alleviated the impact of CC by significantly reducing the

2. Material and methods 2.1. Field conditions, plant materials, and irrigation treatments This research was carried out from 2014 to 2016 in a 0.4-ha vineyard at the IMIDA experimental station in Cehegín (D.O. Bullas), Murcia, SE Spain (38° 6´ 38.13´´N, 1° 40´ 50.41´´W, 432 m a. s. l.). The grapevines (Vitis vinifera L., var. Monastrell, syn. Mourvedre) were +20 years-old and were grafted on five different commercial rootstocks: 140Ru, 1103 P, 41B, 161-49C and 110R. Each rootstock-irrigation method combination was drip irrigated during three consecutive years (2014–2016), using two different DI techniques: RDI and PRI. All combinations were irrigated with similar annual water volumes,

Table 1 Deficit irrigation strategy and water volume applied for each irrigation method, in each phenological period and the total for every year of the experiment. Year

DI strategy 2014 2015 2016 Average (2014-2016)

Irrig. method

PRI RDI PRI RDI PRI RDI PRI RDI

Budburst-Fruit set (mm) April-May

Fruit set-veraison (mm) June-July

Veraison-harvest (mm)

Postharvest (mm)

%ETc

Beginning of August-midSeptember %ETc

mid-September-end of October %ETc

%ETc (10-20) 8.4 8.3 22.5 21.2 34.7 32.9 21.9 20.8

(10) 26.8 27.1 26.7 28.2 21.1 21.2 24.9 25.5

(25-30) 38.6 37.0 33.3 32.8 29.9 26.3 33.9 32.0

(20-30) 10.6 10.5 8.6 8.7 9.6 9.7 9.6 9.6

2

Total annual water volume applied (mm year−1)

84.5 83.0 91.1 90.9 95.3 90.1 90.3 88.0

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applying the same DI strategy (Table 1): 1) no stress or slight stress from budburst to fruit set; 2) moderate-severe water stress from fruit set to veraison 3) partial irrigation recovery to maintain moderate stress from veraison to harvest: 4) full recovery postharvest (Romero et al., 2018). In specific periods, the weekly irrigation was modified according to the midday stem water potential (Ψs) values (the aim being to maintain moderate levels of Ψs, pre-veraison period (fruit set-veraison) and postveraison period (veraison-harvest) between −1.2 and −1.4 MPa) (Romero et al., 2010). The soil, water, and plant characteristics, climatic factors, experimental and irrigation conditions, crop evapotranspiration, ETo and Kc applied, fertilizer usage, and experimental design have been described previously in detail (Romero et al., 2018). Each year at harvest, the yield was collected for 24 vines per rootstock (12 vines per irrigation method). The harvest date was in accordance with the grower´s practice in the area, when ºBrix reached 23.5-24.0. Between 40 and 50 kg of grapes were collected for each combination (R x IM) to perform the microvinifications.

was measured according to Dewanto et al. (2002) with the modifications of Tounsi et al. (2011). The total amino acids in wine were determined by the ninhydrin method (Rosen, 1957). For the analysis of total free amino acids in the must, fruits were taken at harvest (2015) and frozen at -80 °C and they were determined by the AccQ Tag-ultra Ultra Performance Liquid Chromatography (UPLC) method (Waters, 2006, Waters, Milford, MA, USA), as described in detail in Romero et al. (2015). For the mineral analysis of grapes (must), berries were collected in September 2014, 2015, and 2016, and processed in the laboratory as described in Romero et al. (2018). Samples of processed grapes were kept frozen until the analysis. One gram of each sample was digested with HNO3:HClO4 (2:1), according to Chapman and Pratt (1978), and minerals (K, Mg, Ca, Na, P, Fe, Cu, Mn, Zn, and B) were analysed with an inductively coupled plasma optical emission spectrometer (ICP) (Varian MPX Vista, Palo Alto, CA). Minerals in the wine were analysed according to Lopez-Artiguez et al. (1996) by using an ICP. The alcohol percentage was determined by distilling a wine sample, to separate the volatile from the non-volatile components. Glycerol in wine was analysed in an Agilent 1100 liquid chromatograph (HPLC) (Waldbronn, Germany) equipped with a refraction index detector and a 300 × 7.8 mm i.d. CARBOSep CHO-682 LEAD column, with ultra-pure water as the mobile phase at a flow rate of 0.4 mL min−1. Glucose, fructose, and sucrose were analysed by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEPAD), using Thermo Scientific Dionex CarboPac PA20 guard (3 x 30 mm) and separation (3 x 150 mm) columns, and as eluent 10 mM NaOH isocratic with a step to 200 mM KOH at 10 min to regenerate the column. Metabolomic analysis of the wine samples, after ALF in 2016, was performed by nuclear magnetic resonance (NMR) spectroscopy (model: Bruker Avance III HD quantitative 600 MHz; Bruker, Austria, GmbH, Wien, Austria), by the private company Institut Heidger KG (Germany). Thirty-one detectable substances in the wines (NMR wine-metabolite profiling) were analysed simultaneously, including alcohols, organic acids, preservatives, amino acids, and phenolic compounds (Chang et al., 2014; Amargianitaki and Spyros, 2017).

2.2. Microvinifications Microvinifications (3 per combination R x IM) were performed in 2014, 2015, and 2016. After harvest (mid-late September), the grapes were maintained for 2 days in a cold room to cool the grapes and obtain a homogeneous temperature of 4–6 °C. Then, the grapes were destemmed, crushed, and distributed in 40-L tanks. The tanks were introduced in a cool room (4 °C) for 3–4 days to produce the cryofermentation. After that, we removed the tanks from the cool room, to increase their temperature, and selected yeasts (Sacharomyces cerevisiae, Laffort, DSM. Servian, France, 10 g of dry yeast/100 kg grapes) were added to all vinifications. After this, all steps were conducted at 23 ± 1 °C during the alcoholic fermentation (ALF). Throughout the pomace contact period (10–12 days), the cap was punched down twice a day and the temperature and must density were recorded. Later, a light pressing was carried out and the wine was collected, deposited again in the tanks and left to stand for a few days. After a week, the wines were cleaned, protected with sulfurous (8 g SO2/100 kg grapes), and bottled for posterior analysis and sensory tasting. They were analyzed at the end of the ALF (2014, 2015, and 2016).

2.4. Determination of volatile aromatic compounds in the wines Analysis of volatile compounds in the wines was performed by solidphase microextraction (SPME) and gas chromatography-mass spectrometry, according to Gómez-Plaza et al. (2012). For the isolation of volatile compounds by SPME, a divinylbenzene-carboxen-polydimethylsiloxane 50/30 microns (DVB/CAR/PDMS) fibre was used. For the analysis of wine volatile compounds, 10 mL of wine, 4 g of sodium chloride and 25 μL of the internal standard (2-octanol; 250 μg/L) were added to the same vial. The vial was loaded onto a Gerstel autosampling device (Gerstel GmbH & Co.KG, Mellinghofen, Germany). The program of the autosampling device consisted on swirling the vial at 500 r.p.m. for 15 min at 40 °C, then inserting the fibre into the headspace for 30 min at 40 °C then transferring the fibre to the injector for desorption at 240 °C for 5 min. The conditions of the gas chromatograph and the mass spectra can be found in Gómez-Plaza et al. (2012). Injections were done in the splitless. The MS was operated in electron ionization mode at 70 eV and in SCAN mode with the transfer line to the MS system maintained at 240 °C. Peak identification was carried out by comparing mass spectra with those of the mass library (Wiley 6.0) and comparing the calculated retention indices with those published in the literature. Semiquantitative data were obtained by calculating the relative peak area (or TIC signal) in relation to that of the internal standard.

2.3. Wine chemical composition Color intensity (CI) was calculated as the sum of the absorbances at 620 nm, 520 nm and 420 nm (Glories, 1984). CIELab parameters (lightness, L*; redness-greenness, a*; yellowness-blueness, b*) were determined by measuring the transmittance of the wine every 10 nm from 380 to 770 nm, using the D65/10° for the illuminant/observer, with 0.2-cm path length glass cells. The chroma (C*) and hue angle (h*) were calculated by the formulae C* = (a*2 + b*2)½ and h* = (tan−1 b*/a*). Total anthocyanins and total phenols were measured spectrophotometrically, following the methods described by Cayla et al. (2002) and Boulton (2013) respectively. The wine quality index (QI) was calculated as it was previously described by Romero et al. (2016c). Anthocyanins and flavonol derivatives in wine samples were directly analysed by HPLC-UV-VIS (mod. 1260, Agilent Technologies, Santa Clara, CA, USA). Chromatograms were recorded at 360 nm for flavonols and 520 nm for anthocyanins, according to the methodology described by Gomez-Alonso et al. (2007). Resveratrol and piceid were extracted with ethyl acetate, as described by Ribeiro de Lima et al. (1999). Analysis was carried out by HPLC-MS/MS (Agilent Series 1100, Agilent Technologies, Santa Clara, CA, USA) as described by Romero et al. (2018). The test used to determine the antioxidant capacity of the wine was the ABTS•+ radical cation assay, using Trolox to standardize the system (Miller et al., 1993), as described by Navarro et al. (2015). Tannin levels were measured using methylcellulose as a precipitant, according to Sarneckis et al. (2006) and the content of total flavonoids

2.5. Sensory analysis of wines Difference testing was conducted at the end of the ALF (2014, 2015, 2016). A professional panel of five expert tasters was recruited every 3

4 *** ns *** ns

1.09 1.15 1.39 1.46 1.46 1.33 1.38 1.31 1.34 1.23

1.48b 1.56b 0.90a

1.33 1.30

1.12a 1.42c 1.40bc 1.35bc 1.29b

Total tannins (g L−1)

* ns *** ns

6.95 7.27 7.24 8.03 7.99 7.36 8.14 7.76 9.71 7.66

5.93a 9.72c 7.77b

8.00 7.62

7.11a 7.64ab 7.66ab 7.95ab 8.68b

CI

* ns *** ***

1.45abc 1.67bc 1.49abc 1.75c 1.24a 1.51abc 1.83c 1.77c 2.39d 1.28ab

2.33c 1.77b 0.83a

1.68 1.60

1.56b 1.62b 1.38a 1.80c 1.83c

QIwine

** ns *** ns

0.26 0.27 0.24 0.23 0.21 0.22 0.22 0.23 0.26 0.29

0.29b 0.14a 0.30b

0.24 0.25

0.27b 0.23a 0.22a 0.23a 0.28b

Anthocyanins/ tannins

* ns *** ns

15.20 14.60 14.60 14.87 14.67 14.25 15.03 15.03 15.20 14.97

15.50c 14.16a 14.88b

14.94 14.75

14.92ab 14.73ab 14.46a 15.03b 15.08b

º alcohol

*** *** *** ***

0.70a 0.68a 0.68a 0.78c 0.80cd 0.67a 0.84de 0.76bc 0.86e 0.71ab

0.64 – 0.85

0.78 0.72

0.69a 0.73b 0.74b 0.80c 0.79c

OD620nm

*** *** *** ***

2.80a 2.94bc 2.60ab 3.07de 3.27g 2.39cd 3.95g 2.94e 3.78h 2.88f

3.06 – 3.98

3.68 3.36

3.09a 3.23b 3.64c 3.72c 3.93d

OD520nm

*** *** *** ***

2.36abc 2.36ab 2.32bc 2.55d 2.48d 1.98a 3.00e 2.53d 2.70e 2.19c

2.23 – 2.94

2.66 2.51

2.39a 2.59c 2.50b 2.77e 2.67d

OD420nm

* ns *** ns

1.02 1.01 1.11 1.00 1.00 1.25 0.95 1.03 1.05 1.16

0.95 – 1.17

1.03 1.09

1.02ab 1.05b 1,13c 0.99a 1.11c

Tone

** ns *** ns

3.80 3.68 3.74 3.75 3.57 3.71 3.69 3.80 3.59 3.68

3.60 – 3.80

3.67 3.73

3.74c 3.74c 3.63a 3.72bc 3.66ab

pH

ns ** – ns

3.15 3.05 3.15 2.80 3.20 2.68 3.25 3.10 3.13 2.98

– – –

3.18 2.92

3.10 2.98 2.94 3.18 3.05

Reducing sugars (g L−1)

*** ns *** ns

35 46 45 37 55 54 44 59 45 46

55 – 38

45 48

41a 41a 54b 52b 46a

Acetaldehyde (mg L−1)

ns ** – ns

5.37 6.12 6.02 6.60 5.43 6.47 5.78 6.60 6.30 7.04

– – –

5.78 6.56

5.74 6.31 5.95 6.19 6.67

Dissolved oxygen (mg L−1)

‘ns’ not significant; *, **, and *** indicate significant differences at the 0.05, 0.01, and 0.001 levels of probability, respectively. In each column and for each factor or interaction, different letters indicate significant differences according to Duncan’s multiple range test at the 95% confidence level. TPI, Total phenol index; CI, color intensity.

* ns *** ns

401c 216a 261b

** ns *** ns

43c 38b 28a

294 291

33 35 36 37 36 34 40 41 38 34

36 36

283a 286a 283a 299ab 312b

140Ru 1103 P 41B 110R 161-49C Irrigation method (IM) PRI RDI Year 2014 2015 2016 Interaction (R x IM) 140Ru PRI RDI 1103 P PRI RDI 41B PRI RDI 110R PRI RDI 161-49C PRI RDI ANOVA Rootstock (R) Irrigation method (IM) Year Interaction (R x IM)

277 289 279 294 288 277 300 298 326 299

34a 37ab 35a 40b 36ab

Total anthocyanins (mg L−1)

Rootstock (R)

TPI

Table 2 Average values of the chemical composition of wines at the end of alcoholic fermentation, for each rootstock, irrigation method, and year, and the interaction (R x IM). Total anthocyanins, TPI, Total tannins, CI, QIwine, anthocyanins/tannins ratio, º alcohol are from three years (2014, 2015, and 2016). The OD, tone, pH, and acetaldehyde data are from two years (2014 and 2016). The reducing sugars and dissolved oxygen data are from one year (2016).

P. Romero, et al.

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year from D.O. Bullas (Spain), all involved in the winemaking business in the study area. The majority had been formally trained in wine assessment and all had extensive experience in difference testing and aroma and flavor intensity assessment. The differences between the wines were assessed through a blind tasting, following the protocols, recommendations, and tasting sheet of the Instituto Nacional de Denominación de Origen (INDO, Spain). For each sample, 20–30 ml of wine from blind-coded 750-ml bottles were served to tasters in similar tasting glasses, at room temperature and under normal lighting conditions, in a laboratory at the IMIDA (La Alberca, Spain). The glasses were covered with plastic Petri dish lids to retain the headspace aromas. For each wine tasted, the partial and total scores of the tasters were reported on a tasting sheet, in the order: 1) visual phase, 2) olfactory phase (intensity and quality), 3) gustatory phase (intensity and quality), 4) harmony, and 5) total score (the sum of all phases). According to the tasting sheet and protocol used, the lower the score obtained in the different phases and the total score, the better was the sensory and organoleptic quality and the overall evaluation of the wine. Each expert tasted 10 wines (1 per rootstock-irrigation method combination) in each session. Positive and negative sensory attributes in relation to color, smell, and flavor were also reported by the tasters on the tasting sheet. According to the tasters, positive sensory attributes were related more to qualities such as: intensity, darkness, red/purple/violet nuances, vivid, purity, brightness, good expression, body and structure, balance, mellowness, fresh, good harmony, persistent, good aftertaste, good acidity, sweetness, red/dark mature fruits, and floral/fruity notes. In contrast, negative attributes were related more to defects and flaws, such as the presence of atypical aromas (e.g., vegetable-green) or oxidation/chemical aromas, low intensity of fruit, dull color, lack of structure and body, excessive acidity, acetaldehyde notes, bitterness, dryness, and astringency. For each wine, each positive attribute was given a score of 1 point and each negative attribute was given a score of -1, and the sum (positive + negative) of the attributes was calculated (Fig. 1S, supporting information).

components obtained from the PCA, a clusters analysis was carried out to establish a classification/differentiation of the combinations (rootstock x irrigation method) studied. The statistical software IBM SPSS Statistics 21 was used for these studies. 3. Results 3.1. Wine composition, quality index, and nutraceutical potential The wine composition was significantly affected by the rootstock. Thus, low vigor rootstocks (161-49C, 110R) gave a higher polyphenol content (anthocyanins, CI, OD620nm, OD520nm, OD420nm, QIwine,) and lower pH (161-49C) than 1103 P and 140Ru (Table 2). Conversely, high vigor rootstocks such as 1103 P and 140Ru and medium vigor rootstocks (41B) showed the lowest values of TPI, CI, QIwine, OD520nm, OD420nm, and OD620nm and higher pH (1103 P, 140Ru) and acetaldehyde (41B). Wines from 140Ru also had the lowest tannins concentration and wines from 41B the lowest alcohol degree, pH, and QIwine. In addition, wines from 161-49C and 140Ru had the highest anthocyanins/tannins ratio (Table 2). Differences were also found due to the irrigation method. Thus, regardless of the rootstock, PRI resulted in a higher concentration of reducing sugars and a lower concentration of dissolved oxygen (Table 2). Significant interactive effects (R x IM) were also observed. Wines made from grapes grown on 161-49C, 110R, or 41B under PRI had significantly higher OD620nm, OD520nm, and OD420nm than under RDI. The contrary was observed in wines from 1103 P or 140Ru (Table 2). In addition, 161-49C vines under PRI had significantly higher QIwine than 161-49C RDI and the rest of the combinations (R x IM). The year 2014 (a drier and warmer year) was the year with the highest global wine quality index (QIwine), whereas 2016 had the lowest value (Table 2). Regarding the chromatic characteristics, 161-49C wines (measured in 2016) showed lower L*, h*, and b* and higher C* and a* values than those of the other rootstocks, while 140Ru and 1103 P wines showed the highest L* (140Ru), h* and b* (1103 P) values and the lowest C* and a* values (140Ru) (Table 3). The concentrations of several individual derivatives of anthocyanins and flavonols were highest in wines from the 161-49C and 110R rootstocks: malvidin 3-0-monoglucoside (the most abundant anthocyanin), quercetin, and miricetin (flavonols derivatives). Conversely, the concentrations were lowest in 140Ru and 1103 P wines (Table 4). The PRI wines also showed significantly higher concentrations of quercetin 3-Oglucoside. Nevertheless significant interactive effects meant that 161-

2.6. Statistical analysis The data were analyzed using analysis of variance (ANOVA) procedures and the means were separated by Duncan´s multiple range test, using Statgraphics 2.0 Plus software (Statistical Graphics Corporation, USA). A three-way ANOVA procedure was used to discriminate the effects of the rootstock, irrigation method, and year. Based on the results of a set of 23 variables related to different aspects of wine quality that were common in the three years of study (2014, 2015, and 2016), a principal component analysis (PCA) and cluster analysis were carried out. The 23 variables (measured in the three years) chosen initially belonged to five different quality aspects of the wines: 1) polyphenols content, 2) sensory and organoleptic perception, 3) metabolomicstechnological quality, 4) mineral nutrition, and 5) antioxidant activitynutraceutical potential. A preliminary study of the Pearson correlation matrix of all the variables determined the selection of variables for the subsequent analysis. Variables that were linearly related to others were eliminated. Finally, those that had the most significant correlations and represented the largest number of different quality aspects were chosen. Different models were analyzed and the most satisfactory one from the point of view of sampling adequacy, considering the KMO (KaiserMeyer-Olkin) statistics and the Bartlett sphericity test, was chosen. The final selection of nine variables (tannins, anthocyanins/tannins ratio, QIwine, final total score in the sensory analysis, º alcohol, glycerol, K, total macronutrients, and antioxidant activity) presented a value of 0.617 in the KMO test, considering values above 0.5 to be acceptable (Kaiser, 1974), and below 0.05 in the Bartlett test. This confirmed that with this selection of variables we could reject the null hypothesis of sphericity, an essential condition for the factorial analysis of the variables contemplated in this study. In addition, for each of the main

Table 3 Chromatic characteristics (CIElab parameters) of the wines after alcoholic fermentation, for each rootstock and irrigation method in 2016. Rootstock (R)

L*

C*

h*

a*

b*

140Ru 1103 P 41B 110R 161-49C Irrigation method (IM) PRI RDI ANOVA Rootstock (R) Irrigation method (IM) Interaction (R x IM)

40.91c 38.67b 38.11b 36.82ab 35.59a

45.57a 46.75bc 47.24c 46.64b 48.11d

22.71b 23.51b 19.88a 22.88b 19.74a

42.03a 42.87b 44.54c 42.97b 45.28d

17.59b 18.65c 16.07a 18.13bc 16.25a

37.64 38.40

46.88 46.84

21.91 21.58

43.48 43.59

17.47 17.21

** ns ns

*** ns ns

*** ns ns

*** ns ns

*** ns ns

‘ns’ not significant; ** and *** indicate significant differences at the 0.01 and 0.001 levels of probability, respectively. In each column and for each factor or interaction, different letters indicate significant differences according to Duncan’s multiple range test at the 95% confidence level.

5

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Table 4 Mean values of the concentrations of several derivatives of anthocyanins and flavonols in wines after alcoholic fermentation, for each rootstock (R), irrigation method (IM), and year, and the interaction (R x IM). The data are from two years (2014 and 2015). Rootstock (R)

Malvidin 3´-O-monoglucoside (mg L−1)

Quercetin 3´-Ogalactoside (mg L−1)

Quercetin 3´-Oglucoside (mg L−1)

Quercetin 3´-O-glucoronide (mg L−1)

Miricetin 3´-O-glucoside (mg L−1)

140Ru 1103 P 41B 110R 161-49C Irrigation method (IM) PRI RDI Year 2014 2015 Interaction (R x IM) 140Ru PRI RDI 1103 P PRI RDI 41B PRI RDI 110R PRI RDI 161-49C PRI RDI ANOVA Rootstock (R) Irrigation method (IM) Year Interaction (R x IM)

78.52a 76.94a 77.58a 76.91a 91.03b

1.95a 2.31b 2.87c 2.92c 2.91c

10.83a 13.05b 15.48c 16.73d 17.35d

8.56a 9.81b 10.83c 11.65d 11.80d

26.28a 26.66a 31.07b 30.64b 31.05b

81.01 79.38

2.62 2.56

16.02 13.3

11.02 10.04

30.06 28.22

86.28 74.11

3.03 2.15

25.8 3.5

13.19 7.87

50.79 7.49

76.45a 80.58b 77.50ab 76.38a 77.70ab 77.47ab 78.47ab 75.35a 94.93d 87.12c

1.75a 2.15b 2.45c 2.17b 3.10d 2.63c 3.15d 2.68c 2.63c 3.18d

10.12 11.55bc 15.65e 10.45ab 18.67f 12.28 cd 20.15 g 13.32d 15.52e 19.18 fg

8.48a 8.63a 10.43bc 9.18a 12.07e 9.60ab 12.43e 10.87cd 11.68de 11.92de

24.07a 28.48bc 29.93cd 23.38a 32.40de 29.73cd 35.40f 25.88ab 28.48c 33.62ef

*** ns *** ***

*** ns *** ***

*** *** *** ***

*** ns *** **

*** ns *** ***

‘ns’ not significant; *, **, and *** indicate significant differences at the 0.05, 0.01, and 0.001 levels of probability, respectively. In each column and for each factor or interaction, different letters indicate significant differences according to Duncan’s multiple range test at the 95% confidence level.

49C PRI wines had significantly higher concentrations of malvidin 3-Omonoglucoside, but lower concentrations of flavonol derivatives than 161-49C RDI wines. In contrast, 110R PRI wines showed significantly higher concentrations of flavonol derivatives (quercetin and miricetin) than 110R RDI wines (Table 4). Rootstocks 161-49C and 140Ru gave rise to significantly higher concentrations of total amino acids in the must, compared to the other rootstocks (Table 5), while rootstock 41B gave the lowest total amino acid concentrations. In addition, there was a significant effect of the rootstock on the amino acids profile. In general, the most abundant amino acids in the must were Pro, Ala, and Gly. Must from 140Ru had significantly higher Ser, Glu, and Ala levels than that from the rest of the rootstocks, whereas 161-49C led to higher Gly, Val, Phe, and Tyr (Table 5). Wines from rootstock 161-49C also had significantly greater antioxidant activity, higher fructose, glucose, and total sugars

concentrations, and a higher anthocyanins/total sugars ratio (Table 6). In addition, 161-49C and 140Ru wines had significantly higher total amino acid concentrations than the others. Wines from 1103 P had the lowest antioxidant activity and wines from 41B the lowest fructose and total sugars concentrations. Glycerol was significantly less abundant in 161-49C and 41B wines, compared to 110R. There were significant effects of the year, but not the irrigation method, on the nutraceutical potential of the wine (Table 6). Besides, the interaction R x IM showed that, for rootstock 161-49C, PRI wines had significantly higher fructose and total sugars concentrations and anthocyanins/sugars ratios than RDI wines, while, for 110R, RDI wines had significantly higher values of fructose, total sugars, and the anthocyanins/sugars ratio than PRI wines (Table 6). Additional metabolomics analysis after ALF (in 2016) showed that wines from 161-49C had higher concentrations of proline, tartaric acid, and formic acid, higher tartaric/malic ratios, and lower concentrations

Table 5 Amino acid composition and concentration (μmol L−1) of the must, for each rootstock and irrigation method and their interaction (R x IM) in 2015. Rootstock (R)

His

Ser

Arg

Gly

Asp

Glu

Thr

Ala

Pro

Cys

Lys

Tyr

Met

Val

Ile

Leu

Phe

AAtot

140Ru 1103 P 41B 110R 161-49C Irrigation method (IM) PRI RDI ANOVA Rootstock (R) Irrigation method (IM) Interaction (R x IM)

33c 29abc 21a 24ab 31bc

94b 78a 70a 73a 80a

104b 68ab 70ab 45a 40a

244ab 197ab 135a 201ab 296b

25 24 15 18 20

75b 54a 57a 61a 58a

59b 53ab 42a 44a 52ab

177b 134a 108a 116a 134a

524b 471ab 423a 403a 461ab

18c 16abc 12a 12ab 17bc

13bc 11ab 15c 10a 12ab

12a 12a 11a 11a 16b

4b 3ab 2a 3ab 3a

32b 32b 27ab 25a 32b

9abc 10c 8ab 7a 10bc

18bc 19c 16ab 14a 19c

13ab 13ab 11a 11a 14b

1,455c 1,225ab 1,043a 1,079ab 1,294bc

29 26

80 78

85 46

197 232

22 19

62 61

51 49

137 131

471 442

15 15

12 12

13 12

3 3

30 29

9 9

18 17

13 12

1,245 1,193

* ns ns

** ns ns

* ** ns

* ns ns

ns ns ns

* ns ns

* ns ns

** ns ns

* ns ns

* ns ns

** ns ns

** ns ns

* ns ns

* ns ns

* ns ns

** ns ns

* ns ns

** ns ns

‘ns’ not significant; * and ** indicate significant differences at the 0.05, 0.01 levels of probability, respectively. In each column and for each factor or interaction, different letters indicate significant differences according to Duncan’s multiple range test at the 95% confidence level. 6

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Table 6 Nutraceutical quality parameters of wine from Monastrell grapes, for five different rootstocks (140Ru, 1103 P, 41B, 110R, and 161-49) and two different irrigation methods (PRI and RDI), from 2014 to 2016. Antioxidant activity (TEAC index), flavonoids (mg catequin L−1), piceid (mg L−1), resveratrol-4′-O-glucoside (mg L−1), resveratrol (mg L−1), amino acids (mg leucine L−1). The glucose (G), fructose (F), and sucrose (S) (mg L−1), glycerol (mg L−1), sugars (G + F+S) (mg L−1), and anthocyanins/sugars ratio were calculated with data from 2015 and 2016. Rootstock (R)

Antioxidant activity

Flavonoids Piceid Resveratrol-4’- O- Resveratrol Amino glucoside acids

Glucose Fructose Sucrose Sugars Glycerol Anthocyanins/ sugars

140Ru 1103 P 41B 110R 161-49C Irrigation method (IM) PRI RDI Year 2014 2015 2016 Interaction (R x IM) 140Ru PRI RDI 1103 P PRI RDI 41B PRI RDI 110R PRI RDI 161-49C PRI RDI ANOVA Rootstock (R) Irrigation method (IM) Year Interaction (R x IM)

14.4ab 12.7a 14.8b 13.4ab 16.9c

434 456 454 500 453

1.28 1.05 1.36 1.15 1.17

3.76 3.42 3.68 3.18 3.48

1.75 1.32 1.51 1.35 1.74

568b 484a 473a 452a 579b

74.2a 80.1a 71.2a 75.7a 90.2b

64.1b 61.5ab 55.8a 66.2bc 71.8c

26.0 25.9 24.2 24.3 23.7

164b 167b 151a 166b 186c

8,222ab 8,207ab 7,952a 8,523b 8,056a

0.026a 0.028b 0.028b 0.029b 0.032c

14.6 14.3

470 448

1.22 1.18

3.43 3.58

1.43 1.63

508 514

76.7 79.9

64.4 63.4

24.8 24.8

167 167

8,200 8,185

0.028 0.029

11.1 15.0 17.2

349 636 394

2.14 1.32 0.14

5.35 3.48 1.69

2.60 1.81 0.19

615 412 506

– 69.5 87.1

– 62.5 65.3

– 22.8 26.9

– 155 179

– 7,499 8,885

0.028 0.029

13.9 14.9 12.8 12.7 16.3 13.3 13.2 13.6 16.8 16.9

431 436 466 445 494 414 486 514 474 431

1.13 1.43 1.11 0.98 1.44 1.27 1.18 1.11 1.24 1.09

3.29 4.24 3.46 3.39 3.60 3.76 3.19 3.17 3.63 3.32

1.25 2.25 1.35 1.29 1.38 1.64 1.30 1.40 1.88 1.59

569 567 450 518 458 488 472 433 592 565

70.4 77.9 78.3 81.9 70.9 71.5 69.8 81.6 93.9 86.4

67.4abc 60.9ab 61.1ab 61.9ab 56.1a 55.5a 59.5a 73.0bc 77.8c 65.8ab

24.9 27.0 26.4 25.3 24.1 24.2 22.6 26.0 25.9 21.6

163ab 166ab 166ab 169ab 151a 151a 152a 181b 205c 167ab

8,223 8,222 8,376 8,038 7,742 8,162 8,526 8,520 8,131 7,981

0.025a 0.026abc 0.026ab 0.031f 0.028 cde 0.028bcde 0.027abcd 0.030ef 0.035g 0.030def

* ns *** ns

ns ns *** ns

ns ns *** ns

ns ns *** ns

ns ns *** ns

* ns *** ns

** ns *** ns

** ns *** *

ns ns ** ns

* ns ** *

** ns *** ns

** ns ns **

‘ns’ not significant; *, ** and *** indicate significant differences at the 0.05, 0.01, and 0.001 levels of probability, respectively. In each column and for each factor or interaction, different letters indicate significant differences according to Duncan’s multiple range test at the 95% confidence level.

of methanol, fumaric acid, and galacturonic acid, compared to those of the other rootstocks (Table 1S, supporting information). In addition, wines from 110R showed the lowest proline, tartaric acid, and formic acid concentrations, the lowest tartaric/malic ratios, and the highest concentrations of succinic and fumaric acids. The high vigor rootstock 140Ru had lower concentrations of tyrosol and higher concentrations of proline and lactic acid, compared to the rest of the rootstocks. Wines from 41B had higher concentrations of methanol, tyrosol, and galacturonic acid than those of the other rootstocks (Table 1S). Occasionally (in 2016), PRI wines showed lower ethyl acetate concentrations than RDI wines. There were no significant interactive (R x IM) effects. The wine mineral nutrition analysis revealed that wines from 16149C had significantly lower concentrations of P and K than those of the rest of the rootstocks (Table 7). In addition, 161-49C and 41B wines had significantly higher Zn concentrations than 140Ru wines, and 161-49C, 1103 P, and 110R wines had significantly higher B concentrations than 41B wines (Table 7). The K and Zn concentrations in PRI wines were significantly lower than in RDI wines. The year had a significant effect on the mineral content of the wines, and the interaction R x IM had a significant effect on the Zn concentration. Thus, PRI wines from 16149C, 110R, 41B, and 140Ru had lower Zn concentrations than RDI wines, except for 1103 P, where the opposite effect was observed (Table 7). Additional mineral nutrition analysis also showed that 16149C grapes had higher B concentrations than 110R and 140Ru grapes and lower concentrations of Cu, Na and P, than 110R or 1103 P grapes (Table 2S, supporting information)

produced from the Monastrell grapes were arranged in five chemical families (alcohols, esters, terpenes, volatile fatty acids, and others) (Table 8). Alcohols and esters were the major groups in terms of the number and concentration of aromatic compounds identified in all wine samples, followed by volatile fatty acids and terpenes. The aromatic profiles of the wines in 2015 and 2016 showed significant differences due to the year, rootstock, and, to a lesser extent, irrigation method (Table 8). The rootstock affected the concentrations of 11 compounds and the irrigation method only affected the concentrations of two compounds significantly (2-methyl 1- butanol, naphthalene). In general, for all rootstocks, 2016 resulted in more aromatic wines than 2015 and the most abundant aromatic compounds were alcohols (2-methyl 1butanol, phenyl ethanol), esters (ethyl octanoate, ethyl decanoate, ethyl acetate), volatile fatty acids (butanoic acid), and 2 octanone. Wines from rootstocks 1103 P and 41B had the highest concentrations of aromatic compounds, whereas 110R wines had the lowest concentrations. Thus, wines from the high vigor rootstock 1103 P showed higher concentration of alcohols (propanol, hexanol), esters (ethyl 3 OH butanoate, diethyl succinate, ethyl dodecanoate), 2-octanone, and acetic acid than wines from other rootstocks. Conversely, low vigor rootstocks (161-49C and 110R) had significantly lower concentrations of alcohols (propanol, hexanol), esters (3-methyl butyl butanoate, 3-methyl ethyl butanoate, methyl nonanoate, ethyl dodecanoate), 2-octanone, and acetic acid (Table 8). In addition, regardless of the rootstock, PRI wines had significantly higher concentrations of 2-methyl 1-butanol (an alcohol and the most abundant aromatic compound), total higher alcohols, and naphthalene than RDI wines. There were no significant interactions between the rootstock and irrigation method. The score for the sensory analysis of the wines after ALF also differed significantly according to the rootstock, irrigation method, year, and the interaction (R x IM) (Table 9). Wines from rootstocks 161-49C

3.2. Aromatic profile of the wines and sensory analysis The 49 major and minor compounds quantified in the red wines 7

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Table 7 Mineral composition of Monastrell wine after alcoholic fermentation, for different rootstocks (140Ru, 1103 P, 41B, 110R, and 161-49C), irrigation methods (PRI and RDI), and years (2014–2016). The P, K, Ca, Mg, and Na are expressed as mmol kg−1 and Fe, Cu, Mn, Zn, and B as ppm (mg L−1). Rootstock (R)

P

K

Ca

Mg

Na

Fe

Cu

Mn

Zn

B

140Ru 1103 P 41B 110R 161-49C Irrigation method (IM) PRI RDI Year 2014 2015 2016 Interaction (R x IM) 140Ru PRI RDI 1103 P PRI RDI 41B PRI RDI 110R PRI RDI 161-49C PRI RDI ANOVA Rootstock (R) Irrigation method (IM) Year Interaction (R x IM)

280b 279b 269b 303b 229a

1,059b 1,086b 1,065b 1,094b 918a

75.6 72.6 77.1 73.9 71.2

83.8 85.6 81.9 86.7 80.6

12.0 20.7 12.2 16.8 11.0

0.55 0.58 0.64 0.52 0.60

0.15 0.12 0.13 0.14 0.11

0.63 0.57 0.52 0.61 0.62

3.19a 3.42ab 3.63b 3.35ab 3.74b

9.2ab 10.6b 8.4a 10.1b 10.6b

266 279

1,011 1,077

73.0 75.2

84.1 83.4

15.4 13.7

0.58 0.58

0.13 0.14

0.62 0.56

3.20 3.73

9.9 9.7

292 236 288

1,104 1,022 1,006

73.7 85.1 63.5

86.5 83.8 80.9

17.7 15.5 10.4

0.48 0.73 0.53

0.12 0.13 0.15

0.52 0.66 0.59

6.68 1.27 2.44

8.8 10.6 9.9

269 290 287 272 260 279 294 312 219 239

1,052 1,067 1,088 1,084 988 1,141 1,028 1,159 900 936

72.0 79.3 73.9 71.2 75.7 78.4 70.1 77.7 73.3 69.2

83.8 83.9 86.0 85.3 81.4 82.4 86.6 86.9 82.6 78.5

13.5 10.5 24.3 17.0 10.0 10.5 16.1 17.6 9.3 12.7

0.53 0.58 0.60 0.56 0.60 0.68 0.54 0.49 0.61 0.60

0.14 0.17 0.12 0.12 0.12 0.14 0.14 0.14 0.11 0.11

0.71 0.55 0.58 0.56 0.52 0.52 0.62 0.59 0.67 0.56

2.71a 3.66e 3.54de 3.30c 3.29c 3.98f 3.09b 3.60e 3.38cd 4.11f

9.4 8.9 10.4 10.9 8.4 8.5 10.1 10.2 11.4 9.8

* ns ** ns

* * * ns

ns ns *** ns

ns ns ns ns

ns ns ns ns

ns ns *** ns

ns ns ns ns

ns ns * ns

* *** *** *

* ns * ns

‘ns’ not significant; *, **, and *** indicate significant differences at the 0.05, 0.01, and 0.001 levels of probability, respectively. In each column and for each factor or interaction, different letters indicate significant differences according to Duncan’s multiple range test at the 95% confidence level.

and 140Ru (followed by 110R) were the best valued, globally, having the lowest scores in the visual, olfactory, and gustatory phases and harmony, and the lowest final score. Conversely, 41B and 1103 P wines were the worst valued, obtaining the highest scores (Table 9). The year with the best score was 2014. In general, PRI wines obtained worse scores in the olfactory phase (intensity), gustatory phase (intensity and quality), and harmony, and lower final scores, than RDI wines. Nevertheless, there were significant interactive (R x IM) effects. The professional tasters appreciated - in the sensory analysis of the wines (averaged for the three years) - a greater number of positive attributes in 161-49C wines, followed by 140Ru, and more negative attributes for 41B and 1103 P (Fig. 1S).

concentration was significantly correlated with a better score in the gustatory phase (intensity) (Fig. 2). 3.4. Principal component analysis of the wines The analysis of the principal components (PCA) showed a separation between wines from the different combinations (R x IM) (Fig. 3). The first principal component (PC1), explains 38.90%, the second (PC2), explains 26.53% and the third component (PC3) explains 21.99% of the total variance. Macronutrients (included K), antioxidant activity and sugars (glycerol) contributed in this order greatly to PC1 (defined as nutrition-sugars). In the case of PC2 (defined as astringency-bitterness), the principal parameters to explain it were tannins content, sensory analysis score and anthocyanins/tannins ratio. The third component PC3 (defined as flavonoids-alcohol content) was greatly explained by two variables, QIwine and alcohol (Fig. 3). There was a clear separation between 161-49C PRI wines and the rest, mainly in quality attributes related with PC3 (flavonoids-alcohol content). These distinct wines were better characterized by lower K content, greater antioxidant activity, QIwine (related to a high polyphenolic content and color intensity) and high alcohol. The analysis cluster for each component also showed that 161-49C PRI wines were closer to 41B PRI wines and 161-49 RDI wines for PC1 (high antioxidant activity) and 110R RDI wines for PC 2 (lower astringency and better organoleptic evaluation). In addition, 110R PRI wines were better explained by differences in PC1 and PC2 (higher tannins content, lower macronutrients and worse organoleptic perception) compared to 110R RDI wines. In contrast, overall, PCA showed similar component loadings and key variables in 140Ru wines, indicating no clear differences between PRI and RDI wines. In addition, wines from 41B PRI clearly separated from the rest in quality attributes related with PC 2 (related with astringency) (mainly due to a greater tannins content and worse sensory evaluation) and PC 3 (lower QIwine and alcohol) (Fig. 3).

3.3. Relationships between wine quality parameters, aromatic compounds, and sensory analyses According to the Pearson correlation coefficients obtained between the quality parameters and the sensory analysis of the wines, the polyphenolic and alcohol contents were significantly and positively correlated with the global sensory appreciation of the wines (Table 10). Therefore, overall, wines with higher values of IPT, anthocyanins, alcohol, and QIwine obtained better olfactory, gustatory, harmony, and total scores (lower values) in the tasting (Table 10). The values of IPT, CI, tannins concentration, and QIwine were also significantly and negatively correlated with the concentrations of several families of aromatic compounds - such as alcohols, volatile fatty acids, and esters - and with the total aromatic compounds in the wines (Fig. 1). Besides, the concentration of total anthocyanins was correlated negatively with that of volatile fatty acids, but positively with the concentration of alcohols. The concentration of tannins and the CI were also correlated significantly and positively with the volatile fatty acids (Fig. 1). In addition, the greater the concentration of aromatic compounds in the wine, the worse the score in the sensory tasting (Fig. 2). The only exception were the volatile fatty acids, for which a greater 8

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Table 8 Average values of aromatic compounds (concentration and composition) in the wines after alcoholic fermentation (data from 2015 and 2016). Rootstock (R) −1

Alcohols (μg L

)

Propanol 2 methyl 1 propanol Butanol 2 methyl 1 butanol 2,3 butanediol Hexanol Heptanol Octanol Nonanol Decanol Benzethanol Phenyl ethanol Σ Alcohols Esters (μg L−1) Ethyl dodecanoate Ethyl lactate 3 Methyl butyl butanoate 2 Ethyl butanoate Ethyl 3 OH butanoate Ethyl butanoate 3 Methyl ethyl butanoate 3 Methyl 1 butanol acetate (Isoamyl acetate) Ethyl pentanoate Ethyl 3 hexanoate Hexyl acetate Ethyl octanoate Methyl nonanoate Ethyl decanoate Ethyl hexadecanoate Ethyl hexanoate Diethyl succinate Methyl acetate Ethyl acetate Σ Esters Terpenes (μg L−1) D-Limonene Cinamene (+) Linalool Terpineol (+) Citronellol Σ Terpenes Volatile fatty acids (μg L−1) Hexanoic acid Octanoic acid Decanoic acid Butanoic acid Σ fatty acids Others (μg L−1) 2 Octanone 6 Methyl 5,2 heptenone 2,4 Bisphenol Acetic acid Butyrolactone Naphtalene 3,5 Dimethyl benzaldehyde 2,3 Butanedione Σ Others Σ Total (μg L−1)

Odor threshold values (OTV) (μg L−1) 750-830 40,000 150,000 250-7,000 150,000 8,000 1,000 50-120 58 6-210 10,000 14,000 1,500 154,000 18 20 20,000 20 18 17-30 1.5 270 670-1,500 5 4 200-630 2,000 14-200 200,000 3,000 7,500 200 na 6-25.2 250 40 420 500 1,000 173 190 50 na 200,000 35,000 80 na 0.05

Irrigation method (IM)

Year

ANOVA

140Ru

1103P

41B

110R

161-49C

PRI

RDI

2015

2016

R

IM

Year

R x IM

94a 255 24 3,568 411 314a 81 77 85 48 342 2,663 7,962 140Ru 249ab 29 24b 6 158abc 141 56b 975

226b 432 28 4,596 407 471b 104 77 84 57 120 2,841 9,444 1103 P 399b 40 25b 11 187c 206 61b 1,181

112a 569 20 4,227 237 430ab 97 68 88 58 76 2,776 8,759 41B 235ab 26 27b 11 149ab 179 58b 1,340

102a 338 22 4,196 249 401ab 86 61 67 39 83 2,690 8,334 110R 136a 33 12a 9 179bc 170 24a 1,188

112a 282 26 4,150 419 351a 99 64 53 29 80 2,645 8,312 161-49C 203a 39 8a 11 133a 211 41ab 1,372

128 394 27 4,515 367 414 95 69 75 43 91 2,925 9,143 PRI 256 35 19 11 169 194 53 1,276

131 357 21 3,780 322 373 92 69 76 50 189 2,521 7,982 RDI 233 32 19 8 154 169 43 1,146

176 253 17 2,334 347 340 62 33 16 14 152 2,523 6,266 2015 98 46 1.40 9 66 199 – 1,002

82 498 31 5,961 342 447 125 106 135 79 129 2,923 10,854 2016 391 21 37 10 256 164 – 1,420

* ns ns ns ns * ns ns ns ns ns ns ns R * ns ** ns * ns * ns

ns ns ns * ns ns ns ns ns ns ns ns * IM ns ns ns ns ns ns ns ns

** * * *** ns ** *** *** *** *** ns ns *** Year ** * *** ns *** ns – **

ns ns ns ns ns ns ns ns ns ns ns ns ns R x IM ns ns ns ns ns ns ns ns

24 41 89 2,941 39b 1,565 203 578 172a 20 1,123 8,405 140Ru 22 73 71 59 100 325 140Ru 284 589 114 1,257 2,244 140Ru 998b 29 183a 477ab 78 112 227 243 2,211ab 21,147

598 63 72 3,858 19a 1,798 174 878 270b 19 1,006 10,834 1103 P 11 79 70 35 117 312 1103P 307 701 118 1,242 2,368 1103 P 1,233b 33 210a 606b 16 172 254 10 2,512b 25,471

23 56 77 3,239 38b 1,462 140 778 200a 19 1,402 9,430 41B 5 67 75 22 95 264 41B 313 690 96 1,129 2,229 41B 1,091b 35 260a 453ab 16 148 178 12 2,169ab 22,852

24 50 82 2,639 14a 1,112 69 885 170a 17 1,492 8,293 110R 28 43 50 15 81 218 110R 271 581 69 605 1,527 110R 476a 23 276ab 292a 7 181 178 14 1,429a 19,800

26 47 53 3,461 10a 1,612 137 1102 221ab 15 1,278 9,961 161-49C 6 56 60 17 78 218 161-49C 288 639 77 834 1,839 161-49C 452a 29 384b 361a 20 173 205 14 1,617a 21,947

73 52 71 3,381 22 1,605 148 979 219 19 1,253 9,808 PRI 17 65 67 25 95 269 PRI 305 661 84 1,056 2,105 PRI 889 30 279 491 15 182 215 12 2,092 23,416

205 51 78 3,074 26 1,415 141 710 194 17 1,268 8,962 RDI 12 63 64 34 93 266 RDI 280 619 106 971 1,978 RDI 811 29 246 385 40 133 202 105 1,884 21,071

14 33 58 2,955 5 1,054 22 1532 282 1 482 7,857 2015 8 49 27 20 37 141 2015 237 673 108 6 1,023 2015 6 – 328 334 24 279 83 – 1,111 16,400

264 70 92 3,501 43 1,966 268 157 131 35 2,039 10,912 2016 21 79 104 39 151 393 2016 349 607 82 2,021 3,059 2016 1,694 – 198 542 31 36 333 – 2,864 28,087

ns ns ns ns ** ns ns ns * ns ns ns R ns ns ns ns ns ns R ns ns ns ns ns R * ns * * ns ns ns ns * ns

ns ns ns ns ns ns ns ns ns ns ns ns IM ns ns ns ns ns ns IM ns ns ns ns ns IM ns ns ns ns ns * ns ns ns ns

ns ** * ns *** ** ** ** *** *** *** * Year ns * *** ns ** *** Year * ns ns *** ** Year *** ns ** * ns *** *** ns *** **

ns ns ns ns ns ns ns ns ns ns ns ns R x IS ns ns ns ns ns ns R x IS ns ns ns ns ns R x IS ns ns ns ns ns ns ns ns ns ns

‘ns’ not significant; *, **, and *** indicate significant differences at the 0.05, 0.01, and 0.001 levels of probability, respectively. In each column and for each factor or interaction, different letters indicate significant differences according to Duncan’s multiple range test at the 95% confidence level. Sources of OTV for volatile compounds: Hough et al. (1982); Belitz and Grosch (1999); Louw et al. (2010); Pérez-Oliveiro et al. (2014); Cortés-Dieguez et al. (2015); Feng et al. (2015); Wu et al. (2016); Gónzalez-Robles et al. (2016); NIH Pubchem (2018); Bouzas-Cid et al. (2018). na: not available.

4. Discussion

was significantly affected by the rootstock. Overall, wine phenolics mirrored differences in fruit phenolics (Romero et al., 2018), as supported by other studies (Casassa et al., 2015). Wines from low vigor/ high berry quality rootstocks (161-49C, 110R) enhanced significantly the wine phenolic concentration and chromatic characteristics, giving

4.1. Rootstock effect The chemical composition and global quality of Monastrell wine 9

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Table 9 Averaged values of the scores obtained in the tasting and sensory analysis of the wines after alcoholic fermentation, for each rootstock, irrigation method, and year and the interaction (R x IM). The data are from three years (2014–2016).

Rootstock (R) 140Ru 1103 P 41B 110R 161-49C Irrigation method (IM) PRI RDI Year 2014 2015 2016 Interaction (R x IM) 140Ru PRI RDI 1103 P PRI RDI 41B PRI RDI 110R PRI RDI 161-49C PRI RDI ANOVA Rootstock (R) Irrigation method (IM) Year Interaction (R x IM)

Visual phase

Olfactory phase

Gustatory phase

Harmony

Final score

1.8a 3.0b 2.7b 1.9a 1.8a

Int. 4.3a 6.5b 6.5b 5.1a 4.6a

Qual. 5.8ab 7.2c 6.8bc 5.5a 5.6a

Total 10.1a 13.6b 13.3b 10.6a 10.2a

Int. 5.7a 7.8bc 9.8d 8.5cd 6.8ab

Qual. 6.5a 8.5bc 9.8c 7.5ab 6.7a

Total 12.2a 16.3c 19.5d 16.0bc 13.5ab

7.1a 10.7cd 12.2d 9.1bc 8.3ab

31.1a 43.6c 47.7c 37.6b 33.8ab

2.1 2.3

5.8 4.9

6.4 6.00

12.2 10.9

8.5 6.9

8.6 7.0

17.2 13.9

10.3 8.6

41.8 35.8

1.8a 2.4b 2.4b

3.3a 6.0b 6.8c

4.1a 6.4b 8.1c

7.5a 12.4b 14.9c

5.1a 6.3b 11.8c

7.1a 7.9ab 8.4b

12.2a 14.2b 20.2c

6.9a 8.7b 12.9c

28.3a 37.7b 50.3c

1.9ab 1.7a 3.1c 2.8bc 1.8a 3.5c 2.1ab 1.8a 1.6a 1.9ab

4.5ab 4.0a 8.0d 4.9ab 5.9bc 7.1cd 5.4ab 4.8ab 5.3ab 3.8a

6.2 5.5 7.9 6.4 6.3 7.3 6.0 5.0 5.6 5.7

10.7ab 9.5a 15.9d 11.3ab 12.3bc 14.4cd 11.4ab 9.8ab 10.9ab 9.5a

6.6 4.8 9.2 6.4 10.5 9.0 9.8 7.3 6.7 7.0

7.1abcd 5.9ab 9.3def 7.8bcde 10.6f 9.0cdef 9.7ef 5.3a 6.5ab 6.9abc

13.7 10.7 18.5 14.2 21.1 18.0 19.4 12.6 13.2 13.9

7.6a 6.6a 12.8c 8.5ab 12.4c 12.0c 10.9bc 7.3a 7.8a 8.9ab

33.8ab 28.4a 50.3c 36.8b 47.6c 47.9c 43.8c 31.4ab 33.4ab 34.2ab

*** ns * **

*** ** *** **

** ns *** ns

*** * *** **

*** *** *** ns

*** *** ns *

*** *** *** ns

*** ** *** *

*** *** *** **

‘ns’ not significant; *, **, and *** indicate significant differences at the 0.05, 0.01, and 0.001 levels of probability, respectively. In each column and for each factor or interaction, different letters indicate significant differences according to Duncan’s multiple range test at the 95% confidence level.

also higher QIwine scores than high vigor/low berry quality rootstocks (1103 P, 140Ru). In particular, rootstock 161-49C - which was one of the least productive and gave grapes with the highest phenolic content in the berries (total anthocyanins), highest nutraceutical potential (resveratrol derivatives), and better global berry quality (QIoverall) (Romero et al., 2018) - also gave the wines with the highest color intensity, sugars content, antioxidant activity, and global quality (QIwine) (Tables 2 and 6). The HPLC analysis also revealed that the levels of derivatives of anthocyanins (mainly malvidin 3-O-monoglucoside, the most abundant tri-hydroxylated anthocyanin) and flavonols (quercetin, miricetin) were significantly enhanced in wines from 161-49C. An increase in trihydroxylated anthocyanins, especially malvidin derivatives, has been observed also in other DI studies (Castellarin et al., 2007a, b; Santesteban et al., 2011; Brillante et al., 2018) and rootstocks studies (Koundouras et al., 2009), suggesting a shift in the anthocyanin biosynthesis pathway towards higher transcription levels of F3´5´H (rather than F3´H) and stimulation of the activity of the 3´O and 5´O methyltransferase enzymes, resulting in an increase in methylated anthocyanins, mainly malvidin (Romero et al., 2016b). In contrast, in Cabernet Sauvignon (CS), Bindon et al. (2008) found that PRI changed the methylation step of anthocyanin synthesis, increasing non-malvidin anthocyanins, while malvidin‐glucosides were unaffected. The enrichment in trihydroxylated anthocyanins (mainly malvidin 3-O- monoglucosides) constitutes also an enrichment of purple/blue pigments - hence modifying the must and wine quality (Castellarin et al., 2006). The 161-49C wines showed lower L*, h*, and b* and higher C* and a* (Table 3), which means darker and more intense wine color, a greater fraction of blue and red colors, greater purity, and more vivid color. In red wines, these metabolites (malvidin, flavonols) are important components in the determination of wine style and quality, as flavonols can act as copigments and conjugate to anthocyanins, a process called copigmentation that reinforces the long-term stability of

wine color (Castellarin et al., 2012), especially in young wines where the copigmentation fraction is maximal (Heras-Roger et al., 2016). Therefore, 161-49C wines with higher malvidin and flavonols contents, probably increased copigmentation processes, which also involves a wine color shift, towards increased color intensity, darker red hues, lower yellow hues and higher percentage of blue color (darker and purplish wines) (Heras-Roger et al., 2016). This improvement in wine color characteristics may have contributed to the good visual appreciation of these wines (Table 9). Besides, the enhanced presence of malvidin forms of anthocyanins and flavonols derivatives, and greater antioxidant activity, in wines can lead to multiple health benefits in humans (De Vries et al., 2001; Graf et al., 2005; Quintieri et al., 2013; Wang et al., 2018). In contrast, wines from rootstock 140Ru, showed the lowest CI, C*, and a* and the highest L*, which means lighter wine color, lower purity, less vivid color, and a lower fraction of red color (Table 3). The lower berry weight and yield (Romero et al., 2018), and lower must percentage (60.9%) for rootstock 161-49C (data not shown), could also have enhanced the levels of metabolites in the must and wine, due to a greater concentration effect. In contrast, bigger berries (110R and 140Ru, Romero et al., 2018) and a higher must percentage (63.5 and 62.7%, respectively, data not shown) probably produced a dilution effect and reduced the total concentration of flavonoids in the must and wine (Romero et al. 2016). The concentrations of micronutrients, such as Zn and B, were higher in 161-49C wines than in 41B (B) or 140Ru wines (Zn) (Table 7), mirroring what was observed in the leaves (higher B) (Romero et al., 2018) and grapes (higher B) (Table 2S), probably also due to a greater concentration effect. Boron increases the synthesis of carbohydrates and their movement, especially that of sucrose from the leaves to the roots and fruiting bodies, thus increasing the sugar content in the fruit (Batukaev et al., 2016). This could explain the increased sugar content in 161-49C berries, despite the fact that the plants had lower 10

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0.03 −0.46** −0.31 −0.31 −0.25 −0.53**

0.05 −0.52** −0.30 −0.36 −0.28 −0.58***

0.14 −0.53** −0.34 −0.43* −0.35 −0.56**

photosynthesis rates and total leaf area than other rootstocks (Romero et al., 2018). The greater sugar contents in 161-49C berries and musts were also reflected in higher sugar contents after fermentation in 16149C wines (Table 6). On the other hand, the concentrations of some important macronutrients, such as P and K, were reduced in wines from rootstock 161-49C, in accordance with the lower mineral content in grapes (P, Na, Cu, Table 2S) and leaves (N, P, Romero et al., 2018). This was probably due to greater cumulative vine water stress and a lower water uptake/transport capacity in this rootstock (Romero et al., 2018). The content of P in wines is usually related to its levels in their musts, but part of it also comes from soil (Sirén et al., 2015). Rootstocks 161-49C and 140Ru also increased significantly the total amino acids in the must (Table 5) and wines (Table 6), compared to the other rootstocks, which could also have altered the wine metabolomic and aromatic composition. In particular, 161-49C must had higher concentrations of Gly, Tyr, and Phe, while 140Ru must had more Ser, Arg, Glu, Ala, and Pro (Table 5). The amino acids in must (as a N source) serve as precursors for the synthesis of wine volatile aromatic compounds during fermentation, such as higher alcohols (Bell and Henschke, 2005), thus contributing to the overall taste of the wine (Grimplet et al., 2009). Therefore, the increase in the concentration of the major nitrogenous compounds - such as total amino acids, arginine, or glutamine (important N sources for yeast) - and consequently of the yeast assimilable nitrogen (YAN) may have favoured the balance between desirable and undesirable chemical and sensory wine attributes (Bell and Henschke, 2005). The significantly higher concentration of Pro (the most abundant amino acid in must and wines) in 140Ru and 161-49C wines (Tables 6, 1S) could have contributed to an enhancement of sweetness (Deluc et al., 2009). In previous work, Pro and Arg were closely associated with the body and balance scores of the wines in sensory tests (Chang et al., 2014). This is also congruent with the better final scores and greater preference obtained by the wines from these two rootstocks in the sensory analysis (Table 9). The significant (P < 0.05) correlation coefficients between the polyphenol content (anthocyanins, IPTs, tannins, QIwine), alcoholic degree, and score in the wine sensory analysis (Table 10) suggest that, overall, the greater the polyphenolic and alcohol content of the wine, the better valued and more preferred by the tasters it was, as occurred for 161-49C and 110R wines (Table 9). A higher alcohol (ethanol) content in wines leads to a greater perceivable alcoholic aroma and hot mouthfeel (Hopfer et al., 2015), and has been shown to decrease the perception of astringency and roughness (McRae and Kennedy, 2011). In addition, anthocyanins contribute to wine flavor, as high levels of anthocyanins are necessary to obtain not only visual intensity, but also balance and mellowness (Cheynier et al., 1998). Higher concentrations of anthocyanins and polysaccharides (glucose, fructose, total sugars) and a higher anthocyanins/sugars ratio (161-49C wines, Tables 2 and 6) could have contributed to a better mouthfeel perception of these wines, as this has been associated previously with lower astringency ratings of wines and reduction of the unpleasant “puckering” sensation of young wines (McRae and Kennedy, 2011). Another interesting point is that 140Ru wines, despite having lower polyphenolic contents and worse chromatic characteristics, were among the wines that were best valued and most preferred by the tasters (Table 9). This indicates that factors additional to the gross polyphenolic content and chromatic characteristics come into play in the better sensory and organoleptic perception of wines. High quality is not driven by individual sensory descriptors, but is the result of several/ multiple descriptors acting together in the wine matrix (Hopfer et al., 2015). In particular, wine quality has been claimed to depend also on the anthocyanins to tannins ratio (Cheynier et al., 1998). Therefore, lower tannins levels and higher anthocyanins/tannins ratios, associated with a high alcohol content, as observed in wines from rootstocks 140Ru and 161-49C (Table 2), could also have contributed to a better sensory perception of these wines; these properties have been related previously

‘ns’ not significant; *, **, and *** indicate significant differences at the 0.05, 0.01, and 0.001 levels of probability, respectively.

−0.02 −0.13 0.02 −0.19 −0.29 −0.25 0.07 −0.61*** −0.47** −0.33 −0.17 −0.62*** 0.32 −0.60*** −0.40* −0.58*** −0.44* −0.57** 0.29 −0.70*** −0.50** −0.60*** −0.42* −0.68*** 0.33 −0.47** −0.28 −0.53** −0.43* −0.44* 0.08 −0.13 −0.06 −0.24 −0.41* −0.08 −0.13 0.91*** 0.70*** 0.63*** 0.38* −0.56** 0.36* 0.06 0.84*** −0.68*** 0.61*** 0.27 0.13 0.78*** 0.18 IC IPT Tannins Anthoc. Alcohol QIwine

Gustatory phase (Total) Gustatory phase (qual.) Gustatory phase (Int.) Olfactory phase (Total) Olfactory phase (qual.) Olfactory phase (Int) Visual Phase QIwine Alcohol Anthoc. Tannins IPT

Table 10 Matrix of Pearson´s correlation coefficients obtained between wine quality parameters and the sensory analysis of the wines. The data are from three years (2014–2016).

Harmony

Total score of tasting

P. Romero, et al.

11

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Fig. 1. Significant linear relationships found between the polyphenols content (IPTs, anthocyanins, tannins, and QIwine) of the wines and the concentrations of different families of aromatic compounds in the wines (alcohols, esters, volatile fatty acids, and the total aromatic compounds). The data are from two years (2015–2016).

to more intense and balanced wines, mellowness, mouthfullness, lower astringency, and “green” tannin perception which is also possibly enhanced by alcohol (Cheynier et al., 1998).

The NMR metabolomic analysis revealed other positive chemical features that may also have contributed to a greater taste preference for these wines (161-49C and 140Ru): lower methanol, acetic acid, and 12

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Fig. 2. Significant linear relationships found between the score obtained in the sensory analysis of the wines (intensity and quality of olfactory and gustatory phases) and the concentrations of different families of aromatic compounds in the wines (alcohols, esters, terpenes, volatile fatty acids, and the total aromatic compounds). The data are from 2015 and 2016.

galacturonic acid (161-49C), higher tartaric acid (161-49C) and lactic acid (140Ru), and a higher tartaric/malic ratio (161-49C, 140Ru) (Tables 1S and 8). A lower malic acid concentration, higher tartaric/ malic ratio, and higher lactic acid concentration in wines can suppose the replacement of the strong "green" taste of L-malic acid with the less aggressive or slightly sour taste of lactic acid (Volschenk et al., 2006). Tartaric acid is responsible for much of the tart taste of wine and wine acidity (Chang et al., 2014), contributing to both the biological stability and the longevity of wine (Conde et al., 2007), and has more pleasant organoleptic properties than malic acid (Poni et al., 2018). In addition,

140Ru wines had significantly higher concentrations - above the odor threshold value (Table 8) - of an aromatic ester, methyl nonanoate (compared to 1103 P, 110R, or 161-49C wines), which could also confer a coconut, floral, fruit odor on these wines (NIH, Pubchem, 2018). In contrast, wines from rootstocks 41B and 1103 P showed some negative chemical features (defect-causing compounds), such as higher total tannins (41B, 1103 P), greater methanol and acetaldehyde contents (41B), both above their odor threshold values (100 mg L−1 for methanol and 19.2 mg L−1 for acetaldehyde, Gónzalez-Robles and Cook, 2016), greater galacturonic (41B) and acetic acids contents 13

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Fig. 3. Principal component analysis store plot of factorial weight of the main variables of each component (left), factorial stores of different combinations (rootstock x irrigation method) and groupings made from cluster analysis taking into account each component (right). In different colors the results for each component are shown (red, PC1, green, PC 2 and blue, PC3). Data from three years (2014–2016) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

(1103 P), and lower anthocyanins/tannins ratios (41B, 1103 P), ºalcohol (41B), sugar contents (41B), and tartaric/malic ratios (Tables 2,6,8 and 1S). High amounts of tannins are undesirable because they are related to bitter, astringent, and dry sensations (Chang et al., 2014; Cheynier et al., 2006; Sáenz-Navajas et al., 2018). In addition, a lower tartaric/malic ratio confers a green taste on wine and excessive amounts of acetic acid are detrimental to the taste, giving a wine that tastes sour and thin and is less appealing to consumers, with an unpleasant aroma and palate (Joyeux et al., 1984; Conde et al., 2007). A high galacturonic acid content is not desirable either and is associated with the degradation of pectic compounds due to grape infection by Botrytis cinérea (Mihaljevic Zulj et al., 2015). In addition, acetaldehyde (related to wine oxidation) imparts a green, bruised apple, or nutty aroma (Bell and Henschke, 2005) and can increase the perception of astringency (McRae and Kennedy, 2011), while excess methanol is not desirable due to its toxicity and characteristic sweet, pungent, solventlike odor (Gónzalez-Robles and Cook, 2016). These factors may have contributed to the worse overall sensory and organoleptic perception of these wines. The 41B and 1103 P wines showed more negative than positive attributes (Fig. 1S), and were the ones valued worst (Table 9). Among the negative attributes reported by the panel members for these

wines stand out: color with a light layer, light browning, fruits not fully ripe, notes of greenery on the nose and mouth, lack of structure and body in the mouth, slight reduction, poor acidity, and not very intense flavor, notes of pepper and acetaldehyde, astringent and bitter mouthfeel and dryness. This is congruent with the results of Hopfer et al. (2015), who reported that expert tasters associated low wine quality with the presence of defects and flaws, such as microbial spoilage, the presence of atypical aromas (e.g., vegetable-green) or oxidation aromas, or an unbalanced flavour profile. The rootstock also altered the concentrations of volatile aromatic compounds, mainly higher alcohols (HA) and esters. Some of these key odor active compounds - surpassed their odor threshold values (OTV) (Table 9), which may have also altered the sensory perception of the wines (Table 9). Interestingly, the most aromatic wines (1103 P and 41B) had lower polyphenolic contents (in accordance with the inverse relationship between polyphenols and aromatic compounds, Fig. 1) and, besides, were those least preferred by the experts (Table 9). Rather, wines of low or medium aromaticity (161-49C, 140Ru, 110R) were the ones most preferred by the tasters; they had the best scores in the olfactory phase and the best final sensory score, agreeing with the significant inverse relationship between the concentration of aromatic 14

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compounds in the wine and the score obtained in the sensory tasting (Fig. 2). These results are in agreement with other observations where wines of high quality had volatile profiles without extreme concentrations (Hopfer et al., 2015). Therefore, not all volatile aromatic compounds are associated with a pleasant aroma and not all contribute to the aroma equally (Icc et al., 2016). For example, the most aromatic wines from rootstock 1103 P, which was the worst evaluated in the olfactory phase (Table 9), had higher levels of total HA, propanol, hexanol, esters such as ethyl dodecanoate, 3 methyl ethyl butanoate (above its OTV; also in 41B wine), ethyl 3−OH-butanoate, and diethyl succinate, and 2 octanone (above its OTV; also in 41B and 140Ru wines) (Table 8). In wines with high HA levels, red fruit aroma and woody character can be masked or suppressed, thus decreasing the appreciation, preference, and perceived quality of fruity and woody wines (De-La-Fuente-Blanco et al., 2016; De la et al., 2017).In addition, alcohols with six carbon atoms (hexenols and hexanols) bestowed plant (grass) and herbaceous nuances on the wines (Vilanova et al., 2012; Cortés-Dieguez et al., 2015). Propanol may also confer an odor quality described as pungent, harsh, ripe fruit, and alcohol (Cortés-Dieguez et al., 2015). Besides, diethyl succinate and 3 methyl-ethyl-butanoate are compounds related to ageing and/or oxidation reactions, and have been associated with green flavor/dried fruit aroma (Hopfer et al., 2015), while 2 octanone (a methyl ketone) may confer a fatty, green cheese aroma and a bitter, fruity, and camphor taste, giving altogether an unpleasant odor and flavor (Belitz and Grosch, 1999). Similarly, previous studies reported that with increased levels of some alcohols (1butanol) and esters (3-methyl ethyl butanoate, ethyl 2-methyl butanoate, among others) wine experts scored the wines lower for quality (Hopfer et al., 2015). In contrast, 161-49C and 110R wines had lower concentrations of alcohols (mainly hexanol), esters (mainly ethyl dodecanoate), 3 methyl butyl butanoate, ethyl 3−OH-butanoate (16149C), and 2-octanone, which may have contributed to a better olfactory perception of these wines.

PRI wines in the sensory evaluation and organoleptic perception (olfactory and gustatory phases) (Table 9). In CS, PRI increased the presence of volatile C13-norisoprenoids - some with positive sensory characteristics in wines (β-damascenone, β-ionone) and others with negative characteristics, such as a kerosene-like odor (1,1,6-trimethyl1,2-dihydronaphtalene, TDN) (Bindon et al., 2007). Nevertheless, the significant interactive (R x IM) effects on the wine chemical composition indicate that, overall, for low-medium vigor rootstocks (especially 161-49C followed by 41B), PRI was more beneficial (than RDI) since it increased blue, red, and yellow color components, (161-49C, 41B, Table 2), anthocyanin derivatives (malvidin, 161-49C), amino acids (Arg, 161-49C, 41B), reducing sugars (41B, 16149C), fructose (161-49C), total sugars (161-49C), anthocyanins/sugars ratio (161-49C), QIwine (161-49C), flavonol derivatives (41B) and visual perception (41B). In addition, 110R PRI wines showed also some improvements in their phenolic composition compared to 110R RDI wines, but gustatory perception and final sensory score was significantly worse, probably due to lower content of fructose and total sugars (Table 6) and higher content of aromatic compounds (total alcohols, Table 8). On the other hand, for rootstocks of higher vigor and productivity (1103 P, 140Ru) PRI did not produce clear advantages in the wine; rather, RDI was more beneficial for wine composition and global quality due to an improved polyphenolics concentration, QIwine, and sensory perception. PCA-cluster analysis also showed that 161-49C PRI wines were clearly differentiated from the other rootstock-IM combinations, mainly for their best quality (higher QIwine) and alcohol (PC3). In contrast, 41B PRI wines were also differentiated (for PC2 and PC3), mainly for their worst quality: higher tannins content, lower QIwine and alcohol and worse sensory evaluation (Fig. 3). 5. Conclusions Based on these results and those of Romero et al. (2018), we conclude that the choice of the rootstock had a very significant effect on the Monastrell berry and wine composition. The application of low annual water volumes (85–90 mm year−1), with well-designed DI strategies, to low vigor rootstocks (161-49C, 110R) resulted in moderate yields (7,400-9,900 kg ha−1) with higher global berry and wine quality and greater nutraceutical potential, compared to other rootstocks. Interestingly, grafting Monastrell on 140Ru increased substantially the berry yield and WUE (+72%, compared to the average of the other rootstocks), allowing the production of a greater volume of wine. These wines had poorer polyphenolic contents, nutraceutical potential, and chromatic characteristics (due mainly to greater dilution effects), but a high content of lactic acid and amino acids and tartaric/malic and anthocyanins/tannins ratios as well as a low concentration of aromatic compounds (alcohols) and were among the wines valued best and preferred by the tasters. Under semiarid conditions of SE Spain and low water irrigation volumes, the PRI method improved Monastrell berry and wine quality and organoleptic perception for low vigor rootstock (especially 16149C), compared to RDI. These wines showed darker color, higher sugar content and nutraceutical potential as well as better sensory perception compared to other rootstock-IM combinations. In contrast, for high vigor rootstocks (1103 P, 140Ru), RDI was better for wine composition, global quality, and sensory perception.

4.2. Effects of the irrigation method (PRI vs RDI) and its interaction with the rootstock The irrigation method (PRI vs RDI) also altered the chemical composition and aromatic profile of the wines, although to a lesser extent than the rootstock; the differences observed in the berries (Romero et al., 2018) were attenuated in the wines. Thus, PRI wines had significantly greater OD620nm, OD520nm, OD420nm, reducing sugars, arginine, and flavonol derivatives (quercetin 3-O-glucoside) and lower dissolved oxygen, ethyl acetate, and K and Zn concentrations compared to RDI wines. Previous studies in CS wines produced under PRI showed a significant increase (15%) in wine color density (Bindon et al., 2008). Greater color absorbance and higher concentrations of residual sugars (non-fermented) and flavonols derivatives (quercetin 3-O glucoside) may contribute to perception of a more intense wine color, greater perceived sweetness (Hopfer et al., 2015), and increased nutraceutical potential of PRI wines, respectively. Lower dissolved oxygen can also avoid oxidation processes in sensitive, young red wines, which can be beneficial in this type of wine. The lower K concentrations found in PRI wines can be positive, to avoid acidity losses and an increase in pH, but, on the other hand, lower K and Zn contents can decrease the nutritional value of the wine. In addition, overall, PRI wines were more aromatic than RDI wines, since PRI increased significantly the concentrations of total HA (mainly 2-methyl-1-butanol, the most abundant aromatic compound) and naphthalene in the wines (Table 8). Therefore, the higher concentrations of both these aromatic compounds in PRI wines, which surpassed the detectable odor thresholds (Table 8), could have conferred an unpleasant aroma and flavor (both 2-methyl-1-butanol and naphthalene confer strong and characteristic odors - corresponding to alcohol, harsh vinous, and green for 2-methyl-1-butanol and mothballs for naphthalene (NIH, Pubchem, 2018). This was reflected in the poor scores for

Acknowledgements This work was financed by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Subprograma Nacional de Recursos y Tecnologías Agrarias, en coordinación con las Comunidades Autónomas, through the Project RTA2012-00105-00-00, with the collaboration of the European Regional Development Fund. The microvinifications in 2016 and part of the wine composition analysis were financed through the Project “Vitismart ERANET: 652515 Sur Plus Co15

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found UE Program”, financed by INIA. We thank Francisco Javier Martínez López and José del Rio, for field assistance, and Eva María Arques Pardo, for support in laboratory analyses. We also thank “Juaneque”, for the winemaking process, and David J. Walker, for assistance with the manuscript preparation and correction of the written English.

inducing aroma changes and modulating experts preferences in wine models solutions. Aust. J. Grape Wine Res. 23, 162–169. Deluc, L.G., Qulici, D.R., Decendit, A., Grimplet, J., Wheatley, M.D., Schlauch, K.A., Mérillon, J.M., Cushman, J.C., Cramer, G.R., 2009. Water deficits alters differentially metabolic pathways affecting important flavour and quality traits in grapeberries of Cabernet Sauvignon and Chardonnay. BMC Genomics 10, 212. De Vries, J.H.M., Hollman, P.C.H., Van Amersfoort, I., Olthof, M.R., Katan, M.B., 2001. Red wine is a poor source of bioavailable flavonols in men. J. Nutr. 131, 745–748. Dewanto, V., Wu, X., Adom, K.K., Liu, R.H., 2002. Thermal processing enhances the nutritional value of tomatoes by increasing total antioxidant activity. J. Agric. Food Chem. 50, 3010–3014. Feng, Y., Liu, M., Ouyang, Y., Zhao, X., Ju, Y., Fang, Y., 2015. Comparative study of aromatic compounds in fruit wines from raspberry, strawberry, and mulberry in central Shaanxi area. Food Nutr. Res. 59, 29290. Fraga, H., García de Cortazar-Atauri, I., Santos, J.A., 2018. Viticultural irrigation demands under climate change scenarios in Portugal. Agric. Water Manage. 196, 66–74. Fraga, H., Malheiro, A.C., Mountinho-Pereira, J., Santos, J.A., 2013. An overview of climate change impacts on European viticulture. Food Energ. Security 1 (2), 94–110. Glories, Y., 1984. La couleur des vins rouges. 2ème partie. Mesure, origine et interpretation. Connaissance de la Vigne et du Vin 18, 253–271. Gomez-Alonso, S., Garcia-Romero, E., Hermosin-Gutierrez, I., 2007. HPLC analysis of diverse grape and wine phenolics using direct injection and multidetection by DAD and fluorescence. J. Food Anal. 20, 618–626. Gómez-Plaza, E., Mestre-Ortuño, L., Ruiz-García, Y., Fernández-Fernández, J.I., López.Roca, J.M., 2012. Effect of benzothiadiazole and methyl jasmonate on the volatile compound composition of Vitis vinifera L. Monastrell grapes and wines. Am. J. Enol. Vitic. 63 (3), 394–401. Graf, B.A., Milbuty, P.E., Blumberg, J.B., 2005. Flavonols, flavones, flavanones, and human health: epidemiological evidence. J. Med. Food 8, 281–290. Grimplet, J., Wheatley, M.D., Jouira, H.B., Deluc, L.G., Cramer, G.R., Cushman, J.C., 2009. Proteomic and selected metabolite analysis of grape berry tissues under wellwatered and water-deficit stress conditions. Proteomics 9, 2503–2528. Gónzalez-Robles, I.W., Cook, D.J., 2016. The impact of maturation on concentrations of key odour active compounds which determine the aroma of tequila. J. Inst. Brew. 122, 369–380. Guiot, J., Cramer, W., 2016. Climate change: the 2015 Paris agreement thresholds and Mediterranean basin ecosystems. Science 354, 465–468. Heras-Roger, J., Alonso-Alonso, O., Gallo-Montesdeoca, A., Díaz-Romero, C., DariasMartín, J., 2016. Influence of copigmentation and phenolic composition on wine color. J. Food Sci. Technol. 53, 2540–2547. Hopfer, H., Nelson J., Ebeler S.E., and Heymann, H. (2015). Correlating wine quality indicators to chemical and sensory measurements Molecules 20, 8453-8483. Hough, J.S., Briggs, D.E., Stevens, R., Young, T.W., 1982. Malting and Brewing Science Vol 2. Hopped Wort and Beer. Springer-Science-Business media, England B.V 885 pp. Icc, T., Werck-Reichhart, D., Navrot, N., 2016. Meta-analysis of the core aroma components of grape and wine aroma. Front. Plant Sci. 7, 1472. Iglesias, A., Garrote, L., 2015. Adaptation strategies for agricultural water management under climate change in Europe. Agric. Water Manage. 155, 113–124. Joyeux, A., Lafon-Lafourcade, S., Ribéreau-Gayon, P., 1984. Evolution of acetic acid bacteria during fermentation and storage of wine. Appl. Environ. Microb. 48, 153–156. Kaiser, H.F., 1974. An index of factor simplicity. Psychometrika 39, 31–36. Koundouras, S., Hatzidimitriou, E., Karamolegkou, M., Dimopoulou, E., Kallithraka, S., Tsialtas, J.T., Zioziou, E., Nikolaou, N., Kotseridis, Y., 2009. Irrigation and rootstock effects on the phenolic concentration and aroma potential of Vitis vinifera L. Cv. Cabernet Sauvignon grapes. J. Agric. Food Chem. 57, 7805–7813. Lopez-Artiguez, M., Camean, A.M., Repetto, M., 1996. Determination of nine elements in sherry wine by inductively coupled plasma-atomic emission spectrometry. J. AOAC Intern. 79, 1191–1197. Louw, L., Tredoux, A.G.J., Van Rensburg, P., Kidd, M., Nae, T., Nieuwoudt, H.H., 2010. Fermentation-derived aroma compounds in varietal young wines from South Africa. S. Afr. J. Enol. Vitic. 31, 213–225. McRae, J.M., Kennedy, J.A., 2011. Wine and grape tannin interactions with salivary proteins and their impact on astringency: a review of current research. Molecules 16, 2348–2364. Mihaljevic Zulj, M., Puhelek, I., Jagatic Korenika, A.M., Maslov bandic, L., Pavlesic, T., Jeromel, A., 2015. Organic acid composition in Croatian predicate wines. Agric. Conspectus Sci. 80, 113–117. Miller, J., Rice-Evans, C., Davies, M.J., Gopinathan, V., Milner, A., 1993. A novel method for measuring antioxidant capacity and its application for monitoring the antioxidant status in premature neonates. Clin. Sci. 84, 407–412. Molitor, D., Junk, J., 2019. Climate change is implicating a two-fold impact on air temperature increase in the ripening period under the conditions of the Luxembourgish grapegrowing region. OENO one 3, 409–422. NIH Pubchem (2018). Available online: https://pubchemblog.ncbi.nlm.nih.gov. Pérez-Oliveiro, S.J., Pérez-Pont, M.L., Conde, J.E., Pérez-Trujillo, J.P., 2014. Determination of lactones in wines by headspace solid-phase microextraction and gas chromatography coupled with mass spectrometry. J. Anal. Method Chem. 1–10. Poni, S., Gatti, M., Palliotti, A., Zhanwu, D., Duchene, E., Thuy-Thanh, T., Ferrara, G., Stella-Matarrese, A.M., Gallota, A., Bellincontro, A., Mencarelli, F., Tombesi, S., 2018. Grapevine quality: a multiple choice issue. Sci. Hort. 234, 445–462. Pons, A., Allamy, L., Schuttler, A., Rauhut, D., Thibon, C., Darriet, P., 2017. What is expected impact of climate change on wine aroma compounds and their precursors in grapes? Oeno One 51, 141–146. Quintieri, A.M., Baldino, N., Filice, E., Seta, L., Vitetti, A., Tota, B., De Cindio, B., Cerra, M.C., Angelone, T., 2013. Malvidin, a red wine polyphenol, modulate mammalian

Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.agwat.2019.105733. References Alikadic, A., Pertot, I., Eccel, E., Dolci, C., Zarbo, C., Caffarra, A., De Filippi, R., Furlanello, C., 2019. The impact of climate change on grapevine phenology and the influence of altitude: a regional study. Agric. For. Meteor. 271, 73–82. Amargianitaki, M., Spyros, A., 2017. NMR‑based metabolomics in wine quality control and authentication. Chem. Biol. Technol. Agric. 4 (9), 1–12. Ashenfelter, O., Storchmann, K., 2016. The economics of wine, weather and climate change. Rev. Environ. Econom. Policy 10, 25–46. Batukaev, A., Magomadov, A., Sushkova, S., Minkina, T., Bauer, T., 2016. Influence of boron fertilization on productivity of grape plants. BIO Web of Conferences 7, 1–5. Belitz, H.D., Grosch, W., 1999. Food Chemistry. Springer Verlag, Berlin 921 pp. Bell, S.J., Henschke, P.A., 2005. Implications for nitrogen nutrition for grapes. Austr. J. Grape Wine Res. 11, 242–295. Berdeja, M., Nicolas, P., Kappel, C., Dai, W., Z, H., G, P., A, L., M, O., N, Gomés, E., Delrot, S., 2015. Water limitation and rootstock genotype interact to alter berry metabolism through transcriptome reprogramming. Hortic. Res. 2, 1–13. Bindon, K., Dry, P., Loveys, B., 2007. Influence of plant water status on the production of C13-norisoprenoids precursors in Vitis vinifera L. Cv. Cabernet Sauvignon grape berries. J. Agric. Food Chem. 55, 4493–4500. Bindon, K., Dry, P., Loveys, B., 2008. Influence of partial root-zone drying on the composition and accumulation of anthocyanins in grape berries (Vitis vinifera cv. Cabernet Sauvignon). Austr. J. Grape Wine Res. 14, 91–103. Boulton, R.B., 2013. The Copigmentation Assay for Red Wines. 3 pp. available online:. Department of Viticulture & Enology, University of California, Davis. http://boulton. ucdavis.edu/copig.htm. Bouzas-Cid, Y., Falqué, E., Orriol, I., Mirás-Avalos, J.M., 2018. Effects of irrigation over three years on the amino acid composition of Treixadura (Vitis vinifera L.) musts and wines, and on the aromatic composition and sensory profiles of its wines. Food Chem. 240, 707–716. Brillante, L., Martínez-Lüscher, J., Kaan Kurtural, S., 2018. Applied water and mechanical canopy management affect berry and wine phenolic and aroma composition of grapevine (Vitis vinifera L., cv. Syrah) in Central California. Sci. Hort. 227, 261–271. Casassa, L.F., Keller, M., Harbertson, J.F., 2015. Regulated deficit irrigation alters anthocyanins, tannins and sensory properties of Cabernet Sauvignon grapes and wines. Molecules 20, 7820–7844. Castellarin, S.D., Bavaresco, L., Falginella, L., Gonzalves, M.I.V.Z., Di Gaspero, G., 2012. Phenolics in grape berry and key antioxidants. In: Gerós, M. (Ed.), The Biochemistry of Grape Berry. Bentham Science Publishing, Sharjah, U.A.E, pp. 89–110. Castellarin, S.D., Di Gaspero, G., Marconi, R., Nonis, A., Peterlunger, E., Paillard, S., Adam-Blondon, A.F., Testolin, R., 2006. Colour variation in red grapevines (Vitis vinifera L.): genomic organisation, expression of flavonoid 3′-hydroxylase, flavonoid 3′,5′-hydroxylase genes and related metabolite profiling of red cyanidin-/blue delphinidin-based anthocyanins in berry skin. BMC Genomics 7, 12. Castellarin, S.D., Matthews, M.A., Di Gaspero, G.D., Gambetta, G.A., 2007b. Water deficits accelerate ripening and induce changes in gene expression regulating flavonoid biosynthesis in grape berries. Planta 227, 101–112. Castellarin, S.D., Pfeiffer, A., Sivilotti, P., Degan, M., Peterlunger, E., Di Gaspero, G., 2007a. Transcriptional regulation of anthocyanin biosynthesis in ripening fruits of grapevine under seasonal water deficit. Plant Cell Environ. 30, 1381–1399. Cayla, L., Cottereau, P., Renard, R., 2002. Estimation de la maturité phénolique des raisins rouges par la méthode I.T.V. standard. Revue des Oenologues 193, 84–87. Chang, E.H., Jung, S.M., Park, S.J., Noh, J.H., Hur, Y.Y., Nam, J.C., Park, K.S., 2014. Wine quality of grapevine ‘Cheongsoo’ and the related metabolites on proton nuclear magnetic resonance (NMR) spectroscopy at the different harvest times. POJ 7, 80–86. Chapman, H.D., Pratt, P.F., 1978. Methods of analysis for soils, plant and waters. Univ California, Div Agric Sci, Rep 309. Cheynier, V., Fulcrand, H., Brossaud, F., Asselin, C., Moutounet, M., 1998. Phenolic composition as related to red wine flavour. In: Waterhouse, A.L., Ebeler, S.E. (Eds.), Chemistry of Wine Flavour. American Chemistry Society, WA DC, USA, pp. 124–141. Cheynier, V., Dueñas-Patón, M., Salas, E., Mauri, C., Souquet, J.M., Sarni-Manchado, P., Fulcrand, H., 2006. Structure and properties of wine pigments and tannins. Am. J. Enol. Vitic. 57, 298–305. Cortés-Dieguez, S., Rodriguez-Solana, R., Domínguez, J.M., Díaz, E., 2015. Impact odorants and sensory profile of young red wines from four Galician (NW of Spain) traditional cultivars. J. Inst. Brew. 121, 628–635. De-La-Fuente-Blanco, A., Sáenz-Navajas, M.-P., Ferreira, V., 2016. On the effects of higher alcohols on red wine aroma. Food Chem. 210, 107–114. De la, Fuente, Blanco, A., Sáenz-Navajas, M.P., Ferreira, V., 2017. Level of higher alcohols

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Agricultural Water Management 225 (2019) 105733

P. Romero, et al. myocardial and coronary performance and protects the heart against ischemia/reperfusion injury. J. Nutr. Biochem. 24, 1221–1231. Resco, P., Iglesias, A., Bardají, I., Sotés, V., 2016. Exploring adaptation choices for grapevine regions in Spain. Reg. Environ. Change 16, 979–993. Ribeiro de Lima, M.T., Waffo-Téguo, P., Teissedre, P.L., Pujolas, A., Vercauteren, J., Cabanis, J.C., Mérillon, J.M., 1999. Determination of stilbenes (trans-astringin, cisand trans-piceid, and cis- and trans-resveratrol) in Portuguese wines. J. Agric. Food Chem. 47, 2666–2670. Romero, P., Fernández-Fernández, J.I., Martínez-Cutillas, A., 2010. Physiological thresholds for efficient regulated deficit-irrigation management in winegrapes grown under semiarid conditions. Am. J. Enol. Vitic. 61, 300–312. Romero, P., Gil Muñoz, R., Fernández-Fernández, J.I., del Amor, F.M., Martínez-Cutillas, A., García-García, J., 2015. Improvement of yield and grape and wine composition in field-grown Monastrell grapevines by partial root zone irrigation, in comparison with regulated deficit irrigation. Agric. Water Manage. 149, 55–73. Romero, P., Fernández-Fernández, J.I., Gil-Muñoz, R., Botía, P., 2016a. Vigour-yieldquality relationships in long-term deficit-irrigated wine grapes grown under semiarid conditions. Theor. Exp. Plant Physiol. 28, 23–51. Romero, P., García-García, J., Fernández-Fernández, J.I., Gil-Muñoz, R., del Amor, F., Martínez-Cutillas, A., 2016b. Improving berry and wine quality attributes and vineyard economic efficiency by long-term deficit irrigation practices under semiarid conditions. Sci. Hort. 203, 69–85. Romero, P., Fernández-Fernández, J.I., Botía, P., 2016c. Interannual climatic variability effects on yield, berry and wine quality indices in long-term deficit irrigated grapevines, determined by multivariate analysis. Int. J. Wine Res. 8, 3–17. Romero, P., Botía, P., Navarro, J.M., 2018. Selecting rootstocks to improve vine performance and vineyard sustainability in deficit irrigated Monastrell grapevines under semiarid conditions. Agric. Water Manage. 209, 73–93. Rosen, H., 1957. A modified ninhydrin colorimetric analysis for amino acids. Arch. Biochem. Biophys. 67, 10–15. Sáenz-Navajas, M.P., Arias, I., Ferrero-del Teso, S., Fernández-Zurbano, P., Escudero, A., Ferreira, V., 2018. Chemo-sensory approach for the identification of chemical compounds driving green character in red wines. Food Res. Int. 109, 138–148.

Santesteban, L.G., Miranda, C., Royo, J.B., 2011. Regulated deficit irrigation effects on growth, yield, grape quality and individual anthocyanin composition in Vitis vinifera L. Cv. Tempranillo. Agric. Water Manage. 98, 1171–1179. Sarneckis, C.J., Dambergs, R.G., Jones, P., Mercurio, M., Herdewrich, M.J., Smith, P.A., 2006. Quantification of condensed tannins by precipitation with methyl cellulose: development and validation of an optimised tool for grape and wine analysis. Austr. J. Grape Wine Res. 12, 39–49. Sirén, H., Sirén, K., Sirén, J., 2015. Evaluation of organic and inorganic compounds levels of red wines processed from Pinot Noir grapes. Anal. Chem. Res. 3, 26–36. Tóth, J., Végváry, Z., 2016. Future of winegrape growing regions in Europe. Austr. J. Grape Wine Res. 22, 64–72. Tounsi, M.S., Wannes, W.A., Ourghemmi, I., Jegham, S., Njima, Y.B., Hamdaoui, G., Zemni, H., Marzouk, B., 2011. Juice components and antioxidant capacity of four Tunisian Citrus varieties. J. Sci. Food Agric. 91, 142–151. Van leeuwen, C., Destrac-irvine, A., 2017. Modified grape composition under climate change conditions requires adaptations in the vineyard. OENO one 51, 147–154. Vilanova, M., Campo, E., Escudero, A., Graña, M., Masa, A., Cacho, J., 2012. Volatile composition and sensory properties of Vitis vinifera red cultivars from North West Spain: correlation between sensory and instrumental analysis. Anal. Chim. Acta 720, 104–111. Volschenk, H., van Vuuren, H.J.J., Viljoen-Bloom, M., 2006. Malic acid in wine: origin, function and metabolism during vinification. S. Afr. J. Enol. Vitic. 27, 123–136. Wang, J., Hodes, G.E., Zhang, H., Zhang, S., Zhao, W., Golden, S.A., Bi, W., Menard, C., Kana, V., Leboeuf, M., Xie, M., Bregman, D., Pfau, M.L., Flanigan, M.E., EstebanFernández, A., Yemul, S., Sharma, A., Ho, L., Dixon, R., Merad, M., Han, M.H., Russo, S.J., Pasinetti, G.M., 2018. Epigenetic modulation of inflammation and synaptic plasticity promotes resilience against stress in mice. Nat. Commun. 9 (477), 1–14. Wolkovich, E.M., García de Cortazar-Atauri, I., Morales-castilla, I., Nicholas, K.A., Lacombe, T., 2018. From Pinot to Xinomavro in the world´s future wine-growing regions. Nat. Clim. Change 8, 29–37. Wu, Y., Duan, S., Zhao, L., Gao, Z., Luo, M., Song, S., Xu, W., Zhang, C., Ma, C., Wang, S., 2016. Aroma characterization based on aromatic series analysis in table grapes. Sci. Rep. 6, 31116.

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