# Measuring and modelling Syrah red wines evolution:
Ageing tests, color and new molecular indices

Luca Garcia, François Garcia, Cédric Saucier

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## To cite this version:

Luca Garcia, François Garcia, Cédric Saucier. Measuring and modelling Syrah red wines evolution: Ageing tests, color and new molecular indices. Journal of Food Composition and Analysis, 2025, 137, pp.106912. 10.1016/j.jfca.2024.106912. hal-05083170

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Submitted on 24 May 2025

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# Measuring and modelling Syrah red wines evolution: Ageing tests, color and new molecular indices

Luca Garcia, François Garcia, Cédric Saucier*

SPO, Univ Montpellier, INRAE, Institut Agro, Montpellier, France

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## ARTICLE INFO

### Keywords:

Syrah
Wine
Ageing
Colour
Oxidation
Sulfonation
Cork
Polyphenols

## ABSTRACT

A Syrah red wine ageing experiment was set up during 24 months to measure the influence of different micro-agglomerated corks on wine composition and color. Two new wine ageing indices based on targeting phenolic composition ($I_{ox}$, oxidation index and $I_{su}$, sulfonation index) or colorimetric one (Hue) are proposed. The results showed that the slopes of evolution of these indices varied according to the wine as well as the index studied, and that the initial composition played a predominant role in these evolutions. In addition, the oxygen transfer rate (OTR) of the cork modulates the rate of evolution of the oxidation index and color, but had no impact on the sulfonation index. Partial least squares (PLS) regressions were used in order to establish some models that can predict the ageing index slope based on accelerated ageing tests (AATs), voltammetric parameters and phenolic composition measured before bottling. The models involved different explaining variables for each test and showed good predictive capacities with correlation coefficients ($R^2$) > 0.79.

## 1. Introduction

The quality of red wine is significantly influenced by both the attributes of the initial raw materials and the winemaking techniques as well as ageing. The wine ageing process plays a crucial role in the production of top-quality wines, especially red wines. This process consists of two essential phases (Waterhouse et al., 2016): the first phase is maturation in barrels or tanks and the second phase is bottle ageing.

Throughout wine ageing, multiple reactions take place, leading to changes and improvements in sensory characteristics over time (Tao et al., 2014). The aromatic profile of wine undergoes a major transformation throughout ageing. This change results from a complex network of chemical reactions, of which oxygen is a crucial contributor. These reactions follow two main chemical pathways (Ferreira et al., 2014; Ugliano, 2013) which are on one hand, the alteration and the creation of new aromatic compounds through oxidation mechanism such as acetaldehyde or sotolon formation and on the other hand, the formation and degradation of volatile sulphur compounds such as hydrogen sulfide or methanethiol.

In addition to aromatic changes, an important aspect of red wine ageing is the polyphenol composition change and consequently evolution of the wine colour. This transformation results from the reduction in native anthocyanins over time (Alcalde-Eon et al., 2006). This leads to

the formation of new pigments by reaction with flavanols (Salas et al., 2004) or with yeast metabolites and oxidation compounds such as acetaldehyde and pyruvic acid (Es-Safi et al., 1999; Fulcrand et al., 1998). These anthocyanin-derived pigments become more stable, which helps to stabilize the colour of the wine while generating changes in hue, from red-violet to red-orange (Ribéreau-Gayon et al., 1998).

In addition to these aromatic and colorimetric changes, the taste of red wine also undergoes alterations during ageing. There is a partial reduction in astringency and bitterness, due to oxidative and non-oxidative polymerisation, sulfonation reactions and the precipitation of phenolic compounds (Arapitsas et al., 2014; Echave et al., 2021). These transformations can occur rapidly, depending on the type of wine studied, and greatly influence its ageing potential (Langlois et al., 2011). This potential is also influenced by terroir, grape varieties, the wine-making process and the choice of closure. Generally, high-quality red wines require a period of bottle ageing to reach their optimum before being consumed (Chris Somers and Evans, 1977; Gambuti et al., 2013). Ageing potential is closely related to a wine's ability to undergo progressive oxidation over time (Waterhouse and Miao, 2021). Therefore, it is crucial to understand in depth how wine evolves when exposed to oxygen. It is widely recognized that controlled and moderate exposure to oxygen contributes to the quality of a red wine throughout its wine-making and ageing process (Ugliano, 2013). The introduction of oxygen

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: cedric.saucier@umontpellier.fr (C. Saucier).</footnote>
is therefore often necessary, particularly through the closure during bottle ageing. In fact, the closure is the last line of defence for controlling the supply of oxygen to the wine (Karbowiak et al., 2019). The choice of stopper during bottling is therefore of crucial importance for optimizing and developing the characteristics of red wines during ageing.

Nevertheless, the natural ageing process for red wines is a long one. To speed up this process, accelerated ageing tests can be used. These tests simulate the chemical modifications applied to the wine, thus reproducing its evolution in a laboratory setting. They can be based on wine oxygen saturation (Ferreira et al., 2015), the addition of acetaldehyde (Coppola et al., 2021), the addition of hydrogen peroxide (Picariello et al., 2017) or heating the wine (Castro et al., 2014). Recently, three types of accelerated aging tests (AATs) (chemical, thermal, and enzymatic) were developed (Deshaies et al., 2020) with the aim of more quickly assessing ageing potential of red wines. It was shown that the phenolic composition and antioxidant capacity of red wines influence these tests (Deshaies et al., 2021; Garcia et al., 2023).

Predicting the ageing potential of wines from the initial characteristics of the red wine is a challenge. To this end, a study of the natural ageing of six red Syrah wines bottled with four different cork stoppers was carried out over 24 months. Throughout the ageing process, various chemical parameters (colour, chemical composition) were monitored in order to establish evolution kinetics. In parallel, at the start of the study, AATs and electrochemical measurements using cyclic voltammetry were carried out to correlate these tests with the different evolution kinetics. Therefore, the aims of this article are as follows:

* To establish objective ageing indices based on colour or chemical composition.

* Establish models for predicting these indices using AATs.

* Establish models for predicting these indices using electrochemical parameters.

* Study the influence of the micro-agglomerated cork oxygen permeability on the evolution of these indices.

## 2. Materials and methods

### 2.1. Chemicals

Hydrogen peroxide solution 30% (ACS reagent) was obtained from Carlo Erba Reagents (Peypin, France). Laccase from *Trametes versicolor* (0.78 U/mg), was obtained from Sigma-Aldrich (Saint-Quentin Fallavier, France). Oenin chloride (≥ 95%), catechin (≥ 99%), epicatechin (≥ 99%), epigallocatechin (≥ 99%), epicatechin-3-O-gallate (≥ 99%), and proanthocyanidins dimers B1, B2 and B3 (all ≥ 90%) were obtained from Extrasynthese (Genay, France). Vitisin B and epicatechin-SO₃H were synthetised according to Garcia et al. (2024). LC-MS-grade methanol, acetonitrile, water, trifluoroacetic acid and formic acid were purchased from Biosolve (Dieuze, France).

### 2.2. Wine samples and stoppers

A set of six 100% Syrah red wine was acquired from various wineries in the Côtes du Rhône and Languedoc-Roussillon areas. Details such as name, origin, vintage, and ageing method are provided in Table 1. Four different types of micro-agglomerated cork stoppers, each measuring 44 ± 0.2 × 24 ± 0.1 mm, were supplied by DIAM Bouchage (Céret, France). The supplier provided data on oxygen transfer rates (OTR) and oxygen initial rates (OIR) (Table 1). The six wines were bottled using standard winemaking procedures, with free SO₂ levels adjusted to 30 mg.L⁻¹ before bottling. 0.75 L Bordelaise bottles were manually positioned and purged with nitrogen on the filling machine. Following the addition of 0.75 L of wine to each bottle, cork stoppers were randomly inserted using a vacuum corker.

All the biological triplicate combination of bottles and corks were aged during 0, 6, 12 and 24 months (t0, t6, t12 and t24) at 17 °C. At each

<table><caption>**Table 1**

Characteristics of the red wines samples and corks.</caption><thead><tr><th>Code</th><th>Denomination of origin</th><th>Vintage</th><th>Ageing</th></tr></thead><tbody><tr><td>CR1</td><td>Côtes du Rhône</td><td>2020</td><td>Tank</td></tr><tr><td>CR2</td><td>Côtes du Rhône</td><td>2019</td><td>Oak barrel</td></tr><tr><td>LR1</td><td>Languedoc-Roussillon</td><td>2019</td><td>Oak barrel</td></tr><tr><td>LR2</td><td>Languedoc-Roussillon</td><td>2019</td><td>Oak barrel</td></tr><tr><td>LR3</td><td>Languedoc-Roussillon</td><td>2019</td><td>Tank</td></tr><tr><td>LR4</td><td>Languedoc-Roussillon</td><td>2019</td><td>Oak barrel</td></tr><tr><th>Code</th><th>Type</th><th>OIR (mg)</th><th>OTR (mg. year<sup>-1</sup>)</th></tr><tr><td>DIAM 1<br>(D1)</td><td>Micro-agglomerated cork</td><td>0.77 ± 0.03</td><td>0.3 ± 0.1</td></tr><tr><td>DIAM 2<br>(D2)</td><td>Micro-agglomerated cork</td><td>1.72 ± 0.25</td><td>0.9 ± 0.1</td></tr><tr><td>DIAM 3<br>(D3)</td><td>Micro-agglomerated cork</td><td>1.76 ± 0.15</td><td>1.1 ± 0.1</td></tr><tr><td>DIAM 4<br>(D4)</td><td>Micro-agglomerated cork</td><td>2.19 ± 0.13</td><td>1.8 ± 0.4</td></tr></tbody></table>

kinetic point, 72 bottles were opened (6 wines x 4 cork x 3 biological replicate). A total of 252 bottles was necessary during this study.

Subsequently, each opened bottle was aliquoted into 2 mL plastic vials under argon gas and promptly frozen at -80 °C. For each further analysis, biological triplicate (3 vials from 3 different bottles) were used.

### 2.3. Enological parameters

Enological parameters for each wine were assessed post-bottling by Laboratoires Dubernet (Narbonne, France). The alcoholic percentage was measured by FTIR spectroscopy using WineScan equipment from FOSS France (Nanterre, France). Total acidity was determined through titration following the OIV-MA-AS313-01 standard. Volatile acidity, free SO₂, and total SO₂ were analyzed using an automatic colorimetric method employing sequential analyzers. pH was measured via the potentiometric method, while iron and copper content were quantified using microwave-induced plasma atomic emission spectrometry (MP-AES) provided by Agilent France, following the OIV-OENO 637-2021 protocol. All the measured parameters were given in table S1 (Supplementary data).

### 2.4. Accelerated ageing tests (AATs)

Three accelerated ageing tests (ATTs) were conducted on initial red wine samples following the methodology of Deshaies et al. (2020) and Garcia et al. (2023). To begin, each wine sample underwent aeration by vigorously shaking a 500 mL flask containing 50 mL of wine for 10 seconds. After this, the flask cap was opened for 5 seconds to allow fresh air to enter. This aeration process was repeated three times. Subsequently, 11 mL of saturated red wine was transferred into Hermetic Pyrex culture tubes with a volume of 11 mL each (VWR 734-4224, Radnor, PA, USA). These tubes were equipped with Pst3 oxygen sensors from Presens-Precision Sensing GmbH in Regensburg, Germany.

For the enzymatic ATT, 50 µL of a 10 g.L⁻¹ laccase from Trametes versicolor solution was added to the wine. In the case of chemical ATT, 20 µL of a 30% hydrogen peroxide solution was added to the wine sample. Finally, the thermal AAT was initiated by heating the 11 mL of wine at 60 °C. Throughout the experiments, the concentration of dissolved oxygen was continuously monitored using oxoluminescence. Assuming a first-order kinetic reaction, oxygen consumption rate constants (k) were calculated for each of the accelerated ageing tests.

### 2.5. UHPLC-DAD-MS analysis of monomeric anthocyanins

The quantification of monomeric anthocyanins followed the method described by Benbouguerra et al. (2020). After filtration through a 0.45 µm PTFE filter (Macherey-Nagel, Düren, Germany), 7.5 µL of wine
were directly introduced into a UPLC system Waters Acquity (Saint-Quentin-en-Yvelines, France), equipped with a reversed phase UPLC Acquity UPLC BEH C18 column (50 mm × 2.1 mm, 1.7 µm) from Waters. The chromatographic method was a binary gradient with mobile phase A (0.1 % v/v aqueous trifluoroacetic acid) and mobile phase B (acetonitrile). The column temperature was set to 50 °C. The elution program over 40 minutes, with a flow rate of 0.25 mL.min⁻¹, consisted of the following gradient: 1 % B (0 min), 1–8.8 % (0–5 min) B, 8.8–20.6 % (5–30 min) B, 20.6–96 % (30–30.5 min) B, an isocratic with 96 % B (30.5–34 min), 96–1 % (34–34.1 min) B, followed by an isocratic with 1 % B (34.1–40 min). Detection was monitored at 520 nm using a photodiode array detector (PDA). An external malvidin-3-O-glucoside calibration curve was employed, and the results were expressed as mg malvidin-3-O-glucoside equivalent (M3G eq) per liter (L⁻¹).

## 2.6. UHPLC-QqQ-MS/MS analysis of flavanols and ageing markers

Monomeric and dimeric flavanols, vitisin A, vitisin B, and epicatechin-sulfonate were quantified using UHPLC-QqQ-MS/MS with the method described in Garcia et al. (2024). Briefly, an Agilent 1290 Infinity II UHPLC system (Agilent Technologies, Santa Clara, CA, USA) was coupled with a 6470B Agilent Triple Quadrupole mass detector. The analytes were separated using a reversed phase UPLC Acquity UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 µm) from Waters (Saint-Quentin-en-Yvelines, France).

The chromatographic method was a binary gradient consisting of mobile phase A (1 % v/v aqueous formic acid) and mobile phase B (0.1 % v/v formic acid in methanol). The column temperature was set at 40 °C. The elution method, with a flow rate of 0.5 mL.min⁻¹, followed this gradient: an isocratic phase at 1 % B during 2 min, 1–10 % B (2–10 min), 10–28 % B (10–20 min), 28–45 % B (20–24 min), 45–99 % B (24–25.5 minutes), an isocratic phase at 99 % B (25.5–30.5 min), 99–1 % B (30.5–31 min, and finally, an isocratic phase at 1 % B (31–34 min).

Source parameters were set as follows: gas temperature at 230 °C, gas flow at 4 L.min⁻¹, nebulizer at 55 psi, sheath gas temperature at 400 °C, sheath gas flow at 11 L.min⁻¹, capillary voltage at 3000 V in positive mode, nozzle voltage at 0 V, and positive Delta EMV at 200 V. Ionization was performed using positive electrospray (ESI+), and detection was carried out using Multiple Reaction Monitoring (MRM). The performance of the method was given in Table S2 in the Supplementary Data.

## 2.7. Electrochemical characterization of wine samples

Cyclic voltammetry was employed to conduct the electrochemical characterization of red wines samples as described in Deshaies et al. (2021). Briefly, wine samples were diluted 75 times in a model wine solution consisting of 12 % ethanol (v/v), 33 mmol.L⁻¹ tartaric acid, and pH adjusted to 3.6 using 1 mol.L⁻¹ NaOH. Subsequently, 200 µL of the diluted wine were deposited onto the electrode surface. Measurements were carried out over a potential range of −0.2 V to 0.8 V (vs. Ag(s)) at a scan rate of 100 mV.s⁻¹. A screen-printed carbon electrode modified with 4 mm diameter single-walled carbon nanotubes (SWCNTs-SPCE, Dropsens, Oviedo, Spain) was used in a three-electrode setup, with a carbon counter electrode and a silver reference electrode. The experiments were conducted using a potentiostat/galvanostat (Autolab PGSTAT 302 N) controlled by Nova 2.1.5 software (Metrohm, Herisau, Switzerland).

Various charges (µC) corresponding to antioxidant capacities can be derived from voltammograms, which involve calculating the area under the anodic curve up to a selected potential (Chevion et al., 2000). The total charge, Q<sub>800 mV</sub>, represents all oxidizable phenolic compounds present in the wine. Q<sub>240 mV</sub> is specifically attributed to the most readily oxidizable polyphenols with the highest reducing capacity. Q<sub>520 mV</sub>

encompasses all compounds that undergo oxidation up to this potential. The difference Q<sub>520 mV</sub> – Q<sub>240 mV</sub> corresponding mainly to malvidin type pigments is then determined. Ultimately, the ratio Q<sub>240 mV</sub>/Q<sub>800 mV</sub> indicates the proportion of highly antioxidant compounds contributing to the overall antioxidant capacity of the wines.

## 2.8. Spectrophotometric colorimetric measurements

Total color intensity, which is the summation of absorbance values measured at 420, 520, and 620 nm wavelengths, as well as the hue (calculated as the ratio between Absorbance 420 nm and Absorbance 520 nm), were assessed using a UV-visible spectrophotometer (Agilent Carry 60 spectrometer, Agilent, Les Ulis, France). These measurements were conducted following the protocol outlined in reference method OIV-MA-AS2-07B as documented in the "Compendium of International Methods of Analysis" published by the International Organisation of Wine and Wine (OIV) in 2009.

## 2.9. Statistical analysis

XLSTAT software (Addinsoft version 19.02, Paris, France) was used to performed ANOVA, correlation, and PLS (Partial Least-Squares) regression tests. The Tukey test was employed, and data were considered significant when *p*-values were < 0.05. Pearson's correlation coefficient was employed to determine correlations. Initially, PLS regression models were constructed using all studied variables, and further simplification occurred through iterative elimination of variables with smaller contributions to the model. The optimal model was defined as the one yielding the highest coefficient of determination with the fewest variables. All analyses, experiments, and tests were carried out in triplicate.

# 3. Results and discussion

It is widely recognized that the ageing potential of red wines can impact the variation in their evolution characteristics. Indeed, some wines exhibit a relatively fast ageing process, reaching their peak quality within the first years, while others require extended ageing before achieving optimal quality (Chris Somers and Evans, 1977). From an organoleptic point of view, these changes are characterised by a more or less rapid evolution in the wine colour, from a red-violet hue to a brick-red hue. This change is caused by the appearance of new pigments derived from anthocyanins, such as pyranoanthocyanins, which have yellow-orange hue (Mateus and de Freitas, 2001). They consequently modify the proportion of yellow colour in the aged wine at the same time as the degree of oxidation. Concerning taste properties, the wine structure (astringency and bitterness) change with ageing. Recently, a new type of flavanol resulting from sulfonation reactions (adducts of bisulfite with type B procyanidins) has been identified as a marker of non-oxidative ageing (Arapitsas et al., 2014). Furthermore, these compounds, due to their ionic properties, have the ability to reduce the intensity of astringency (Ma et al., 2018).

As highlighted in previous study (Garcia et al., 2024), a significant decrease in grape polyphenol content (anthocyanins, flavanols) occurred during bottle ageing, accompanied by an increase in vitisin type pyranoanthocyanins and flavanol sulfonates such as epicatechin-SO₃H, so these compounds can be used as markers of early wine ageing. However, in terms of objective indices correlated with the wine age, hue measured by spectrophotometric analysis is one of the most widely used in oenology (Chira et al., 2011). Nevertheless, as there are many factors which influence the colour of a wine (grape varieties, vinification techniques, ...), it appears difficult to use only this index to determine the wine age (Fan et al., 2023). This is the reason why, to better characterize the oxidative and non-oxidative evolutions of wines
<figure><img src="image_5.png" /><figcaption>Fig. 1. Ageing indices curve over 24 months. A: Index of oxidation; B: Index of sulfonation; C: Wine hue. Values represent means of triplicate biological determination ± standard deviation.</figcaption></figure>

during ageing, we have introduced two new indices based on the relative proportions of “aged” and “native” polyphenols. The oxidation index (I<sub>ox</sub>) evaluates the ratio of vitisin pigments (sum of vitisin A and B), produced through oxidative reactions, relative to malvidin-3-O-glucoside, the most important native anthocyanin in wine. The sulfonation index (I<sub>su</sub>) quantifies the ratio between sulfonated epicatechin (formed by sulfonation) and epicatechin, the major constituent of native condensed tannins in wine. These two ratios provide insights into the transformation of polyphenols under oxidative and non-oxidative processes. The formulas for these indices are as follows:

$$ \bullet \text{ Index of oxidation (I}_{\text{ox}}\text{)} = \frac{\text{Vitisin A} + \text{Vitisin B concentration(mg.L}^{-1}\text{)}}{\text{Malvidin-3-O-glu concentration(mg.L}^{-1}\text{)}} \times 100 $$

$$ \bullet \text{ Index of sulfonation (I}_{\text{su}}\text{)} = \frac{\text{Epicatechin-SO3H concentration(mg.L}^{-1}\text{)}}{\text{Epicatechin concentration(mg.L}^{-1}\text{)}} \times 100 $$

This approach makes it possible to quantify the evolution of a wine as a function of specific phenolic markers.

### 3.1. Evolution of ageing indices during bottle storage

In order to study the overall index evolution kinetics until 24 months (t0, t6, t12 and t24) as a function of the red wine matrix and not the variability associated with the cork stopper, this section will be based only on the results for the D1 stopper (all experiments were performed in biological replicates with triplicates for each analysis).

The slope values from the linear regressions of the indices (I<sub>ox</sub>, I<sub>su</sub>, and hue) were obtained as time function (Fig. 1). These slope values can offer information on the rate of ageing and, consequently, the ageing potential of the red wines. All the kinetic curves are shown in Fig. 1 and detailed indices of those wine kinetics are given in table S3 (Supplementary data).
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 2.** Slopes after 24 months for each wine/cork combination. A: Index of oxidation; B: Index of sulfonation; C: Wine hue. Values represent means of triplicate biological determination ± standard deviation. Different letters indicate the significant differences between samples in one column according to Tukey's test, *p* < 0.05.</figcaption></figure>

The oxidation index ($I_{ox}$) (Fig. 1.A.) shows the rate of transformation of malvidin-3-O-glucoside into vitisin pigments (A and B) via oxidation reactions (Mateus et al., 2002). At the beginning of ageing (t0) just after bottling, this index varied between 0.163 for CR1 (youngest wine) and 0.981 for CR2. As depicted in Fig. 1.A., the $I_{ox}$ value changed during the two years of bottle ageing with an increase that follows a linear profile over ageing time. There is no difference in the type of evolution profile for this index depending on the type of wine studied.

The upward trend in the value of this index is due to the reduction of the original grape anthocyanins and the formation of new pigments, particularly malvidin-3-glucoside derivatives as observed in previous studies (Garcia et al., 2024; Wirth et al., 2010).

However, the slope of kinetics varied among different wines and ranged from $0.086 I_{ox} \cdot month^{-1}$ for CR1 (youngest wine) to $0.454 I_{ox} \cdot month^{-1}$ for CR2. These differences for this slope is then 5.3 times

<caption>**Table 2**</caption>

PLS regression coefficients and quality parameters for the different models explaining $I_{ox}$ slope.

<table><thead><tr><th>Modelling parameters</th><th>PCs</th><th>R<sup>2</sup></th><th>I<sub>ox</sub> Slope- PLS regression models</th></tr></thead><tbody><tr><td>1. AATs</td><td>1</td><td>0.794</td><td>AAT<br>chemical<br>0.891</td></tr><tr><td>2. Electrochemical</td><td>3</td><td>0.969</td><td>Q240<br>11.067</td></tr><tr><td>3. Composition</td><td>4</td><td>0.989</td><td>Malvidin<br>3-O-Glc<br>-0.755<br>AAT<br>chemical<br>0.862</td></tr><tr><td>4. Total</td><td>4</td><td>0.997</td><td>Cyanidin 3-<br>O-coumaroyl-<br>Glc<br>-0.359<br>Q520-240<br>-0.265</td></tr></tbody></table>

higher for CR2 than for CR1. This means that the rate of formation of vitisin-type pigments is much faster for CR2 than for the other wines studied. An influence of the initial $I_{ox}$ value is also observed: indeed, a wine with a low initial value will have a lower slope of kinetics. These differences in kinetics rates are closely linked to the initial polyphenol content, particularly anthocyanins. According to Singleton (1987), the total phenolic composition of a wine can give an estimate of its ability to resist to oxidation. These observations are consistent with the initial concentrations of polyphenols in wines: CR1 has indeed the highest initial concentration of anthocyanins and total flavanols while CR2 has the lowest (Supplementary data, Table S1). Therefore, wines that are less resistant to oxidation will tend to form acetaldehyde more quickly via the Fenton reaction (Wildenradt and Singleton, 1974) and therefore to form vitisin-type pigments more quickly. This will change the sensory properties of the red wine over time, giving it a more orange tint (Echave et al., 2021; J. Oliveira et al., 2009).

The sulfonation index ($I_{su}$) (Fig. 1.B.) illustrates the rate of formation of epicatechin-SO$_3$H by reaction between flavanols and sulfites present in the wine (Arapitsas et al., 2014). Initially, this index varied between 0.66 for CR1 and 4.44 for LR1 and whatever the wine studied, the increase in this index over 24 months of ageing followed a linear profile. This observation is in line with the literature where a linear increase in epicatechin-SO$_3$H during wine ageing was observed (Arapitsas et al., 2014; Garcia et al., 2024). There is no clear impact of the phenolic composition and initial $I_{su}$ on the slope of this index. A different pattern for the evolution of $I_{su}$ is then observed compared to $I_{ox}$, as this index does not take oxidative mechanisms into account. The gradual and continuous rise of this index during ageing is attributed to the reaction between flavanols and sulfites present in the wine. However, as with $I_{ox}$ indices, the slopes of these kinetics differ depending on the wine, varied from $0.072 I_{su} \cdot month^{-1}$ for CR1 and $0.382 I_{su} \cdot month^{-1}$ for CR2. These variations indicate distinct rates of change in the $I_{su}$ among wines and that the formation rate of epicatechin-SO$_3$ is higher in CR2 than in the other examined wines. On an organoleptic point of view, this epicatechin-SO$_3$H formation rate could also modify the sensation of astringency by modifying the structure and reducing the size of condensed tannins by sulfonation (Ma et al., 2018).

Concerning the chromatic index called the hue (Fig. 1.C.), a linear increase was also observed over the two years of ageing for all the wines studied. These observations are in agreement with previous studies (Castellanos et al., 2021; Wirth et al., 2010).

At bottling, hue values ranged from 0.60 for CR1 (youngest wine) to 0.75 for LR2, typical values for young wines (Somers and Vérette, 1988). Like the $I_{ox}$ and $I_{su}$ indices, the change is linear, so we were able to calculate the slopes for the evolution of this chromatic parameter. Although after two years the increase in hue was small ($\Delta T_{24-T_{0max}} = 0.080$ for CR1), there were significant differences in the slope values, ranging from $0.0015$ hue \cdot month$^{-1}$ for LR2 to $0.0034$ hue \cdot month$^{-1}$ for
<table><caption>**Table 3**
PLS regression coefficients and quality parameters for the different models explaining I<sub>su</sub> slope.</caption><thead><tr><th>Modelling parameters</th><th>PCs</th><th>R<sup>2</sup></th><th colspan="5">I<sub>su</sub> slope- PLS regression models</th></tr></thead><tbody><tr><td>(1) AATs</td><td>1</td><td>0.919</td><td>AAT enzymatic</td><td></td><td></td><td></td><td></td></tr><tr><td>(2) Electrochemical</td><td>4</td><td>0.915</td><td>-0.959</td><td>Q240</td><td>Q520</td><td>Q800</td><td></td></tr><tr><td>(3) Composition</td><td>3</td><td>0.984</td><td>3.944</td><td>Malvidin 3-O-Glc</td><td>1.275</td><td>-3.013</td><td></td></tr><tr><td>(4) Total</td><td>3</td><td>0.991</td><td>-0.302</td><td>Malvidin 3-O-acetyl-Glc</td><td>-1.062</td><td>Petunidin 3-O-coumaroyl-Glc</td><td>B1</td></tr><tr><td></td><td></td><td></td><td>Q520</td><td>Malvidin 3-O-acetyl-Glc</td><td>-0.610</td><td>0.469</td><td>0.188</td></tr><tr><td></td><td></td><td></td><td>0.344</td><td></td><td></td><td>B1</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>0.226</td><td></td></tr></tbody></table>

CR1. In contrast to I<sub>ox</sub>, the greater the initial hue value was, the lower the slope of this index in the first few months of bottle ageing was. As Hue is a global index, many direct and indirect parameters can influence its evolution during ageing. Its increase could be attributed mainly to the increase in the percentage of direct anthocyanin condensation products such as pyranoanthocyanins and the decrease in the percentage of anthocyanins (Boido et al., 2006). A rapid change in this index shows the tendency of a wine to change rapidly from a red-purple colour to the red-orange tint typical of older wines.

Linear regression analyses of oxidation (I<sub>ox</sub>), sulfonation (I<sub>su</sub>) and hue indices reveal distinct ageing potentials among wines. Slope variations underscore the wines diverse ageing trajectories, highlighting the influence of initial composition on their evolution.

### 3.2. Impact of cork OTR on I<sub>ox</sub>, I<sub>su</sub> and hue indices

As observed in the previous section, the overall evolution of ageing indices for 24 months varied depending on the type of red wine studied. However, it is important to also consider the influence of the cork stopper on the slope of ageing index evolution, especially for the colour changes. During bottle ageing, the oxygen transfer rate (OTR) appears as a crucial parameter that can impact the shelf life of the wine (Pons et al., 2022) and the conversion of ageing markers (Garcia et al., 2024; Wirth et al., 2010). In this study, wines were bottled using four micro-agglomerated corks with increasing OTR, denoted as D1, D2, D3 and D4 (Table 1) and Fig. 2 illustrates the slope of the ageing indices over 24 months for each wine/stopper combination.

Examining the influence of OTR on the slope of I<sub>ox</sub> (Fig. 2.A.) an increase of slope value was evident over time and also with increasing OTR, regardless the red wine sample. In general, higher oxygen transfer rates (OTR) through the cork led to a reduction in grape native polyphenols and an increase in aging markers due to red wine oxidation. Previous researches demonstrated that an elevated OTR promotes various oxidation reactions involving anthocyanins and flavanols, resulting in the formation of compounds such as pyranoanthocyanins and ethyl-linked pigments by reaction with acetaldehyde formed by oxidation of ethanol during ageing (Garcia et al., 2024; Wirth et al., 2010, 2012). The slope values increased (Δ<sub>slope</sub>) between the two extreme corks (D1 and D4) and ranged from minimum +0.04 for CR1 to maximum +0.11 for CR2. Consequently, the increase in OTR favoured an increase of the oxidation index, thus contributing to an increase of its slope.

When the impact of the cork oxygen transfer rate on the evolution of I<sub>su</sub> slope after 24 months of ageing (Fig. 2.B.) no significant difference in the slope value among the various corks for all the analyzed wines was observed. Indeed, it has been shown in a previous study that the OTR of the closure does not exert an influence on the formation of flavanols sulfonates (Garcia et al., 2024). Indeed, they are formed through a non-oxidative chemical process involving acid depolymerization of tannins followed by nucleophilic addition of sulfites (Arapitsas et al., 2014, 2018; Ma et al., 2018). The slope of I<sub>su</sub> appears to be predominantly independent of oxygen presence.

Concerning the influence of the cork OTR on the chromatic index (Hue) (Fig. 2.C.), we observed, as for the I<sub>ox</sub> index, an increase in slope values of Hue evolution with increasing OTR, regardless of the specific wine studied. However, depending on the wine, the OTR has a greater or lesser influence on the slope values, with Δ<sub>slope</sub> values ranging from minimum +1.13 for LR2 to maximum +4.7 for CR2. This highlights the influence of ageing potential and the type of bottle closure on the rapid development of the wine orange tint. As shown in a previous study (Caillé et al., 2010), the increase in oxygen supply via the cork OTR will modulate the evolution of the wine colour during ageing, notably by the decrease in native red pigments and the increase in orange and brown pigments such as pyranoanthocyanins or other polymerised pigments (Garcia et al., 2024; Wirth et al., 2012).

Our study reveals different 24-months ageing index evolution for different red wines, emphasizing the role of cork stoppers in influencing the slope of color changes. Increased oxygen transfer rates (OTR) through the cork led to reduced polyphenols and increased ageing markers. OTR influences oxidation and color evolution. The I<sub>su</sub> slope, related to flavanols sulfonates, remains independent of OTR as the reaction involved is only acid-catalyzed and does not involve oxidation. These findings highlight the intricate relationship between wine composition, cork OTR, and ageing dynamics.

### 3.3. PLS-R modelling of indices slopes from initial parameters

Partial least squares regression (PLS-R) models were calculated to predict indices slopes calculated after 24 months of bottle ageing from initial parameters measured before bottling i.e. accelerated ageing tests (AATs), cyclic voltammetry parameters and phenolic composition. All the measured parameters are given in the supplementary data (Tables S4, S5, S6 and S7) and all the iterative PLS models are given in the supplementary data (Table S8, S9 and S10). These models could

<table><caption>**Table 4**
PLS regression coefficients and quality parameters for the different models explaining hue slope.</caption><thead><tr><th>Modelling parameters</th><th>PCs</th><th>R<sup>2</sup></th><th colspan="5">Hue slope - PLS regression models</th></tr></thead><tbody><tr><td>(1) AATs</td><td>2</td><td>0.841</td><td>AAT chemical</td><td>AAT enzymatic</td><td></td><td></td><td></td></tr><tr><td>(2) Electrochemical</td><td>2</td><td>0.906</td><td>-1.728</td><td>-1.022</td><td></td><td></td><td></td></tr><tr><td>(3) Composition</td><td>3</td><td>0.995</td><td>-1.111</td><td>Q240</td><td>Q800</td><td></td><td></td></tr><tr><td>(4) Total</td><td>4</td><td>0.998</td><td>-0.564</td><td>Peonidin 3-O-coumaroyl-Glc</td><td>0.174</td><td>Epicatechin-3-O-gallate</td><td>Vitisin</td></tr><tr><td></td><td></td><td></td><td>AAT thermal</td><td>-0.383</td><td></td><td>0.392</td><td></td></tr><tr><td></td><td></td><td></td><td>1.037</td><td>Q240</td><td>Q240/800</td><td></td><td>B3</td></tr><tr><td></td><td></td><td></td><td></td><td>-1.078</td><td>-1.108</td><td></td><td>0.364</td></tr></tbody></table>
offer the possibility to predict the rate of change of these indices and therefore the ageing potential of red wine, based on measurements done at the beginning of the wine life.

First, PLS models were established for the I<sub>ox</sub> slope and the results of the most relevant variables and their normalized coefficients in the regression equation were summarised in Table 2. Four different PLS regression models were established considering: only AATs (1), vol-tammetric parameters (2), phenolic composition (3) and all the measured parameters (4).

PLS model 1 showed a correct correlation between the predicted and experimental values of I<sub>ox</sub> slope with a linearity coefficient of 0.794. On the other hand, PLS models 2, 3 and 4 showed higher and good prediction capacity with R<sup>2</sup> values of 0.969, 0.989 and 0.997 respectively (Table 2).

PLS models 1 and 4 suggest that wines with a higher oxygen consumption catalysed by H₂O₂ in the chemical AAT may have a faster increase in I<sub>ox</sub> slope value and thus a faster increase in vitisin types pigments during the first months of bottle ageing. This results in accordance with the literature (C. M. Oliveira et al., 2011) because for the chemical AAT, when hydrogen peroxide is added, the Fenton reaction is accelerated and accentuates the formation of acetaldehyde. This acetaldehyde may react with acetaldehyde-reactive-polyphenol (ARP) such as anthocyanins to form vitisin types pigments. PLS model 2 suggests the high prediction capacity of electrochemical parameters and especially the charge of most oxidisable compound Q<sub>240 mV</sub>, the total charge Q<sub>800 mV</sub> and the contribution of most oxidisable compounds to total charge Q<sub>240 mV</sub>/Q<sub>800 mV</sub>. The negative contribution of the total charge Q<sub>800 mV</sub> equivalent of the total antioxidant capacity highlight the impact of wine antioxidant compounds in the protection of wine against oxidation during ageing. According to PLS models 3 and 4, anthocyanins have a negative influence, so wines with a large I<sub>ox</sub> slope are wines with low initial concentrations of anthocyanins and malvidin pigments and corresponding Q<sub>520 mV</sub>-Q<sub>240 mV</sub> charge in particular. In fact, a red wine with a low concentration of polyphenols is a wine that will be more sensitive to oxidation (Singleton, 1987), and will then increase in the value of the slope. On the contrary, we observe the positive influence of catechins for model 2 where we note the positive influence of the Q<sub>240 mV</sub> charge which corresponds to the charge of the most easily oxidisable compounds such as flavanols (Deshaies et al., 2021; Kilmartin et al., 2001).

As with I<sub>ox</sub>, different PLS models for the sulfonation index (I<sub>su</sub>) were established, and all the PLS models showed high prediction capacity between the predicted and experimental values of I<sub>su</sub> slope with a linearity coefficient of 0.919, 0.915, 0.984 and 0.991 respectively (Table 3).

PLS models (1) suggests that wines with a higher oxygen consumption catalysed by laccase in the enzymatic AAT may have a slower increase in I<sub>su</sub> slope value and thus a slower sulfonation mechanism during the first years of bottle ageing. This observation could be due to the fact that sulfonation mechanisms during wine ageing mainly impact the polymeric chain of tannins, but these compounds are not highly reactive towards polyphenols oxidase such as laccases due to the steric hindrance of tannins (Nicolas et al., 2003). PLS model (2) suggests that wines with high Q<sub>240 mV</sub> and Q<sub>520 mV</sub> loadings are wines with greater sulfonation reactions. This may be due to the fact that these charges are proportional to the quantity of easily oxidisable compounds such as flavanols with catechol and pyrogallol moieties (Deshaies et al., 2021; Newair et al., 2018) and these molecules form part of the constituent units of tannin chains that are precursors to sulfonated flavanols. Concerning the models (3) and (4) that take into account the phenolic composition of the wine, a negative effect of glycosylated and acetylated malvidin-type anthocyanins on sulfonation kinetics is noted. Monomeric anthocyanins can undergo nucleophilic addition of hydrogenosulfites ions under oenological conditions, resulting in strong bleaching (Berké et al., 1998). However, on the contrary, a positive influence of petunidin-3-O-coumaroylglucoside was observed, a possible

explanation could be that at low concentrations coumaroylated anthocyanins can form insoluble aggregates in water via intramolecular interactions (Mendoza et al., 2020) which could prevent reactions with sulfites. In addition, these models show that wines rich in B1 proanthocyanidins (epicatechin-catechin) will have greater sulfonation kinetics, which is logic because in an acidic medium this dimer can depolymerise to release an epicatechin carbocation which can then react with sulfites to form epicatechin-SO<sub>3</sub>H (Arapitsas et al., 2014; Garcia et al., 2024).

Finally, PLS models were built for the hue slope as for the last ageing index (Table 4).

All the PLS models showed high prediction capacity between the predicted and experimental values of hue slope with a linearity coefficient of 0.841, 0.906, 0.995 and 0.998 (Table 4).

The PLS model (1) taking into account only the accelerated ageing tests (AATs) suggests that the wines with the highest color increase kinetics during the first years of ageing are those with low oxygen consumption kinetics during the chemical and enzymatic AATs. Indeed, as shown by Carrascon et al. (Carrascón et al., 2018), low O₂ consumption by the wine can lead to higher acetaldehyde accumulation by ethanol oxidation. Furthermore, in the case of chemical ATT, the presence of hydrogen peroxide will lead to the formation of acetaldehyde and therefore the formation of acetaldehyde-derived pigments with a more orange tint. This same model suggests the same observation for enzymatic AAT, in fact a high oxygen consumption in the presence of laccase could lead to enzymatic browning and increase the yellow tint of the wine as shown in a previous study (Deshaies et al., 2020) where an increase in absorbance at 420 nm was observed in the wines after this enzymatic accelerated ageing test. The PLS model (2), which takes into account only the electrochemical parameters, shows the negative influence of the Q<sub>240 mV</sub> charge on the change in hue. One possible explanation could be that this charge is proportional to the concentration of the most easily oxidisable compounds in the wine. If this charge is low, these compounds will not protect the wine, such as oxygen and compounds with a high oxidation potential as malvidins (Deshaies et al., 2021; Garcia et al., 2024) and lead to the formation of derivative pigments with a more orange hue than the native compounds. This model demonstrates that wines with a significant total charge of 800 mV will have faster kinetics in hue evolution. This could show the indirect effect of antioxidant capacity on the hue evolution of red wine.

Focus on the PLS model taking into account the phenolic composition (3) shows negative impact of peonidin-3-O-coumaroyl-glucoside, which can be explained by the fact that coumaroylated anthocyanins cause a bathochromic shift of the maximum absorption wavelengths, leading to an increase in the red/purple hue (Hoshino et al., 1980), which may reduce the effect of hue enhancement in the early stages of ageing. The negative influence of epicatechin-3-O-gallate on the hue slope value was also observed. Indeed, it is known that this flavanol acts as a co-factor in copigmentation (Liao et al., 1992), interacting with anthocyanins to cause a bathochromic shift, hence a shift in hue towards red/purple. Conversely, this model suggests that wines with a high initial concentration of pyranoanthocyanins such as vitisins will have faster hue evolution kinetics as these compounds may increase the orange tint of the wines. Finally, the PLS model (4), taking into account all parameters considered in this study, follows the model (2) concerning electrochemical parameters with a negative impact of the Q<sub>240 mV</sub> charge as well as the Q<sub>240 mV</sub>/Q<sub>800 mV</sub> ratio. This model also reflects the positive impact of thermal AAT, as during this test, the preferential targets are ARP, including anthocyanins (Garcia et al., 2023), which react with acetaldehyde to form orange pigments, thus modifying the hue value of the wine. Positive impact of the initial concentration of proanthocyanidins B3 is also observed. An explanation could be that during ageing, dimers can react with anthocyanins to form colorless polymers and yellow pigments of the xanthylium type (Jurd and Somers, 1970), which could lead to a decrease in red color and an increase in the orange-yellow hue of the wine.
## 4. Conclusion

In conclusion, the ageing potential of red wines significantly impacts their evolution characteristics as evidenced by changes in oxidation, sulfonation and hue indices over 24 months of bottle storage. The varying slopes of these kinetics indices among wines underscore distinct ageing trajectories, influenced by initial composition. Wines with higher initial polyphenol content resist better to oxidation and show slower derived pigment formation, resulting in a slower shift to an orange tint. Conversely, wines with lower initial polyphenol content age faster, leading to quicker color evolutions. Cork oxygen transfer rate (OTR) impacts the targeted polyphenols in our study, affecting oxidation indices and color (hue) evolution. However, the sulfonation index remains unaffected by the OTR. Predictive models (PLS-R) based on initial parameters showed good predicting capacity of ageing potential, highlighting the importance of accelerated aging tests (AATs), electrochemical parameters and phenolic composition.

### CRediT authorship contribution statement

**François Garcia:** Writing – review & editing, Validation, Supervision, Methodology, Conceptualization. **Cédric Saucier:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Conceptualization. **Luca Garcia:** Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Formal analysis.

### Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

We acknowledged Diam bouchage company for financial support of this project including the PhD grant of Luca Garcia (Research contract with University of Montpellier - UM200893). Winemakers from Languedoc-Roussillon and Côtes du Rhône are thanked for allowing sampling in their cellars. Vivelyes is acknowledged for its help in wine bottling.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jfca.2024.106912.

### Data availability

Data will be made available on request.

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