# Oenological Performances of New White Grape Varieties

Aécio Luís de Sousa Dias, Charlie Guittin-Leignadier, Amélie Roy, Somaya

Sachot, Faïza Maçna, Damien Flores, Emmanuelle Meudec, Jean-Claude

Boulet, Nicolas Sommerer, Aurélie Roland, et al.

► To cite this version:

Aécio Luís de Sousa Dias, Charlie Guittin-Leignadier, Amélie Roy, Somaya Sachot, Faïza Maçna, et al. . Oenological Performances of New White Grape Varieties. Beverages, 2025, 11 (3), pp.90. 10.3390/beverages11030090 . hal-05162461

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Article

# Oenological Performances of New White Grape Varieties

Aécio Luís de Sousa Dias <fnref n="1" />,<fnref n="2" />,* Ⓙ, Charlie Guittin-Leignadier <fnref n="1" />, Amélie Roy <fnref n="3" />, Somaya Sachot <fnref n="1" />, Faïza Maçna <fnref n="1" />, Damien Flores <fnref n="1" />,<fnref n="2" />, Emmanuelle Meudec <fnref n="1" />,<fnref n="2" /> Ⓙ, Jean-Claude Boulet <fnref n="1" />,<fnref n="2" />, Nicolas Sommerer <fnref n="1" />,<fnref n="2" /> Ⓙ, Aurélie Roland <fnref n="1" />, Marie-Agnès Ducasse <fnref n="3" /> and Jean-Roch Mouret <fnref n="1" />,*

**Abstract:** The wine industry aims to reduce pesticide use by utilizing disease-resistant grape varieties, although their oenological potential remains underexplored. This study aimed to evaluate their oenological potential compared to traditional ones. Musts from resistant (Souvignier Gris, Sauvignac, Voltis, and Floreal) and traditional (Chardonnay, Sauvignon Blanc, and Viognier) varieties were fermented at laboratory scale with online CO₂ monitoring, and two yeasts were used to study varietal responses to yeast impact. Wines were analyzed for metabolites from central carbon metabolism, aromas (varietal thiols, ethyl esters, acetate esters, and higher alcohols), and phenolic compounds (hydroxy-benzoic acids, hydroxycinnamic acids, flavan-3-ols, and flavonols) using (U)HPLC methods. Principal component analysis (PCA) of all variables revealed Souvignier Gris grouped with a Sauvignon Blanc sample, partially due to varietal thiols. PCA of aromas (PC1: 37.7%, PC2: 17.8%) showed that Souvignier Gris and Sauvignac exhibited similar behavior to Sauvignon Blanc. The heat map of 19 phenolics showed Sauvignac and Sauvignon Blanc clustered, with lower phenolic abundance. This preliminary work contributes to a detailed characterization of the oenological potential of these new varieties and constitutes an essential step in identifying which traditional and well-known varieties they resemble. This will then enable the recommendation of cellar itineraries adapted to their profile.

**Keywords:** wine; aromas; fermentation; disease-resistant grape varieties; oenological parameters; varietal thiols; polyphenols; multivariate statistical analysis

## 1. Introduction

The wine industry is currently facing environmental challenges, including the reduction of pesticide use and adaptation to climate change. In this context, breeding programs have developed grape varieties resistant to vine diseases, particularly against downy mildew, caused by the oomycete *Plasmopara viticola*, and powdery mildew, caused by the ascomycete *Erysiphe necator*, to minimize pesticide applications [1–3]. Moreover, these resistant varieties may also be of interest as alternatives for climate change adaptation, from a certain point of view, since extreme weather events, such as the increase in ambient temperature [4] and excessive rainfall [2], could increase the prevalence of microorganism-related diseases. However, in this case, this potential utility must be evaluated in relation

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check for
updates

Academic Editor: Andrea Versari

Received: 9 May 2025

Revised: 3 June 2025

Accepted: 4 June 2025

Published: 11 June 2025

**Citation:** de Sousa Dias, A.L.; Guittin-Leignadier, C.; Roy, A.; Sachot, S.; Maçna, F.; Flores, D.; Meudec, E.; Boulet, J.-C.; Sommerer, N.; Roland, A.; et al. Oenological Performances of New White Grape Varieties. Beverages **2025**, *11*, 90. https://doi.org/10.3390/beverages11030090

**Copyright:** © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ SPO, University of Montpellier, INRAE, Institut Agro, Montpellier, France; somaya.sachot@inrae.fr (S.S.); faiza.macna@inrae.fr (F.M.); emmanuelle.meudec@inrae.fr (E.M.); jean-claude.boulet@inrae.fr (J.-C.B.); nicolas.sommerer@inrae.fr (N.S.); aurelie.roland@institut-agro.fr (A.R.)</footnote>

<footnote n="2">² INRAE, CALIS Research Infrastructure, PROBE Research Infrastructure, PFP Polyphenols Analysis Facility, Montpellier, France</footnote>

<footnote n="3">³ Institut Français de la Vigne et du Vin, Domaine de Pech Rouge, 11430 Gruissan, France; amelie.roy@vignevin.com (A.R.); marie-agnes.ducasse@vignevin.com (M.-A.D.)</footnote>

<footnote>* Correspondence: aecio-luis.de-sousa-dias@inrae.fr (A.L.d.S.D.); jean-roch.mouret@inrae.fr (J.-R.M.)</footnote>
to their ability to resist certain climate change-related conditions, such as water stress and heat.

The quality of wines is one of the main concerns surrounding disease-resistant grape varieties (both current and future), as, ideally, they should not only be resistant but also possess organoleptic properties appreciated by consumers [2]. This quality depends on chemical composition, interactions between volatile and non-volatile compounds, and consumer perception [5]. The analytical parameters used to assess wine quality are varied, with the most studied being physicochemical parameters, aroma compounds, phenolic compounds, proteins, polysaccharides, and lipids [3].

Aromas and phenolic compounds are central elements in the quality of white and red wines. Aromas can be categorized into three main classes: varietal aromas, fermentative aromas, and post-fermentation aromas [6]. The first two classes are mainly responsible for the fruity aromas of wines. Among varietal aromas, varietal thiols are particularly important for the wine typicity of Sauvignon Blanc-like wines and have been intensely studied in the last decades [7]. The 3-mercaptohexan-1-ol reminiscent of grapefruit aromas [8], 3-mercaptohexyl acetate described as passion fruit [9], and 4-methyl-4-mercapto-pentan-2-one with boxwood/cassis bud descriptor [10] are very powerful aroma compounds. IUPAC's updated nomenclature recommends replacing mercapto- with sulfanyl- [7]. Therefore, these compounds are also known as 3-sulfanylhexan-1-ol (3SH), 3-sulfanylhexyl acetate (3SHA), and 4-methyl-4-sulfanylpentan-2-one (4MSP) [11].

These varietal aromas are derived from odorless and non-volatile precursors present in varying amounts depending on the grape variety [12]. During fermentation, these precursors are assimilated by the yeasts via specific transporters, then degraded by an enzyme (β-lyase) which cleaves the bond between the volatile and non-volatile parts, releasing the aroma (3SH or 4MSP). An exception is 3SHA, which is not derived from a non-volatile precursor. Instead, it is the acetylated derivative of 3SH, formed through yeast-mediated acetylation during alcoholic fermentation [13].

Fermentative aromas, including higher alcohols and esters, are synthesized by yeast during fermentation. The type and concentration of these aromas vary depending on several factors, such as fermentation temperature, yeast strain, lipid content, and nitrogen availability in the must [14]. Higher alcohols are generally considered to have a negative impact on overall wine quality, particularly when their concentration exceeds 400 mg/L, as they can impart a strong, pungent odor to the wine [15]. One exception is 2-phenylethanol, which gives a floral note with an odor comparable to that of roses [16]. There are two classes of esters: ethyl esters and acetate esters. Most ethyl esters contribute to the aroma of young wines and are the source of pleasant fruity notes, while among acetate esters, only a few (amyl, isoamyl, and 2-phenylethyl acetate) contribute more to floral and fruity notes [16].

Phenolic compounds comprise a large group of specialized plant metabolites. They are also related to the sensory properties of wine and have beneficial health effects when wine is consumed in moderation, mainly due to their antioxidant and anti-inflammatory activities [17]. Their chemical diversity is influenced by genetic factors but also by pedological conditions and winemaking processes [18,19]. Consequently, they have been evaluated as chemical markers associated with these variables [20]. In white wines, the most represented phenolic families are phenolic acids (including both hydroxybenzoic and hydroxycinnamic acids), flavan-3-ols (in both monomeric and oligomeric forms), flavonols, and stilbenes [17].

The sensory properties of phenolic compounds in white wine are primarily associated with astringency, bitterness [21], and color [17]. In particular, phenolic compounds play a key role in wine browning, which is related to enzymatic oxidation catalyzed by polyphenol oxidase (PPO) and non-enzymatic oxidation of phenolic compounds. In plant cells, phenolic compounds and PPO are compartmentalized. However, during grape
crushing and pressing, this compartmentalization is disrupted, allowing direct interaction
between these chemical species in the presence of oxygen, thereby initiating oxidative reac-
tions [22]. These reactions lead to the formation of o-quinones, which are highly reactive
electrophilic intermediates. These compounds can undergo further chemical reactions with
nucleophilic species, including non-phenolic and other phenolic compounds, triggering
a cascade of oxidative polymerization reactions that ultimately result in the formation of
colored polymeric compounds [22]. Furthermore, o-quinones can react with peptides and
bisulfite, forming stable adducts [23]. One of the most abundant adducts identified in white
wines is 2-(S-glutathionyl)-*trans*-caftaric acid, also known as the grape reaction product
(GRP), which arises from the reaction between caftaric acid and glutathione (γ-L-glutamyl-
L-cysteinyl-L-glycine; GSH), a natural tripeptide present in grapes and wines [24]. The
relative abundance of GRP serves as a chemical marker for the extent of must and wine
exposure to enzymatic oxidation [25].

There are few studies on the different oenological parameters of resistant grape varieties, particularly regarding the varieties examined in this study (Floreal, Voltis, Souvignier Gris, and Sauvignac), partly because they are relatively recent. These varieties are mainly resistant to downy mildew and powdery mildew [1,2] and were introduced into the Vitis International Variety Catalogue (VIVC) in 2018, with the exception of Souvignier Gris, which was included in 2012 [2]. Dournes and co-workers [26] identified and quantified 3SH and 3SHA in Floreal wine. Leis and co-workers [27] analyzed volatile compounds and conducted sensory evaluations on six resistant grape varieties, including Souvignier Gris.

Comparative studies on wines produced from resistant and traditional grape varieties have also focused on a limited set of quality parameters, including phenolic compounds [28] and varietal thiols [29]. Comparative studies that employ a comprehensive approach, integrating several quality parameters, as well as a standardized winemaking process to allow for objective comparisons, are essential for deepening the understanding of the oenological potential of wines produced from resistant varieties. Wines from resistant varieties that exhibit oenological profiles comparable to conventional wines may be strong candidates for the current wine market and may even serve as potential substitutes for wines from reference varieties. Conversely, those with oenological characteristics that differ significantly from traditional wines may face lower consumer acceptance due to the potential presence of unusual organoleptic properties [3]. From another perspective, these same wines can also offer opportunities for innovation, as they have the potential to reach new markets interested in novel wine styles or more eco-friendly wines, given that the wine industry and consumer preferences are constantly evolving.

Chemometrics is one of the most widely used tools for evaluating wine quality based on different oenological variables. It has been applied not only for the classification of wine samples in terms of grape varieties but also in relation to their geographical origin and processing conditions [30]. Among the various statistical methods employed, unsupervised methods such as principal component analysis (PCA) and heat map analysis have been widely used [27,31,32]. An example of a comparative study was conducted by Claudel and co-workers [31]. They compared wines from Floreal with wines from traditional varieties, including Chardonnay, applying chemometric methods to physicochemical parameters and aroma data, including varietal thiols. Their results indicated a similarity between Floreal and Chardonnay.

Therefore, the objective of this study was to evaluate the oenological potential of white
wines produced from disease-resistant grape varieties by comparing them with wines
from traditional varieties. To achieve this, several oenological variables obtained during
alcoholic fermentation on a laboratory scale with online CO2 monitoring were analyzed
using chemometric methods.
## 2. Materials and Methods

### 2.1. Must Preparation

A total 10 samples of 7 different grape varieties from 5 locations in France and harvested in 2022 were used for this study, as shown in Table 1. The reference varieties were Chardonnay (CB and C), Sauvignon Blanc (SNM and SN), and Viognier (VB), while the resistant varieties were Voltis (VM), Floreal (FL and FLV), Sauvignac (SCM), and Souvignier Gris (SG). The musts were obtained from these grapes of comparable technological maturity, selected based on the winegrowers' expertise. This ensured sample comparability in terms of ripeness. Each of the musts was supplemented with nitrogen using a 20 g/L solution of di-ammonium phosphate (Merck, Darmstadt, Germany) to achieve an initial assimilable nitrogen content of 250 mgN/L. The musts were also sulphited to prevent oxidation, with a solution of potassium metabisulphite (Merck, Darmstadt, Germany) at 10% SO₂, to achieve 2 g/hL SO₂.

<table><caption>**Table 1.** Samples of white grapes from disease-resistant varieties and traditional varieties.</caption><thead><tr><th>Sample Code</th><th>Grape Variety</th><th>Origin (City—Postal Code</th><th>Resistance to Downy Mildew and Powdery Mildew <sup>a</sup></th><th>Variety Number VIVC <sup>b</sup></th><th>Pedigree <sup>b</sup></th></tr></thead><tbody><tr><td>SN</td><td>Sauvignon Blanc</td><td>Nantes—44000</td><td>NO</td><td>10790</td><td>Traminer X unknown</td></tr><tr><td>SNM</td><td>Sauvignon Blanc</td><td>Vix—85770</td><td>NO</td><td>10790</td><td>Traminer X unknown</td></tr><tr><td>SCM</td><td>Sauvignac</td><td>Vix—85770</td><td>YES</td><td>22322</td><td>(Sauvignon X Riesling) X unknown</td></tr><tr><td>SG</td><td>Souvignier Gris</td><td>Vix—85770</td><td>YES</td><td>22629</td><td>Cabernet Sauvignon X Bronner</td></tr><tr><td>FL</td><td>Floreal</td><td>Vix—85770</td><td>YES</td><td>25805</td><td>Villaris X a descendant of Muscadinia rotundifolia (MTP 3159-2-12)</td></tr><tr><td>FLV</td><td>Floreal</td><td>Nantes—44000</td><td>YES</td><td>25805</td><td>Villaris X a descendant of Muscadinia rotundifolia (MTP 3159-2-12)</td></tr><tr><td>VM</td><td>Voltis</td><td>Rodilhan—30230</td><td>YES</td><td>25807</td><td>Villaris X a descendant of Muscadinia rotundifolia (MTP 3159-2-12)</td></tr><tr><td>C</td><td>Chardonnay</td><td>Gruissan—11170</td><td>NO</td><td>2455</td><td>Heunisch Weiss X Pinot noir</td></tr><tr><td>CB</td><td>Chardonnay</td><td>Bourdic—30190</td><td>NO</td><td>2455</td><td>Heunisch Weiss X Pinot noir</td></tr><tr><td>VB</td><td>Viognier</td><td>Bourdic—30190</td><td>NO</td><td>13106</td><td>Unknown</td></tr></tbody></table>

### 2.2. Fermentation

Triplicate fermentations were carried out at 20 °C in 300 mL fermenters containing 250 mL of must inoculated with 10 g/hL of active dry yeast: Zymaflore X5 (yeast «x») from LAFFORT® (Bordeaux, France) and Anchor Alchemy II (yeast «a») from Oenobrands SAS (Montferrier-sur-Lez, France), both previously rehydrated for 30 min at 37 °C in a 50 g/L glucose solution (Merck, Darmstadt, Germany). The conditions for laboratory-scale fermentation with online CO₂ monitoring, along with data management methods, were previously described [33]. This system calculates, for each time point, (1) the amount of CO₂ released from weight loss (g/L), which is proportional to the amount of sugars consumed at that time, and (2) the fermentation rate, which corresponds to the rate of CO₂ production, in g CO₂/L/h (proportional to the rate of sugar consumption).

<footnote><sup>a</sup> The resistance factors against downy mildew and powdery mildew are found in the work of Töpfer and Trapp [2];</footnote>

<footnote><sup>b</sup> according to Vitis International Variety Catalogue (www.vivc.de; accessed on 10 February 2025).</footnote>
## 2.3. Chemical Analysis of Wine

### 2.3.1. Metabolites of Central Carbon Metabolism

At the end of fermentation, samples were analyzed via high performance liquid chromatography (HPLC) to determine the concentrations of ethanol, glycerol, succinate, and acetate in g/L, following the same analytical conditions described in a previous study [33].

### 2.3.2. Analysis of Fermentative Aromas

The concentrations (mg/L) of ethyl esters (ethyl propanoate, ethyl isobutanoate, ethylbutanoate, ethyl-2-methylbutanoate, ethyl-3-methylbutanoate, ethyl pentanoate, ethyl hexanoate, ethyl lactate, ethyl octanoate, ethyl-3-methylthiopropionate, ethyl decanoate, diethylsuccinate, and ethyl dodecanoate), acetate esters (propyl acetate, isobutylacetate, 2-methylbutylacetate, isoamyl acetate, hexyl acetate, and 2-phenylethylacetate), and higher alcohols (1-propanol, 2-methylpropanol, 2-methylbutanol, 3-methylbutanol, 1-hexanol, 3-methylthiopropanol, and 2-phenylethanol) were measured in the liquid phase after sample pretreatment using double liquid-liquid extraction with dichloromethane (Merck, Darmstadt, Germany) in the presence of deuterated standards. Samples were analyzed via gas chromatography-mass spectrometry under the same conditions described in a previous study [33].

### 2.3.3. Analysis of Phenolic Compounds

The phenolic compounds of wine samples were analyzed via ultra-high-performance liquid chromatography coupled to high resolution mass spectrometry (UHPLC-HRMS), using a UHPLC-ESI-Q-Orbitrap MS system (Thermo Fisher Scientific, Germering, Germany). Liquid chromatography separation was performed using the same equipment and conditions as described in a previous study [34].

Similarly, HRMS analysis was performed in the negative ion mode using nearly the same parameters described by Dias and co-workers [34]. However, in the present study, the sheath, auxiliary, and sweep gases were set to 40, 10, and 2, respectively, and the vaporizer temperature was set to 300 °C.

Wine samples, a blank sample (solvent), and a quality control sample (mixture containing a fraction of each wine sample) were injected into the UHPLC-HRMS system. The wine samples were injected in triplicate using the full scan mode and a resolution set to 240,000. The quality control sample was injected in triplicate using a data-dependent mode to obtain HRMS and HRMS/MS spectra with a resolution set to 30,000 also for identification purposes. The precursor ions were fragmented in the higher-energy collisional dissociation cell against nitrogen gas with a normalized collision energy set to 35%.

The HRMS data were processed using Compound Discoverer 3.2 software to extract UHPLC-HRMS features, characterized by their retention times (RT) and *m/z* values. This data processing resulted in 87 features. Feature annotation was conducted based on predicted molecular formulas, MS/MS fragmentation patterns, comparisons with analytical standards when available and literature data. Among the detected features, 19 were identified as phenolic compounds. The peak areas of these compounds were used for statistical analyses.

### 2.3.4. Varietal Thiol Analysis

Varietal thiols (3SH and 3SHA) were analyzed via liquid chromatography-tandem mass spectrometry (Agilent Technologies, Santa Clara, CA, USA) and quantified using stable isotope dilution assay (SIDA), according to the procedure described by Dournes and co-workers [35].
## 2.4. Statistical Analysis

All experiments were conducted in experimental triplicates. PCA was performed using Scilab 2025.1.0 software (https://www.scilab.org/; accessed on 21 January 2025) with the FACT toolbox 1.5, after centering and standardization of the variables. The heat map of phenolic compounds was generated using Compound Discoverer 3.2 software, applying the center and scale options and normalized areas.

# 3. Results and Discussion

## 3.1. Overview of the Oenological Profiles of Wines

As an initial approach, the various measured variables related to fermentation and the sensory properties of the wine samples were analyzed using PCA to evaluate the overall relationships among the samples and among the variables. All quantified aroma compounds described in Sections 2.3.2 and 2.3.4 were considered for the PCA. In order to facilitate global interpretation and reduce model complexity, the concentrations of compounds within the same aroma family were summed. The families included ethyl esters, acetate esters, higher alcohols, and varietal thiols. Similarly, phenolic compounds were grouped into hydroxybenzoic acids, hydroxycinnamic acids (including naturally occurring and processing-derived compounds), flavan-3-ols, and flavonols, as described in Table S1 [36–39]. The PCA included all identified phenolic compounds. Section 3.3.1, which specifically addresses phenolic compounds, provides explanations on their identification and classification. In the case of central carbon metabolism compounds, the concentration data of the individual compounds were taken into account for the analysis. Finally, the fermentation kinetics parameters selected for analysis were the maximal CO₂ release rate (Vmax) and fermentation time.

Figure 1 presents the selected score and loading plots from the PCA. In the score plot of Figure 1a, principal components 1 and 2 (PC1 and PC2) explain 32.7% and 14.5% of the dataset variability, respectively. The PC1 axis primarily drove varietal differentiation, while the PC2 reflected more the yeast effect. FL, C, CB, VB, and SCM samples were slightly separated on the PC1 axis. The VM, FLV, SN, SG, and SNM samples were grouped together and slightly separated from the SCM samples along the PC2 axis.

The loading plot (Figure 1b) indicates that the separation of the samples towards the negative side of PC1 was mainly influenced by flavonols, flavan-3-ols, hydroxybenzoic acids, acetate esters, and Vmax. On the other hand, the positioning of the samples on the positive side of the PC1 axis was more influenced by varietal thiols, ethyl esters, and fermentation time.

Among these variables, flavonols, flavan-3-ols, and hydroxybenzoic acids, followed by varietal thiols, are the most associated with the intrinsic characteristics of each grape variety. These phenolic compounds are naturally present in grapes [19], while varietal thiols are formed during fermentation from precursors whose presence and abundance are characteristic of grape varieties [7]. Regarding Vmax and fermentation time, these variables were negatively correlated in the loading plot, as they are inversely proportional in the fermentation process.

Figure 1a illustrates a slight trend of separation among samples from different yeast strains for each grape variety, primarily along the PC2 axis. In general, samples associated with yeast «x» exhibited higher scores than those corresponding to yeast «a». This trend was more pronounced for FL, C, CB, SG, SN, and SCM, while a weaker distinction was also observed for VB, VM, and SNM.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Score (a) and loadings (b) plots from the principal component analysis on white wine samples, considering the following variables: fermentative aromas (ethyl esters, acetate esters, and higher alcohols), varietal thiols, fermentation kinetics parameters (fermentation time and Vmax), metabolites of central carbon metabolism (acetate, glycerol, succinate, and ethanol), and phenolic compound families (hydroxybenzoic acids, native hydroxycinnamic acids, hydroxycinnamic acid derivatives, flavan-3-ols, and flavonols). The wine sample codes are detailed in Table 1. The « x » and « a » following the sample codes indicate the yeast strains Zymaflore X5 and Anchor Alchemy II, respectively.</figcaption></figure>

This slight separation can be mostly explained by fermentation-related factors, such as fermentative aromas, varietal thiols, metabolites of central carbon metabolism, and fermentation kinetics parameters. The loading plot (Figure 1b) indicates that the main contributors to the higher PC2 scores were glycerol and acetate for CBx, FLx, VBx, VMx, and SNx; fermentation time for SNMx and SGx; ethyl esters for SCMx; and ethanol and acetate for Cx. Additionally, Vmax and acetate esters strongly influenced the positioning of FLa. The lowercase letters « x » and « a » following the sample codes indicate the yeast strains used in the vinification process, as described in Figure 1 and the Section 2. It is worth noting that fermentative aromas and varietal thiols are also influenced by must composition, which is closely linked to grape variety. Therefore, this separation between samples of the same variety, but fermented by different yeasts, may also be influenced by a differential interaction of the different yeasts with the grape matrix.

Conversely, FLV was the only sample exhibiting an inverse trend relative to the strain factor. The FLVa samples were positioned higher than FLVx along the PC2 axis, which was more strongly influenced by succinate, varietal thiols, and glycerol, as indicated in the loading plot (Figure 1b).
Overall, the distribution of samples in Figure 1a shows that resistant varieties and reference varieties do not form two distinct groups. This can also be observed in Figure S1, which corresponds to Figure 1, but uses a color code to distinguish between these varieties. This reinforces the interest in studying the behavior of different resistant varieties in comparison to well-established reference varieties to assess their oenological potential.

It is important to note that the SNM samples, corresponding to the reference Sauvignon Blanc variety (Table 1), were closely positioned to the SG samples, which represent a resistant variety, in the score plot for both yeasts used (Figure 1a). This behavior can be partially explained by the total thiol content (Figure 1b). This aligns with the literature, at least for the Sauvignon Blanc samples, as this variety is known for its characteristic thiol aroma [9,40].

Other samples of resistant varieties (FLV, VM, and SCM), as well as another sample of Sauvignon Blanc (SN), were also located in the same region of the score plot, but generally farther from the SNM and SG samples.

The SG and SCM sample varieties exhibit a certain degree of genetic similarity to the Sauvignon Blanc variety (Table 1), suggesting that genetic factors may have contributed to their proximity in the PCA score plot (Figure 1a). Additionally, SG and SNM are from the same location (Table 1), which may have also contributed to their proximity in the plot.

On the other hand, FLV and FL, which represent the same variety, displayed opposite positions in the PCA score plot (Figure 1), with FL being separated from all other varieties. The PCA loading plot of this figure shows that FL samples have a higher ethanol concentration, suggesting that the grapes from FL reached a higher level of maturity at harvest, which may have contributed to the separation between these samples.

This figure also indicates a higher abundance of flavonols, flavan-3-ols, and hydroxybenzoic acids in FL. The concentrations of phenolic compounds in plants are generally influenced by environmental and viticultural factors [18]. Since the FL and FLV samples come from different geographical origins (Table 1), it is also possible that the differences in phenolic compounds between these samples and their separation in the PCA score plot are also due to different climatic and pedological conditions.

FLV was more associated with the sum of varietal thiols than FL (Figure 1). Previous studies also detected 3SH and 3SHA in Floreal [26] and higher 4MSP levels in Floreal compared to Chardonnay [31]. Thus, the relationship between FLV and varietal thiols seems to be consistent with the existing literature. Since varietal thiols are chemically unstable and highly prone to oxidation [7], the lower levels found in FL may result from greater exposure to oxidation processes. This discussion is further addressed in Section 3.3.2, which focuses on phenolic compound analysis.

The VM sample is closely related to the FL and FLV samples in genetic terms (Table 1), but it was only associated with the FLV sample. This relationship was partly due to the sum of varietal thiols, as shown in Figure 1a,b.

The two Chardonnay variety samples (C and CB) were positioned at the center of the sample distribution in the PCA score plot (Figure 1a). However, they were likely separated due to climatic and pedological differences between the two samples, as they come from different locations (Table 1). The VB sample, representing another traditional variety, also occupied a central position in the plot but was closer to CB than to C.

It is worth noting that the global interpretation of Figure 1 based on compound families should be considered with caution, as it may not reflect the behavior of individual compounds. For this reason, in the following sections, individual compounds are considered in the analyses, and certain variables are analyzed separately to further enrich the discussions.
## 3.2. Fermentative Aromas and Varietal Thiols

Since fermentative aromas and varietal thiols contribute significantly to the final quality of wines, the wine samples were also compared based exclusively on these variables. Among these, fermentative aromas are highly influenced by fermentation conditions, including the yeast strains used, while varietal thiols are variety-dependent. Only compounds that exhibited high variability among the samples were selected to enhance sample separation in the PCA. Consequently, compounds with a low relative standard deviation (<10%) were excluded from the analysis. The variables considered for PCA were as follows: ethyl esters (ethyl propanoate, ethyl butanoate, ethyl hexanoate, ethyl lactate, ethyl octanoate, and ethyl decanoate), acetate esters (propyl acetate, isobutyl acetate, 2-methylbutyl acetate, isoamyl acetate, hexyl acetate, and 2-phenylethyl acetate), higher alcohols (1-propanol, 2-methylpropanol, 2-methylbutanol, 3-methylbutanol, 3-methylthiopropanol, and 2-phenylethanol), and varietal thiols (3SH and 3SHA).

Figure 2 presents the score and loading plots of the PCA. PC1 (37.7%) and PC2 (17.8%) together accounted for 55.5% of the total dataset variability. The PCA score plot (Figure 2a) reveals a slight general tendency toward separation between samples derived from different yeast strains, following a trend from higher PC1 and lower PC2 values (samples derived from yeast «a») to lower PC1 and higher PC2 values (samples derived from yeast «x»).

The variability observed in Figure 2a can be primarily attributed to fermentative aromas (Figure 2b), which were the main drivers of the slight separation among samples according to the yeast strain employed. In contrast, individual varietal thiols had a limited impact on sample positioning in the PCA score plot, as indicated by their greater distance from the correlation circle in the loading plot (Figure 2b), unlike the pattern observed in the Section 3.1 for the sum of thiols.

In Figure 2b, higher alcohols generally had negative loadings on PC2, except for 1-propanol, which exhibited an opposite behavior. This atypical behavior of 1-propanol compared to other higher alcohols has been previously reported in the literature [14]. Additionally, acetate esters generally had positive loadings on PC2, except for 2-phenylethyl acetate. Finally, ethyl esters did not exhibit any clear trend and were dispersed in the PCA loading plot.

Figure 2a and Figure S2 did not reveal any clear tendency for separation between resistant and traditional varieties. In Figure 2a, Sauvignon Blanc samples, SN and SNM, were located closer to each other for both yeast strains than in the analysis with all variables (Figure 1). Notably, the resistant variety samples, SG and SCM, were also located close to the Sauvignon Blanc samples, especially those associated with the yeast «x» (SGx and SCMx). These findings suggest an aromatic similarity between these traditional and resistant varieties. Additionally, these results are partially consistent with the Section 3.1, where the SG was found to be closer to the Sauvignon Blanc when all variables were considered.

The FLV and VM samples, and primarily FL from resistant varieties, were not clustered with the samples from traditional varieties (VB, C, CB, SN, and SNM) when compared based on the yeast used, suggesting relatively distinct aromatic properties between these two groups of samples.

The CB, C, and VB samples were located at the center of the sample distribution in the score plot (Figure 2), with the samples related to yeast «x» being closer to each other. Additionally, FL samples were located in a more peripheral position relative to all the other samples. These results were consistent with the pattern observed in Figure 1a for the full set of variables.

Additionally, Voltis (VM sample) and Viognier (VB sample) were the varieties least affected by the different yeast strains in terms of aromatic profile. Although one Floreal sample (FLV) was only slightly impacted by the yeasts, the other sample (FL), originating
from a different region, was markedly affected. A similar pattern was observed between
the two Chardonnay samples (CB and C).

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Score (a) and loadings (b) plots from the principal component analysis on white wine samples, considering the following variables: ethyl esters (ethyl propanoate, ethylbutanoate, ethyl hexanoate, ethyl lactate, ethyl octanoate, and ethyl decanoate), acetate esters (propyl acetate, isobuty-lacetate, 2-methylbutylacetate, isoamyl acetate, hexyl acetate, and 2-phenylethylacetate), higher alcools (1-propanol, 2-methylpropanol, 2-methylbutanol + 3-methylbutanol, 3-methylthiopropanol, and 2-phenylethanol), and varietal thiols (3SH and 3SHA). The wine sample codes are detailed in Table 1. The « x » and « a » following the sample codes indicate the yeast strains Zymaflore X5 and Anchor Alchemy II, respectively.</figcaption></figure>

3.3. *Phenolic Compounds*

3.3.1. Identification

Phenolic compounds (hydroxycinnamic acids, flavan-3-ols, and flavonols) are impor-
tant for the quality of white wines, being involved in the oxidative browning of musts and
certain taste properties such as bitterness. Nineteen phenolic compounds were identified
(or tentatively) (Table S1). The compounds were classified into different families in order
to perform a general evaluation, as discussed in Section 3.1. Among the hydroxybenzoic
acids, gallic acid was identified by comparison with the reference standard, based on its
UV spectrum, RT, and exact mass, despite the absence of MS/MS fragmentation.

The hydroxycinnamic acids were classified as native or derived compounds. The
native compounds included caftaric acid, coutaric acid (cis and trans isomers) and fertaric
acid. The differentiation between the cis and trans isomers of coutaric acid was confirmed
based on their elution profiles in a reversed-phase column, with the *cis* isomer eluting first [41].

The hydroxycinnamic acid derivatives included *cis* and *trans* isomers of GRP, sulfonated caftaric acids, *p*-coumaric acid, and caffeic acid. The *cis* and *trans* forms of GRP were distinguished based on their elution behavior on a reverse-phase [42].

In winemaking, bisulfite is commonly added to must to prevent microbial spoilage and oxidative degradation. It can also react with o-quinones leading to the formation of stable bisulfite-adduct species [23]. In this case, it competes with GSH for nucleophilic attack on the oxidized caftaric acid-o-quinone [23]. In the present work, three sulfonated caftaric acids were tentatively identified based on their MS/MS fragmentation patterns (Table S1).

Caffeic acid and *p*-coumaric acid can naturally occur in grapes and must as products of biosynthesis, but in this case, they are generally found in low concentrations [43]. In grapes, they are predominantly found in their conjugated forms as caftaric and coutaric acids, which can subsequently undergo hydrolysis to release caffeic acid and *p*-coumaric acid during vinification, even in the early stages such as fermentation [44]. Therefore, these latter compounds were classified as derived compounds.

Three flavan-3-ols were identified in their monomeric forms ((+)-catechin and (−)-epicatechin) and in a dimeric form (B-type procyanidin). Flavonols were detected in both their aglycone form (quercetin) and glycosylated forms (quercetin 3-glucoside and quercetin 3-glucuronide).

### 3.3.2. Comparison of Wines

All 19 identified phenolic compounds were considered individually for comparing different wines based on the heat map analysis (Figure 3). This figure shows a clear clustering for each wine sample, regardless of the yeast strain used for fermentation. This result indicates that phenolic compounds are good markers of wine samples, as they are influenced by genetic, climatic, pedological, and winemaking factors [18–20].

The heat map (Figure 3) divided the samples into two major clusters referred to as green and red groups. The green group consists of VM, SG, CB, SN, SCM, and SNM, while the red group includes FLV, VB, C, and FL. It also classified the variables into two main categories. One category contained only hydroxycinnamic acid derivatives (sulfonated caftaric acid, *p*-coumaric acid, and caffeic acid) and native hydroxycinnamic acids (caftaric acid and coutaric acid, both *cis* and *trans* forms). The other category included the remaining variables, consisting of hydroxybenzoic acids (gallic acid and protocatechuic acid-hexoside), a native hydroxycinnamic acid (fertaric acid), hydroxycinnamic acid derivatives (GRP, both *cis* and *trans* forms), flavan-3-ols ((−)-epicatechin, (+)-catechin, and a B-type procyanidin dimer), and flavonols (quercetin, quercetin 3-glucoside, and quercetin 3-glucuronide).

Overall, the red sample group exhibited a relatively higher abundance of both categories of phenolic compounds compared to the green group. More specifically, FLV and VB samples were particularly rich in phenolic compounds belonging to the category that included only hydroxycinnamic acids, whereas C and FL samples showed a higher abundance of compounds from the other category. However, two exceptions were observed: VM displayed a particularly high abundance of two sulfonated caftaric acids, while SG had elevated levels of (−)-epicatechin and fertaric acid.

Although FLV, VB, C, and FL belong to the same group, their phenolic profiles were notably different. These differences were influenced by varietal characteristics and pedological conditions but may also result from specific chemical transformations. FLV exhibited higher proportions of caftaric acid, *trans*-coutaric acid, and *p*-coumaric acid than FL. Generally, *trans*-coutaric acid is more abundant than the *cis* form in wines [45], but FL showed
the opposite, suggesting chemical transformations. While UV radiation can convert *trans* to *cis*-coutaric acid [46], the similar levels of the *cis* form in both samples and the reduced abundance of the *trans* form in FL suggests the *trans* form was primarily converted into other compounds. One possible pathway involves the conversion of coutaric acid to caftaric acid via the cresolase activity of PPO [25]. Additionally, it can undergo enzymatic and chemical oxidation resulting in the formation of polymers and adducts [47].

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Heat map of phenolic compound profiles in wines produced from disease-resistant (VM, SG, SCM, FLV, and FL) and traditional (CB, SN, SNM, VB, and C) grape varieties using two yeast strains. The « x » and « a » following the sample codes indicate the yeast strains Zymaflore X5 and Anchor Alchemy II, respectively. Two groups of samples are represented in green and red (at the bottom). The colors of the variables represent phenolic families: violet (hydroxybenzoic acids), blue (hydroxycinnamic acids, including native and derived compounds), light green (flavan-3-ols), and black (flavonols). The sample codes are detailed in Table 1.</figcaption></figure>

Additionally, the high proportion of *p*-coumaric acid in FLV may result from the hydrolysis of *trans*-coutaric acid itself or other compounds conjugated with *p*-coumaric acid during must preparation and fermentation [44].

FL exhibited higher proportions of GRPs (*cis* and *trans* forms), (+)-catechin, a B-type procyanidin, gallic acid, protocatechuic acid-hexoside, and quercetin. The high proportion of GRPs, which was the highest among all the samples, indicates that this sample contained a significant amount of caftaric acid, similar to the FLV sample. Furthermore, this suggests that FL was more exposed to enzymatic oxidation during must preparation.

The increase in quercetin derivative concentrations in wine grapes as a result of sunlight exposure has been reported [48,49]. This could be a possible explanation for the higher proportion of quercetin in FL, potentially influenced by specific agricultural practices or climatic conditions.

It is interesting to note that the relative abundances of quercetin and quercetin 3-glucuronide in samples FL, C, and VB, as well as quercetin 3-glucoside in sample C, were higher in samples fermented with yeast «x» compared to those fermented with yeast «a». The evaluation of the ratios between the two yeasts for quercetin glycosides and quercetin in samples C and VB did not indicate an increase in quercetin abundance at the expense of quercetin glycosides. Thus, a possible enzymatic activity converting quercetin glycosides into quercetin aglycone appears to be unlikely.
Instead, these results suggest a differential interaction between these compounds (flavonols) and yeast components, which was more pronounced in yeast «a» and led to a reduction in the abundance of these compounds in the corresponding samples. These interactions may have occurred with cell wall molecules or yeast metabolites.

Notably, protocatechuic acid-hexoside (a hydroxybenzoic acid) and fertaric acid (a hydroxycinnamic acid) also exhibited higher abundances in the samples derived from yeast «×», particularly in sample C for both compounds and in sample SG for fertaric acid. The absence of a yeast-related effect on these compounds across all samples suggests that interactions with phenolic compounds may also depend on variety-specific matrix components.

The higher relative abundance of B-type procyanidin in FL compared to FLV may also suggest a difference in maturity between the samples. Some studies have reported that polymeric flavonoids are more abundant than flavanol monomers in grapes of greater maturity [41,50]. As discussed in Section 3.1, Figure 1a shows a higher ethanol content in the FL samples compared to FLV, which may indicate that the FL grapes were harvested at a more advanced stage of maturity.

As previously discussed, the FL sample was more exposed to oxidation than the FLV sample. These oxidative processes may have negatively affected the varietal thiol content in the FL sample, as thiols can react with quinones formed through enzymatic oxidation [51]. This could at least partially explain why varietal thiols were more strongly associated with the FLV samples than with FL, as shown in Figure 1.

The VB sample exhibited higher proportions of sulfonated caftaric acid, caffeic acid, and quercetin 3-glucuronide than the other red group samples. The high proportion of sulfonated caftaric acid suggests that this sample initially contained elevated caftaric acid levels in the grapes. The abundant presence of caffeic acid may result from the hydrolysis of caftaric acid [44] or other conjugated molecules. As previously discussed, quercetin 3-glucuronide may be linked to increased sun exposure. However, in this case, no other samples of the same variety from different regions are available for comparison. Therefore, the high abundance of this compound could also be a varietal characteristic. Nevertheless, this hypothesis requires further investigation with a larger number of samples.

The Chardonnay samples C and CB exhibited distinct phenolic profiles, leading to their separation into different groups in the heat map (Figure 3). Sample C had notably higher proportions of quercetin 3-glucuronide and quercetin 3-glucoside than CB. Sample C originates from Gruissan, a sunnier and more coastal region than CB's origin (Bordic). These climatic differences may have influenced the compound abundances and contributed to the separation of these samples in the heat map. This aligns with the PCA score plot in Figure 1a, where these samples were primarily separated along PC2 due to the phenolic compounds.

Regarding the green sample group, the phenolic profiles of CB, SN, SCM, and SNM are highly similar. The most notable difference concerns the protocatechuic acid-hexoside content in SN, which was not equally abundant in SNM, despite both samples representing the Sauvignon Blanc variety. This suggests that the higher abundance of this compound is more associated with pedological factors.

The SG and VM samples exhibited differences not only between themselves but also in comparison to the other samples in the green group (Figure 3). The SG sample exhibited high levels of fertaric acid and (–)-epicatechin. The latter is found in varying proportions depending on the grape variety [52], grape ripeness [53], and agricultural practices [49]. On the other hand, fertaric acid is generally present in lower proportions compared to courtaric and caftaric acids (including their adduct forms) in white wines [45]. Therefore, the high
abundance of fertaric acid combined with epicatechin in SG may be a characteristic of this variety, which needs to be confirmed in further studies.

The VM sample displayed a high proportion of caftaric acid in its sulfonated form. FL and FLV samples, genetically related to the Voltis variety, also had a high abundance of caftaric acid in their GRP and free forms, respectively. Despite this shared characteristic, the phenolic profile of VM remains notably different from those of FL and FLV (Figure 3). Consequently, they were separated in the heat map. This was not observed in the other analyses considering all the variables (Figure 1a) and the aromas (Figure 2a), where VM and FLV showed a similar positioning in the PCA score plot, partially due to the varietal thiols.

When comparing the phenolic profiles of the resistant varieties to those of the reference varieties, it was observed in Figure 3 that mainly SCM exhibited profiles most similar to the Sauvignon Blanc varieties (SN and SNM). This similarity can be partially attributable to the genetic proximity between these samples (Table 1).

On the other hand, as already discussed, the samples FLV and FL, although they belong to the same broad group that also includes VB and C, have phenolic compositions that differ significantly from those of the latter. Similarly, SG has many similarities with CB and SN; however, they possess specific chemical differences that are notable.

## 4. Conclusions

The multivariate statistical analyses revealed varying degrees of distinction among wines from different varieties and locations. The heat map of 19 phenolic compounds classified the ten samples. The PCA of selected fermentative aromas (6 ethyl esters, 6 acetate esters, 5 higher alcohols) and varietal thiols (3SH and 3SHA) logically demonstrated an effect of the yeast strain but also revealed some clustering by variety and location.

Taken together, all these different analyses revealed that the disease-resistant and traditional grape varieties did not form two distinct groups based on oenological parameters, but rather showed a dispersed pattern, with varying degrees of similarity. Notably, Souvignier Gris closely resembled Sauvignon Blanc, particularly in the aroma analysis. For Sauvignac, this similarity was more evident in the aroma and phenolic compound analyses. These results suggest that Souvignier Gris and Sauvignac may share oenological characteristics with Sauvignon Blanc, indicating potential as substitutes for this traditional variety in the context of pesticide reduction. This substitution may be relevant in the context of climate change, which is expected to increase fungal diseases due to excessive rainfall and rising temperatures.

The Floreal and the Voltis did not exhibit a consistent similarity with the wines from traditional varieties across the different analyses. This suggests that these resistant varieties may have different organoleptic properties. If confirmed by future studies, they could be valuable for developing new wine styles for emerging markets. Additionally, their oenological parameters might align with those of traditional varieties not included in this study.

Further studies will be needed to support the current findings. A main limitation of the present work is the small sample size. A greater number of samples per variety and region, along with more yeast strains, would allow for additional statistical analyses to reinforce the results discussed.

The approach used in this study, combining laboratory-scale fermentation with online CO₂ monitoring and multivariate analysis, could be applied to red grape varieties. Furthermore, it may also be valuable for selecting future grape varieties by allowing the early integration of oenological data with genetic and agronomic criteria, thanks to the feasibility of small-scale wine production. This preliminary work offers a detailed characterization of the oenological potential of these new varieties and is a key step in identifying
which traditional and well-known grape varieties they resemble. This will then enable the recommendation of cellar itineraries adapted to their profile.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/beverages11030090/s1. Table S1: Identification of phenolic compounds of white wine samples by UHPLC-ESI-Q-Orbitrap MS analyses. Figure S1: Score plot from the principal component analysis on white wine samples, considering the following variables: fermentative aromas, varietal thiols, fermentation kinetics parameters, metabolites of central carbon metabolism, and phenolic compound families. Figure S2: Score plot from the principal component analysis on white wine samples, considering the following variables: ethyl esters, acetate esters, higher alcohols, and varietal thiols.

**Author Contributions:** Conceptualization, M.-A.D. and J.-R.M.; data curation, A.L.d.S.D., J.-C.B., A.R. (Aurélie Roland), and J.-R.M.; formal analysis, A.L.d.S.D., D.F., E.M., J.-C.B., and J.-R.M.; funding acquisition, M.-A.D.; investigation, A.L.d.S.D., M.-A.D., and J.-R.M.; methodology, A.L.d.S.D., C.G.-L., A.R. (Amélie Roy), S.S., F.M., D.F., E.M., J.-C.B., A.R. (Aurélie Roland), M.-A.D., and J.-R.M.; project administration, M.-A.D.; resources, M.-A.D. and J.-R.M.; software, A.L.d.S.D., S.S., F.M., D.F., E.M., and J.-C.B.; supervision, N.S., A.R. (Aurélie Roland), M.-A.D., and J.-R.M.; validation, M.-A.D. and J.-R.M.; visualization, A.L.d.S.D., E.M., J.-C.B., N.S., M.-A.D., and J.-R.M.; writing—original draft, A.L.d.S.D.; writing—review and editing, D.F., E.M., J.-C.B., N.S., A.R. (Aurélie Roland), M.-A.D., and J.-R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the "CASDAR", project n° 7368335, national fund from the French ministry of agriculture, which aims to coordinate and federate agricultural development actions by articulating the objectives of agricultural policy and the needs of farmers.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are available upon request.

**Acknowledgments:** This study was supported by the UMT Actia Oenoypage (Joint Technological Unit), a partnership tool shared by IFV, INRAE, and Institut Agro Montpellier, which has been established and supported by the French Ministry responsible for Food. The authors would also like to thank the Plateau d'Analyse des Volatils (PTV) of UMR SPO as well as Lylia El Aichouchi for their contributions to the laboratory analyses.

**Conflicts of Interest:** The authors declare no conflicts of interest.

**Abbreviations**

The following abbreviations are used in this manuscript:

<table><tr><td>(U)HPLC</td><td>(Ultra-)high performance liquid chromatography</td></tr><tr><td>HRMS</td><td>High resolution mass spectrometry</td></tr><tr><td>ESI</td><td>Electrospray ionization</td></tr><tr><td>RT</td><td>Retention time</td></tr><tr><td>m/z</td><td>Mass-to-charge ratio</td></tr><tr><td>PCA</td><td>Principal component analysis</td></tr><tr><td>3SH</td><td>3-sulfanylhexan-1-ol</td></tr><tr><td>3SHA</td><td>3-sulfanylhexyl acetate</td></tr><tr><td>PPO</td><td>Polyphenol oxidase</td></tr><tr><td>GSH</td><td>γ-L-glutamyl-L-cysteinyl-L-glycine</td></tr><tr><td>GRP</td><td>Grape reaction product</td></tr><tr><td>Yeast « x »</td><td>Zymaflore X5 yeast from LAFFORT®</td></tr><tr><td>Yeast « a »</td><td>Anchor Alchemy II yeast from Oenobrands SAS</td></tr><tr><td>Vmax</td><td>Maximal CO<sub>2</sub> release rate</td></tr></table>
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