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Article

Revealing the Unique Characteristics of Greek White Wine Made from Indigenous Varieties Through Volatile Composition and Sensory Properties

by
Evangelia Nanou
1,
Maria Metafa
2,*,
Susan E. P. Bastian
3 and
Yorgos Kotseridis
1,*
1
Laboratory of Oenology & Alcoholic Drinks (LEAD), Department of Food Science & Human Nutrition, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece
2
Institute of Technology of Agricultural Products, Hellenic Agricultural Organization—DIMITRA, 1 Sofokli Venizelou, 14123 Lycovrisi, Greece
3
Discipline of Wine Science, School of Agriculture, Food and Wine, Waite Campus, University of Adelaide, Urrbrae, Adelaide 5064, SA, Australia
*
Authors to whom correspondence should be addressed.
Beverages 2025, 11(2), 33; https://doi.org/10.3390/beverages11020033
Submission received: 15 December 2024 / Revised: 11 February 2025 / Accepted: 17 February 2025 / Published: 27 February 2025
(This article belongs to the Section Wine, Spirits and Oenological Products)
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Abstract

:
Greek wines made from the indigenous grape varieties Assyrtiko, Malagousia, Moschofilero and Roditis are attracting the interest of wine producers and consumers due to their aromatic characteristics. However, there are few studies that focus on the unique wine characteristics of each variety and the relationship between the composition of volatile compounds and sensory properties. Monovarietal white wines (2018 vintage) were analyzed by gas chromatography–mass spectrometry to quantify 34 volatile compounds. Multivariate statistical analyses were used to investigate correlations between volatiles and sensory attributes identified by a trained panel. The results showed that the strongest aroma compounds were a group of terpenes, isoamyl acetate and phenylethyl acetate. Terpenes such as geraniol, α-terpineol, linalool and cis-rose oxide correlated with floral notes, especially in Moschofilero wines. In addition, isoamyl acetate contributed to the aroma of tropical fruits, especially banana, in the Roditis wines, while phenylethyl acetate correlated with rose, vanilla and fruity notes in both the Moschofilero and Roditis samples. The Assyrtiko wines and the Malagousia wines were mainly associated with compounds such as cis-3-hexen-1-ol and cis- and trans-furan linalool oxides, which may enhance fresh fruit and citrus aromas through synergistic effects. The common background aroma of the studied wines was mainly determined by higher alcohols, fatty acids and ethyl esters. This study provides a basis for understanding the typical aroma of white wines from indigenous Greek grape varieties, which will help producers develop targeted wine styles and will be useful for consumer promotion.

1. Introduction

Wines of Greece are becoming increasingly appealing to consumers in both local and international markets. Assyrtiko, Malagousia, Moschofilero and Roditis are among the emblematic white grape varieties of Greek viticulture and wine [1]. Assyrtiko wines are mainly produced in the Santorini PDO region and are famous for their minerality [2], high acidity [3] and citrus aromas [4], while Malagousia, a grape variety that was developed in Central Greece, has a characteristic aroma that ranges from fruity to floral [4,5] and can also have herbal notes [6]. The Moschofilero grape is primarily produced in the Peloponnese, associated with the Mantineia PDO region, and is characterized by high aromatic intensity with floral and citrus aromas [4]. Finally, Roditis is a grape variety that is widely planted in Greece and whose wines have been characterized by fruitiness, specifically tropical fruit and banana, as well as vanilla [4].
Aroma is one of the most important factors that determine the quality of wine and its acceptance by consumers [7]. Several factors influence wine aroma, such as grape variety, terroir, viticultural and winemaking practices and bottle aging [8]. Volatile compounds formed during these processes stimulate our olfactory system and produce different sensations, i.e., aromas or odors [9]. Depending on the origin of these volatile compounds, aromas are categorized as primary or varietal when they come from the grape, secondary when originating from fermentation and other early processes, and tertiary or bouquet when they result from aging, including contributions from oak contact [10,11]. Several reviews have focused on the understanding of the impact of the aforementioned factors and the role of volatile compounds on aroma formation [12,13].
Gas chromatography–mass spectrometry (GC-MS) is one of the most applied analytical techniques for the identification and quantification of volatile compounds since it offers high sensitivity and accuracy. In order to determine the sensory relevance of each compound, the odor activity value (OAV) is widely used [9]. The OAV allows an estimation of the impact of an odor compound based on its concentration relative to its odor threshold. Nevertheless, it is impossible to fully explain the perceived aroma of wines solely by the analysis of their volatile composition. Interactions between volatile compounds, such as synergistic and suppression effects of odorants [14], and interactions with the non-volatile wine matrix [15,16] also affect the impact of the odorants on aroma perception. Thus, it is necessary to investigate wine aroma not only based on chemical analysis but also in relation to sensory analysis that takes into consideration human perception [17,18].
Understanding wine aroma is crucial as it enables producers to select the appropriate vineyard and winemaking processes to enhance desirable characteristics and reduce undesirable ones. In addition, this knowledge helps producers focus on specific flavor characteristics that are appreciated by consumers, thus increasing the wine’s market potential. Although there is already a lot of research on volatiles in wines, the characterization of volatile and sensory profiles of wines from Greek grape varieties remains scarce. Recent studies have investigated the volatile composition of wines produced from the Moschofilero [19], Assyrtiko and Malagousia [19,20] varieties. However, other studies have focused mainly on individual varieties and on the effects of specific aspects such as yeast strains [3,5,21,22,23,24] or pre-fermentative processes [25] on volatile or sensory properties. Hence, there is a lack of comparative studies that focus on the exploration of the typical aroma character of different Greek varieties using a comprehensive approach to investigate the correlation between volatile and sensory data. Therefore, the aim of this study was to investigate the volatile profile of white wines from the Greek indigenous varieties Moschofilero, Malagousia, Assyrtiko and Roditis, highlighting differences and similarities between them and exploring correlations between the volatile compounds and the sensory attributes of these wines, thus highlighting their unique characteristics.

2. Materials and Methods

2.1. Wine Samples

Monovarietal white wines made from four indigenous Greek grape varieties were included in this study. Seven commercially available Moschofilero (MSF) wines from Peloponnese, four Malagousia (MLG) wines, one from Attica, two from Macedonia and one from Evia, three Assyrtiko (ASR) wines, one from Santorini and two from Macedonia, and three Roditis (ROD) wines from Peloponnese were analyzed. All samples were from the 2018 vintage and had no contact with wood. The wine samples were selected as typical according to the recommendations of the National Inter-Professional Organization for Vine and Wine and were checked to ensure that they were free of off-flavors before analysis. The volatile compounds analysis took place between January and February 2020 and the sensory evaluation in December 2019. The results of the physicochemical and sensory analysis have already been reported by Nanou et al. [4] (see Table S1, Supplementary Material). Here, we present the results of the volatile compound analysis and an investigation of the correlations between volatile compounds and the sensory data. The coding of the wines is consistent with the article by Nanou et al. [4] to facilitate referencing for the reader (Table S1).

2.2. Reagents and Standards

The reagents used in this study included methanol (HPLC grade) and absolute ethanol, both sourced from Panreac (Barcelona, Spain), as well as dichloromethane (HPLC grade), obtained from Sigma–Aldrich (Steinheim, Germany). Water was purified using a Milli-Q water purification system from Millipore (Bedford, MA, USA). Sodium sulfate and L(+)-tartaric acid (purity > 99%) were also purchased from Panreac (Barcelona, Spain). Chemical standards for identifying and quantifying aroma compounds were provided by Sigma–Aldrich (Steinheim, Germany), Aldrich (Steinheim, Germany), Merck (Darmstadt, Germany), Fluka (Buchs, Switzerland), PolyScience (Niles, IL, USA), Panreac (Barcelona, Spain), Alfa Aesar (Karlsruhe, Germany), and SAFC (St. Louis, MO, USA), with all standards having a purity of over 95%. Additionally, ISOLUTE® ENV+ 200 mg/6 mL cartridges were obtained from Biotage (Uppsala, Sweden).

2.3. Quantification of Volatile Compounds

2.3.1. Extraction Methods

Two different extraction techniques were employed to recover volatile compounds from the wine samples, depending on their concentration and origin. Both techniques were applied to the wine samples in triplicate, using three separate bottles of wine for each extraction. For the extraction of fermentation-related (major) aroma compounds, such as ethyl esters, acetates, volatile fatty acids, and higher alcohols, a modified liquid–liquid extraction (LLE) method was utilized, as described by Ivanova et al. [26]. Specifically, a 50 mL filtered wine sample was placed in a glass conical flask and was spiked with 100 μL of a 2-octanol internal standard solution (2 mg/L final concentration). Then, 25 mL of dichloromethane (in a 2:1 ratio) was added, and the flask was placed in an ice bath at 0 °C. The sample was stirred for 30 min using a magnetic stirrer. After centrifugation (10,000× g, 4 °C, 20 min), the dichloromethane layer was separated, evaporated under vacuum at 35 °C to approximately 1.2 mL and finally reduced to 250 μL under a nitrogen stream before being transferred to a glass vial for analysis.
To extract primary aroma compounds, including terpenes, C6 higher alcohols, C13-norisoprenoids, and volatile phenols, solid-phase extraction (SPE) was performed using Isolute ENV+ cartridges, following a method developed and validated by Metafa and Ecomomou [27], with slight adjustments. The SPE cartridges were conditioned sequentially with 10 mL of methanol and 20 mL of water. A 25 mL filtered wine sample, diluted with an equal volume of water, was spiked with 100 μL of a 2-octanol internal standard solution (0.5 mg/L final concentration). The sample was passed through the conditioned SPE cartridge, which was subsequently washed with 20 mL of water. The analytes were eluted using 35 mL of dichloromethane, dried over 1.8 g of sodium sulfate, and concentrated to approximately 1.5 mL using a Vigreux column. The final step involved further reducing the solvent volume to 250 μL under a nitrogen stream, resulting in a concentration factor of 100.

2.3.2. GC-MS Analysis

A 1 μL aliquot of the concentrated sample was injected into an Agilent 6890N gas chromatograph coupled with an Agilent 5972 mass selective detector. Separation was achieved on an Innowax polyethylene glycol capillary column (25 m × 0.2 mm i.d., 0.2 μm film thickness). The temperature program began at 60 °C for 5 min, followed by an increase to 140 °C at 1.5 °C/min and to 205 °C at 3 °C/min. Helium (grade 6) was used as a carrier gas at a flow rate (constant flow) of 1 mL/min. The injector temperature was 200 °C and the transfer line’s temperature was 280 °C. A splitless injection for 1 min (45 psi) was followed by a split ratio of 1:50. Mass spectra were acquired over the range of 50–400 m/z at one scan per second.
The quantification of both major and minor volatile compounds relied on five-point calibration curves prepared in a synthetic wine matrix (12% ethanol, 4 g/L tartaric acid, pH 3.4, adjusted with sodium hydroxide). The calibration solutions were spiked with standard analytes and a fixed concentration of the internal standard (2-octanol: 2 mg/L for major volatiles and 0.5 mg/L for minor volatiles). LLE and SPE procedures, as described, were performed in triplicate for each concentration level. Analyte detection was carried out in full-scan mode for the major volatiles and in the selected ion monitoring mode (SIM) for minor (primary aroma) compounds. Analyte identification was based on retention times, NIST library mass spectra (https://webbook.nist.gov/chemistry/) (match factor ≥ 90), and ion peak intensity ratios compared with standard solutions. Relative peak areas of analytes to the internal standard were used for quantification, ensuring accurate identification and reliable results.

2.4. Odor Activity Values

Odor activity values (OAVs) were calculated as the ratio between the concentration of each volatile and its odor threshold. They were used to determine the relative contribution of each volatile to the wine aroma, with higher OAVs indicating a greater potential impact [9,11]. Odor thresholds, determined in model wine matrices, were retrieved from the literature [28,29,30,31,32,33].

2.5. Sensory Analysis

The protocol for sensory training and wine evaluation is described in detail by Nanou et al. [4]. In summary, 23 panelists were trained to evaluate white wines orthonasally over a period of four months in a total of 21 sessions of 45 min. In this study, orthonasal evaluation was chosen as this is the primary olfactory pathway. However, it is recognized that retronasal evaluation may reveal different in-mouth flavors depending on factors such as pH, enzyme activity and cross-modal interactions [34]. Training included familiarization with the odor reference standards and the study wines, vocabulary development, and training in the method of sensory evaluation based on frequency of mentions [35,36]. When the training was completed and the panelists were ready for the evaluation process, they tested the 17 wines in duplicate under controlled conditions, in four sessions over two weeks. The blind-coded samples were served monadically in random order, and panelists were instructed to select two to five sensory attributes from a list of twenty-five odor descriptors.
In the present work, we aimed to extend our understanding by examining the relationship between the five most frequently mentioned attributes for each wine (as identified by Nanou et al. [4]; see Table S2, Supplementary Material) and the volatile compounds detected in this study. This approach allowed us to include not only the sensory attributes that distinguished the samples but also those that were consistent across all samples and still important for describing the overall sensory space.

2.6. Statistical Analysis

The quantified results are presented as the mean concentrations ± standard deviation (SD) of each volatile compound calculated from triplicate extractions for each wine sample and then averaged across all samples of each grape variety. One-way analysis of variance (ANOVA) was used to examine differences in the concentration of the volatiles across varieties, followed by the Tukey HSD post hoc test for pairwise comparisons. All analyses were performed on a 5% level of statistical significance (α = 0.05). Principal Component Analysis (PCA) on standardized variables was run to visualize correlations between the volatile compounds and the wines. Subsequently, Agglomerative Hierarchical Clustering (AHC) using Ward’s method and Euclidean distance was applied on the first three components of the aforementioned PCA in order to gain insight into the groups of wines that were formed according to their volatile profile. Next, the derived clusters were submitted to PCA to further explore any correlations with volatile compounds. Finally, a Partial Least Squares (PLS) regression analysis was performed with sensory data on the Y matrix and volatile compounds on the X matrix to visualize and explore correlations between the two matrices and the study wines. A Variable Importance in Projection (VIP) of 1 was used as a critical point to decide on the inclusion of variables on the X matrix after taking into consideration OAVs as well. One-way ANOVA was run in IBM SPSS Statistics version 26 (IBM Corporation, Armonk, New York, NY, USA) and all multivariate data analyses were performed with XLSTAT 2024 (Addinsoft, New York, NY, USA).

3. Results and Discussion

In total, 34 volatiles, primary and fermentation aroma compounds, were identified and quantified through GC-MS analysis. Concentrations are averaged across varieties and are shown in Table 1. Total concentrations of the volatile compounds (see Supplementary Figure S1) show that higher alcohols were the most abundant group of volatiles in all varieties, ranging from a total of 350 mg/L to 437 mg/L. Alcohols in wine are generally found in high concentrations and play an important role in the formation of the overall aroma in wines. Furthermore, they interact with other compounds and they contribute to aroma complexity [37,38]. Fatty acids and esters followed with 19 to 27 mg/L and 17 to 26 mg/L, respectively, across the four varieties. Volatile fatty acids affect wine aroma moderately, although individually, these compounds impart unpleasant odors [39]. Esters, known for contributing to the fruity aroma [17], were found in relatively high quantities in all varieties, especially in Assyrtiko and Moschofilero. The group of two carbonyl compounds, i.e., γ-butyrolactone and 2,3-butanedione, ranged between 5 and 10 mg/L. Last but not least, terpenes and the single volatile phenol, i.e., vanillin, were found in quantities of 30 to 500 μg/L and 1 to 3 μg/L, respectively, across the varieties. Notably, terpenes were more than five times higher in Moschofilero wines compared to Roditis and Assyrtiko wines and had about double the concentration in Moschofilero compared to Malagousia wines. Terpenes are important for the aroma of wines in that they impart floral and fruity aromas [40]. Furthermore, interactions among terpenes can add to the effect of a single terpene compound by enhancing the overall aroma intensity [41]. Vanillin was found in traces; however, in Assyrtiko, it was three times as high compared to Moschofilero and Roditis and double in comparison to Malagousia.
Regarding individual volatile compounds, 15 out of the 34 differed significantly across the varieties (Table 1). In the group of alcohols, isoamyl alcohol, which imparts fermented notes [42], was higher in Assyrtiko wines than in Moschofilero. This level of isoamyl alcohol (340 ± 64 mg/L) in the current study is comparable to the one in a previous study on young white wines from Chardonnay [43]. Methionol, known for its cooked potato and sweet aroma [42], was found in lower concentrations in Roditis compared to Malagousia and Moschofilero. The low concentration of methionol in Roditis is similar to the one found previously in Macabeo white wine [32], while the higher ones relate to those found in Chardonnay wine [43].
As for the acetate esters, Roditis contained a higher amount of isoamyl acetate, associated with a banana aroma [42] and phenylethyl acetate, linking to rose and honey notes [42], compared to the rest of the varieties. Similar levels of isoamyl acetate were found in wines of Zalema, which is an autochthonous Spanish white variety [44], while phenylethyl acetate was found in lower concentrations in the latter wines compared to Roditis. Ethyl esters, in particular ethyl hexanoate and ethyl octanoate, known for contributing to fruity notes [42], were lower in Malagousia wines compared to their counterparts. These compounds are formed through esterification of fatty acids during fermentation [11]. Their lower concentrations in Malagousia may suggest variations in the fermentation process or in the availability of precursors. Indeed, Malagousia wines have a lower concentration of the respective fatty acids (Table 1).
Regarding terpenes and terpenoids, Malagousia had higher levels of trans- and cis-furan linalool oxide compared to the rest of the varieties. Linalool oxide is a derivative coming from the oxidation of linalool and it may indirectly affect the aroma of wines due to its high odor threshold by enhancing fresh scents [33]. In Moschofilero, which had the highest total concentration of terpenes, linalool and α-terpineol were detected in significantly higher levels compared to the Assyrtiko and Roditis samples. Last, cis-rose oxide, that is responsible for floral notes in wine [45] and for notes that link to the aroma of the tropical fruit lychee [46,47], was higher in Moschofilero than in the rest of the wines, contributing probably to the rose aroma that has been reported as an attribute specifically related to Moschofilero [4]. A recent study on the volatile composition of Greek wines also indicated the high terpene concentration of Moschofilero wines compared to Assyrtiko and Malagousia wines [19]. These findings relate to the varieties of Riesling, Muscat and Gewürztraminer, which are known for their rich terpene aromatic profiles [41,47,48,49,50], implying similarities in their volatile composition to Moschofilero wines. Particularly, cis-rose oxide has been indicated as a major contributor of the overall aroma in Gewürztraminer wines [51]. Furthermore, a recent study depicted the importance of cis-rose oxide to linalool and α-terpineol ratios in the perceived aroma of white wines [52].
The odor activity value (OAV) is widely used in wine research to highlight the importance of volatile compounds, suggesting that compounds with higher values play a more critical role in defining the characteristic aromas of a wine. Largely, a value over 1 is considered as the critical point for an OAV. However, several studies have used lower cut-off points such as 0.5 [39], 0.2 [53] or even 0.1 [32], suggesting that compounds with a lower value may also play a significant role since there are other effects as well than solely that of the relationship between the concentration of each compound and its odor threshold, which can affect wine aroma. These effects not only include synergistic or masking phenomena among volatiles that occur in a complex matrix such as that of wine [14] but also interactions between the volatile and non-volatile matrix [15,16,17].
Table 2 shows thresholds and aroma descriptions from the literature, as well as the calculated OAV for each volatile compound per variety. As can be seen, some esters and volatile fatty acids have particularly high OAVs, indicating low thresholds and/or high concentrations of these compounds in the wines. Specifically, all fatty acids have an OAV over 0.5 and more than half of them have an OAV over 10. However, fatty acids have a moderate impact on the overall aroma of wines, even though each one is associated with unpleasant smells [39]. On the contrary, esters in our study wines are present at similar concentrations with similar OAVs as fatty acids, yet they do play a significant role in the perceived aroma in wines [17]. Terpenes have typically low thresholds, which, together with their low concentrations in wine, lead to moderate or small OAVs. However, their impact on wine aroma is high [40]. Notably, β-damascenone, a C13 norisoprenoid, exhibits a particularly high OAV due to its very low odor threshold, suggesting its high influence on wine aroma. Previous research has suggested that its presence may enhance the fruity character of wine [54].
A PCA was run to visualize relationships among the identified volatile compounds and the wine samples. From this point onwards, samples will be mentioned in their abbreviated form for simplicity of reporting and to correspond with figures and tables, i.e., ASR for Assyrtiko, MLG for Malagousia, MSF for Moschofilero, and ROD for Roditis wines. Specifically, in Figure 1, axes F1 and F2 explain 47% of the variance in our data. F1 represents primarily terpenes on the right side, while F2 represents mainly esters and fatty acids on the bottom side and some alcohols, like cis-3-hexen-1-ol and methionol, along with some terpenoids like cis- and trans-furan linalool oxide in the upper side of the axis. Higher alcohols are spread all over the PCA plot. Regarding the wines, MSF9 and MSF10 were closely related to the positive side of F1 and thus to terpenes, of which some had a high OAV (cis-rose oxide, geraniol, linalool) and some a low one (nerol and α-terpineol). This suggests an important contribution of these compounds, even with a low OAV. MLG wines were all grouped together in the upper left side of the PCA biplot and were characterized mainly by the linalool oxides and cis-3-hexenol, all having a low OAV. ROD samples were placed in the lower left part of the biplot and correlated strongly with some esters with high OAVs, such as isoamyl acetate and ethyl decanoate. The space around the center of the axes and towards the bottom side was shared by ASR and other MSF samples, indicating that these may have some shared volatile profiles consisting of esters and acids of high OAVs. A PCA plot with volatile compounds colored according to their OAVs can be seen in Supplementary Figure S2.
A cluster analysis of the previously identified PCA factors resulted in four groups based on their volatile profile (Supplementary Table S3). These clusters were further described after performing PCA with clusters as rows and volatiles as columns (Supplementary Figures S3 and S4). To simplify data presentation, the clustered wines are shown in Figure 1, with wines of the same cluster having the same color; cluster 1 is shown in blue, cluster 2 in black, cluster 3 in brown, and cluster 4 in green. According to this analysis, cluster 1 was positively correlated with cis- and trans-furan linalool oxide and with cis-3-hexenol as well as isobutanol. Most esters such as phenylethyl acetate, ethyl octanoate, ethyl hexanoate and ethyl butyrate were negatively correlated with this cluster, indicating that their concentrations in the respective wines are low compared to the other clusters. The MLG wines were all grouped in this cluster, indicating that these samples had a relatively high concentration of cis- and trans-furan linalool oxide and cis-3-hexenol, while most esters were found in low concentrations.
Cluster 2 correlated mainly with component F3 and therefore showed a strong correlation with vanillin, decanoic acid, ethyl decanoate and ethyl 3-hydroxybutyrate on this axis (Supplementary Figure S4), while it showed a clear negative association with terpenes. From this, we can conclude that the ASR samples, which were all categorized in cluster 2, were rich in vanillin and some esters but had low levels of terpenes. Cluster 3, consisting of MSF9 and MSF10, was positively related to terpenes, mainly α-terpineol, linalool, citronellol, cis-rose oxide and geraniol. It also correlated with β-damascenone and some esters. This strong terpene profile suggests that these wines may have a distinct floral character.
Finally, cluster 4 correlated positively with isoamyl acetate, ethyl decanoate and trans-3-hexen-1-ol, while β-damascenone, citronellol and other terpenes were negatively associated with this cluster. Since this cluster consisted exclusively of ROD samples, we can conclude that these wines have a fruity character such as banana notes, as isoamyl acetate is strongly correlated with this odor. The remaining MSF wines were distributed between clusters 1 and 2, suggesting a more complex aroma character than MSF9 and MSF10, probably with additional fruity notes for cluster 2 and fresh notes for cluster 1 attributed to the esters and cis-3-hexen-1-ol, respectively, apart from the floral notes of the terpenes.
The grouping of the samples in clusters in this study is remarkably similar to the clustering result according to sensory attributes in the previous research of Nanou et al. [4] that showed that the same samples were grouped into four groups, two of them being the same as in the current work, i.e., cluster 3, with the two MSF samples, and cluster 4 with the ROD samples. In the former study, ASR samples also formed part of cluster 2, but this one was also shared with some MLG and MSF samples, while cluster 1 consisted of MSF and MLG wines. Furthermore, the sensory profile of those clusters seems quite relevant to the volatile profile of the current study. Cluster 1, in Nanou et al. [4], was characterized among others by floral notes (citrus blossoms) that could be related to the terpene concentration that was found in the current study; cluster 2 exhibited a citrus fruit character that can likely be related to the high ester content found here; cluster 3 showed floral and honey attributes possibly related to the high terpene concentration that was reported in the current study; and cluster 4 was characterized by banana and vanilla notes that can partly be explained by the high isoamyl acetate content that we detected in this study. It should be noted that the samples in both studies were of the same origin, and thus, this association can be investigated directly.
Therefore, we performed PLS regression to explore the global relationships between sensory and volatile data, as well as the samples (Figure 2). In the model, we used sensory attributes that were among the most frequently cited across all wines. The total number of attributes emerged from the results of Nanou et al. [4], and the five most frequently mentioned attributes for each wine were included. Further details can be found in Table S2 and Figure S5 (Supplementary Material) that describe the sensory profile of the wines. From the final PLS model, we removed the volatile compounds that had both an OAV < 0.5 and a VIP < 1 (Figure S6) since they did not contribute significantly to the overall aroma of the wine nor did they play an important role in driving differences among samples. These volatiles were ethyl 3-hydroxybutyrate, γ-butyrolactone, 1-hexanol, trans-3-hexen-1-ol, citronellol and vanillin. We particularly kept in the model cis- and trans-furan-linalool oxide because it showed a high correlation with fresh-cut grass and citrus fruit odors, even though it had a VIP < 1. Next, we kept in the model volatiles with both an OAV > 0.5 and a VIP > 1, as these can be seen as key odor compounds playing a significant role in perceived wine aroma. In our study, key aroma compounds, i.e., compounds for which direct associations with odors can be made, were isoamyl acetate, phenylethyl acetate, methionol, geraniol, linalool, α-terpineol and cis-rose oxide. We also chose to keep in the model volatiles with both an OAV < 0.5 and a VIP > 1 because even if they have a limited role individually (OAV < 0.5), they play a significant role in explaining variability within the dataset (VIP > 1). Hence, they may have a significant contribution through synergistic effects on other volatiles and enhance their aroma. The results showed that such indirect associations could be made with diethyl succinate, ethyl lactate, trans-rose oxide, cis-3-hexen-1-ol, and nerol. Finally, we included volatiles with both an OAV > 0.5 and a VIP < 1 because even though they are not important for explaining differences between wine samples (VIP < 1), they may be important for the common background aroma in all wines (OAV > 0.5). Such volatiles were the vast majority of ethyl esters, the volatile fatty acids and some alcohols such as isoamyl alcohol, phenylethyl alcohol and isobutanol.
The PLS model (Figure 2) explained 41% of the variance in the sensory data (Y matrix) and 48% of the variance in the volatile compounds (X matrix). On the left side of the plot, floral scents like jasmine and rose, and also honey and citrus blossoms, correlated strongly with the terpenes: linalool, geraniol, nerol, α-terpineol, cis rose oxide, trans-rose oxide, but also, even though to a lesser extent, with some esters like ethyl octanoate, ethyl hexanoate, ethyl butyrate and ethyl lactate. The attribute honey was mainly correlated with phenylethyl alcohol and, to a lesser extent, with β-damascenone. Phenylethyl acetate correlated with rose, vanilla and tropical fruit scents. The latter aroma compound has been previously associated with rose odor [55]. Odors coming from tropical fruit like banana, pineapple, melon were strongly correlated with isoamyl acetate. Vanilla also correlated positively with these odors and volatiles, i.e., with isoamyl acetate and phenylethyl acetate. However, it is known that this scent is predominantly derived from vanillin, though such an association was not shown here. Vanillin was not associated with vanilla odor and was left out of the final model as it had both an OAV < 0.5 and VIP < 1. As a sweet scent, vanilla is probably more closely associated with odors that relate to sweet fruit, like tropical fruit, or to rose odor that is frequently found in sweets that may, for example, contain rose water.
On the right side of the biplot, alcohols such as isoamyl alcohol, trans-3-hexen-1-ol, and isobutanol, and also a fatty acid, butyric acid, were related to odors such as mushroom, nuts, earthy, and fresh-cut grass. Furthermore, cis-3-hexen-1-ol was related with the fresh-cut grass odor and with citrus fruit notes, like lemon, lime and grapefruit. Cis- and trans-linalool oxides also correlated positively with the latter odors and with cis-3-hexen-1-ol and contributed probably synergistically to enhance these notes in the study wines. Regarding the wines’ distribution in the aroma space, MSF samples were plotted on the left side of the graph indicating the significant role of terpenes in their aroma profile, with MSF9 and MSF10 having a remarkably high correlation with this chemical group and floral scents compared to the rest of the MSF samples, which were closer to the center of the plot, suggesting the influence of more variables on their aroma. ROD samples were placed on the bottom side, and all of them, especially ROD1, correlated with tropical fruit odors, vanilla, isoamyl- and phenylethyl acetate. ASR and MLG samples were placed in the upper right side of the plot, sharing citrus fruit and earthy odors, with MLG samples being generally more closely related to citrus fruits and fresh odors. All in all, ROD wines and MSF9 and MSF10 exhibited a clearer aroma profile compared to the rest of the wines, attributed mainly to high ester–low terpene concentration for the Roditis variety and high terpene content for the two aforementioned MSF samples. The rest of the MSF samples were defined not only by terpenes but also by esters, which made their aroma profile more complex. ASR wines exhibited an earthy mushroom aroma profile, attributed mainly to isobutanol and isobutyric acid and to the much lower terpene concentration compared to the rest of the samples. However, they also had fresh and citrus fruit notes that were shared with and mainly present in MLG wines.
In this study, commercially available wines were analyzed; therefore, we acknowledge that winemaking techniques and grape maturation conditions may vary among producers. These factors have a strong influence on the aromatic profile of wines as variations in yeast selection, fermentation conditions and other enological practices can affect the expression of volatile compounds [8,11]. However, the goal of this study was to examine if differences in the varietal character of the wines are evident despite these variations, reflecting real-market conditions where consumers encounter wines from different producers and may therefore exhibit unique characteristics due to regional and winemaking influences. In the present work, the observed differentiation among the four varieties suggests that intrinsic varietal characteristics contribute significantly to the volatile and sensory profile of these wines. Future research could complement these findings by applying a standardized vinification protocol to investigate further the specific influence of grape variety independent of winemaking procedures.

4. Conclusions

This is the first study to provide a comprehensive approach to understanding the aroma profile of wines of the Greek white grape varieties Moschofilero, Assyrtiko, Malagousia and Roditis by not only describing but also by differentiating these profiles, using volatile compound analysis and sensory data. Moreover, to our knowledge, this is the first time that data on the volatile profile of the Roditis variety have been published in this context.
The quantification of the volatile compounds and the multivariate statistical analyses showed that two of the seven Moschofilero wines and all Roditis wines formed two distinct and homogeneous groups. The Moschofilero wines were characterized by a high content of terpenes, while the Roditis wines were characterized by a high content of esters, especially isoamyl acetate, and a low content of terpenes. The Malagousia wines, which are all grouped together but share overlap with some Moschofilero wines, showed a correlation mainly with cis- and trans-furan linalool oxides and cis-3-hexen-1-ol. The Assyrtiko wines, also grouped in a different cluster but consistent with some other Moschofilero wines, showed a higher concentration of esters and vanillin, albeit exhibiting low terpene content. Apart from the two Moschofilero samples, which formed a separate group, the other wines of this variety showed a more complex volatile profile, consisting not only of terpenes but also of esters and higher alcohols.
The most important aroma compounds were the terpene and terpenoid compounds geraniol, α-terpineol, linalool and cis-rose oxide, which were directly and strongly related to the floral scent in the wines, especially in the Moschofilero wines, and caused the clearest differences between the wines studied. In addition, isoamyl acetate contributed to the tropical fruit aroma and especially the banana aroma of the Roditis wines, while phenylethyl acetate induced a sweet aroma correlated with rose, vanilla and fruit notes in both the Moschofilero and Roditis samples. The wines from Assyrtiko and Malagousia exhibited a less pronounced ester and terpene profile, while they were mainly associated with compounds such as cis-3-hexen-1-ol and cis- and trans-furan linalool oxides, which can enhance fresh fruit and citrus aromas through synergistic effects. The common background aroma of the study wines was mainly determined by higher alcohols, fatty acids and ethyl esters.
With these initial data, an attempt has been made to cover a high level of variability by using wines from different varieties, regions and winemakers. However, it is important that future studies focus on the analysis of a larger number of samples per variety, from multiple vintages, in order to draw more vigorous conclusions about the typical aromas of these varieties. Also, standardized vinification protocols could eliminate any variability due to different winemaking practices. All of this, along with further research into consumer preferences, will allow winemakers to focus on winemaking practices that enhance certain volatiles and sensory attributes, increasing the uniqueness and acceptability of wines from these varieties.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/beverages11020033/s1, Table S1: Wine samples included in the study, along with their physicochemical characteristics. All parameters were measured in triplicate. Values are shown as means ± standard deviation. Adapted from Nanou et al. [4]; Table S2: The five most frequently selected descriptors (proportion of selection in parenthesis) by the panel for each wine. Adapted from Nanou et al. [4]; Table S3: Clusters of wines according to their volatile compound profiles as created through Agglomerative Hierarchical Cluster (AHC) analysis. Figure S1: Bar graphs showing the total concentration in each group of volatile for the wines of the study varieties; Figure S2: Principal Component Analysis (PCA) biplot showing volatile compounds and wine samples on F1 and F2. Volatile compounds are colored according to different OAV categories; Figure S3: Principal Component Analysis (PCA) biplot showing volatile compounds and wine clusters on F1 and F2; Figure S4: Principal Component Analysis (PCA) biplot showing volatile compounds and wine clusters on F1 and F3; Figure S5: Sensory space of the wines including the five most cited attributes for each wine. Adapted from Nanou et al. [4]; Figure S6: Variable Importance in Projection (VIP) values for the volatile compounds as calculated through Partial Least Squares (PLS).

Author Contributions

Conceptualization, E.N., M.M., S.E.P.B. and Y.K.; methodology, E.N., M.M. and Y.K.; software, E.N.; validation, E.N. and M.M.; formal analysis, E.N. and M.M.; investigation, E.N. and M.M.; resources, Y.K. and M.M.; data curation, E.N. and M.M.; writing—original draft preparation, E.N. and M.M.; writing—review and editing, E.N., M.M., S.E.P.B. and Y.K.; visualization, E.N.; supervision, M.M. and Y.K.; project administration, Y.K.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was performed within the framework of the project “The Vineyard Roads, Subproject 2: Chemical/organoleptic characterization of varieties—bio-synthetic paths—vinification”. This research has been financed by Greek national funds through the Public Investments Program (PIP) of the General Secretariat for Research and Technology (GSRT), under the action “The Vineyard Roads” (project code: 2018SE01300000; title of project: “Emblematic research action of national scope for the exploitation of new technologies in the agri-food sector, specializing in genomic technologies and pilot application in the value chains of “olive”, “grapevine”, “honey”, and “livestock””).

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki of 1975, as revised in 2013, and the protocol was approved by the Ethics Committee of the Agricultural University of Athens (Reg. No. 18) on 14 February 2019.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding authors.

Acknowledgments

The authors would like to thank Sophie Tempère for her insightful comments and valuable feedback on the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Principal Component Analysis (PCA) biplot showing volatile compounds and wine samples on F1 and F2. The two axes together account for 47% of the total variation in the dataset, with F1 accounting for 29% and F2 explaining 18% of the total variation. Wines with the same color belong to the same cluster according to Agglomerative Hierarchical Cluster (AHC) analysis; cluster 1 is shown in blue, cluster 2 in black, cluster 3 in brown, and cluster 4 in green. ASR stands for Assyrtiko; MLG stands for Malagousia; MSF stands for Moschofilero; ROD stands for Roditis.
Figure 1. Principal Component Analysis (PCA) biplot showing volatile compounds and wine samples on F1 and F2. The two axes together account for 47% of the total variation in the dataset, with F1 accounting for 29% and F2 explaining 18% of the total variation. Wines with the same color belong to the same cluster according to Agglomerative Hierarchical Cluster (AHC) analysis; cluster 1 is shown in blue, cluster 2 in black, cluster 3 in brown, and cluster 4 in green. ASR stands for Assyrtiko; MLG stands for Malagousia; MSF stands for Moschofilero; ROD stands for Roditis.
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Figure 2. Partial Least Squares (PLS) biplot with sensory data on the Y matrix (blue letters) and volatile compounds on the X matrix (black letters). ASR stands for Assyrtiko; MLG stands for Malagousia; MSF stands for Moschofilero; ROD stands for Roditis.
Figure 2. Partial Least Squares (PLS) biplot with sensory data on the Y matrix (blue letters) and volatile compounds on the X matrix (black letters). ASR stands for Assyrtiko; MLG stands for Malagousia; MSF stands for Moschofilero; ROD stands for Roditis.
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Table 1. Concentration of wine volatile compounds, presented as means and standard deviation (SD), for the four varieties and p-values (α = 0.05) as derived from one-way ANOVA. Values with different letters in the same row are significantly different according to the Tukey HSD post hoc test (α = 0.05).
Table 1. Concentration of wine volatile compounds, presented as means and standard deviation (SD), for the four varieties and p-values (α = 0.05) as derived from one-way ANOVA. Values with different letters in the same row are significantly different according to the Tukey HSD post hoc test (α = 0.05).
ASR (n = 3)MLG (n = 4)MSF (n = 7)ROD (n = 3)p-Value
MeanSDMeanSDMeanSDMeanSD
Alcohols (mg/L)
Isobutanol576.8832460176420ns
Isoamyl alcohol340 a64283 ab26252 b44324 ab340.038
1-Hexanol1.50.71.40.51.20.51.10.2ns
Phenylethyl alcohol38143283614268ns
Methionol0.94 ab0.121.2 a0.11.1 a0.40.41 b0.010.017
trans-3-Hexen-1-ol0.03 a0.010.01 b0.01ND0.02 ab0.020.005
cis-3-Hexen-1-ol0.020.020.040.030.020.010.010.00ns
Total alcohols437 400 350 416
Carbonyl compounds (mg/L)
2,3-Butanedione4.51.53.11.12.61.71.90.7ns
γ-Butyrolactone5.91.56.11.75.31.73.60.7ns
Total carbonyl compounds10.4 9.2 7.9 5.5
Esters (mg/L)
Ethyl butyrate0.310.040.220.070.370.250.360.07ns
Isoamyl acetate0.07 a0.050.14 a0.050.16 a0.040.83 b0.44<0.001
Ethyl hexanoate0.74 a0.180.38 b0.020.70 a0.120.62 ab0.060.002
Ethyl lactate1421121717112ns
Ethyl octanoate0.68 a0.090.44 b0.070.73 a0.120.70 a0.050.003
Ethyl decanoate0.110.060.050.020.110.080.140.06ns
Diethyl succinate9.6 a1.95.7 b0.24.9 bc1.42.5 c0.8<0.001
Phenylethyl acetate0.14 a0.040.20 a0.100.23 a0.110.49 b0.220.020
Ethyl 3-hydroxy-butyrate0.140.040.140.020.100.050.130.06ns
Total esters26.0 18.7 24.9 17.0
Fatty Acids (mg/L)
Isobutyric acid1.00.31.20.40.60.40.980.27ns
Butyric acid3.00.22.30.32.90.62.50.3ns
Hexanoic acid11 a17.6 b0.412 a1.510 ab0.10.002
Octanoic acid10 ab27.0 a1.89.0 ab2.012 b0.70.028
Decanoic acid1.50.91.10.31.71.11.60.4ns
Total fatty acids26.5 19.2 26.2 27.0
Terpenes/terpenoids/
norisoprenoids (μg/L)
cis-Rose oxideND0.25 a0.170.90 b0.470.30 ab0.200.006
trans-Rose oxideND0.080.050.170.100.200.20ns
trans-Furan linalool oxide5.9 a2.551 b3218 a48.9 a120.011
cis-Furan linalool oxide3.5 a1.424 b159.1 a2.24.9 a6.10.014
Linalool1.9 a0.843 ab23100 b4814 a120.004
α-Terpineol9.1 a0.9122 ab31212 b14724 a300.033
CitronellolND0.330.220.390.400.070.12ns
Nerol0.070.120.600.5018221.50.3ns
β-Damascenone7.21.48.25.51144.50.2ns
Geraniol2.40.3104.1331714.32.6ns
Total terpenes/terpenoids/
norisoprenoids		30.1 260 503 63.4
Volatile phenols (μg/L)
Vanillin3.52.41.81.61.10.31.20.3ns
Total volatile phenols3.5 1.8 1.1 1.2
ASR stands for Assyrtiko; MLG stands for Malagousia; MSF stands for Moschofilero; ROD stands for Roditis; n represents the number of wine samples in each variety; ND stands for ‘not detected’; ns stands for ‘no significant difference’.
Table 2. Odor activity values (OAVs) of the volatile compounds identified in the study wines presented as average values for each variety.
Table 2. Odor activity values (OAVs) of the volatile compounds identified in the study wines presented as average values for each variety.
Odor Threshold ReferenceOdor Descriptors *ASRMLGMSFRODOAV Category **
Alcohols (mg/L)
Isobutanol40[28]wine, solvent, bitter1.432.061.501.61>1
Isoamyl alcohol30[28]whiskey, malt, burnt11.39.458.3910.7>10
1-Hexanol8[28]resin, flower, green0.180.180.140.14<0.5
Phenylethyl alcohol14[29]honey, spice, rose, lilac2.702.262.591.86>1
Methionol1[29]sweet, potato0.941.191.060.41>1
trans-3-Hexen-1-olunknown moss, fresh
cis-3-Hexen-1-ol0.4[28]grass0.060.090.060.03<0.5
Carbonyl compounds (mg/L)
2,3-Butanedione 0.1[28]butter44.831.425.518.7>10
γ-Butyrolactoneunknown caramel, sweet
Esters (mg/L)
Ethyl butyrate0.02[28]apple15.411.018.618.0>10
Isoamyl acetate0.03[28]banana2.474.705.2027.5>10
Ethyl hexanoate0.014[29]apple peel, fruit52.827.350.144.3>10
Ethyl lactate154[31]fruit0.090.070.110.07<0.5
Ethyl octanoate0.005[29]fruit, fat13588146139>10
Ethyl decanoate0.2[29]grape0.550.230.550.69>0.5
Diethyl succinate200[31]wine, fruit0.050.030.020.01<0.5
Phenylethyl acetate0.25[28]rose, honey, tobacco0.540.800.931.96>1
Ethyl 3-hydroxy-butyrate20[30] 0.010.010.010.01<0.5
Fatty Acids (mg/L)
Isobutyric acid2.3[29]rancid, butter, cheese0.490.530.270.42>0.5
Butyric acid0.173[29]rancid, cheese, sweat17.313.116.814.4>10
Hexanoic acid0.42[29]fat, cheese, barnyard25.218.027.624.4>10
Octanoic acid0.5[29]sweat, cheese20.414.018.723.9>10
Decanoic acid1[29]rancid, fat1.511.131.741.65>1
Terpenes (μg/L)
cis-Rose oxide0.2[28]sweet, rose, green, flower0.001.254.501.50>1
trans-Rose oxideunknown flower
trans-Furan linalool oxideunknown
cis-Furan linalool oxideunknown
Linalool25[29]flower, lavender0.081.733.990.58>1
α-Terpineol250[29]oil, anise, mint0.040.490.850.10<0.5
Citronellol100[28]rose0.000.000.000.00<0.5
Nerol400[33] sweet0.000.000.050.00<0.5
β-Damascenone0.05[28]apple, rose, honey14416522590>10
Geraniol36[32]rose, geranium0.070.293.690.12>1
Volatile phenols (μg/L)
Vanillin60[30]vanilla0.060.030.020.02<0.5
ASR stands for Assyrtiko; MLG stands for Malagousia; MSF stands for Moschofilero; ROD stands for Roditis. In ref. [28], the matrix is 10% v/v ethanol in water. In ref. [29], the model wine matrix is 11% v/v ethanol, 7 g/L glycerin, 5 g/L tartaric acid, pH adjusted to 3.4 with 1 M NaOH. In refs. [30,32], the matrix is 10% v/v ethanol in water, 5 g/L tartaric acid, pH 3.2. In ref. [31], thresholds are calculated in 12% v/v ethanol in water. * Odor descriptors are retrieved from the Flavornet website [42]. ** The OAV category was based on the highest average value; <0.5 stands for OAVs lower than 0.5; >0.5 stands for OAVs between 0.5 and 1; >1 stands for OAVs between 1 and 10; >10 stands for OAVs greater than 10.
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Nanou, E.; Metafa, M.; Bastian, S.E.P.; Kotseridis, Y. Revealing the Unique Characteristics of Greek White Wine Made from Indigenous Varieties Through Volatile Composition and Sensory Properties. Beverages 2025, 11, 33. https://doi.org/10.3390/beverages11020033

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Nanou E, Metafa M, Bastian SEP, Kotseridis Y. Revealing the Unique Characteristics of Greek White Wine Made from Indigenous Varieties Through Volatile Composition and Sensory Properties. Beverages. 2025; 11(2):33. https://doi.org/10.3390/beverages11020033

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Nanou, Evangelia, Maria Metafa, Susan E. P. Bastian, and Yorgos Kotseridis. 2025. "Revealing the Unique Characteristics of Greek White Wine Made from Indigenous Varieties Through Volatile Composition and Sensory Properties" Beverages 11, no. 2: 33. https://doi.org/10.3390/beverages11020033

APA Style

Nanou, E., Metafa, M., Bastian, S. E. P., & Kotseridis, Y. (2025). Revealing the Unique Characteristics of Greek White Wine Made from Indigenous Varieties Through Volatile Composition and Sensory Properties. Beverages, 11(2), 33. https://doi.org/10.3390/beverages11020033

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