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. 2022 Dec 19;12(12):1295. doi: 10.3390/metabo12121295

A Targeted and an Untargeted Metabolomics Approach to the Volatile Aroma Profile of Young ‘Maraština’ Wines

Ana Boban 1, Urska Vrhovsek 2, Silvia Carlin 2, Ana Mucalo 1, Irena Budić-Leto 1,*
Editor: Yonghuan Yun
PMCID: PMC9780986  PMID: 36557333

Abstract

This study investigated the detailed volatile aroma profile of young white wines of Maraština, Vitis Vinifera L., produced by spontaneous fermentation. The wines were produced from 10 vineyards located in two Dalmatian subregions (Northern Dalmatia and Central and Southern Dalmatia). Volatile compounds from the wine samples were isolated by solid-phase extraction (SPE) and analyzed by an untargeted approach using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC/TOF-MS) and a targeted approach by gas chromatography–tandem mass spectrometry (GC-MS/MS). A comprehensive two-dimensional GC×GC analysis detailed the total volatile metabolites in the wines due to its excellent separation ability. More than 900 compounds were detected after untargeted profiling; 188 of them were identified or tentatively identified. A total of 56 volatile compounds were identified and quantified using GC-MS/MS analysis. The predominant classes in Maraština wines were acids, esters, and alcohols. The key odorants with odor activity values higher than one were β-damascenone, ethyl caprylate, ethyl isovalerate, ethyl 2-methylbutyrate, ethyl caproate, isopentyl acetate, ethyl butyrate, and phenylacetaldehyde. The metabolomics approach can provide a large amount of information and can help to anticipate variation in wines or change winemaking procedures.

Keywords: aroma, volatile compounds, two-dimensional gas chromatography, Maraština wines, spontaneous fermentations

1. Introduction

Aroma is one of the most important quality attributes of wine, and the perceived flavor is the result of complex interactions between all the volatile and nonvolatile compounds. The aroma of young wine consists of compounds derived from grapes and those produced from alcoholic fermentation [1]. Traditional winemaking practices rely on the microbiota naturally present in the grapes and in the winery environment. Yeasts belonging to the genera Saccharomyces cerevisiae will eventually dominate and complete fermentation, but it takes time to establish the fermentation. During this time, many other indigenous yeast genera belonging to the non-Saccharomyces species have a greater role in flavor development than S. cerevisiae through extracellular enzymes. They can liberate glycosidically bound constituents and contribute significantly to the character and quality of the final wine [2]. There is a growing interest in native microflora towards possible contribution to the aroma features linked to terroir influences and the expression of these attributes. Nowadays, authors have pointed out that the presence of natural microbiota in wine fermentation that relies on wine regions significantly contributes to the specific flavor characteristics of wine. Wines made from the same grape variety but from different geographical locations are appreciated for their diversity [3,4]. Recently, 25 different fungal genera present in Maraština grapes have been characterized in the indigenous microbiota of Maraština grapes collected from vineyards located within the Croatian coastal wine-growing region of Dalmatia (Northern Dalmatia, Dalmatian hinterland, and Central and Southern Dalmatia) [4].

Many different methods for studying wine aroma have been developed using a targeted approach, especially one-dimensional gas chromatography (1D-GC) coupled with different detectors [5]. One-dimensional gas chromatography coupled with tandem mass spectrometry (1D-GC-MS/MS) is one of the most efficient analytical techniques for metabolomics studies [6]. Due to the rapid development of analytical chemistry within the last decades encompassing a tandem mass detector, the determination of the exact concentrations of compounds present in trace amounts is a challenge. A triple quadrupole detector mass spectrometer (QqQ-MS) operating in a selected reaction monitoring mode is the advanced method in more detailed quantitative metabolomics studies. Besides its sensitivity, this instrument has a very good linear dynamic range, which allows excellent quantification of the metabolites of different chemical classes in a five-fold and even higher concentration range [7]. However, this targeted approach does not provide full information about volatile components. The 1D-GC volatile fractions are hampered by frequent co-elution, even when high-efficiency capillary columns, selective stationary phases, and programmed oven temperature conditions are used. An untargeted metabolomics approach by comprehensive two-dimensional gas chromatography coupled with a time-of-flight mass spectrometer (GC×GC/TOF-MS) emerged as a more powerful analytical technique for the detailed analysis of the volatile compounds of complex samples such as wines [8]. This technique utilizes a long non-polar column with a short polar column connected by a modulator. The instrument’s heart is the modulator because it ensures that separation is comprehensive and multidimensional. GC×GC allows the separation of a large number of compounds in a single chromatographic run due to the added selectivity of the second column and inherently high peak capacity [9]. Using this instrumental approach, compounds co-eluting from the first column undergo additional separation in the second one [10]. Therefore, the separation potential, with higher peak capacity, selectivity is greatly enhanced when compared to the one-dimensional GC.

Maraština, a Croatian autochthonous variety of grape, is one of the most important white cultivars in the Adriatic coastal region of Croatia and has the potential for producing high-quality monovarietal and dessert wines [11,12]. Maraština wines are characterized by a higher intensity of yellow color and distinguished from Chardonnay, Istrian Malvasia, and Muscat blanc wines by the more intense body, viscosity, astringency, and tannin quantity [13]. Maraština wines produced from different vine-growing subregions in Dalmatia have indicated significantly different basic physico-chemical parameters of the must and color intensities of wine [14]. According to the legislation, Croatian wines produced from different viticultural areas of Dalmatia can be labeled with a protected designation of origin (PDO) (Regulation EU, No. 1308/2013) [15].

In this study, we thoroughly examined the volatile aroma profile in experimental young Maraština wines produced by spontaneous fermentation in two vine-growing subregions of Dalmatia (the Northern Dalmatia subregion and the Central and Southern Dalmatia subregions) located along the Adriatic coast. This study aimed to discriminate the wines produced in those two subregions based on volatile aroma profiling. To date, information on the volatile composition of Maraština wines produced from spontaneous fermentation has not been found, so this investigation fulfills the knowledge of Croatian wines. Profiling by comprehensive GC×GC/TOF-MS was combined with a conventional GC-MS/MS analysis of volatile compounds to obtain wine volatile metabolome.

2. Methods and Materials

2.1. Chemicals and Reagents

Ethanol 99.8%, n-heptanol 99.9%, dichloromethane 99.8%, and methanol for HPLC 99.9% were purchased from Sigma-Aldrich (Sigma-Aldrich, St. Luis, MO, USA). Milli-Q water was used for the extraction of samples and the preparation of standard solutions. Cartridges with 200 mg of stationary phase based on styrene–divinylbenzene for solid-phase extraction (SPE) were purchased from Isolute® ENV+ (Biotage, Uppsala, Sweden).

2.2. Vineyard Parcel Characteristics

The Maraština vineyards were selected to represent the major soil and climate types of the two Dalmatian subregions. The five commercial vineyards and the germplasm collection at the Institute for Adriatic Crops in the Central and Southern subregions (CSD) and four commercial vineyards in the Northern Dalmatia (ND) subregion were chosen for the production of experimental wines. Table S1 summarizes the main characteristics of the vineyard parcels under study, such as soil type, plantation year, altitude, row distance, and row orientation. Row orientation in the vineyards was north–south in both subregions. The vineyards in the CSD subregion were situated on reddish-brown soil on limestone with a sandy loam texture at an altitude from 14 to 94 m above sea level (a.s.l.). The three vineyards in the ND subregion were situated on brown soil on limestone with a sandy texture, whereas one vineyard was on reclaimed karst. The vineyards were at altitudes from 60 to 260 m a.s.l. All the vines were trained to the vertical shoot-positioned bilateral cordon system and were cultivated without irrigation. The canopy management techniques were the same in both subregions, all vines were pruned to four cuttings with two buds, and thinning was performed when the shoots were 15 cm long.

2.3. Wine Samples

A total of 30 wines made from the Maraština variety were produced by spontaneous fermentation, without added inoculated yeast. In each vineyard, nine representative vines were chosen randomly within the vineyard during the 2021 vintage. The grapes were harvested in technological maturity separately determined for each subregion due to the different climatic conditions. From each vineyard, 15 kg of grapes were harvested and stored in a cooler during transport to Institute for Adriatic Crops. The grapes were destemmed, crushed, and treated with potassium metabisulfite to give a total concentration of SO2 in the wine of approximately 50 mg/L. The must was separated and cold-stabilized for 24 h at 4 °C. The stabilized must from each repetition was decanted in 500 mL Erlenmeyer flasks and protected from light with aluminum foil. The fermentations were carried out at 20 °C. The fermentation progress was monitored daily by measuring the sugar content and fermentation temperature. Samples of the young wines from the end of the fermentation were taken in 50 mL falcon tubes and stored at −80 °C until the metabolomics analysis of aroma.

2.4. Climate Data

The climate in Northern Dalmatia subregion and Central and Southern Dalmatia subregions is Mediterranean, based on climate data from the meteorological station (Vela Luka, Split, Zadar, and Benkovac). The average temperature in the period from January 2021 to September 2021 was 16.8 °C for ND and 18.0 °C for CSD subregions. June was the driest month with an average precipitation of 3 mm (ND) and 5 mm (CSD). Most of the precipitations during the growing season occurred in April (50 mm in ND) and May (66 mm in CSD). More detailed climatological data for the year 2021 are reported in Table S2.

2.5. Solid-Phase Extraction for GC-MS/MS and GC×GC/TOF-MS Analysis

Sample preparation and extraction were performed according to the modification of the previously described method [16]. Isolute® ENV+ solid-phase extraction cartridges were supplied by Biotag (Uppsala, Sweden) filled with 200 mg of stationary phase. The cartridge was pre-conditioned with 4 mL of dichloromethane, followed by 4 mL of methanol and 4 mL of model wine solution. A total of 50 mL of wine mixed with 100 µL of internal standard (n-heptanol, 250 mg/L) was added to the cartridge, washed with 3 mL of Milli-Q water, and dried for 10 min. The extracted compounds were eluted directly into the injection vial from the cartridge with 2 mL of dichloromethane.

2.6. GC-MS/MS Analysis

Analysis was performed using the Agilent Intuvo 9000 system for fast GC coupled with an Agilent 7010B triple quadrupole mass spectrometer (QqQ) (Agilent Technologies, Santa Clara, CA, USA) equipped with an electronic ionization source operating at 70 eV. Separation was obtained by injecting 1 µL in split mode (1:10) into a DB-Wax Ultra Inert column (30 m × 0.25 mm id × 0.25 µm film thickness, Agilent Technology, Santa Clara, CA, USA). The initial temperature of the GC oven was 40 °C for 2 min, increased by 10 °C/min to reach 55 °C, then by 20 °C/min until 165 °C, by 40 °C/min to 240 °C for 1.5 min, and, finally, by 50 °C/min to 250 °C and kept at this temperature for an additional 4 min (16 total runtimes). Helium was used as a carrier gas (with a flow of 1.2 mL/min). The mass spectra were acquired in multiple reaction monitoring modes. Nitrogen was used as the collision gas, with a flow of 1.5 mL/min, in addition to Helium at 4.0 mL/min as a quench gas. The transfer line and source temperature were set at 250 °C and 230 °C, respectively. The data acquisition and subsequent analyses were performed using the MassHunterWorkstation software 10.0.368 (Agilent Technologies, Santa Clara, CA, USA) [16].

2.7. GC×GC/TOF-MS Analysis

The GC×GC system consisted of an Agilent 7890N (Agilent Technologies, Palo Alto, CA, USA) coupled with a LECO Pegasus IV time-of-flight mass spectrometer (TOF-MS) (Leco Corporation, St. Joseph, MI, USA) equipped with a Gerstel MPS autosampler (GERSTEL GmbH & Co. KG, Mülheim an der Ruhr, Germany), as described in previous studies with modifications [17]. A volume of 1 µL of wine extract (SPE) was injected at 250 °C in split mode (1:10). The oven was equipped with a 30 m × 0.25 mm × 0.25 µm film thickness VF-WAXms column (Agilent Technologies, Santa Clara, CA, USA) in the first dimension (1D) and a 1.5 m × 0.15 mm × 0.15 µm film thickness Rxi 17Sil MS column (Restek, Bellefonte, PA, USA) in the second dimension (2D). The primary oven temperature was kept at 40 °C for 4 min, then raised at 6 °C/min to 250 °C, and then finally maintained at this temperature for an additional 5 min. The secondary oven was maintained at 5 °C above the temperature of the primary oven throughout the chromatographic run. As described previously [18], the modulator was offset by +15 °C in relation to the secondary oven; the modulation time was 7 s with 1.4 s of hot pulse duration. Helium was used as a carrier gas at a constant flow of 1.2 mL/min. The MS parameters included electron ionization at 70 eV, with ion source temperature at 230 °C, a detector voltage of 1317 V, a mass range of 40–350 m/z, an acquisition rate of 200 spectra/s, and an acquisition delay of 120 s. Automated peak finds and spectral deconvolution with a baseline offset of 0.8 and a signal-to-noise (S/N) ratio of 100 were performed using LECO ChromaTOF software version 4.32 (Leco Corporation, St. Joseph, MI, USA). Peak width limits were set to 42 s and 0.1 s in the first and the second dimension, respectively. Adaptive integration was not used. The required match (similarity) to combine peaks was set to 650. Under these conditions, 938 putative compounds were detected. Volatile compounds were identified by comparing their retention times and mass spectra with those of pure standards and with mass spectra from NIST 2.0, Wiley 8, and FFNSC 2 (Chromaleont, Messina, Italy). Mass spectrometric information of each peak was compared to NIST mass spectra libraries, with a minimum library similarity match of 750. A mix of 122 compounds was injected under identical conditions to identify compounds by comparison with pure standards. Tentative identification of wine aroma compounds and/or confirmation of their identities was achieved by comparing experimental linear temperature-programmed retention index (LTPRI) with those from the literature for conventional one-dimensional GC obtained using columns of equal or equivalent polarity (NIST 2.0, Wiley 8, FFNSC 2, VCF).

2.8. Data Analysis

The statistical analyses of the volatile compounds were carried out by using IBM®SPSS® Statistica for Windows program package version 23.0 (SPSS Inc., Chicago, IL, USA). Statistically significant differences between mean values at p < 0.05 were obtained by one-way ANOVA and the least significant difference (LSD) test. Multivariate analyses were performed on reduced data sets. The Fisher F-ratio was used for the selection of the parameters. The initial GC-MS data set of 56 volatile compounds was reduced to 15 variables. This reduced data set was used for principal component analysis. Additionally, the initial data set of 188 volatile compounds determined by GC×GC/TOF-MS was reduced to 56 compounds for performing hierarchical clustering. Heatmap was generated by Ward algorithm and Euclidean distance analysis using the metabolomics data analysis program MetaboAnalyst v.5.0. (http://www.metaboanalyst.ca) (accessed on 5 November 2022) created at the University of Alberta, Canada [19].

3. Results and Discussion

The wine subregion according to the legislation of the Republic of Croatia (Regulation NN 32/2019) has been proposed as a marker for the production of wine with a protected designation of origin. The vine-growing subregion represents a geographically limited area with similar climatic and pedological conditions and other agrobiological conditions, which enable the production of wine with the specific characteristic of the subregion. The results of this study were obtained from experimental wines belonging to the two vine-growing subregions: Northern Dalmatia (ND) and Central and Southern Dalmatia (CSD). In the current study, the vineyards of the ND subregion were located at higher altitudes, mainly situated on brown soil on limestone. The vineyards of the CSD subregion, located in the central and southern parts of Dalmatia, were planted on reddish-brown soil. Regarding the temperature data, vineyards in ND were exposed to a 1.3 °C lower average temperature and lower average precipitation during the vegetation period. Numerous studies show that soil type, climate, training systems, canopy, and cultural practices strongly impact the shoot growth, yield per vine, and the aroma composition of the berries [11,17,20].

3.1. GC-MS/MS Analysis

The concentrations of all quantified volatile aroma compounds in young Maraština wines by targeted approach with the GC-MS/MS method are presented in Table 1. The compounds are sorted by chemical classes and descending Fisher F-ratio in each group. A total of 56 volatile compounds were quantified, including terpenic compounds (14), C13-norisoprenoids (3), esters (17), alcohols (4), acids (5), phenols (4), aldehydes (2), ketones (2), lactones (4), and indole (1).

Table 1.

Concentration (µg/L) of volatile aroma compounds (mean ± standard deviation) in young Maraština wines from Northern Dalmatia (ND) and Central and Southern Dalmatia (CSD) subregions determined by GC-MS/MS.

No. Compound tR (min:s) LOQ (µg/L) F-Ratio Concentration (µg/L) S
ND CSD
1 cis-Rose oxide 07:38.3 0.036 15.954 0.07 ± 0.03 0.04 ± 0.01 *
2 trans-Rose oxide 07:46.0 0.014 13.046 0.02 ± 0.01 0.01 ± 0.01 *
3 cis-Linalool oxide 08:29.9 0.114 10.211 0.27 ± 0.07 0.34 ± 0.06 *
4 trans-Linalool oxide 08:18.2 0.082 4.889 0.46 ± 0.11 0.54 ± 0.10 *
5 trans-Terpin 11:23.9 0.100 2.490 0.30 ± 0.14 0.23 ± 0.10 ns
6 1,8-Cineole 06:24.8 0.050 2.292 0.09 ± 0.04 0.07 ± 0.01 ns
7 α-Terpineol 09:51.2 0.100 1.634 1.71 ± 0.43 1.50 ± 0.45 ns
8 Eugenol 11:38.8 0.150 1.626 0.17 ± 0.07 0.22 ± 0.12 ns
9 Geraniol 10:28.8 0.250 1.062 5.23 ± 1.14 4.74 ± 1.35 ns
10 Terpinen-4-ol 09:21.5 0.075 0.475 0.13 ± 0.09 0.22 ± 0.44 ns
11 β-Ionone 10:55.2 0.050 0.340 0.07 ± 0.01 0.06 ± 0.03 ns
12 β-Citronellol 10:07.7 1.000 0.233 10.01 ± 3.79 9.45 ± 2.57 ns
13 Linalool 08:55.3 0.100 0.153 6.88 ± 1.23 6.63 ± 1.95 ns
14 Safranal 09:38.6 0.100 0.009 0.12 ± 0.06 0.12 ± 0.05 ns
∑ Terpenic compounds 25.51 ± 3.84 24.18 ± 5.16 ns
15 β-Damascenone 10:27.8 0.100 38.577 1.89 ± 0.64 0.84 ± 0.27 *
16 TDN 10:09.0 0.050 16.381 0.68 ± 0.16 0.45 ± 0.15 *
17 Vitispirani (mix of isomers) 08:56.5 0.500 4.149 0.64 ± 0.31 0.39 ± 0.35 ns
∑ C13-norisoprenoids 3.21 ± 0.76 1.67 ± 0.51 *
18 Ethyl caprylate 08:12.4 1.000 33.678 221.09 ± 68.24 176.73 ± 65.92 ns
19 Diethyl succinate 09:42.4 0.250 19.764 399.80 ± 195.07 170.80 ± 82.73 *
20 Ethyl valerate 05:37.9 0.050 4.846 1.22 ± 0.37 1.52 ± 0.35 *
21 Ethyl laurate 10:29.2 0.075 3.699 28.64 ± 46.24 7.47 ± 7.29 ns
22 Ethyl heptanoate 07:27.1 0.050 3.170 1.03 ± 0.24 1.42 ± 0.75 ns
23 Ethyl caprate 09:32.9 0.050 3.170 73.77 ± 73.89 45.76 ± 24.78 ns
24 Ethyl isovalerate 04:53.6 0.100 3.147 11.06 ± 6.23 8.02 ± 3.15 ns
25 Ethyl 2-methylbutyrate 04:42.5 0.050 3.097 7.55 ± 4.18 5.39 ± 2.56 ns
26 Ethyl leucate 08:55.9 0.250 2.878 36.78 ± 11.11 14.24 ± 9.94 *
27 Butyl acetate 04:55.8 0.150 1.549 0.59 ± 0.38 0.76 ± 0.38 ns
28 Ethyl caproate 06:36.0 0.050 1.532 178.75 ± 23.78 161.34 ± 44.5 ns
29 Isoamyl acetate 05:29.6 0.250 1.166 491.93 ± 309.43 579.53 ± 126.75 ns
30 Ethyl phenylacetate 10:16.3 0.050 1.067 4.73 ± 1.46 4.13 ± 1.62 ns
31 Isobutyl acetate 04:13.2 0.500 0.501 11.24 ± 6.80 12.49 ± 2.70 ns
32 Hexyl acetate 06:56.6 0.075 0.127 1.93 ± 2.62 2.24 ± 2.04 ns
33 Phenylethyl acetate 10:24.5 0.075 0.051 113.91 ± 30.45 110.87 ± 39.36 ns
34 Ethyl butyrate 04:30.0 0.100 0.001 67.10 ± 16.39 66.89 ± 15.94 ns
∑ Esters 1651.12 ± 437.54 1369.58 ± 291.48 *
35 Benzyl alcohol 10:37.4 0.150 4.216 11.06 ± 3.39 16.07 ± 7.94 *
36 cis-3-Hexen-1-ol 07:49.3 0.014 1.664 52.28 ± 32.29 40.97 ± 15.4 ns
37 trans-3-Hexen-1-ol 07:39.6 0.050 0.590 28.76 ± 18.30 24.57 ± 11.70 ns
38 1-Hexanol 07:34.8 0.075 0.005 301.42 ± 75.5 303.70 ± 98.49 ns
∑ Alcohols 393.53 ± 117.99 385.30 ± 115.34 ns
39 Geranic acid 12:13.3 5.000 8.813 4.32 ± 4.56 8.22 ± 2.65 *
40 Octanoic acid 11:14.2 50.000 5.916 2453.81 ± 420.15 2083.9 ± 400.08 *
41 Decanoic acid 11:57.5 50.000 4.083 967.72 ± 337.98 766.39 ± 209.3 ns
42 Nonanoic acid 11:35.3 10.000 1.833 18.18 ± 4.73 20.18 ± 3.36 ns
43 Valeric acid 09:59.8 5.000 0.903 41.03 ± 9.07 43.7 ± 6.34 ns
∑ Acids 3480.74 ± 669.79 2914.16 ± 575.91 *
44 4-Vinylguaiacol 11:44.5 5.000 5.536 155.17 ± 107.71 271.54 ± 146.66 *
45 4-Ethyl phenol 11:37.7 0.050 4.260 0.08 ± 0.05 0.13 ± 0.06 *
46 Guaiacol 10:34.0 0.100 0.501 0.07 ± 0.07 0.09 ± 0.05 ns
47 4-Ethyl guaiacol 11:10.9 0.075 0.304 0.09 ± 0.03 0.10 ± 0.03 ns
∑ Phenols 155.41 ± 107.82 271.85 ± 146.72 *
48 Phenylacetaldehyde 09:35.0 1.000 5.401 39.96 ± 6.79 31.85 ± 10.70 *
49 Benzaldehyde 08:51.4 0.150 1.056 0.21 ± 0.26 0.31 ± 0.27 ns
∑ Aldehydes 40.16 ± 6.89 32.15 ± 10.78 *
50 2-Aminoacetophenone 11:52.7 0.050 1.017 0.21 ± 0.06 0.24 ± 0.08 ns
51 Zingerone 14:34.6 0.050 0.610 2.92 ± 1.81 3.42 ± 1.65 ns
∑ Ketones 3.13 ± 1.79 3.66 ± 1.66 ns
52 γ-nonalactone 11:14.2 0.150 0.989 2.85 ± 2.11 2.27 ± 1.12 ns
53 γ-octalactone 10:50.6 0.100 0.568 1.90 ± 1.42 2.46 ± 2.28 ns
54 γ-decalactone 11:50.1 0.100 0.529 0.75 ± 0.15 0.80 ± 0.25 ns
55 δ-decalactone 11:38.0 0.150 0.009 8.61 ± 2.29 8.69 ± 2.26 ns
∑ Lactones 14.11 ± 3.70 14.22 ± 3.86 ns
56 Benzothiazole 11:00.6 0.500 2.937 1.04 ± 0.76 0.73 ± 0.08 ns
∑ Indole 1.04 ± 0.76 0.73 ± 0.08 ns
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tR—retention time; LOQ—limit of quantification; S—statistical differences; ns—no significant differences; and * —significant differences (p < 0.05). Cis and trans indicate geometric isomers and are written in italic type.

In Maraština wines, 15 volatile compounds were significantly different among the two vine-growing subregions in Dalmatia. The obtained results are in agreement with previous studies on Australian wines from different wine-growing regions, which showed the influence of climate conditions on alternations of volatile precursors, which can modify the fermentation medium and lead to changes in the aroma profile of wine [17]. It was shown that compounds associated with wines from the cooler climate were grape-derived volatiles, such as monoterpenes, C6 compounds, and some C13-norisoprenoids [17]. The higher rainfall promotes a decrease in the concentration of volatiles [21].

Terpenic compounds were the largest group of primary aroma compounds identified in the wines of Maraština. In this research, the two vine-growing subregions were significantly different in the concentration of trans-linalool oxide, cis-linalool oxide, cis-rose oxide, and trans-rose oxide (p < 0.05). β-citronellol (10.01 µg/L), linalool (6.88 µg/L), geraniol (5.23 µg/L), and α-terpineol (1.71 µg/L) were determined in the highest concentration in young Maraština wines derived from the ND subregion. Additionally, similar concentrations were determined in the CSD region, which shows a match with previous studies on white wines [22,23]. Linalool has characteristic citrus-like, sweet, and flowery notes; β-citronellol, α-terpineol, and geraniol exhibit flowery and sweet aromas [24]. In this study, all identified terpenic compounds were present in concentrations lower than their sensory threshold. Still, with a relatively wide array of present fruity–sweet–citric–flowery notes, there is a synergistic contribution to wine aroma [25]. Luzzini and co-workers reported a higher concentration of trans-linalool and cis-linalool oxide in spontaneous fermentation [26]. Rose oxide is a typical compound in Traminette wine with a lychee aroma [27], but it was not identified in wines produced from spontaneous fermentation [26]. The Gewürztraminer wine, with concentrations of linalool, α-terpineol, and rose oxide, which are similar in concentrations to our results, was described with notes of tropical fruit and ginger aromas [28]. Among the compounds that are related to discrimination with grape varieties, terpenic compounds were found to be highly discriminant and, thus, confirm the fact as being good markers of origin [13,29]. Additionally, it has been observed that concentrations of terpenic compounds were impacted by different yeasts in alcoholic fermentation [30].

C13-noriseprenoids are the second group of compounds belonging to the varietal aroma. Grapes accumulate a wide range of C13-noriseprenoids whose aglycones contribute highly desirable flavor and aroma properties [31]. In Maraština wines from both vine-growing subregions, β-damascenone, 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN), and vitispirani (mix of isomers) were detected. The concentration of β-damascenone (1.89 µg/L in ND) in Maraština is above the odor perception threshold, 0.05 µg/L [32] (Table S3). β-damascenone had a direct impact on wine aroma with an odor reminiscent of honey, prunes, or overmatured plums. In the ND subregion, significantly higher concentrations of TDN and β-damascenone were detected. This observation is consistent with Loyd and co-workers [33], who emphasize the importance of grape growing conditions in relation to concentrations of β-damascenone. TDN has been highlighted as a compound whose concentration increases for grapes grown under higher sunlight exposure, which is related to the ND subregion [34].

Esters contribute to the fruity and floral characteristics and aroma complexity of wines, even at concentrations below their odor threshold, by synergistic effect [35]. Through fatty acid acyl- and acetyl coenzyme A (CoA) pathways, yeasts produce ethyl fatty acid esters during alcoholic fermentation. On the other hand, acetate esters are produced through the condensation of higher alcohols with acetyl-CoA, which are under the control of esterase enzymes [36]. The most abundant ester in this study was isoamyl acetate, with concentrations of 491.93 µg/L in the ND subregion and 579.53 µg/L in the CSD subregion, followed by ethyl caprylate. Those esters contribute to the fresh fruity aromas of young white wines by commonly surpassing their low odor threshold, such as 30 µg/L for isoamyl acetate and 2 µg/L for ethyl caprylate [37]. The average concentration of ethyl isovalerate, ethyl 2-methylbutyrate, ethyl caproate, and ethyl butyrate surpassed their corresponding odor thresholds [38] (Table S3) and defined the fruity–flowery component of the aroma profile of Maraština. Furthermore, the significantly higher total concentration of esters in wines from the ND subregion compared to the CSD subregion can be related to the colder climate and higher concentrations of fatty acids in the ND subregion [39]. The concentration of ethyl acetate and acetate esters increased in spontaneous fermentation compared to different S. cerevisiae strain-inoculated fermentations of Corvina and Corvinone wines [26]. Additionally, Canonico and co-workers [40] reported a positive effect of spontaneous fermentation on Verdicchio wine by producing the highest content of isoamyl acetate (653 µg/L).

An important part of the compounds derived from grape metabolism is C6 alcohols. Three of them, cis-hexen-1-ol, trans-3-hexen-1-ol, and 1-hexanol were quantified in Maraština. The most abundant C6 alcohol was 1-hexanol (303.70 µg/L in CSD), which could be related to the grape origin giving the vegetal character of wine [11]. C6 alcohol rarely directly participates in wine aroma due to a high odor perception threshold, such as 2500 µg/L for 1-hexanol [41]. The total concentrations of C6 alcohols were similar in both subregions (385.30 µg/L in the CSD subregion and 393.53 µg/L in the ND subregion). Some other studies on Corvina [26] and Chardonnay wines [42] showed that wines produced from spontaneous fermentation had lower concentrations of alcohols than other co-fermentations.

Quantitatively, fatty acids were the larger group of secondary aroma compounds, followed by esters and alcohols. The total concentrations of fatty acids were significantly different between the two subregions. The major medium-chain fatty acids (MCFAs) quantified in Maraština wines are octanoic (2453.81 µg/L in ND), decanoic (967.72 µg/L in ND), and nonanoic (20.18 µg/L in CSD) acids. The concentration of octanoic acid was higher than their corresponding odor threshold of 500 µg/L [43] and significantly higher in the ND subregion. This trend was already observed by Petronilho and co-workers, who characterized the volatile fraction of the white wines Arinto and Bical and showed that fatty acids contribute to a large part of the aroma profile [44]. Yeasts are the primary producers of these fatty acids, which are worth mentioning because of their ability to convert to ethyl ester [45]. The different grape microbiotas of the wine subregions in Dalmatia described by Milanović [4] might influence the significant statistical difference in the acid content of young wine Maraština. Medina and co-workers reported elevated concentrations of MCFA during spontaneous fermentation in Chardonnay [42]. Inoculation with non-Saccharomyces and Saccharomyces cerevisiae can modify the chemical profile and bring benefits to regulating the content of fatty acids since their presence may have a negative impact on aromas with greasy and cheesy notes [46].

Volatile phenols are considered a characteristic compound in wine, but their influence on the final aroma can be positive or negative depending on their concentration. The main volatile phenols in wines are 4-ethylguaiacol, 4-vinylguaiacol, and 4-vinylphenol, which were all identified and quantified in examined Maraština wines too [41]. Volatile phenols can be produced from phenolic acids by yeast enzymatic activity or acid hydrolyses of their glycosides. The concentration of 4-vinylguaiacol in all investigated wines from CSD and ND subregions was higher than the odor percipient threshold of 40 µg/L [47], which is connected to negative clove notes. The presence of these compounds in wine is associated with Brettanomyces yeasts present in native microbiota [48].

Aldehydes and ketones are highly volatile constituents formed from yeasts during fermentation by decarboxylation of 2-oxo-3-phenylpropanoic acid or a chemical oxidation process [49]. Phenylacetaldehyde was significantly different in the Maraština wines from both subregions, and its concentrations were about 10 times higher than the sensory threshold of 4 µg/L [50]. Phenylacetaldehyde with OAV > 10 highly contributed to wine aroma with the key odorant of honey showing a significantly higher concentration in wines from ND. Similar data were found in other studies of wines where concentrations of phenylacetaldehyde were above the corresponding threshold, especially in young white wines [11,51].

Lactones are volatile organic compounds derived from lipid metabolism in grapes [52] and are naturally present in wine, especially γ-lactones and δ-lactones. These compounds had low perception thresholds (γ-nonalactone 25 µg/L and γ-octalactone 7 µg/L) [53,54] and very powerful odor descriptors that range from peach-like and coconutty to creamy and floral. Allamy reported concentrations of γ-nonalactone were low in white wines (about 5.9 μg/L), similar to our concentrations of 2.85 µg/L in ND and 2.27 µg/L in CSD [55]. Benzothiazole was the only indole detected in this study with similar concentrations in the ND (1.04 µg/L) and CSD (0.73 µg/L) subregions.

3.2. GC×GC/TOF-MS

Table 2 presents the volatile compounds that were identified or tentatively identified through a comparison of the experimental literature retention indices (LRIexp) and mass spectral data with corresponding data reported in the NIST database (LRIlit). A total of one hundred and eighty-eight identified or tentatively identified compounds included terpenic compounds (7), C13-norisoprenoids (1), esters (48), alcohols (25), acids (36), phenols (5), aldehydes (5), ketones (6), lactones and furanoids (22), sulfur-containing compounds (9), nitrogen-containing compounds (12), and other compounds (12). Compounds are listed according to different chemical classes and in order of decreasing F-ratio. It is evident that there are a large number of compounds that are co-eluted in the first dimension and which obviously cannot be properly observed with 1D-GC-MS. The use of GC×GC analyses resulted in 188 tentatively identified metabolites, a number that is three times higher than the one obtained by 1D-GC-MS. GC×GC/TOF-MS provides much-increased separation capacity and chemical selectivity for the analysis of metabolites present in a complex wine matrix. Wine metabolites are expressed as peak area and area percentage in two vine-growing subregions (CSD and ND) with their respective retention times in the first (1 tR) and in the second (2 tR) chromatographic dimensions, literature retention indices (LRIlit), and experimental retention indices (LRIexp) obtained in GC×GC/MS analyses.

Table 2.

Chromatographic area and area percentage (%) of volatile aroma compounds in young Maraština wines from Northern Dalmatia (ND) and Central and Southern Dalmatia (CSD) subregions determined by GC×GC/TOF-MS, sorted by compound class, and in descending Fisher F-ratio.

No. Compound m/z 1 tR
(min:s)		 2 tR
(min:s)		 LRIexp LRIlit ND CSD F S
Area % Area %
1 8-Hydroxylinalool 101 30:42.0 00:01.2 2300 2294 6426 0.01 5214 0.01 5.608 *
2 Hotrienol 71 18:48.0 00:01.3 1604 1605 4887 0.00 2844 0.00 5.067 *
3 2,3-Dihydrofarnesol 69 30:07.0 00:01.6 2273 2265 18,585 0.02 15,003 0.02 1.936 ns
4 β-Citronellol 69 21:50.0 00:01.4 1757 1762 20,982 0.02 18,151 0.02 1.571 ns
5 Linalool 93 17:31.0 00:01.4 1547 1544 15,457 0.01 14,273 0.02 0.674 ns
6 Geraniol 69 23:14.0 00:01.4 1844 1839 18,645 0.02 17,447 0.02 0.477 ns
7 trans-Farnesol 69 31:24.0 00:01.6 2350 2355 12,911 0.01 14,418 0.02 0.233 ns
∑ Terpenic compounds 97,893 0.09 87,349 0.10 1.057 ns
8 3-Oxo-α-ionol 108 35:15.0 00:01.4 2641 - 27,266 0.03 31,116 0.04 0.921 ns
∑ C13-norisoprenoids 27,266 0.03 31,116 0.04 1.808 ns
9 Ethyl 2-hydroxy-4-methylvalerate 69 17:31.0 00:01.3 1547 1547 188,279 0.18 74,936 0.09 33.650 *
10 Ethyl isopentyl succinate 101 24:17.0 00:01.7 1900 1897 52,727 0.05 20,056 0.02 23.051 *
11 Isoamyl lactate 45 18:06.0 00:01.3 1583 1583 136,300 0.13 75,713 0.09 22.459 *
12 Diethyl butanedioate 101 20:12.0 00:01.5 1686 1679 5,015,783 4.84 1,878,508 2.19 21.671 *
13 Decyl 2,2-dimethylpropanoate 70 23:14.0 00:01.3 1844 - 13,321 0.01 6138 0.01 14.324 *
14 Ethyl 3-hydroxypropionate 73 18:34.0 00:01.1 1597 - 185,916 0.18 83,103 0.10 14.318 *
15 Ethyl 3-formylpropionate 85 28:01.0 00:01.2 2145 - 24,799 0.02 13,960 0.02 13.896 *
16 Diethyl 2-hydroxypentanedioate 85 28:36.0 00:01.3 2143 - 418,513 0.40 209,095 0.24 12.786 *
17 Ethyl 2-phenylethyl oxalate 104 31:03.0 00:01.4 2337 - 7245 0.01 2795 0.00 12.337 *
18 Diethyl 2-methylbutanedioate 115 31:17.0 00:01.1 2346 - 6585 0.01 3881 0.00 12.317 *
19 Ethyl hydrogen succinate 128 31:45.0 00:01.1 2363 2368 6,682,871 6.44 5,000,549 5.84 11.107 *
20 Ethyl pyruvate 43 11:27.0 00:01.2 1268 1267 114,024 0.11 181,394 0.21 9.854 *
21 Ethyl 2-acetamido-4-methylpentanoate 128 27:47.0 00:01.4 2117 - 7613 0.01 3598 0.00 9.168 *
22 Methyl 4-hydroxybutanoate 74 21:50.0 00:01.1 1757 - 7964 0.01 16,086 0.02 8.420 *
23 Diethyl malate 117 26:37.0 00:01.3 2031 2041 211,154 0.20 128,189 0.15 7.498 *
24 Methyl ethyl succinate 115 19:23.0 00:01.4 1641 1632 23,799 0.02 10,165 0.01 7.464 *
25 Ethyl 2-hydroxypropanoate 45 13:05.0 00:01.1 1344 1353 2,411,977 2.33 1,963,777 2.29 5.328 *
26 2-Phenylethyl propionate 104 24:03.0 00:01.7 1892 - 7125 0.01 5276 0.01 4.853 *
27 Methyl 2-methyl-3-oxobutanoate 88 26:02.0 00:01.2 2000 - 7922 0.01 10,553 0.01 4.573 *
28 Ethyl linoleate 105 34:47.0 00:02.2 2517 - 38,773 0.04 22,431 0.03 4.163 ns
29 Ethyl laurate 88 23:21.0 00:02.2 1844 1846 127,797 0.12 27,741 0.03 3.102 ns
30 Ethyl 2-phenylacetate 91 22:18.0 00:01.6 1793 1786 83,794 0.08 65,586 0.08 2.936 ns
31 Ethyl octanoate 88 15:11.0 00:02.0 1440 1440 1,534,067 1.48 1,216,408 1.42 2.934 ns
32 Ethyl heptanoate 88 12:58.0 00:01.9 1322 1327 8473 0.01 11,930 0.01 2.919 ns
33 α-Terpinyl acetate 59 20:33.0 00:01.5 1696 1693 13,056 0.01 10,422 0.01 2.915 ns
34 Ethyl undecenoate 152 38:24.0 00:01.2 2883 - 8752 0.01 4496 0.01 2.913 ns
35 Ethyl pentadecanoate 88 30:00.0 00:02.3 2268 2161 75,987 0.07 41,633 0.05 2.587 ns
36 Ethyl vanillate 151 35:15.0 00:01.3 2641 2653 4552 0.00 7188 0.01 2.452 ns
37 Ethyl dec-9-enoate 88 20:26.0 00:02.0 1693 1703 67,674 0.07 42,785 0.05 2.026 ns
38 Ethyl decanoate 88 19:30.0 00:02.1 1645 1642 330,113 0.32 201,810 0.24 1.944 ns
39 Ethyl 4-hydroxybutanoate 87 22:32.0 00:01.2 1800 1796 6,613,274 6.38 7,833,802 9.14 1.694 ns
40 Ethyl acetaminoacetate 72 28:22.0 00:01.2 2155 - 25,266 0.02 21,895 0.03 1.583 ns
41 Ethyl 3-cyclohexylpropanoate 88 31:10.0 00:01.4 2341 - 7943 0.01 6381 0.01 1.527 ns
42 Ethyl 3-hydroxyoctanoate 117 24:03.0 00:01.4 1892 1892 29,452 0.03 24,961 0.03 1.226 ns
43 Methyl 2,3-dihydroxybenzoate 136 30:00.0 00:01.2 2268 - 11,774 0.01 6125 0.01 1.179 ns
44 Ethyl 2-(4-hydroxyphenyl)acetate 180 38:52.0 00:01.2 2904 - 6564 0.01 7742 0.01 0.953 ns
45 Ethyl 3-hydroxybutanoate 71 16:56.0 00:01.2 1510 1505 53,677 0.05 44,603 0.05 0.890 ns
46 Ethyl 3-hydroxyhexanoate 71 20:12.0 00:01.3 1686 1690 9462 0.01 8390 0.01 0.559 ns
47 N-Acetyl-L-valine ethyl ester 72 26:23.0 00:01.4 2019 - 17,871 0.02 14,638 0.02 0.366 ns
48 Methyl pyruvate 43 21:57.0 00:01.4 1761 1217 122,105 0.12 137,910 0.16 0.199 ns
49 Ethyl hexanoate 88 10:38.0 00:01.8 1232 1238 810,509 0.78 862,228 1.01 0.155 ns
50 Ethyl 4-acetoxybutanoate 87 21:01.0 00:01.5 1732 - 30,519 0.03 32,622 0.04 0.110 ns
51 2-Phenylethyl acetate 104 22:53.0 00:01.6 1811 1811 1,474,492 1.42 1,411,052 1.65 0.084 ns
52 2-Methylbutyl acetate 43 08:04.0 00:01.6 1131 1128 3,851,619 3.71 4,093,266 4.78 0.072 ns
53 Ethyl 4-hydroxybenzoate 121 40:09.0 00:01.2 2996 - 13,799 0.01 14,950 0.02 0.060 ns
54 Ethyl 2-phenylethyl dimethylmalonate 104 37:35.0 00:01.4 2811 - 12,272 0.01 11,503 0.01 0.059 ns
55 Diisoproply phthalate 149 36:04.0 00:01.8 2702 - 7430 0.01 7370 0.01 0.001 ns
56 Hexyl acetate 43 11:34.0 00:01.7 1270 1275 46,249 0.04 45,864 0.05 0.000 ns
∑ Esters 30,961,232 29.85 25,925,505 30.26 6.558 *
57 2,4,7,9-Tetramethyl-5-decyne-4,7-diol 109 27:26.0 00:01.3 2106 - 2419 0.00 4759 0.01 16.214 *
58 3-Methylpentan-1-ol 56 12:44.0 00:01.1 1316 1340 1,029,814 0.99 510,705 0.60 8.382 *
59 2,7-Dimethyloctane-4,5-diol 69 21:22.0 00:01.1 1743 - 56,054 0.05 37,359 0.04 7.861 *
60 2-(4-Methoxyphenyl) ethanol 121 31:03.0 00:01.3 2337 2335 54,485 0.05 41,628 0.05 7.723 *
61 2-Phenylethanol 45 24:24.0 00:01.2 1904 1909 9,329,193 8.99 4,663,562 5.44 7.509 *
62 Nonan-2-ol 45 16:56.0 00:01.4 1510 1528 60,114 0.06 44,710 0.05 7.000 *
63 4-Methylpentan-1-ol 56 12:23.0 00:01.1 1306 1301 138,557 0.13 57,418 0.07 5.757 *
64 4-Hexen-3-ol 71 28:08.0 00:01.2 2148 - 31,532 0.03 27,392 0.03 4.185 ns
65 cis-4-Hydroxymethyl-2-methyl-1,3-dioxolane 103 20:05.0 00:01.1 1682 - 40,436 0.04 112,028 0.13 3.949 ns
66 3-heptyn-2-ol 43 24:10.0 00:01.1 1896 - 140,268 0.14 193,684 0.23 3.516 ns
67 3-Ethyl-4-methyl-1-pentanol 69 16:42.0 00:01.3 1503 1507 3100 0.00 15,929 0.02 2.985 ns
68 trans-4-hydroxymethyl-2-methyl-1,3-dioxolane 103 18:55.0 00:01.1 1607 35,623 0.03 116,338 0.14 2.303 ns
69 3-Hexen-1-ol 67 14:01.0 00:01.2 1386 1380 173,450 0.17 117,245 0.14 2.180 ns
70 3-Ethoxy-1-propanol 59 13:47.0 00:01.1 1363 1377 44,867 0.04 24,178 0.03 2.092 ns
71 2,6-Dimethyl-7-octen-2,6-diol 71 25:27.0 00:01.2 1959 1964 7915 0.01 7044 0.01 1.734 ns
72 Heptan-1-ol 70 15:39.0 00:01.3 1453 1456 2,585,574 2.49 2,431,741 2.84 1.035 ns
73 Phenoxyethanol 94 28:15.0 00:01.2 2151 2142 10,304 0.01 12,013 0.01 0.683 ns
74 1-Butanol 56 08:25.0 00:01.1 1140 1146 140,783 0.14 154,607 0.18 0.429 ns
75 Isoamyl alcohol 55 10:10.0 00:04.9 1221 1230 445,386 0.43 395,568 0.46 0.357 ns
76 Pentan-1-ol 42 10:59.0 00:01.1 1241 1244 43,391 0.04 37,469 0.04 0.219 ns
77 2-Methyl-3-butene-1,2-diol 71 23:49.0 00:02.2 1863 - 17,499 0.02 16,048 0.02 0.175 ns
78 Butane-1,3-diol 45 18:06.0 00:01.0 1583 1576 1,564,242 1.51 1,473,432 1.72 0.109 ns
79 (2S,3S)-Butane-2,3-diol 45 17:24.0 00:01.0 1543 1545 5,140,609 4.96 5,339,535 6.23 0.084 ns
80 (3,4,5-Trimethoxyphenyl) methanol 198 38:31.0 00:01.4 2889 - 9875 0.01 10,396 0.01 0.020 ns
81 4-Methyl-5-thiazoleethanol 113 30:42.0 00:01.2 2300 2311 17,739 0.02 18,441 0.02 0.019 ns
∑ Alcohols 21,123,230 20.37 15,863,228 18.52 7.556 *
82 Hexanoic acid 60 23:21.0 00:01.1 1848 1854 7,588,282 7.32 5,184,808 6.05 10.652 *
83 2-Oxopentanedioic acid 101 37:35.0 00:01.1 2811 - 177,608 0.17 121,733 0.14 9.957 *
84 Dodecanoic acid 60 33:16.0 00:01.2 2489 - 62,061 0.06 24,576 0.03 9.084 *
85 Isovaleric acid 60 20:05.0 00:01.0 1682 1680 7,103,710 6.85 5,799,112 6.77 8.570 *
86 Butanoic acid 60 19:16.0 00:01.0 1638 1637 1,073,005 1.03 828,020 0.97 7.530 *
87 Acetic acid 60 15:39.0 00:01.0 1453 1465 499,185 0.48 741,473 0.87 7.121 *
88 Caprylic acid 60 26:58.0 00:01.1 2050 2046 6,104,658 5.89 4,619,455 5.39 6.266 *
89 Octanoic acid 60 27:12.0 00:01.1 2098 2096 6,104,658 5.89 4,619,455 5.39 6.266 *
90 Succinic acid 56 30:49.0 00:01.0 2304 - 375,686 0.36 211,649 0.25 4.411 *
91 Butanedioic acid 56 37:00.0 00:00.9 2782 - 308,343 0.30 147,377 0.17 4.215 ns
92 Dec-9-enoic acid 69 31:10.0 00:01.1 2341 2341 279,052 0.27 184,202 0.22 3.734 ns
93 Decanoic acid 60 30:14.0 00:01.2 2279 2275 1,334,398 1.29 965,662 1.13 3.674 ns
94 4-Methyl-2-oxovaleric acid 57 15:25.0 00:01.3 1447 - 66,486 0.06 19,862 0.02 3.495 ns
95 5-Oxotetrahydrofuran-2-carboxylic acid 103 38:31.0 00:01.1 2889 - 80,224 0.08 60,831 0.07 3.393 ns
96 2-Methylbutanoic acid 74 20:05.0 00:01.1 1682 1674 5,108,137 4.93 4,341,982 5.07 2.652 ns
97 3-Hexenoic acid 68 25:13.0 00:01.0 1952 - 6614 0.01 3529 0.00 2.602 ns
98 Malic acid 71 37:28.0 00:01.0 2806 277,579 0.27 215,559 0.25 2.528 ns
99 2-Hydroxy-4-methylpentanoic acid 76 33:44.0 00:01.0 2592 - 75,528 0.07 37,324 0.04 1.723 ns
100 Hexadecanoic acid 60 38:45.0 00:01.4 2900 2900 77,788 0.07 60,127 0.07 1.506 ns
101 5-Hexenoic acid 60 24:24.0 00:01.0 1904 1900 53,357 0.05 34,362 0.04 0.869 ns
102 4-Methoxy-4-oxobutanoic acid 101 31:17.0 00:01.0 2346 - 40,500 0.04 33,313 0.04 0.776 ns
103 o-Anisic acid 105 37:07.0 00:01.2 2792 - 8878 0.01 6783 0.01 0.748 ns
104 Heptanoic acid 60 25:13.0 00:01.1 1952 1960 46,171 0.04 52,421 0.06 0.716 ns
105 Homovanillic acid 137 40:02.0 00:01.4 2992 3099 6456 0.01 5866 0.01 0.648 ns
106 Pyruvic acid 85 32:20.0 00:01.0 2411 - 7576 0.01 6720 0.01 0.574 ns
107 trans-3-Hexenoic acid 68 24:59.0 00:01.0 1923 1915 4188 0.00 3508 0.00 0.445 ns
108 2-(4-Hexyl-2,5-dioxofuran-3-yl)acetic acid 126 27:40.0 00:01.6 2113 2110 23,221 0.02 16,851 0.02 0.437 ns
109 2-Propenoic acid 45 19:30.0 00:01.0 1645 - 22,808 0.02 33,948 0.04 0.414 ns
110 Benzeneacetic acid 91 34:19.0 00:01.1 2572 2565 489,308 0.47 537,374 0.63 0.368 ns
111 Propionic acid 74 17:24.0 00:01.0 1543 1547 58,564 0.06 56,625 0.07 0.137 ns
112 Benzoic acid 105 32:34.0 00:01.1 2423 2423 26,705 0.03 25,854 0.03 0.104 ns
113 Isobutyric acid 73 18:06.0 00:01.0 1583 1581 539,147 0.52 559,134 0.65 0.027 ns
114 trans-2-Hexenoic acid 73 25:27.0 00:01.0 1959 1969 9305 0.01 9517 0.01 0.027 ns
115 7-Octenoic acid 57 27:54.0 00:01.1 2120 - 14,525 0.01 14,436 0.02 0.010 ns
116 Pentanoic acid 60 21:22.0 00:01.0 1743 1744 76,463 0.07 77,245 0.09 0.008 ns
117 Nonanoic acid 60 28:36.0 00:01.1 2162 2165 12,275 0.01 12,228 0.01 0.001 ns
∑ Acids 38,142,449 36.78 29,672,925 34.64 17.718 *
118 2-Ethoxy-6-(methoxymethyl)phenol 137 31:17.0 00:01.4 2346 - 6209 0.01 2609 0.00 46.882 *
119 4-Vinylguaiacol 135 29:11.0 00:01.3 2200 2203 287,108 0.28 483,388 0.56 3.766 ns
120 2,4-Ditert-butylphenol 191 30:49.0 00:01.3 2304 2316 8225 0.01 6636 0.01 1.416 ns
121 2,6-Ditert-butyl-4-methylphenol 205 24:31.0 00:02.0 1908 1906 85,342 0.08 91,286 0.11 1.015 ns
122 Phenol 94 26:09.0 00:01.1 2000 2008 15,548 0.01 16,802 0.02 0.864 ns
∑ Phenols 402,432 0.39 600,722 0.70 3.701 ns
123 4-Hydroxybenzaldehyde 121 39:48.0 00:01.2 2960 2958 140,741 0.14 97,154 0.11 4.282 *
124 Benzaldehyde 106 17:10.0 00:01.4 1536 1534 6935 0.01 8158 0.01 3.959 ns
125 6,6-Trimethyl-1-cyclohexene-1-propenal 163 24:59.0 00:01.6 1923 3759 0.00 4395 0.01 1.300 ns
126 Benzeneacetaldehyde 91 19:37.0 00:01.4 1648 1648 52,327 0.05 56,856 0.07 0.159 ns
127 Hydroxy methyl furfural 97 33:30.0 00:01.1 2500 - 22,283 0.02 22,031 0.03 0.027 ns
∑ Aldehydes 226,044 0.22 188,594 0.22 2.768 ns
128 2-Methyl-4-phenyl-3-pentanone 105 25:27.0 00:02.0 1959 - 18,102 0.02 104,172 0.12 8.945 *
129 3-Hydroxy-2-butanone 45 11:48.0 00:01.1 1276 1280 59,058 0.06 21,029 0.02 4.706 *
130 1-Phenylethanone 105 19:44.0 00:01.4 1652 1656 18,532 0.02 21,882 0.03 3.013 ns
131 4,5-Dimethyl-1,3-dioxol-2-one 114 27:54.0 00:01.1 2120 - 22,813 0.02 24,528 0.03 0.452 ns
132 Acetovanillone 151 35:29.0 00:01.2 2650 2651 93,840 0.09 85,845 0.10 0.162 ns
133 Zingerone 137 37:21.0 00:01.3 2800 2790 7157 0.01 6754 0.01 0.083 ns
∑ Ketones 219,503 0.21 264,211 0.31 0.484 ns
134 2-Benzofuran-1(3H)-one 105 31:38.0 00:01.4 2359 2356 4335 0.00 6958 0.01 18.421 *
135 δ-Valerolactone 42 22:46.0 00:01.3 1808 - 28,596 0.03 63,074 0.07 11.676 *
136 DL Mevalolactone 71 34:12.0 00:01.1 2566 - 36,733 0.04 54,671 0.06 10.112 *
137 2,3-Dihydro-1-benzofuran 120 31:59.0 00:01.1 2371 2389 574,035 0.55 1,642,628 1.92 7.665 *
138 5-(Hydroxymethyl)dihydrofuran-2(3H)-one 85 35:50.0 00:01.1 2664 - 3,008,921 2.90 2,223,477 2.60 5.411 *
139 δ-Octalactone 99 25:34.0 00:01.5 1963 1965 29,764 0.03 21,141 0.02 4.794 *
140 3-Hydroxy-4,4-dimethyldihydrofuran-2(3H)-one 128 26:30.0 00:01.1 2025 - 2356 0.00 2954 0.00 4.547 *
141 4-Hydroxy-2-ethyl-5-methyl-3(2H)-furanone 56 27:26.0 00:01.1 2106 22,976 0.02 16,587 0.02 4.410 *
142 4-(1-Hydroxyethyl)-γ-butanolactone 86 31:03.0 00:01.2 2337 2328 51,285 0.05 39,634 0.05 2.961 ns
143 5-Ethoxydihydro-2(3H)-furanone 85 21:08.0 00:01.4 1735 1728 96,235 0.09 67,581 0.08 2.558 ns
144 δ-Hexanolactone 42 22:25.0 00:01.4 1796 1792 53,111 0.05 28,094 0.03 2.431 ns
145 α-Amino-γ-butyrolactone 57 28:43.0 00:01.1 2165 - 15,428 0.01 17,230 0.02 2.156 ns
146 cis-4-Hydroxy-3-methylundecanoic acid lactone 99 25:41.0 00:01.6 1967 - 22,731 0.02 17,913 0.02 1.168 ns
147 γ-Hexalactone 85 20:47.0 00:01.4 1704 1703 12,815 0.01 14,975 0.02 0.815 ns
148 3-Hydroxy-4,4-dimethyldihydrofuran-2(3H)-one 71 26:37.0 00:01.1 2031 2034 108,610 0.10 115,306 0.13 0.672 ns
149 δ-Decalactone 99 29:11.0 00:01.6 2200 2192 26,224 0.03 24,117 0.03 0.420 ns
150 5-(Hydroxy[methoxy(5-oxotetrahydro-2-furanyl)methoxy]methyl)dihydro-2(3H)-furanone 85 31:24.0 00:01.2 2360 - 30,839 0.03 25,851 0.03 0.411 ns
151 γ-Octalactone 85 24:38.0 00:01.5 1911 1916 40,055 0.04 46,792 0.05 0.210 ns
152 γ-Nonalactone 85 28:22.0 00:01.6 2155 7610 0.01 7212 0.01 0.165 ns
153 3,4-Dihydroxy-5-methyl-dihydrofuran-2-one 60 38:59.0 00:01.1 2908 - 20,306 0.02 21,262 0.02 0.056 ns
154 Ƴ-Butyrolactone 68 22:32.0 00:01.1 1800 - 3,075,844 2.97 3,137,551 3.66 0.014 ns
155 γ-Heptalactone 85 22:39.0 00:01.4 1804 1796 9714 0.01 9998 0.01 0.012 ns
∑ Lactones and Furanoids 7,278,522 7.02 7,605,007 8.88 0.347 ns
156 Ethyl 3-methylthiopropanoate 74 18:06.0 00:01.5 1583 1580 15,294 0.01 38,729 0.05 6.358 *
157 3-(Methylthio)propionic acid 61 30:35.0 00:01.0 2295 2298 119,593 0.12 72,314 0.08 6.004 *
158 2-Methyldihydrothiophen-3(2H)-one 60 17:17.0 00:01.5 1540 - 822,873 0.79 550,872 0.64 3.146 ns
159 3-(Ethylthio)propanol 61 22:04.0 00:01.2 1785 1802 30,717 0.03 40,517 0.05 2.897 ns
160 N-acetylmethionine ethyl ester 99 35:50.0 00:01.4 2664 - 14,010 0.01 15,774 0.02 0.919 ns
161 3-Methylthiopropyl acetate 61 19:16.0 00:01.5 1638 1633 6213 0.01 7236 0.01 0.669 ns
162 S-(3-Hydroxypropyl) ethanethioate 74 25:48.0 00:01.2 1971 - 76,117 0.07 86,477 0.10 0.660 ns
163 5-Acetyldihydrofuran-2(3H)-one 85 27:05.0 00:01.2 2092 2096 23,593 0.02 22,169 0.03 0.388 ns
164 3-Methylmercapto-1-propanol 106 20:54.0 00:01.2 1707 1715 1,677,408 1.62 1,720,142 2.01 0.035 ns
∑ Sulfur-containing compounds 2,785,819 2.69 2,554,230 2.98 0.601 ns
165 2-Ethylbutan-1-amine 101 35:08.0 00:01.1 2636 - 192,419 0.19 98,372 0.11 26.914 *
166 N-Phenethylacetamide 104 34:33.0 00:01.3 2583 2590 10,957 0.01 72,921 0.09 8.741 *
167 3-Methylpiperazine-2,5-dione 85 34:05.0 00:01.0 2560 - 5863 0.01 4539 0.01 5.000 ns
168 Benzothiazole 135 25:20.0 00:01.5 1956 1959 8013 0.01 4187 0.00 3.423 ns
169 N,N-Dibutylformamide 72 21:57.0 00:01.7 1761 1767 9573 0.01 12,233 0.01 1.776 ns
170 N-Acetylcysteamine 60 15:18.0 00:01.3 1443 - 16,226 0.02 30,705 0.04 1.708 ns
171 1H-indole 117 32:48.0 00:01.2 2434 2435 9900 0.01 34,798 0.04 1.101 ns
172 N-(3-Methylbutyl)acetamide 72 23:35.0 00:01.2 1856 1855 3509 0.00 7045 0.01 1.042 ns
173 1H-Isoindole-1,3(2H)-dione 120 39:13.0 00:01.3 2940 - 137,672 0.13 122,523 0.14 0.982 ns
174 3-Ethyl-4-methyl-1H-pyrrole-2,5-dione 139 30:14.0 00:01.2 2279 2260 20,059 0.02 22,456 0.03 0.453 ns
175 2-(Oxan-4-yl)ethanamine 85 28:22.0 00:01.1 2155 - 10,950 0.01 10,492 0.01 0.138 ns
176 2-Propoxyethylamine 68 36:39.0 00:00.9 2730 - 3229 0.00 3729 0.00 0.037 ns
∑ Nitrogen-containing compounds 428,370 0.41 424,000 0.49 0.003 ns
177 4-Hydroxy-6-pentyltetrahydro-2H-pyran-2-one 102 37:28.0 00:01.5 2806 - 6418 0.01 2634 0.00 14.628 *
178 1,1-Di(2-methyl butoxy)ethane 71 12:16.0 00:02.5 1303 - 169,495 0.16 688,162 0.80 7.905 *
179 Succinic acid anhydride 56 35:01.0 00:01.0 2631 - 450,941 0.43 285,403 0.33 7.558 *
180 2-Methyl-2-propyl-1,3-dioxolane 87 35:43.0 00:01.3 2659 - 4732 0.00 6170 0.01 1.151 ns
181 Isothiocyanatocyclohexane 141 20:12.0 00:02.0 1686 1670 17,086 0.02 17,710 0.02 1.141 ns
182 1,4-Dioxanyl hydroperoxide 115 20:05.0 00:01.7 1682 - 7069 0.01 9086 0.01 0.946 ns
183 2,3-Diphenylbutane 105 18:55.0 00:01.6 1607 - 3817 0.00 4115 0.00 0.157 ns
184 2-Methyl-2H-pyran-3,4,5 (6H)-trione 142 30:35.0 00:01.1 2296 - 2767 0.00 2992 0.00 0.137 ns
185 Methylsuccinic anhydride 68 23:07.0 00:01.1 1841 1855 4237 0.00 5186 0.01 0.086 ns
186 4,5-Dimethyl-2-pentadecyl-1,3-dioxolane 101 33:37.0 00:01.1 2500 52,080 0.05 50,362 0.06 0.045 ns
187 Ethoxy-1-pentoxyethane 73 07:43.0 00:02.0 1103 1104 1,298,503 1.25 1,373,564 1.60 0.043 ns
188 trans-7-tetradecene 83 15:25.0 00:02.8 1447 1435 8217 0.01 8055 0.01 0.034 ns
∑ Other compounds 2,025,363 1.95 2,453,438 2.86 0.775 ns
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1 tR—retention time in first chromatographic dimensions; 2 tR—retention time in second chromatographic dimensions; LRIlit—linear retention index from the literature; LRIexp—linear retention index obtained experimentally; S—statistical differences; -—not found; ns—no significant differences; and *—significant differences (p < 0.05). Cis and trans indicate geometric isomers and are written in italic type.

Furthermore, a quantitative analysis would be necessary for a precise definition of the impact of volatile metabolites on wine aroma. Aroma descriptors found in the literature are employed for a general discussion regarding the influence of the presence of a volatile compound on the wine aroma. A discussion regarding the potential contribution of a few important metabolites is presented as follows. Terpenic compounds, including 8-hidroxylinalool, 2,3-dihydrofarnesol, hotrienol, β-citronellol, trans-farnesol, linalool, and geraniol were identified. The only identified C13-norisoprenoid was 3-oxo-α-ionol (0.04%) with a very similar chromatographic area in both subregions. These compounds have an impact on the aroma with notes that are floral with a slight woody note and notes of flowers, rose, and geranium. The number of detected terpenic compounds and C13-norisoprenoids was higher using the targeted approach—GC-MS/MS—because it is more sensitive and allows the quantification of terpenic compounds and norisoprenoids, even at very low concentrations.

The abundant classes were acids and esters with peak areas of 64.90% (CSD) and 66.63% (ND) showing correspondence with the results of GC-MS analysis. The compound with higher area percentages was hexanoic acid (7.32%). Additionally, the peak area of hexanoic acid showed significant differences in the two subregions, as well as 2-oxopentanedioic acid, acetic acid, isovaleric acid, butanoic acid, caprylic acid, octanoic, and succinic acid. Esters represented one of the most dominant classes of compounds, which is in the agreement with studies provided by GC×GC/TOF-MS [56,57], especially in the ND subregion. The higher areas of esters in Maraština wines belong to ethyl hydrogen succinate (6.44%), followed by ethyl 4-hydroxybutanoate (6.38%), diethyl butanedioate (4.84%), 2-methylbutyl acetate (4.78%), and ethyl 2-hydroxypropanoate (2.33%). Among the alcohols, the three major ones were: 2-phenylethanol (8.99%), butane-2,3-diol (6.23%), and heptan-1-ol (2.84%). The 2-phenylethanol, for example, contributes a positive rose aroma, and its presence was observed in the aroma of Merlot [57]. The next more abundant group was lactones and furanoids. 5-(hydroxymethyl) dihydrofuran-2(3H)-one (2.97%) and γ-butyrolactone (3.66%) had higher areas. γ-butyrolactone has sensory descriptors such as creamy and oily. The volatile sulfur compounds in wines come mainly from the metabolism of yeast and contribute mainly to unpleasant aromas in wines. Significantly different sulfur-containing compounds in the CSD and ND subregions were ethyl 3-methylthiopropanoate and 3-(methythio)propionic acid. The most abundant was 3-methylmercapto-1-propanol (1.98%). Moreira reported high levels of S-methyl thioacetate, 3-mercapto-1-propanol, 3-(ethylthio)-1-propanol, and 3-methylthiopropionic acid in white wines such as Alvarinho, Loureiro, and Avesso [58]. Out of a total of five phenols, 4-vinylguaiacol had the highest chromatography areas (0.56%). Nitrogen in wine is sourced from the degradation of amino acids and is used by yeast for the production of other nitrogen compounds. The most abundant nitrogen metabolites in the CSD and ND subregions were 2-ethylbutan-1-amine (0.19%), followed by N-phenethylacetamide, which were not identified by GC-MS. Among carbonyl compounds, 4-hydroxybenzaldehyde (0.14%) was found as a major chromatographic peak. Rodríguez-Bencomo and co-workers [59] reported this compound as one of the useful precursors that showed contents in grapes comparable to the levels observed in wine volatile compounds. The most important ketone was acetovanillone (0.10%). Acetovanillone is a component that is formed during wine oxidation [60].

3.3. Multivariate Statistical Analysis

The principal component analysis performed on the GC-MS data set allowed a good separation of Maraština wines derived from two vine-growing subregions. In a projection of 15 volatile compounds that defined the principal components PC1 and PC2, the first two principal components explained 95.7% of the variability (Figure 1). PC1 accounted for 79.1% of total variability, while PC2 accounts for 16.6% variability. Wines from the ND subregion were clearly differentiated from the wines from the CSD subregion along the direction of PC1 and gravitated to higher positive PC2 values.

Figure 1.

Figure 1

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Separation of Maraština wines according to ND and CSD vine-growing subregions in three-dimensional space defined by the first three principal components PC1, PC2, and PC3.

Hierarchical clustering analysis performed on the GC×GC/TOF-MS data set confirmed the discrimination of the Maraština wine volatile profile among the vine-growing subregions (Figure 2). On the heatmap, for the CSD subregion, a darker color in the column was evident for 10 compounds: δ-valerolactone, DL mevalolactone, 2,3-dihydro-1-benzofuran, hydroxy-4,4-dimethyldihydrofuran-2(3H)-one, acetic acid, methyl 4-hydroxybutanoate, methyl 2-methyl-3-oxobutanoate, ethyl pyruvate, 2,4,7,9-tetramethyl-5-decyne-4,7-diol, N-phenethylacetamide, 2-methyl-4-phenyl-3-pentanone, 1,1-di(2-methyl butoxy)ethane, ethyl 3-methylthiopropanoate, and 2-benzofuran-1(3H)-one. The rest of the 46 compounds had a higher chromatographic peak area in the ND subregion and mostly belong to terpenic compounds, esters, alcohols, and acids. Compounds with a darker color, which correspond to the higher chromatographic peak area, were 8-hydroxylinalool, hotrienol, decyl 2,2-dimethylpropanoate, 2-phenylethyl propionate, 3-methylpentan-1-ol, 4- methylpentan-1-ol, 4-hydroxybenzaldehyde, 4-hydroxy-6-pentyltetrahydro-2H-pyran-2-one, and 3-hydroxy-2-butanone.

Figure 2.

Figure 2

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Hierarchical clustering representation corresponding to the 56 most significant volatile compounds of the Maraština wines from the two subregions (ND and CSD) obtained by GC×GC/TOF-MS analysis. The rows in the heat map represent compounds, and the columns indicate samples. Compounds are designated by numbers that correspond to those in Table 2. The relative content of each compound is illustrated through a chromatic scale (from dark-blue, minimum, to dark-red, maximum).

4. Conclusions

In conclusion, young Maraština wines produced from the Northern Dalmatia subregion had a higher concentration of total volatile compounds than the Southern and Central Dalmatia subregions, especially the following compounds: cis-rose oxide, trans-rose oxide, β-damascenone, TDN, ethyl leucite, diethyl succinate, phenylacetaldehyde, benzaldehyde, and octanoic acid. The aroma profile of all experimental wines was dominated by esters, followed by acids and alcohols. Furthermore, the low odor thresholds and higher concentrations of compounds such as β-damascenone, ethyl caprylate, ethyl isovalerate, ethyl 2-methylbutyrate, ethyl caproate, isopentyl acetate, ethyl butyrate, and phenylacetaldehyde directly contribute to the aroma of young Maraština wines from both subregions with key odorants of fruity (apple, banana, strawberry, prune, and lemon) and honey notes. Spontaneous fermentations were characterized by the high concentration of esters regardless of grape origin and reflected in the distinctive aroma character of the wines. The methodology applied proved successful for the most detailed screening of metabolites in young Maraština wines produced by spontaneous fermentation reported to date. Different metabolomics approaches in this study (targeted and untargeted) made it possible to identify one hundred and eighty-eight compounds by GC×GC/TOF-MS and fifty-six compounds by GC-MS/MS. The metabolomics approach can provide a large amount of information and can help to anticipate variation in wines or change winemaking procedures. Multivariate analysis proved good separation and discrimination of Maraština wines from two Dalmatian subregions.

Acknowledgments

Authors would like to thank wine producers from Dalmatia (Croatia, EU) for donating the grapes of the Maraština variety: Anito Pecotić, Marija and Josip Pecotić, Neven Baničević, Dušan Didović, Vlado Perišin, Rajko Arbanas, Ivica Ražnjević, Boris Miletić, Ante Marić, and Stipe Knežević.

Supplementary Materials

The following supporting information can be downloaded at: www.mdpi.com/article/10.3390/metabo12121295/s1, Table S1: General vineyard parameters; Table S2: Climate data obtained from the Croatian Meteorological and Hydrological Service in the period from January 2021 to September 2021 and represented the average value of four vineyards for the Northern Dalmatia subregion and six vineyards for the Central and Southern Dalmatia subregions; Table S3: Odor activity value (OAV) of the main odorants in young Maraština wines quantified by gas chromatography–mass spectrometry (GC-MS/MS) in Central and Southern Dalmatia and Northern Dalmatia subregions.

Click here for additional data file. (218.5KB, zip)

Author Contributions

Conceptualization, A.B. and I.B.-L.; methodology, A.B., U.V., S.C., A.M. and I.B.-L.; formal analysis, A.B. and S.C.; writing—original draft preparation, A.B.; writing—review and editing, A.B., U.V., S.C., A.M. and I.B.-L.; visualization and supervision, I.B.-L., U.V. and S.C.; project administration, I.B.-L.; funding acquisition, I.B.-L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are contained within the article and supplementary materials.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This research was funded by Research project IP-2020-02-1872; “Impact of native non-Saccharomyces wine yeast on wine aromas–WINE AROMAS” and “Young Researchers’ Career Development Project–Training New Doctoral Students–DOK-2021-02” funded by the Croatian Science Foundation; and COST Action CA 17111 INTEGRAPE “Data integration to maximize the power of OMICs for grapevine improvement”.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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