Influence of geographical location, site and silvicultural parameters, on volatile composition of Quercus pyrenaica Willd. wood used in wine aging

Forest Ecology and Management 262 (2011) 124–130

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Influence of geographical location, site and silvicultural parameters, on volatile composition of Quercus pyrenaica Willd. wood used in wine aging ˜ a,∗ , M. Soledad Pérez-Coello a , Ignacio J. Díaz-Maroto b , Pedro J. Martín-Alvarez c , M. Elena Alanón Pablo Vila-Lameiro b , M. Consuelo Díaz-Maroto d a

Área de Tecnología de los Alimentos, Facultad de Ciencias Químicas, Universidad de Castilla-La Mancha, Avd. Camilo José Cela, 10, 13071 Ciudad Real, Spain Departamento de Ingeniería Agroforestal, Escuela Politécnica Superior, Campus Universitario, s/n, Universidad de Santiago de Compostela, 27002 Lugo, Spain c Instuto de Fermentaciones Industriales, Consejo Superior de Investigación Científica, Juan de la Cierva 3, 28006 Madrid, Spain d IRICA (Instituto Regional de Investigación Científica Aplicada), Universidad de Castilla La-Mancha, Campus Universitario, s/n, 13071 Ciudad Real, Spain b

a r t i c l e

i n f o

Article history: Received 28 January 2011 Received in revised form 7 March 2011 Accepted 8 March 2011 Available online 7 April 2011 Keywords: Quercus pyrenaica Wine aging Volatile compounds Geographical location Silvicultural parameters

a b s t r a c t For the current study, the volatile and semi-volatile composition of several samples of Quercus pyrenaica wood from NW Spain were analyzed and compared. The research was performed on a wide sample set of more than 100 samples (test tubes) obtained from different stands of this species. The relationship between some silvicultural and site parameters and volatile composition was studied. Altitude seemed to be the most influential parameter on the volatile composition. However, other factors such as distance from tree center, average annual precipitation, and number of trees per hectare whose effects on the volatile compounds were not significant. The influence of soil texture was not a determining factor while geographical location seemed to have a more specific impact on the extractive volatile content. The content of all extractable compounds studied allowed a good separation of oak samples of the same species according to their geographical origin. © 2011 Elsevier B.V. All rights reserved.

1. Introduction The aging process, by means of oak barrels or by using oak chips, plays a significant role in winemaking due to its effect on the flavor, color, tannin profile and texture of the resulting wines. These new features, acquired for aged wines during their period of maturation, are appreciated and well-valued by consumers. Traditionally, three oak species have been widely utilized in cooperage for barrel production with aging purposes (Quercus alba L., Quercus petraea (Matt.) Liebl. and Quercus robur L.). Forests in America, for the former species, and France for the rest, are the main sources of these oak wood species. Indeed, in the field of oenology, the type of oak wood commonly used in the maturation process is known as American or French oak wood. Therefore, due to their broad employment, the chemical composition of oak wood from forests of these regions has been widely studied (Chatonnet and Dubourdieu, 1998; Doussot et al., 2002; Nishimura and Matsuyama, 1989; Pérez-Coello and Díaz-Maroto, 2009; Prida and Puech, 2006). But the extensive exploitation of many of these forests has generated the necessity to look for alternatives for these oak wood species. For that reason, new types of oak wood such as Q. petraea and Q. robur species from Eastern European countries like Ukraine,

∗ Corresponding author. Tel.: +34 926295300x3425; fax: +34 926295318. ˜ E-mail address: [email protected] (M.E. Alanón). 0378-1127/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2011.03.011

Russia, Romania, Hungary, etc., (Prida and Puech, 2006) and other species like Q. pyrenaica have been considered for their employment in cooperage (Fernández de Simón et al., 2006, 2008). From an oenological point of view, some of the main extractable oak wood components are volatile compounds. These compounds are mainly responsible for the flavor and taste of aged wines which implies an improvement in their organoleptic properties. Among volatile compounds found in oak wood, especially remarkable for their sensory impact are: cis- and trans-ˇ-methyl--octalactones, also known as oak lactones, a key compound in aged wines due to their oak or coconut flavor and low perception threshold (Abbott et al., 1995; Günther and Mosandl, 1986); vanillin and its derivatives, described as responsible for the vanillin flavor and small free phenols such as eugenol and guaiacol, characterized by spice and smoke flavors (Boidron et al., 1988; Díaz-Maroto et al., 2008). Several terpenic and norisoprenoid compounds have been also described in oak wood. Although these compounds are not found in high quantities, their presence is important due to their sensorial properties described as floral, fruity, tea, and tobacco (Sefton et al., 1990). Other semi-volatile compounds also detected in oak wood samples and less studied are triterpenic components such as ˜ sterols and vitamin E (Alanón et al., 2009; Guillén and Manzanos, 2001). Q. pyrenaica is an autochthonous oak species of the Iberian Peninsula, known as “rebollo” or “melojo”, whose natural distribution covers a wide area in Spain. Although traditionally its wood

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125

Table 1 Overall information on sampling and silvicultural parameters (n = 107). Provenance

Samples

Lugo

45

Ourense

57 5

Pontevedra a b c d e f

Soil texture Sandy-loam Loamy Clay-silt Loamy-sand

d centera

ALTb

NTHc

Precipd

tempe

2–10

387–1172

376.6–4230.8

823–1457

10.7–12.8

Sandy-loam Loamy Loamy-sand

1.5–8.5

550–1136

384.0–3266.7

748–1419

7.4–13.1

Sandy-loam

3.3–7.5

772

700

1290

11.8

MOf 1.87–11.30

1.09–5.62 4.69

Distance to center expressed in cm. Altitude expressed in m. Number of trees per hectare. Annual average precipitation expressed in mm3 year−1 . Annual average temperature expressed in ◦ C. MO: soil organic matter.

has been undervalued, being used mainly for industrial uses, firewood and charcoal (Díaz-Maroto et al., 2007), recent studies of ellagitannins, phenolic composition and volatile compounds have demonstrated its suitability for carrying out wine maturation processes. Phenolic composition of Q. pyrenaica seems to be similar to those of other species commonly used in cooperage, showing some quantitative differences which are more significant when compared to American rather than French oak wood species (Fernández de Simón et al., 2006). Regarding its behaviour in the aging process, red wines matured with this type of wood were characterized by high levels of eugenol, guaiacol and other volatile phenols and furanic aldehydes. And with regard to phenolic aldehydes and ketones, they seem to have intermediate levels compared to wines aged in French or American barrels (Fernández de Simón et al., 2003, 2008; Rodriguez-Bencomo et al., 2009). It goes without saying that the amount of extractable volatile components from oak wood will be a decisive factor in the final wine aroma. Therefore, the study of the volatile composition of the Q. pyrenaica species is of vital importance. However, although knowledge of the wood chemical composition is the first step in predicting which components could be expected in the final product after aging, little information has been found about volatile composition of “rebollo” Spanish stands (Cadahía et al., 2003; Fernández de Simón et al., 2006). Furthermore, the size of the sample set was often small. This fact is the main drawback observed, due to the great variability of the chemical composition of wood samples, even among those belonging to the same species. Several studies have pointed out a strong variability in oak wood within the same species, stave, tree, stand, forest, etc., as a result of the interaction between different natural factors and geographical provenance (Doussot et al., 2002; Guchu et al., 2006; Mosedale et al., 1999). Another drawback is that sometimes geographic location and species were unknown or not mentioned or, in other cases, samples were collected from the same stand which implies an insufficient and not statistically representative sampling. Therefore, to draw conclusions about the volatile profile of Q. pyrenaica, a detailed study with a large sample set of Spanish oak stands from different regions is necessary. Our objectives were to carry out a study of Spanish Q. pyrenaica forests regarding volatile and semi-volatile compounds, and to evaluate the influence of geographical location, site and silvicultural parameters on them. Considering the high natural variability of oak composition, sampling methods acquire great importance and a rigorous sampling procedure was applied to obtain a set of 107 test tubes, from several “rebollo” Spanish stands, carefully selected to guarantee the identification of the wood samples according to their geographic location and silvicultural parameters. Furthermore, all

samples were stabilized to the same water content and preparation was carried out identically for all samples with the aim of excluding errors and providing reliable conclusions 2. Materials and methods 2.1. Wood samples collection The sample set was constituted for 107 samples belonging to Q. pyrenaica. The samples were thoroughly collected from several stands of some geographic locations in the northwester of Iberian Peninsula (Ourense, Lugo and Pontevedra provinces). Table 1 shows all the information related to location and silvicultural parameters of wood samples. The collection procedure followed the pattern provided by the Spanish Association for Standardisation and Certification (UNEAENOR, 56528). From each tree selected, disks of wood were obtained at a height of 1.3 m from the base of the stem. From each disk, test tubes (heartwood) measuring 20 mm × 20 mm × 40 mm were taken. In order to standardize the water content of all samples, the wooden blocks were dried as follows: test tubes were saturated with water, to saturated state, and then stabilized to 12% of internal moisture at 20 ± 2 ◦ C and 65% relative moisture. Finally, samples were heated to dryness (0% internal moisture) in an oven at 103 ± 2 ◦ C. For its analysis, wooden samples were ground with a mechanical mill and sieved (size <1 mm) in order to obtain a homogenous sawdust. 2.2. Reagents and standards Dichloromethane, as extract agent, was supplied by Scharlau Chemie S.A. (Barcelona, Spain) while the standards used for the identification and quantification process were obtained from Sigma–Aldrich (Madrid, Spain). 2.3. Extraction of volatile compounds Isolation of volatile compounds was performed by means of pressurized liquid extraction using an accelerated solvent extractor ASE 200 (Dionex Corp, Sunnyuale, CA). 1 g of sawdust mixed with 500 mg of diatomaceous earth as a dispersant agent, were placed in an inox extraction cells of 11 mL. Extractions were carried out using optimized conditions described in a previous research ˜ (Alanón et al., 2009): dichloromethane was used as extraction solvent at 60 ◦ C, two cycles of 10 min each one were carried out under 1500 psi of pressure. Rinse of the complete system was performed to avoid any carry-over. Extracts obtained were concentrated using

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Table 2 Range selected compounds concentrations (␮g g−1 ) and their monitoring ions. LRI

Compoundsa

Selected ions (SIM)

Range

SD

1105 1204 1307 1328 1342 1367 1371 1417 1464 1502 1609 1654 1656 1667 1684 1756 1816 2009 – – – – –

Guaiacol ␣-Terpineol trans-ˇ-Methyl--octalactone Vinyl guaiacol* cis-ˇ-Methyl--octalactone Syringol Eugenol Vanillin Isoeugenol Acetovanillone Butyrovanillone -Cadinene‡ ı-Cadinene‡ 3-Oxo-˛-ionol# Syringaldehyde Coniferaldehyde Vomifoliol≈ Sinapaldehyde Vitamin E ˇ-Sitosterol ˇ-Amyrin† ˛-Amyrin (viminalol) Amyrin derivative†

109/124 59/93/121/136 99 135/150 99 139/154 164 151/152 164 151/166 151 161 161 108 181/182 178 124 208 165/430 81/95/107/145/213/414 203/218 203/218 203/218

0.12–1.73 0.27–2.93 0.79–16.20 0.16–2.06 2.48–60.55 0.02–4.67 0.16–22.87 3.99–42.10 0.64–6.33 0.14–2.07 0.91–6.92 0.07–23.33 0.03–52.64 0.02–2.43 5.59–79.92 1.74–43.16 0.02–14.17 2.32–64.03 0.70–63.53 18.97–439.02 1.43–67.54 0.75–67.39 1.22–73.16

0.38 0.52 3.58 0.47 11.81 0.72 3.30 7.59 1.40 0.44 1.18 3.84 5.22 0.54 14.58 10.22 2.18 15.02 12.04 93.68 17.71 14.00 10.30

a

Response factor used when pure reference standard was not available: * guaiacol,  vanillin, ‡ guaiaol, # ␣-ionol, ≈ ␤-damascone, † ␣-amyrin.

first a rotary evaporator at 40 ◦ C under atmospheric pressure and further under nitrogen flow to a final volume of 200 ␮L. Extracts were kept at −20 ◦ C until their subsequent GC–MS analysis. 2.4. Gas chromatography–mass spectrometry analysis GC–MS analyses were performed on an HP-G 1800B GCD system coupled to a mass detector (Agilent Technologies, Palo Alto, CA, USA). The carrier gas was helium at a linear velocity o 33 cm s−1 . Analytes were separated on an HP5 capillary column (30 m × 0.25 mm i.d., 0.25 ␮m film thickness; Supelco Ltd., Bellefonte, PA, USA). The oven temperature was held at 70 ◦ C for 3 min, increased to 250 ◦ C at 3 ◦ C min−1 and then to 290 ◦ C at 8 ◦ C min−1 and finally held at 290 ◦ C for 30 min. The injector temperature was 300 ◦ C and 1 ␮L of each extracts was injected in splitless mode over 0.3 min. The temperatures of ion source and transfer line were 230 ◦ C and 270 ◦ C respectively. Positive ion electron impact spectra were recorded at ionisation energy of 70 eV in SCAN mode in order to identify and select the target compounds which were selected based on their oenological importance from both the sensorial and functional point of view. After the choice of aimed compounds, all extracts were injected in SIM mode based on the more characteristic ions, Table 2. Chromatographic peaks were identified by comparing their linear retention index (LRI) values and mass spectra with those of pure standards. For substances for which no commercial standards were available, tentative identification was carried out by comparing their LRI values and mass fragmentation patterns with those in either the literature or commercial libraries. Quantification was carried out by means of response factors of standard compounds injected in the same way. 2.5. Statistical analysis Variance correlation analysis, variance analysis (ANOVA) and least significant difference (LSD) were the statistical analyses applied to chemical data using the STATISTICA program for windows, version 7.1 (StatSoft, Inc., 2005). Furthermore, linear discriminate analysis (LDA) was implemented to identify some canonical functions capable of distinguishing among wood samples.

3. Results and discussion Solvent extraction of wood components at high pressure and at 60 ◦ C, followed by GC–MS analysis of the extracts, allowed the detection of a large number of volatile and semi-volatile constituents of oak wood. The qualitative profile obtained was quite similar to those reported in the literature for other oak wood ˜ species using the same technique (Alanón et al., 2009; Natali et al., 2006; Vichi et al., 2007). Table 2 shows the concentrations of selected compounds found in Q. pyrenaica set samples. Based on the results, it is possible to observe the high variability found among samples of the same species. High variabilities on the levels of volatile compounds in wood samples among individual trees, forests and geographical locations, have also been established by other authors (Doussot et al., 2002; Guchu et al., 2006; Mosedale et al., 1999). However, this variability seems to be much less important than that found among the different species (Guchu et al., 2006). Q. pyrenaica samples showed high quantities of cis- and trans-ˇmethyl--octalactone and in all samples the cis-stereoisomer was the predominant form. These compounds are relevant not only for their sensorial impact, but also for their use in differentiating oak wood species (Masson et al., 1995; Pérez-Coello et al., 1997). Phenolic aldehydes such as vanillin, syringaldehyde, coniferaldehyde and sinapaldehyde were found in great concentrations. These compounds come from fragments of lignin, one of the main polymers of wood. Phenolic aldehydes do not play an important sensorial role, except for vanillin and their derivatives (acetovanillone, butyrovanillone, etc.) due to their characteristic vanilla flavor (Boidron et al., 1988; Díaz-Maroto et al., 2008). Other types of compounds considered were phenolic alcohols whose occurrence is also due to lignin degradation. Among them, it is important to point out guaiacol, syringol, eugenol and their derivatives because they impart characteristic spicy, clove and oak aromas (Boidron et al., 1988; Díaz-Maroto et al., 2008). Eugenol and isoeugenol were the major phenolic compounds found in all Q. pyrenaica samples. Terpenic and norisoprenoids compounds such as ˛-terpineol, -cadinene, ı-cadinene, 3-oxo-␣-ionol and vomifoliol were detected in Q. pyrenaica. These compounds have not been widely studied, despite their importance for fruity and floral aromas (Díaz-Maroto et al., 2008; Sefton et al., 1990).

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Table 3 Correlation coefficients between components and silvicultural parameters. Component

d center

Altitude

Precip

Temp

MO

NTH

Guaiacol ˛-Terpineol trans-ˇ-Methyl--octalactone Vinyl guaiacol cis-ˇ-Methyl--octalactone Syringol Eugenol Vanillin Isoeugenol Acetovanillone Butyrovanillone -Cadinene ı-Cadinene 3-Oxo-˛-ionol Syringaldehyde Coniferaldehyde Vomifoliol Sinapaldehyde Vitamin E ˇ-Sitosterol ˇ-Amyrin ˛-Amyrin Amyrin derivative

0.23* 0.23* −0.01 0.30* −0.11 0.14 0.09 0.10 0.04 0.07 0.03 −0.11 0.10 0.00 0.15 0.18 0.29* 0.08 0.10 0.18 0.25* 0.04 0.07

−0.23* −0.54* −0.25* −0.36* −0.01 −0.28* −0.40* −0.28* −0.11 −0.22* −0.02 0.29* 0.28* −0.21* −0.16 −0.28* −0.05 −0.07 −0.47* −0.37* −0.21* 0.12 0.15

−0.05 0.09 −0.09 0.33* −0.26* −0.05 0.15 −0.03 −0.07 −0.07 −0.19* −0.19 −0.05 −0.24* −0.14 0.02 −0.00 0.03 0.01 −0.05 0.09 −0.10 −0.04

0.16 0.39* 0.31* 0.25* 0.12 0.07 0.30* 0.33* −0.03 0.06 −0.01 −0.11 −0.29* 0.21* −0.02 0.16 −0.02 −0.10 0.39* 0.12 −0.01 −0.40* −0.43*

−0.04 −0.22* 0.02 −0.17 0.03 −0.19 −0.09 −0.21* −0.00 −0.32* −0.04 0.04 0.02 −0.28* −0.21* −0.34* −0.15 −0.38* −0.27* −0.45* −0.45* −0.29* −0.26*

−0.27* −0.43* −0.10 −0.30* −0.11 −0.10 −0.38* −0.12 0.09 −0.03 0.12 0.40* 0.11 0.02 0.11 0.01 0.04 0.20* −0.17 −0.06 0.00 0.27* 0.12

d center: distance to center; precip: annual average precipitation; temp: annual average temperature; MO: soil organic matter; NTH: number of tress per hectare. * Significant correlations at P < 0.05.

Among semi-volatile compounds, the major component detected was ˇ-sitosterol, a phytosterol widely distributed in the plant kingdom. These semi-volatile compounds have scarcely been ˜ studied and few references about them have been found (Alanón et al., 2009; Guillén and Manzanos, 2001). These compounds are important from a functional point of view, due to their antioxidant properties (Guillén and Manzanos, 2001). 3.1. Relationship between wood volatile compounds and silvicultural parameters The influence of studied parameters such as distance from tree center, altitude, average annual precipitation and temperature, organic matter and number of trees per hectare on the volatile composition of Q. pyrenaica samples was studied. Table 3 shows the correlation coefficients found between volatile components and silvicultural parameters. Although they were low, probably due to the multiple factors which affect volatile composition, some significant correlations were found at P < 0.05. Among them, the most influential parameter on the volatile composition of wood was altitude. For the majority of compounds a significant correlation was found, with the major coefficients being those found for ˛-terpineol, eugenol and vitamin E. It is worth noting that practically all correlation coefficients were negative, implying that the higher the altitude, the lower the concentration of volatile compounds. Organic matter and average annual temperature also showed certain influence on some volatile compounds such as ˛-terpineol, vanillin, oxo-˛-ionol, vitamin E and ˛-amyrin among others. On the other hand, some parameters such as distance from tree center, average annual precipitation and number of trees per hectare hardly ever showed significant correlations with the volatile concentrations. 3.2. Relationship between wood volatile compounds and soil texture In order to study the influence of soil type with respect to the wood volatile composition, samples of Q. pyrenaica growing in dif-

ferent soil textures were grouped and analyzed. Four texture types were studied: loamy, loamy-sand, clay-silt and sandy-loam. Volatile composition of the studied samples with different textures was quite similar. Table 4 shows the mean values of concentrations for each compound and each type of soil texture. The analysis of variance was performed for texture and some compounds showed significant differences at P < 0.05. Among them, it was possible to find volatile aldehydes such as syringaldehyde, sinapaldehyde and coniferaldehyde (these compounds do not have flavoring properties but they are found in important quantities in oak wood), triterpenic compounds like ˇ-amyrin and its derivatives, and ˇ-sitosterol, interesting components from a functional point of view, due to their antioxidant activity (Guillén and Manzanos, 2001). Furthermore, significant differences were found for other compounds with important sensorial properties such as ˛-terpineol and 3-oxo-˛-ionol, both with floral flavor, and transˇ-methyl--octalactone whose aroma is very characteristic and described as coconut and/or woody. The least significant difference test, LSD, showed in detail the differences found for each volatile compound for each soil texture studied. Based on these results (Table 4), we can observe that for practically all of the variables among loamy-sand, claysilt and sandy loam texture there were no differences. However, the greatest differences were found for the loamy texture. Samples grown in loamy soil showed lower concentrations of volatile aldehydes, triterpenic compounds and trans-ˇ-methyl--octalactone. Loamy soils are poorer in organic matter and nitrogen content than loamy-sand, clay-silt and sandy loam textures (Díaz-Maroto and Vila-Lameiro, 2006), a fact that could explain the differences found. Using all the data concerning volatile and semi-volatile compounds, a linear discriminate analysis (LDA) was applied in order to classify samples according to their soil texture. A rather clear distinction of samples grown in sandy-loam soil was observed. The percentage of predicted classifications for this texture membership was 92.6%. On the other hand, the percentage of correct classification was also good for samples belonging to loamy-sand soil: 82.3%. Meanwhile, the predicted classification for the remaining samples was not good since their correct percentage of classification was very low; 66.6% and 50.0% for loamy

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Table 4 Mean concentration (␮g g−1 ) of Q. pyrenaica samples growing in different soil textures and analysis of variance. Compound

Loamy

Loamy-sand

Clay-silt

Sandy-loam

F

P

Guaiacol ˛-Terpineol trans-ˇ-Methyl--octalactone Vinyl guaiacol cis-ˇ-Methyl--octalactone Syringol Eugenol Vanillin Isoeugenol Acetovanillone Butyrovanillone -Cadinene ı-Cadinene 3-Oxo-˛-ionol Syringaldehyde Coniferaldehyde Vomifoliol Sinapaldehyde Vitamin E ˇ-Sitosterol ˇ-Amyrin ˛-Amyrin Amyrin derivative

0.77a,b 0.81a,b 3.31a 0.93a 23.32b 0.61a 2.60a 16.95a 2.41a 0.81b 3.57a 3.13a 0.64a 0.30a 22.88b 12.89b 1.12a 15.39b 4.47a 57.85a 9.53b 5.70b 7.06b

0.98b 0.66a 6.04b 1.08a 19.30a,b 0.81a 2.00a 20.67a 2.59a 1.28a 4.07a 1.50a 2.04a 0.28a 32.96a 24.06a 2.22a 32.89a 9.54a,b 92.79a,b 20.68a,b 15.77a 16.09a

0.78a,b 1.55c 1.71a 1.07a 7.62a 0.87a 3.96a 20.75a 3.50a 0.95a,b 3.47a 0.27a 0.16a 0.34a,b 39.41a 21.15a,b 0.38a 23.06a,b 14.34a,b 103.91a,b 32.02a 9.85a,b 11.75a,b

0.72a 0.93b 4.74a,b 1.09a 19.00a,b 0.88a 2.68a 20.86a 2.29a 1.12a 3.89a 1.77a 1.52a 0.60b 37.92a 21.29a 1.92a 27.94a 11.09b 136.07b 25.16a 14.35a 14.27a

2.28 3.92 2.72 0.58 2.07 0.68 0.42 1.32 1.05 4.14 0.71 0.94 0.29 2.76 6.02 4.56 1.45 5.12 1.66 4.04 4.57 2.20 3.01

0.0841 0.0107* 0.0482* 0.6319 0.1081 0.5634 0.7388 0.2725 0.3730 0.0081* 0.5501 0.4241 0.8308 0.0457* 0.0008* 0.0048* 0.2328 0.0024* 0.1805 0.0093* 0.0047* 0.0920 0.0335*

Different superscript letters in the same row indicate differences according to the least significant difference test. * Significant differences at P < 0.05.

and clay-silt respectively. Therefore, it was possible to conclude that a classification of samples based on the soil texture is a bad discriminant parameter (Díaz-Maroto et al., 2006a,b). 3.3. Relationship between wood volatile compounds and geographical location of Q. pyrenaica trees The data collection set was composed of samples of Q. pyrenaica from some different geographical origins all located in NW Spain, but with different site characteristics, from areas with typical oceanic climate to others with Mediterranean influence. Data obtained was submitted to different statistical analyses in order

to study the influence of geographical location on the volatile composition. Table 5 shows the mean values of oak wood volatile compounds for each geographical location. Wood samples from Lugo and Pontevedra hardly ever showed differences between them with respect to their volatile composition. Although mean values of several volatile compounds were lower for those samples belonging to Ourense, just significant differences were found for guaiacol and its derivative vinyl guaiacol, (which are responsible for smoky odor), syringol, eugenol with a marked clove aroma, ˛-terpineol with floral odor, and coniferaldehyde. Therefore, oak wood samples from Ourense seem to have lower quantities of

Table 5 Mean concentration (␮g g−1 ) of Q. pyrenaica samples growing in different geographical location and analysis of variance. Compound

Lugo

Pontevedra

Ourense

Fð

P‡

Guaiacol ˛-Terpineol trans-ˇ-Methyl--octalactone Vinyl guaiacol cis-ˇ-methyl--octalactone Syringol Eugenol Vanillin Isoeugenol Acetovanillone Butyrovanillone ı-Cadinene -Cadinene 3-Oxo-˛-ionol Syringaldehyde Coniferaldehyde Vomifoliol Sinapaldehyde Vitamin E ˇ-Sitosterol ˇ-Amyrin ˛-Amyrin Amyrin derivative

1.00a 1.15a 5.86b 1.19a 20.89a 1.15b 3.84b 21.39a 2.61a 1.19b 4.03a 0.60a 2.26a 0.54a,b 35.53a 22.62a 2.11a 26.36a 16.23a 143.06b 24.13a 24.13a 11.35a

0.94a 1.11a 4.69a,b 1.50a 24.81a 1.02a,b 3.17a,b 25.24a 1.62a 1.23b 4.34a 0.46a 1.77a 1.01b 43.19a 30.23a 1.79a 40.40b 8.89a,b 100.36a,b 41.30b 41.30a 15.94a

0.57b 0.67b 3.59a 0.93b 17.65a 0.54a 1.57a 18.75a 2.31a 0.99a 3.66a 2.13a 0.21a 0.41a 33.21a 17.62b 1.51a 25.26a 4.90b 93.82a 18.77a 18.77a 14.52a

23.34 14.19 5.52 6.72 1.52 10.85 6.66 2.78 1.42 2.84 1.72 1.18 0.71 3.28 1.22 6.00 0.95 2.39 13.87 3.71 4.52 1.18 1.38

0.0000* 0.0000* 0.0053* 0.0018* 0.2228 0.0000* 0.0019* 0.0665 0.2460 0.0630 0.1839 0.3116 0.4918 0.0414* 0.2985 0.0034* 0.3891 0.0963 0.0000* 0.0275* 0.0130* 0.3116 0.2564

Different superscript letters in the same row indicate differences according to the least significant difference test. ð F: critical values for the F-distribution at a probability level of P < 0.05. ‡ P: statistical significance testing. * Significant differences at P < 0.05.

M.E. Ala˜ nón et al. / Forest Ecology and Management 262 (2011) 124–130 Table 6 Coefficients of classification functions for provenance given by the linear discriminant analysis. Compound Guaiacol ˛-Terpineol trans-ˇ-Methyl--octalactone Vinyl guaiacol cis-ˇ-Methyl--octalactone Syringol Eugenol Vanillin Isoeugenol Acetovanillone Butyrovanillone -Cadinene ı-Cadinene 3-Oxo-˛-ionol Syringaldehyde Coniferaldehyde Vomifoliol Sinapaldehyde Vitamin E ˇ-Sitosterol ˇ-Amyrin ˛-Amyrin Amyrin derivative Constant

Lugo 17.532 4.021 1.073 8.712 0.059 0.744 −0.880 0.359 1.733 0.568 1.407 0.430 −0.255 −4.575 0.030 −0.385 0.720 −0.026 0.191 −0.026 0.113 −0.146 0.222 −17.024

Pontevedra 7.402 2.265 0.603 10.001 0.309 4.610 −1.640 0.438 −1.276 −0.013 2.353 0.259 −0.277 0.661 −0.077 −0.203 −0.393 0.110 −0.462 −0.069 0.750 −0.760 0.241 −31.560

Ourense 6.602 3.745 0.772 6.556 0.049 −1.562 −1.056 0.493 1.003 1.715 1.432 0.081 −0.095 −2.592 0.017 −0.586 0.323 0.184 0.045 −0.014 −0.008 −0.132 0.298 −12.692

volatile compounds than samples belonging to the Lugo or Pontevedra provinces. A linear discriminate analysis, LDA, was also performed by regrouping samples according to their geographical origin. In this case, the estimation classification was rather good for all geographical origins, above all for samples from Ourense. The percentages of correct classification were 80.0, 82.2 and 94.7% for samples from Pontevedra, Lugo and Ourense provinces respectively. Table 6 shows variables included in the LDA analysis and their respective coefficients in the three functions originated for each oak wood provenance together with the constant for each equation. ˛-terpineol, trans-ˇ-methyl--octalactone, vinyl guaiacol, isoeugenol, butyrovanillone and 3-oxo-˛-ionol were the most important variables in distinguishing samples from Lugo. For samples from both Pontevedra and Ourense, the most important variables were ˛-terpineol, vinyl guaiacol, syringol, eugenol, isoeugenol and butyrovanillone, and also acetovanillone and 3oxo-˛-ionol for samples from Ourense. The graph of sample distribution according to their geographical origin discrimination is shown in Fig. 1. It confirms the goodness of this analysis is for

Fig. 1. Distribution of Quercus pyrenaica samples according to their geographical location discrimination.

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discriminating oak samples from Pontevedra. Meanwhile, in the case of samples from Ourense and Lugo, there is a certain overlapping that shows more similarity between oak samples from these provenances. Therefore, despite the high variability found in samples, the species is not the only factor to take into account, since different geographical locations could also establish differences regarding the chemical composition of wood samples of the same species. This fact has been also observed in the case of French forests where there are traditional differences between French oak of various regions (Allier, Limousin, Nevers, Vosges, Tronc¸ais, etc.). The oak wood from these regions can be differentiated on the basis of their differing oak lactone, vanillin and eugenol contents (Pérez-Coello and Díaz-Maroto, 2009) and for this reason their quality and price is different. 4. Conclusions It has been demonstrate the value of Q. pyrenaica species due to the presence of important volatile compounds with a view to its use in cooperage. It can be concluded that among silvicultural parameters, altitude, organic matter and average annual temperature seem to influence the volatile composition of Q. pyrenaica samples. On the other hand, factors such as distance from tree center, average annual precipitation and number of trees per hectare whose effects on volatile compounds were not found. Results also showed that although samples grown in loamy textures showed significantly lower concentrations of some volatiles compared to the rest of the soils, the soil texture was not a determining factor in classifying samples. Meanwhile, geographical location seemed to have a specific impact on the extractive volatile content and allowed a good classification. Therefore, geographical location should also be a consideration in wood selection with aging purposes, since differences on the volatile content modify the organoleptic characteristics of wine. Acknowledgements Authors would like to thank to Ministerio de Ciencia e Innovación for the award of a doctoral grant of MEA and for their financial support towards the Coordinate Project AGL2008-04913CO2-01/ALI – AGL2008-04913-CO2-02/ALI. References Abbott, N., Puech, J.L., Bayonove, C., Baumes, R., 1995. Determination of the aroma threshold of the cis and trans racemic forms of ˇ-methyl--octalactone by gas chromatography-sniffing analysis. Am. J. Enol. Vitic. 46, 292–294. ˜ Alanón, E., Ramos, L., Diaz-Maroto, M.C., Pérez-Coello, M.S., Sanz, J., 2009. Extraction of volatile and semi-volatile components from oak wood used for aging wine by miniaturised pressurised liquid technique. Int. J. Food Sci. Technol. 44, 1825–1835. Boidron, J.N., Chatonnet, P., Pons, M., 1988. Effects of wood on aroma compounds of wine. Conn. Vigne. Vin. 22, 275–294. Cadahía, E., Fernández de Simón, B., Jalocha, J., 2003. Changes in volatile compounds in Spanish, French and American oak wood after natural seasoning and toasting. J. Agric. Food Chem. 51, 5923–5932. Chatonnet, P., Dubourdieu, D., 1998. Comparative study of the characteristics of American white oak (Quercus alba) and European oak (Quercus petraea and Q. robur) for production of barrels used in barrel ageing of wines. Am. J. Enol. Vitic. 49, 79–85. Díaz-Maroto, I.J., Vila-Lameiro, P., 2006. Comparison between the chemical properties and the macronutrient content in natural stands of Quercus robur L. and Q. pyrenaica Willd. soils in Galicia, Spain. Agrochimica 50, 200–209. Díaz-Maroto, I.J., Fernández-Parajes, J., Vila-Lameiro, P., 2006a. Autecologie of rebollo oak (Quercus pyrenaica Willd.) in Galicia (Spain). Ann. Forest Sci. 63, 157–167. Díaz-Maroto, I.J., Vila-Lameiro, P., Díaz-Maroto, M.C., 2006b. Autecology of sessile oak (Quercus petraea) in the north-west Iberian Peninsula. Scand. J. Forest Res. 21, 458–469.

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