# In vino veritas: A metabolomics approach for authenticating Provence Rosé wines

Katia Mekbel, Elnur Garayev, Nathalie Pouzalgues, Aurélie Chevallier, Gilles Masson, Sok-Siya Bun, Béatrice Baghdikian, Gérald Culioli, Jean-Charles Martin

► To cite this version:

Katia Mekbel, Elnur Garayev, Nathalie Pouzalgues, Aurélie Chevallier, Gilles Masson, et al. In vino veritas: A metabolomics approach for authenticating Provence Rosé wines. Food Chemistry, 2025, 465, pp.141950. 10.1016/j.foodchem.2024.141950. hal-04929301

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# In vino veritas: A metabolomics approach for authenticating Provence Rosé wines

Katia Mekbel<sup>a</sup>, Elnur Garayev<sup>b</sup>, Nathalie Pouzalgues<sup>c</sup>, Aurélie Chevallier<sup>c</sup>, Gilles Masson<sup>c,d</sup>, Sok-Siya Bun-Llopet<sup>b</sup>, Béatrice Baghdikian<sup>b</sup>, Gérald Culioli<sup>e</sup>, Jean-Charles Martin<sup>a,*</sup>

## ARTICLE INFO

Keywords:

Rosé wines

Polyphenols

Machine learning

Untargeted LC-MS-based metabolomics

Food authenticity

## ABSTRACT

Provence rosé wines have gained global popularity, making them vulnerable to fraud. This study aimed to identify specific chemical markers to detect counterfeit Provence rosé wines. An untargeted LC-MS-based metabolomics analysis was performed on a set of 30 wines classified as "Provence," "Non-Provence," and "Provence imitations." Using the Molnotator workflow, 1300 potential metabolites were generated, and five key chemomarkers were selected through a machine learning pipeline. Further targeted analysis and bioinformatics using *in silico* MS/MS fragmentation systems confidently annotated three specific chemomarkers for "Provence" rosé: acuminoside, tetrahydroxydimethoxyflavone, and 5'-methoxycastavinol. A composite score using a PLS model combining the 3 chemomarkers effectively distinguished authentic wines, with high accuracy (sensitivity 83.3 %, specificity 100 %, accuracy 93.3 %).

## 1. Introduction

Rosé wine probably originated in Provence (France), introduced by Greek colonists from the city of Phocaea approximately two thousand six hundred years ago. The rosé of Provence constitutes also an important worldwide market of nearly 62 million bottles wines exported in 2022. Like other successful consumer products, Provence rosé wine is susceptible to counterfeiting, leading to significant economic losses and reputational damage. Consequently, there is strong interest in developing reliable methods to authenticate Provence rosé wines and differentiate them from counterfeit alternatives. Polyphenols are key components of wines, as they are responsible for colour, taste and ultimately the quality of the wines. Their occurrence in wine depends on several factors such as grape variety, wine-making process, geographic origin and age of the wine (Lambert et al., 2015). In recent years, several authors investigated the ability of polyphenols to assess the geographical origin and "terroir" of wines. Some illustrative examples are mentioned as follows, such as the case of Chinese (Wu et al., 2021),

South America (Carneiro et al., 2020), Mendoza (Argentina) (Urvieta et al., 2018), Spanish (Serrano-Lourido et al., 2012) and Czech (Pavloušek & Kumša, 2013) wines. However, contrary to red wine, rosé wine polyphenolic composition has not been widely investigated and only a few studies have been published on this subject so far (Gil et al., 2020; Lambert et al., 2015; Leborgne et al., 2023; Pérez-Magariño et al., 2004; Puértolas et al., 2011; Wirth et al., 2012).

Several analytical techniques have been already applied successfully in red wine geographical authentication, such as ¹H NMR metabolomics, developed to semiautomatically quantify the main components of wine (Gougeon et al., 2018), spectrofluorometric analysis combined with machine learning method, which uses absorbance, transmission, and fluorescence excitation emission matrixes (A-TEEM) (Ranaweera et al., 2021). Nevertheless, LC-MS methods seem best suited for wine polyphenols identification and quantification, tandem MS being highly relevant for structural elucidation and quantification of low-level constituents (De Villiers et al., 2012). Today, UHPLC coupled to MS/MS is one of the most widely employed techniques in food analysis and the

<footnote><sup>a</sup> C2VN, INRAE1260, INSERM1063, Aix-Marseille Université, BIOMET, Marseille, France</footnote>

<footnote><sup>b</sup> IMBE, Aix-Marseille Université, Avignon Université, CNRS, IRD, Marseille, France</footnote>

<footnote><sup>c</sup> Centre du Rosé, 70 Av. du Président Wilson, 83550 Vidauban, France</footnote>

<footnote><sup>d</sup> Institut Français de la Vigne et du Vin, domaine de l'Espiguette, 30240 leGrau du Roi, France</footnote>

<footnote><sup>e</sup> IMBE, Aix-Marseille Université, Avignon Université, CNRS, IRD, Avignon, France</footnote>

<footnote>* Corresponding author at: C2VN, Plateforme BIOMET, Faculté SMPM La Timone, 27 boulevard Jean Moulin, 13385 Marseille, France.</footnote>

<footnote>E-mail addresses: katia.mekbel@univ-amu.fr (K. Mekbel), elnur.garayev@univ-amu.fr (E. Garayev), npouzalgues@centredurose.fr (N. Pouzalgues), achevallier@centredurose.fr (A. Chevallier), gilles.masson@vignevin.com (G. Masson), sok-siya.bun@univ-amu.fr (S.-S. Bun-Llopet), beatrice.baghdikian@univ-amu.fr (B. Baghdikian), gerald.culioli@univ-avignon.fr (G. Culioli), Jean-charles.martin@univ-amu.fr (J.-C. Martin).</footnote>
number of works focusing on the determination of polyphenols in this research field is growing up as it has been reviewed (Lucci et al., 2017). More precisely, High Resolution Mass Spectrometry (HRMS) has been successfully employed to the analysis of phenolic compounds in several matrices such as fruit products and vegetables, including coffee and coffee-based products (Rodríguez-Carrasco et al., 2018), but also in Rosé musts and wines (Leborgne et al., 2023). Untargeted LC-MS metabolomics approach therefore appears relevant in detecting frauds since it allows scanning a wide choice of potent and robust chemomarkers. Thus, a specific and robust method to authenticate provence rosé wines would also necessitate to find the most appropriate chemomarkers analyzed through the LC-MS workflow. This can be achieved by machine learning techniques, using training samples to calibrate and validation samples to challenge the classification model. This approach closely mirrors the workflows recommended in clinical biomarker discovery studies for patient stratification (Glaab et al., 2021), and has also been applied, albeit more discreetly, in food authenticity testing (Mialon et al., 2023).

The main objective of this study therefore, was to differentiate “Provence” from “Non-Provence” and “Provence imitations” rosé wines primarily founded on polyphenol profiling through LC-MS analysis combined with chemometric analysis and machine learning. To achieve this, we employed untargeted HRMS metabolomics to scan all detectable wine metabolites, processed using a molecular workflow algorithm that translates both positive and negative mass spectral features into neutral molecules (MolNotator) (Breaud et al., 2022) (Olivier-Jimenez et al., 2021). The resulting dataset of putative neutral metabolites was then fed into a biomarker statistical workflow that we originally developed for clinical applications (Chikh et al., 2024; Fraser et al., 2020). The putatively annotated metabolites were further refined using a targeted MS/MS fragmentation, analyzed with predictive mass fragmentation algorithms (SIRIUS, CFM-ID, and the In Silico DataBase ISDB). This methodology led to the discovery of a distinct set of chemomarkers characteristic of “Provence” rosé wines. Finally, a PLS regression equation, based on these specific chemomarkers, was used to calculate a score value with a threshold allowing to distinguish “Provence” rosé wines from “Provence imitation” and “Non-Provence” rosé wines.

## 2. Materials and methods

### 2.1. Chemicals and reagents

All chemicals and reagents used in the experiments were of LC-MS grade. Ultrapure water (Milli-Q, lab water solutions, Merck, RIOS 8 model), acetonitrile (Sigma Aldrich; CAS-No 75–05-8) and formic acid (Fisher chemicals; —No 64–18-6) were used for the LC-MS mobile phase preparation. Ultrapure water (Milli-Q, lab water solutions, Merck, RIOS 8 model) and methanol (CARLO ERBA reagents; CAS-No 67–56-1) were used for solid phase extraction (SPE).

### 2.2. Wine samples

Beginning with a collection of 703 rosé wines, we selected experimental wine samples from various origins, including Provence, non-Provence, and Provence imitations, based on oenological characteristics. This selection process followed a systematic workflow, detailed in Supplementary Fig. 1. In this selection process, the imitation Provence rosé wines were chosen to be as oenologically similar as possible to the authentic Provence rosé wines. The characteristics analyzed included colour intensity (CI = OD420 + OD520 + OD620), nuance (OD420/520), sugar content (glucose-fructose in g/l), alcohol percentage (% v/v), pH, total acidity (TA in g/l H2SO4), tartaric acid (in g/l), malic acid (in g/l), the tartaric acid/malic acid ratio, and total sulfur dioxide (in mg/l). In the final stage, a total of 30 rosé wines were randomly selected, with ten wines chosen from each of the three groups identified during the conventional oenological screening. The oenological parameters for

these 30 wines can be found in Supplementary Table 1. The metadata of these wines is available on Table 1. These 30 rosé wines are from the 2021 harvest and bottled in 2022.

### 2.3. Sample preparation

Polyphenols Solid Phase Extraction (SPE) was performed on Oasis Hydrophilic-Lipophilic Balance (HLB) cartridges (Waters) using a vacuum manifold. The cartridges used contained 30 mg of sorbent with a

<caption>**Table 1**</caption>

Description of the 30 rosé wine samples used in this study (wine names cannot be legally disclosed).

<table><thead><tr><th>Category</th><th>Producing country</th><th>Producing region</th><th>Grape variety</th></tr></thead><tbody><tr><td>Non-Provence</td><td>France</td><td>Languedoc-Roussillon</td><td>Tempranillo</td></tr><tr><td>Non-Provence</td><td>France</td><td>Languedoc-Roussillon</td><td>Garnacha</td></tr><tr><td>Non-Provence</td><td>France</td><td>Val de Loire</td><td>Blend of Tempranillo, Viura, and Albillo</td></tr><tr><td>Non-Provence</td><td>France</td><td>Vallée du Rhône</td><td>Kékfrankos</td></tr><tr><td>Non-Provence</td><td>Hungary</td><td>Grande plaine hongroise</td><td>Blend of Kékfrankos, and Kadarka</td></tr><tr><td>Non-Provence</td><td>Hungary</td><td>Transdanubie du sud</td><td>Blend of Pinot Noir, Cinsault, and Grenache</td></tr><tr><td>Non-Provence</td><td>Italy</td><td>Abruzzo</td><td>Blend of Grenache, Cinsault, and Syrah</td></tr><tr><td>Non-Provence</td><td>Spain</td><td>Castilla y León</td><td>Blend of Grenache, and Syrah</td></tr><tr><td>Non-Provence</td><td>Spain</td><td>Castilla y León</td><td>Carberneter franc</td></tr><tr><td>Non-Provence</td><td>Spain</td><td>Navarre</td><td>Montepulciano</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Grenache, Cinsault, and others</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Syrah, Grenache, and others</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Grenache, and Cinsault</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Syrah, and Grenache</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Grenache, Syrah, and others</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Grenache, Cinsault, and Syrah</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Grenache, and Cinsault</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Grenache, Syrah, and Cabernet-Sauvignon</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Grenache, Syrah, and Rolle</td></tr><tr><td>Provence</td><td>France</td><td>Provence</td><td>Blend of Cinsault, Grenache, and Carignan</td></tr><tr><td>Provence imitation</td><td>Bulgaria</td><td>Thrace</td><td>Blend of Pinot Noir, and Syrah</td></tr><tr><td>Provence imitation</td><td>Spain</td><td>Castilla-La Mancha</td><td>Tempranillo</td></tr><tr><td>Provence imitation</td><td>France</td><td>Bordeaux</td><td>Merlot</td></tr><tr><td>Provence imitation</td><td>France</td><td>Corse</td><td>Blend of Syrah, Grenache Noir, and Mourvèdre</td></tr><tr><td>Provence imitation</td><td>France</td><td>Languedoc-Roussillon</td><td>Merlot</td></tr><tr><td>Provence imitation</td><td>France</td><td>Languedoc-Roussillon</td><td>Blend of Grenache, and Syrah</td></tr><tr><td>Provence imitation</td><td>France</td><td>Languedoc-Roussillon</td><td>Blend of Grenache Noir, and Cinsault</td></tr><tr><td>Provence imitation</td><td>France</td><td>Val de Loire</td><td>Blend of Endemic Corsican varieties, and others</td></tr><tr><td>Provence imitation</td><td>France</td><td>Vallée du Rhône</td><td>Merlot</td></tr><tr><td>Provence imitation</td><td>Italy</td><td>Abruzzes</td><td>Not communicated</td></tr></tbody></table>
loading capacity of 10 mL. The HLB sorbent is a universal polymeric reversed-phase sorbent produced using a lipophilic divinylbenzene copolymer and an hydrophilic N-vinylpyrrolidone copolymer. It allowed the extraction of a wide range of polar to apolar and acidic to basic organic compounds. First, SPE cartridges were conditioned with 1 mL of methanol, then equilibrated with 1 mL of ultrapure water, and 10 mL of wine were loaded with a flow rate of 1 mL min⁻¹. A washing step using 1 mL of ultrapure water was performed. Finally, the polyphenol fraction was eluted using 3 mL of methanol with a flow rate of 1 mL min⁻¹. The methanol fractions were evaporated (SpeedVac) and reconstituted in 1 mL of ultrapure water acidified with 0.1 % formic acid.

Three blank samples were prepared in parallel to wine samples by doing all the SPE steps but without loading wine. The thirty-three resulting fractions were filtered on 0.2 µm polytetrafluoroethylene (PTFE) filters (cytiva, Whatman Uniflo™). Then, a pooled quality control (QC) sample was prepared by pooling 100 µL of each wine sample and it was divided into seven QC to be injected between every five wine samples and a pool equilibration (pool Eq) sample. The pool Eq sample was injected after solvent blanks in order to condition the LC-MS system to the biological matrix (wine). The thirty filtered samples were each separated into two parts: (i) one part of 200 µL for UHPLC-MS/MS analysis, (ii) a second part of 200 µL for storage at -20 °C in a 96-well freezing plate in case further analyses are required. Wine samples were then analyzed according to a randomized order.

## 2.4. UHPLC-MS/MS analysis

### 2.4.1. Untargeted analysis

LC-MS/MS analysis was performed on Vanquish (Thermo Scientific) Ultra High-Performance Liquid Chromatography system (UHPLC) coupled to an Orbitrap Exploris 240 high-resolution mass spectrometer (HRMS) from Thermo Fisher, equipped with an electrospray ionization source (ESI).

The chromatographic separation was carried on an Agilent Zorbax Eclipse Plus C18 column (2.1 × 100 mm, 1.8 µm) at 43 °C. Ultrapure water (A) and acetonitrile (B), both acidified with 0.1 % formic acid, were used as mobile phases. The multistep linear gradient used was as follows: 0–2 min, 95 % A isocratic; 2–17 min, 95–50 % A; 17–27 min, 50–0 % A; 27–29 min, 0 % A isocratic; 29–30 min, 0–95 % A; 30–33 min, 95 % A isocratic. The injection volume was 20 µL and the flow rate of the mobile phase was 0.8 mL min⁻¹. The samples were kept at +4 °C throughout the sequence.

MS data were collected by separate runs in ESI positive and negative modes from *m/z* 80 to 1200 with an acquisition speed of 8 spectra s⁻¹. The source parameters were set as follows: capillary, 3 kV for negative scans and 3.5 kV for positive scans; source temperature, 325 °C; sheath gas 50; auxilliary gas 12 and sweep gas 0 arbitrary units.

For MS/MS acquisition, a top-three data-dependent acquisition (DDA) protocol was used; therefore, MS/MS fragmentation spectra were obtained only for the three most abundant precursor ions using a collision energy of 30 eV. External calibration of the instrument was performed prior to LC-MS/MS analysis by direct infusion of Pierce FlexMix Calibration solution (Thermo Scientific; Ref A39239) to obtain high mass accuracy (< 1 ppm).

### 2.4.2. Targeted analysis

LC-MS/MS analysis was performed on the same instrument with the same chromatographic parameters as the untargeted analysis. The targeted experiment used the retention time and *m/z* of the chemomarkers selected by random forest (RF), and fragmented with six different collision energies 10, 20, 30, 40, 50, and 60 eV.

## 2.5. Data processing and chemomarkers annotation

For the untargeted analysis, the raw data obtained from the UHPLC-MS/MS system were converted into the “.mzXML” open format using MS

Convert (Chambers et al., 2012).

For the targeted analysis, the raw data were converted to six mzML files per sample using MS Convert to process the six collision energies 10, 20, 30, 40, 50 and 60 eV, separately.

For the untargeted analysis, the exported mzXML data were pre-processed using MZmine package, version 3.4.27 (Schmid et al., 2023). The processing workflow includes mzXML data file import, mass detection, chromatogram building, chromatogram deconvolution, feature list deisotoping, alignment between analytical replicates, and filtering. The *m/z* features present in blank samples were removed from the final feature list. The first thirty seconds and the last 3 min of the run were excluded from data processing. Parameters of each step used for processing are given in Supplementary Tables 2a and 2b. Exploratory analysis by principal component analysis to study the initial distribution of the data and to check the clustering of the QC samples to ensure proper analytical conditions is displayed in Supplementary Fig. 2.

Processed data were exported (“.mgf” and “.csv” files) and applied to MolNotator package (Olivier-Jimenez et al., 2021) to calculate neutral molecules from all the ions in which a molecule is detected (its adducts in positive and negative mode, as well as its in-source fragments). First, the AdNotator module was used to identify the adduct ions while FragNotator was applied to detect in-source fragment ions (Olivier-Jimenez et al., 2021). The AdNotator precursor was set to 0.002 Da and the retention time tolerance to 6 s.

For the targeted analysis, the exported mzML data were pre-processed using MZmine package, version 3.9.0 and following the same data processing steps as the untargeted analysis. A mgf file containing the entire feature list was exported to SIRIUS for annotation. Additionally, multiple mgf files per targeted metabolite, representing different collision energies, were exported to target the *In Silico* Database (ISDB database) and analyzed using Spectral library matcher (https://github.com/mandelbrot-project/spectral_lib_matcher). ISDB is a database of *in silico* predicted MS/MS spectra of 170,602 Natural Products from the Universal Natural Products Database (UNPD). Therefore, the chemomarkers were tentatively annotated by comparing the results of SIRIUS and the matching with the ISDB database (Allard et al., 2016; Allen et al., 2014; Allen et al., 2015; Allen et al., 2016; Djoumbou-Feunang et al., 2019; Dührkop et al., 2019; Wang et al., 2021; Wang et al., 2022).

## 2.6. Chemometrics analysis

Partial least square regression, logistic regression and RF are machine learning algorithms that we used as feature selection means and/or as statistical modeling tools, as explained below.

For PLS-Discriminant Analysis (PLS-DA) conducted on 181 Provençal and 236 imitation Provençal rosé wines using oenological parameters, the Variable Importance in Projection (VIP) scores were employed to rank the discriminative power of each parameter. Parameters were then sequentially removed in ascending order of their VIP scores to assess the impact on model performance (Supplementary Table 3).

For RF analyses, the following parameters were used: the Gini index was used to calculate the variable importance (VIP) value. The sample fraction was 66 %, the number of trees 500, the number of chemomarkers 50, estimate prediction of error was obtained by out-of-bag estimation, randomness was on.

Data obtained from MolNotator were filtered by the coefficient of variation (CV) of each feature in the QC samples, which equals to the standard deviation divided by the mean multiplied by 100. The molecules with a CV value superior to 30 % were excluded from the final dataset. In addition, only features present in each three technical replicates were kept. The mean intensity of technical replicates was then determined.

The filtered data were used for statistical analysis to find specific chemomarkers for “Provence” rosé wines and identify “Provence
imitations". The ten "Provence" rosé wines and the ten "Non-Provence" wines were used to build and train the statistical model, whereas the ten "Provence imitation" wines were used to challenge the model performance. This setting demonstrated the best predictive capability. The statistical workflow consisted of the following steps:

(i) five iterations were done by randomly leaving 20 % samples out five times, in order to select the best variables (metabolites) upon each RF analysis (MetaboAnalyst 5.0) (Xia et al., 2009), The importance of each metabolite in discriminating "Provence" from "Non-Provence" was based on the variable importance (VIP) calculated by the algorithm. The significant threshold of VIP was calculated using a normal probability plot indicating which VIP values of the corresponding metabolites deviated the most from normal distribution (Chikh et al., 2024; Fraser et al., 2020).

(ii) the most common variables shared in at least three iterations were retained and tentatively identified as described above,

(iii) they were included into a logistic regression (Tanagra, 1.4.41, Riko Rakotomalala; Lyon, France) or a PLS model (SIMCA P12; Umea, Swesen) to enable binary classification ("Provence" rosé wines vs "Non-Provence" rosé wines) and to calculate the regression coefficients and constants of a predictive equation combining the individual chemomarkers.

(iv) ROC analysis was carried out for class assignment of "Provence imitations" using "Provence" and "Non-Provence" samples as calibrators and the performance of this model was evaluated by calculating sensibility (true positives/(true positives+false negatives), specificity (true negatives/(true negatives+false positives), accuracy ((true positives+true negatives)/("Provence" +"Non-Provence")) and error rates (100-%correctly assigned) (MetaboAnalyst 5.0) (Xia et al., 2009). The complete workflow is summarized in Fig. 1.

## 3. Results

### 3.1. LC-MS/MS-based metabolomics analysis

Base peak chromatograms (BPCs) of three wines, representing the three categories of rosé, acquired in both negative and positive ionization modes, are provided in Supplementary Fig. 3. The final features lists contained 10,510 *m/z* features in positive mode and 9151 *m/z* features in negative mode. The use of MolNotator package allowed to identify the different adducts generated during the ionization process using a triangulation method and to predict the corresponding neutral metabolites (Olivier-Jimenez et al., 2021). The algorithm also allowed the identification of in-source fragment ions with a high efficiency. The MolNotator algorithm proposed 2462 probable metabolites out of the

19,661 *m/z* features. These resulting metabolites were sorted according to their CV in the QCs to retain only statistically relevant molecules. This filtering step resulted in a final dataset of 1300 probable metabolites that will be used for statistical analysis (Supplementary Table 4).

### 3.2. Random Forest (RF) variable selection and variable annotation

In the RF settings, many classification and regression trees are constructed using randomly selected training datasets and random subsets of predictor variables for modeling outcomes. Results from each tree are aggregated to give a prediction for each observation (Speiser et al., 2019). RF has been chosen to select the most discriminant variables of the "Provence" rosé wines vs the "Non-Provence" ones as calibrators, since this setting achieved a better final predictive model than comparing with the "Provence imitation" group (data not shown). Five most common variables shared in at least three iterations were selected (Supplementary Fig. 4). and tentatively identified using our annotation workflow. For this, we used SIRIUS (Dührkop et al., 2019) which is a machine learning method that annotates metabolites through multiple kernel learning on fragmentation trees (Shen et al., 2014). Moreover, the experimental fragments of the five chemomarkers at different collision energies were matched against the ISDB database. Chemomarker 1 (acuminoside) has been identified by using SIRIUS annotation algorithm, whereas the chemomarker 2 (5'-methoxycastavinol) has been identified by ISDB. The structure hypothesis obtained with two different annotation approaches have been merged, this allowed the annotation of the chemomarker 3 (5,7,3',5'-tetrahydroxy-8,4'-dimethoxyflavone with a tanimoto similarity of 79 % by SIRIUS or 5,7,3',4'-tetrahydroxy-3,5'-dimethoxyflavone with a matching score of 79 % with ISDB in the negative mode).

Chemomarker 1 (acuminoside) has been validated by matching the MS/MS spectrum of its [M + Na]⁺ adduct at *m/z* 471.2189 with MS/MS² spectra in pubchem (2R,3S,4S,5R,6R)-2-[[[(2R,3R,4R)-3,4-dihydroxy-4-(hydroxymethyl)oxolan-2-yl]oxymethyl]-6-[(2E)-3,7-dimethylocta-2,6-dienoxy]oxane-3,4,5-triol | C21H36O10 | CID 21630845 - PubChem (nih.gov) from MoNA database at 40 eV collision energy. All experimental fragment ions of [M + Na]⁺ acuminoside adduct matched with the theoretical data of MoNA database MassBank of North America (ucdavis.edu) that were obtained by an LC-ESI-QTOF instrument at *m/z* 335.0936; 121.0949; 107.0856 and 105.0698.

Chemomarker 2 (5,7,3',5'-tetrahydroxy-8,4'-dimethoxyflavone or 5,7,3',4'-tetrahydroxy-3,5'-dimethoxyflavone), showed in its MS/MS spectra acquired in negative mode a main ion at *m/z* 330.0382, which was assigned to the loss of a methyl group from the precursor deprotected ion. The loss of the second methyl was observed through the occurrence of a fragment ion at *m/z* 315.0145 at a weaker intensity that could be less favored under the operational conditions. Those are the

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The developed statistical workflow used in this study which is derived from the publications of (Fraser et al., 2020; Martin et al., 2023).</figcaption></figure>
predominant fragmentations occuring in most methoxylated flavonoids under the negative ionization mode, which are indicative of the presence of an OCH3 group in the molecule. An even weaker signal at m/z 268.0483 maybe assigned to an additional loss of 29, which has been described as characteristic of the fragmentation pattern of dimethoxy- lated flavonoids and corresponds to the loss of an HCO- radical (Favre et al., 2018).

Chemomarker 3 (5'-methoxycastavinol) was proposed by ISDB matching results in positive mode at a collision energy of 10 eV with a matching score of 55 % and also in negative mode at a collision energy of 20 eV with a matching score of 66 %. Moreover, the predicted MS/MS spectra in HMDB at 20 eV in both positive (https://hmdb.ca/spectra/ms_ms/101095) and negative (https://hmdb.ca/spectra/ms_ms/166615) modes matched with the experimental MS/MS data (fragment ions at m/z 401.1227, 563.1765, and 419.1342 in positive mode and at m/z 399.1080 and 357.0974 in negative mode).

The annotated chemomarkers are described in Table 2. Spectral data acquired in both positive and negative ionization modes used for the annotation are also displayed in Supplementary Fig. 5. Putative annotations were classified following the levels of confidence proposed by (Schymanski et al., 2014): level 1 (L1): structure confirmed by the reference standard with MS, MS/MS spectra, and retention time matching; level 2a (L2a): probable structure using library spectrum match or literature match; level 2b (L2b): diagnostic of structure using MS/MS fragments or ionization behavior, with no literature confirmation; level 3 (L3): tentative candidates with uncertainties (for example positional isomers). Three annotated chemomarkers over the five selected were finally kept as further classifiers testing since they satisfied to a good identification score (Table 2) and did not compromised the prediction power.

Distribution of the intensities of the three RF chemomarkers in the three groups of rosé wines is represented by whisker boxes in Fig. 2. Chemomarker 1 was mostly present in “Provence” rosé wines, whereas chemomarkers 2 and 3 were mostly present in “Non-Provence” rosé wine samples.

### 3.3. Classification of rosé wines using PLS regression and validation

A supervised multivariate analysis uses the class assignment information of observations. As supervised methods, we compared logistic regression and PLS regression. PLS-DA outperformed Logistic regression using the 3 out of 5 RF selected variables that were most robustly annontated (Supplementary Table 5) and was retained for further class prediction and “Provence imitations” identification. The methods allow to combine linearly the “X” variables using a regression equation to predict a 0 or 1 type “Y” binary variable (e.g. “Provence” or “Non-Provence” rosé wines). This combination produces a score that is close to the binary theorical 0 or 1 value related to “Non-Provence” and “Provence” rosé wines, respectively (Fig. 3A). The performance of the predictive model using the 3 best chemomarkers to detect “Provence imitation” rosé wines was then assessed for its specificity and sensitivity using a ROC curve estimator (Fig. 3). The % of correct assignement was as a mean of 93.3 % (strict cut off) or 83.3 % (95 % confidence interval), much higher than was achieved while using conventional oenological parameters (62–65 %) (Supplementary Table 3).

### 3.4. ROC curve estimator

ROC curves show how the specificity and sensitivity change as the classification boundary is varied among the range of identified bio-markers. Receiver Operating Characteristic (ROC) curves have been previously used to classify wines according to grape varieties by using NMR and MS analytical techniques (Crook et al., 2021; Gómez-Meire et al., 2014; Mascellani et al., 2021). Here, ROC curves has been used as a second statistical modeling to detect “Provence imitations” using “Non-Provence” and “Provence” wine samples as calibrators. The results

of ROC prediction can be seen in Fig. 3. First, the ROC estimator performance was evaluated by determining the Area Under the ROC Curve (AUC) and compare the composite score to its individual components. The AUC is a single scalar value that measures the overall performance of a binary classifier (Hanley & McNeil, 1982). The AUC value is within the range [0.5–1.0], where the minimum value represents the performance of a random classifier, and the maximum value would correspond to a perfect classifier. The 3 chemomarkers considered all together as a score had an AUC of 0.98, and always superior to each of it's separate components (Supplementary Table 6). These results showed that combining the 3 chemomarkers all together is the best way to detect the “Provence imitations” with the highest accuracy. The cut-off value determined at the asymptote of the curve (false positive rate vs true positive rate) was 0.512 (Fig. 3B). ROC estimator assigned 9/10 of “Provence imitations” as “Non-Provence” rosé wines (Fig. 3E). The performance of this model was gauged through the calculation of the following parameters: sensibility (probability to detect “Provence imitation”) was 83.3 %; specificity (probability to recognize a “Provence” rosé wine as such) was 100 %; accuracy was 93.3 % and error rate was 5 % using the strict cut-off value at 0.512. To confirm class assignment with more confidence and take into account uncertainty, a confidence interval at 95 % was calculated. The values that were inside this interval could not be reliably classified, whereas the values that were outside the interval were assuredly assigned to their corresponding classes. As a result, only 1 (95 % confidence interval) “Provence imitation” was undetermined (Fig. 3D).

## 4. Discussions and perspectives

Previous researches on wine authentication have primarily relied on adducts and in-source fragment data of metabolites, both produced during the ionization process. In contrast, our innovative study advances the field of wine metabolomics by being the first to directly analyze wine metabolites.

For this, we used the MolNotator package, which predicts neutral molecules through triangulation calculations.

We further employed RF analysis to select a distinct set of chemomarkers of “Provence” rosé wines, combining them in a PLS regression score to differentiate “Provence” from “Non-Provence” and “Provence imitations” rosé wines. The robustness of this score was validated using the ROC curve estimator.

Additionally, targeted MS/MS fragmentation on these chemomarkers, supported by bioinformatics tools such as SIRIUS and ISDB, enabled their confident annotation. The overall performance of the model allow 93.3 % of correct rosé wine class assignement, vs only 62–64 % when using macroscopic conventional oenological parameters (Supplementary Table 3).

The 3 chemomarkers, acuminoside, 5,7,3',5'-tetrahydroxy-8,4'-dimethoxyflavone or 5,7,3',4'-tetrahydroxy-3,5'-dimethoxyflavone and 5'-methoxycastavinol, were all present in the 3 types of rosé wines. Only their level differed, acuminoside being present in higher amounts in “Provence” rosé wines than in the 2 other rosé types.

Acuminoside (geranyl 6-O-β-D-apiofuranosyl-β-D-glucopyranoside) is a terpene glycoside found in grape (Vitis vinifera) and it has already been characterized in Muscat of Frontignan, Muscat of Alexandria, Muscat Ottonel, Muscat of Hambourg, and Gewurztraminer. It has been previsouly identified by gas chromatography on the basis of its trime-thylsilyl (TMS) and trifluoroacetyl derivatives (TFA) in Hypoxis acuminata plants in 1989 (Voirin et al., 1990).

Acuminosides are composed of acuminose moiety and and aglycone part. Acuminose is β-D-apiofuranosyl-(1 → 6)-β-D-glucopyranose which is an apiose-derived glycone. Apiose is a branched pentose naturally occurring either as a component of the plant cell wall polysaccharides or as a sugar moiety present in numerous plant secondary metabolites. Acuminose occurs in various glycosides such as acuminosylated terpenes, flavonoids, stilbenes and coumarins.
<caption>**Table 2**</caption>

Data and putative annotation of the five selected chemomarkers of "Provence" rosé wines by UHPLC-MS/MS analysis in positive and negative ion modes. Metabolites are sorted by retention times (RT). MN ID: MolNotator identifier; IC: identification confidence (Schymanski et al., 2014).

<table><thead><tr><th rowspan="2">MN ID</th><th rowspan="2">RT (min)</th><th rowspan="2">Putative annotation</th><th rowspan="2">Molecular formula</th><th rowspan="2">IC</th><th colspan="5">MS</th><th colspan="2">MS/MS</th><th rowspan="2">Reference</th></tr><tr><th>[M + H]<sup>+</sup> (Error in ppm)</th><th>SIRIUS Score (in %)</th><th>ISDB Score (in %)</th><th>[M - H]<sup>-</sup> (Error in ppm)</th><th>SIRIUS Score (in %)</th><th>ISDB Score (in %)</th><th>[M + H]<sup>+</sup> Fragments (relative intensity in %)</th><th>[M - H]<sup>-</sup> Fragments (relative intensity in %)</th></tr></thead><tbody><tr><td>18,664</td><td>1.47</td><td>Naphtylglucoronide</td><td>C<sub>16</sub>H<sub>16</sub>O<sub>7</sub></td><td>L3</td><td>321.0983 (-2.8)</td><td>60</td><td></td><td></td><td></td><td></td><td>159,0915 (100); 303,0973 (84); 144,0807 (34); 130,0650 (24)</td><td></td></tr><tr><td>18,820</td><td>7.26</td><td>5,7,3',5'-Tetrahydroxy-8,4'-dimethoxyflavone or 5,7,3',4'-Tetrahydroxy-3,5'-dimethoxyflavone</td><td>C<sub>17</sub>H<sub>14</sub>O<sub>8</sub></td><td>L2a</td><td>347.0758 (+2.59)</td><td>57</td><td>81</td><td>345.0613 (-0.87)</td><td>79</td><td>79</td><td>329,0653 (100); 347,0759 (97); 301,0706 (37); 221,0444 (31)</td><td>330,0382 (100); 345,0614 (61); 302,0433 (10); 331,0418 (10)</td></tr><tr><td>19,101</td><td>8.96</td><td>5'-Methoxycastavinol</td><td>C<sub>27</sub>H<sub>32</sub>O<sub>14</sub></td><td>L2b</td><td>581.186 (+1.72)</td><td></td><td>55</td><td>579.1722 (-1.55)</td><td></td><td>66</td><td>581,1859 (100); 401,1227 (25); 419,1332 (5); 563,1755 (2)</td><td>579,1710 (100); 205,0505 (16); 399,1079 (15); 373,1135 (8) 447,2226 (100); 149,0454 (59); 89,0244 (36); 101,0243 (17)</td></tr><tr><td>17,633</td><td>10.55</td><td>Acuminoside</td><td>C<sub>21</sub>H<sub>36</sub>O<sub>10</sub></td><td>L2a</td><td></td><td></td><td></td><td>447.2233 (-0.67)</td><td>99</td><td></td><td></td><td>73,0648 (100); 55,0543 (13); 114,0915 (1); 145,1224 (1)</td><td></td></tr><tr><td>17,875</td><td>18.71</td><td>9(or 10)-Methyl-18-dihydroxy-10(or 9)-methoxyoctadecanoate</td><td>C<sub>20</sub>H<sub>40</sub>O<sub>5</sub></td><td>L3</td><td>361.2946 (+1.94)</td><td>67</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Whisker boxes representing the distribution of the area's under peaks of the three RF chemomarkers in the three rosé wine classes (autoscaled values). Provence imititation in red, Non-Provence in green and Provence in blue. <sup>a,b</sup> indicate statistical differences in the intensity level of the corresponding metabolites across the wines (ANOVA, Padj < 0.05, Tukey post-hoc test). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

Acuminose is also a constituent of substrates of glycosidase-driven plant alarm and defense systems as well as an odor releasing glucosidase mechanism in grapes. The enzymatic hydrolysis of diglycosides like acuminosides may generally proceed by two distinct mechanisms.

In the sequential mode, β-apiosidase initially cleaves the intersugar bond between the terminal sugar and β-D-glucoside. In the second step, a β-glucosidase catalyzes the hydrolysis of the resulting β-D-glucoside and liberates the corresponding aglycone and glucose. In the one-step mode, the diglycosidase catalyzes direct cleavage of the glycosidic linkage between the aglycone and the disaccharide (Karnišová Potocká et al., 2021).

Therefore, given the heterogeneity of the grapes used in the pro-
duction of each rosé wine, accuminoside levels may be influenced by the
specific abiotic conditions of the region and/or the winemaking process.

The second chemomarker was 5,7,3',5'-tetrahydroxy-8,4'-dimethoxy-
yflavone or 5,7,3',4'-tetrahydroxy-3,5'-dimethoxyflavone, being higher
in "Non-Provence" rosé wines. This metabolite belongs to the chemical
family of flavonols and its chemical structure is very close to those of
syringetin (3,5,7,4'-tetrahydroxy-3',5'-dimethoxyflavone), a flavonoid
derivative commonly described in grape and wine (Hsu et al., 2009).

Flavonols in wine are mostly present as glycosides even if they are
also present in smaller amounts in their aglycone form produced by acid
hydrolysis of the flavonols glycosides over the wine making process and
the wine aging (Castillo-Muñoz et al., 2007). It has been reported by
Castillo-Muñoz et al the presence of new methoxylated flavonols derived
from aglycones different from the classic kaempferol, quercetin, myr-
icetin and isorhamnetin.

Besides, they have succeeded to use the flavonol profiles based on the
aglycone type for cultivar authenticity (Castillo-Muñoz et al., 2007).
This compound distinguished the “Non-Provence” rosés from both
“Provence” and “Provence imitations” types equally. The reasons for
these specificity are not easily explained, as grape variety, winemaking
processes, and abiotic conditions vary greatly across the different wine
samples in each type.

The third chemomarker was 5'-methoxycastavinol. It is a

“castavinol” compound, a polyphenol-glucoside originally described in
Bordeaux red wines (Castagnino & Vercauteren, 1996). It have been
implied in the colour changes during aging of red wines. The carbonyl
group of diacetyl which is naturally produced during malolactic
fermentation of wine, is highly reactive and it is plausible that this group
reacts with other components present in wine.

One such reaction has been proposed by Castagnino and Vercauteren (1996), who hypothesise the reaction between polyphenol glucosides (malvidin-3-glucoside) of red wines and diacetyl to form castavinol derivatives. It is proposed that C4 compounds, such as diacetyl react with anthocyanins through a nucleophilic addition mechanism to give the castavinol derivatives (Bartowsky & Henschke, 2004).

Similar to the second chemomarker, 5'-methoxycastavinol was also
observed in higher amounts in "Non-Provence" vs "Provence" and
"Provence imitations" rosé wines. Once again it is not easy to find the
causes explaining the specific levels of this compound in our 3 types of
rosé wines.

An important finding is that combining the three chemomarkers into a PLS score outperformed their individual contributions in distinguishing the three types of wines studied (Supplementary Table 6). This highlights the advantage of using a ‘multiplex’ scoring approach over single indicators for class prediction. In this combination, each chemomarker appears to provide complementary quantitative information, enhancing the accuracy of class assignment.

To conclude, this study constitutes a proof-of-concept to use the
molecular information to authenticate genuine rosé wines of Provence
from their imitations. Indeed, the concomitant use of an untargeted
metabolomics approach followed by a targeted MS/MS study on the
selected chemomarkers, and the design of a robust chemometric work-
flow derived from clinical biomarkers studies allowed to build a confi-
dent molecular authentication score. Furthermore, our finding also
materialized at the molecular level the concept of terroir for rosé wines.
Nonetheless, the analysis and the mathematical models for constructing
the predictor have been carried out with a limited number of samples.
Therefore, the relevance of the approach remains to be refined on a
A Rosé Provence score value = - 4.81E-07*[Acuminoside] - 4.22E-07 *[5,7,3',5'-Tetrahydroxy-3,5' (or 8,4')- dimethoxyflavone] - 1.61E10-7*[5'-Methoxycastavinol] + 0.0305335

<figure><img src="image_6.png" /><figcaption>Fig. 3. The performance of ROC curve estimator in detecting with robustness the Provence imitations of Provence rosé wines. A, rosé Provence score value. B. Receiver Operating Characteristic (ROC) curve using Non-Provence and Provence as calibrators. The cut-off value determined at the asymptote of the curve (false positive rate vs true positive rate) was 0.512. C, class assignment of the thirty rosé wines by the ROC classifier. D, class assignment of the thirty rosé wines by the ROC classifier, with a confidence interval of 95 %. E, class assignment of the Provence imitations by ROC curve, associated with probabilities. PR, Provence wines, NPR, Non-Provence wines and FP, False Provence (Provence imitation) wines.</figcaption></figure>

larger panel of rosé wines and on different harvest years, as well as on adulterated rosé wines. In addition, the score must be calculated based on the absolute quantification of each of its components in order to generalize its potential use, which necessitate the chemical synthesis or purification of the corresponding yet non-available chemical standards.

## Funding

The CDR (Centre du Rosé) and the CIVP (Centre interprofessionnel des vins de Provence) provided the financial support and the Centre du Rosé provided the wine samples.

## CRediT author statement

Sok-Siya Bun-Llopet, Béatrice Baghdikian participated to sample preparation and analyses and to critical article reviewing. Nathalie Pouzalgues, Gilles Masson, Aurélie Chevallier and Gérald Culioli contributed to design the protocol and the research question. Katia Mekbel and Elnur Garayev perfomed all the LC-MS/MS analyses and spectra bioinformatic annotation workflow and article writing. Jean-Charles Martin contributed to design the protocol, performed statistics and article writing.

## CRediT authorship contribution statement

**Katia Mekbel:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Formal analysis, Data curation.
**Elnur Garayev:** Writing – review & editing, Visualization, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Nathalie Pouzalgues:** Writing –

review & editing, Resources, Project administration, Investigation, Funding acquisition, Conceptualization.
**Aurélie Chevallier:** Conceptualization.
**Gilles Masson:** Writing – review & editing, Validation, Resources, Project administration, Investigation, Funding acquisition, Conceptualization.
**Sok-Siya Bun-Llopet:** Supervision, Formal analysis.
**Béatrice Baghdikian:** Visualization.
**Gérald Culioli:** Writing – review & editing, Visualization, Validation, Supervision, Software, Resources, Methodology, Data curation.
**Jean-Charles Martin:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Jean-Charles Martin reports financial support was provided by the CDR and the CIVP. Gilles Masson reports a relationship with the CDR that includes: board membership and employment. Nathalie Pouzalgues reports a relationship with the CDR that includes: employment. Aurelie Chevallier reports a relationship with the CDR that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

Data will be made available on request.
## Acknowledgments

We are grateful to the CDR and the CIVP for the funding of the project and the supply of rosé wine samples. Great thanks to Ljubica Svilar who is an engineer in mass spectrometry at C2VN for her valuable assistance in preparing the LC-MS/MS methods and her helpful advice. The authors also thank Catherine Tardivel, who is an engineer in biological techniques at C2VN for her important support in the project. We also thank Fathi Mabrouki and Myriam Bertolotti, who are technicians at IMBE, for their help in preparing the samples.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.foodchem.2024.141950.

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