# New insights into yeast acclimation for sparkling wine elaboration

A. BORRULL, E. MIRÓ, G. LÓPEZ-MARTÍNEZ, M. POBLET, R. CORDERO-OTERO, N. ROZÈS*

Department of Biochemistry and Biotechnology, Oenology Faculty, University Rovira i Virgili, Campus Sescelades, 43007 Tarragona, Spain

Ethanol – ROS – oxygen – vacuole – lipids

## Introduction

Sparkling wines produced according to the *méthode traditionnelle* are found in many viticulture countries. The more famous are Champagne (France), Cava (Spain), Asti Spumante (Italy), Premium Sekt (Germany), and Cap Classique (South Africa). In Spain, the 95% of bottle-fermented sparkling wines are mainly produced within the Penedès region (Catalonia). The achievement of secondary fermentation mainly depends on some factors such as chemical composition of base wine, acclimation process of yeast strains and secondary fermentation temperature. The wine yeast strains have to be acclimated to these unfavourable growth conditions to ensure a regular and complete fermentation. Although some industrial yeast acclimation processes are widely used to conduct the *prise de mousse* [1], little information exists for improving yeast performance.

The aim of this study was to analyze the influence of some acclimation growth parameters on the yeast capacity to improve its performance during the secondary fermentation.

## Materials and methods

After a four-day yeast acclimation which mimics the industrial procedure [1], a commercial wine yeast strain (*Saccharomyces cerevisiae* EC1118, Lallemand) was used to inoculate a defined base wine to realize the secondary fermentation in bottle at 25°C. Briefly, the acclimation conditions were aerobiosis (OXI) or semi-anaerobiosis (NOXI) in presence of sugar, and with limited-sugar content in aerobiosis (ETOXI) through the process. Samples taken at time 4 days were evaluated for ROS accumulating cells [2], microvacuolation formation and vacuolar activity [3], fatty acid [2] and sterol [4] compositions. The *prise de mousse* was monitored by measuring the bottle pressure inside through aphrometers. At 2, 4 and 6 bars, samples were taken to analyze sugar content (enzymatic method), alcoholic degree (ebulliometry method) and cell viability (flow cytometry method) [2].

## Results and discussion

The 4-days process was performed on a defined wine base medium to permit us to point out the main physiological parameters which could play an essential role in this feature such as cell lipid composition, vacuolar activity and ROS intracellular accumulation. As observed in Figure 1, yeast cells had consumed all disposable sucrose during the first 24 hours, producing an increase of ethanol content in the acclimation medium. After this activation phase (P1), cells placed in different conditions developed a different response in relation to the availability of carbon source and oxygen. Concretely, cells were placed in no limited oxygen cultures in which sugar content was added or not during the second phase of acclimation (P2), OXI and ETOXI conditions, respectively. These features allowed us to measure the behaviour of yeast cells in respiro-fermentative (OXI) and respiratory (ETOXI) conditions.

Another condition was developed to determine the incidence of a fermentative behaviour (NOXI) in which cells were acclimated in presence of sugar in limited oxygen culture. In presence of sugar, OXI and NOXI supported the same production curve of ethanol rising to 13.61 ± 0.15% (v/v) for OXI and 14.78 ± 0.19%
(v/v) for NOXI along the 3-days of P2 (Fig. 1). Finally, by this way, we had three types of acclimated yeast cells by which three adaptive features were found.

<figure><img src="image_162.png" /><figcaption>Fig. 1 - Time course of cell acclimation process to ethanol. Et: Ethanol; Su: Sugar; OXI: Aerobiosis + sugar addition; NOXI: Semi-anaerobiosis + sugar addition; ETOXI: aerobiosis without sugar addition.</figcaption></figure>

To observe the induced features during the acclimation process, secondary fermentations in bottles were conducted at 25°C in a base wine with OXI, OXI and ETOXI cells at 2 10⁶ cells/ml. Only, the secondary fermentation was completed with NOXI cells, 35 days, while the others had a stuck fermentation (experiment stopped at time 40 days) (results not shown).

<table><caption>**Table 1** - Evolution of some parameters during the secondary fermentation of bottled base wine at 25°C with acclimated *Saccharomyces cerevisiae* EC11118. The conditions used were: OXI, Aerobiosis + sugar addition; NOXI, Semi-anaerobiosis + sugar addition; ETOXI, Aerobiosis without sugar addition.</caption><thead><tr><th></th><th>OXI</th><th>NOXI</th><th>ETOXI</th></tr></thead><tbody><tr><td>Final sugar content (g/l)</td><td>5.41</td><td>2.24</td><td>3.51</td></tr><tr><td>Alcoholic degree (%, v/v)</td><td>10.30</td><td>10.90</td><td>10.55</td></tr><tr><td>Days to reach 4 bars</td><td>16</td><td>10</td><td>13</td></tr><tr><td>Cell viability at 4 bars (cell/ml)</td><td>1.18 10<sup>6</sup></td><td>1.34 10<sup>6</sup></td><td>1.30 10<sup>6</sup></td></tr></tbody></table>

If we observed the time to reach 4 bars, we could note some differences due to the type of acclimation used for cells. In this case, the fastest was NOXI, the slowest was OXI with a 6-day difference between both. Concerning the final sugar and ethanol contents, NOXI condition showed the highest alcoholic degree with 10.90% (v/v) and the lowest sugar level with 2.24 g/l. Little differences were observed in relation to the viability at pressure 4 bars but no viable cells were found at time 40 days for both OXI and ETOXI. In NOXI condition, the viability remained ca 25% of the total population (2.55 10⁶ cells/ml) (results not shown).

Looking at the cytological parameters such as vacuole morphology and activity, or ROS accumulation, we could observe a best state of cells acclimated in the NOXI condition (Fig. 2). They significantly have the highest vacuolar activity, the best microvacuolation (fragments into multiple small vesicles) and the lowest ROS accumulation. Vacuoles are involved in many processes including the homeostasis of cell pH and modified their morphology depending on the growth conditions [5]. In our conditions of acclimation to ethanol, cells placed in NOXI condition seemed to be better prepared to the secondary fermentation challenge because of their high degree of vacuole fragmentation and their capacity to maintain an acid pH into the vacuole. Under osmotic or ionic stress, vacuole must be able to fuse and fragment at will to adjust to different cellular challenges [5].
<figure><img src="image_163.png" /><figcaption>**Fig. 2** - Microvacuolation, vacuolar activity and ROS determinations of *Saccharomyces cerevisiae* EC1118 cells through the acclimated process at 25°C. The conditions used were: LSA-R, Active Dry Yeast Rehydrated in water during 30 min; LSA-W, Active Dry Yeast Rehydrated in base wine during 30 min; P1, activation phase (24h); OXI, Aerobiosis + sugar addition; NOXI, Semi-anaerobiosis + sugar addition; ETOXI, Aerobiosis without sugar addition.</figcaption></figure>

On the other hand, ROS accumulation was increased in constant aeration, OXI and ETOXI, compared with NOXI condition. Besides, a significant decrease in presence of no fermentable carbon source (ETOXI) with respect to OXI condition was observed.

<table><caption>**Table 2** - Lipid composition of *Saccharomyces cerevisiae* EC1118 cells through the acclimated process at 25°C. The conditions used were: OXI, Aerobiosis + sugar addition; NOXI, Semi-anaerobiosis + sugar addition; ETOXI, Aerobiosis without sugar addition.</caption><thead><tr><th></th><th>OXI</th><th>NOXI</th><th>ETOXI</th></tr></thead><tbody><tr><td>UFA/SFA</td><td>2.58<sup>b</sup></td><td>1.60<sup>a</sup></td><td>2.84<sup>c</sup></td></tr><tr><td>C18:1/C18</td><td>3.11<sup>b</sup></td><td>2.29<sup>a</sup></td><td>3.35<sup>c</sup></td></tr><tr><td>Ergosterol (%)</td><td>54.33<sup>a</sup></td><td>53.98<sup>a</sup></td><td>55.08<sup>a</sup></td></tr><tr><td>Ergosterol/Squalene</td><td>8.70<sup>b</sup></td><td>6.40<sup>a</sup></td><td>9.99<sup>c</sup></td></tr><tr><td>Ergosterol/Zymosterol</td><td>3.37<sup>b</sup></td><td>3.30<sup>b</sup></td><td>3.00<sup>a</sup></td></tr></tbody></table>

Concerning the lipid composition, it was interesting to note that no changes occurred in the ergosterol percentage regardless of the condition used during the acclimation. However, it seemed that this acclimation process (NOXI) induced a significant decrease of membrane fluidity because the relations between UFA and SFA, ergosterol and squalene, and oleic (C18:1) and stearic (C18) acid were the lowest. On the other hand, in situation of catabolite derepression (exhaustion of fermentable sugars, ETOXI condition), the results were in agreement with those of Quain and Haslam [6]. Indeed, we observed a decrease of the ratio ergosterol/zymosterol for one ergosterol content slightly higher in ETOXI than in both OXI and NOXI conditions. Such as described by [6], steryl esters increased when yeast cells grew on ethanol (ETOXI). In our conditions, this fact was observed with the ratio ergosterol/zymosterol.

## Conclusion

In conclusion, our results seem to show that the best cell acclimation to ethanol depends on the cell sugar metabolism and other physiological factors such as cell oxidative state, vacuolar activity and lipid composition at 25°C. Some works are necessary to confirm these features at lower temperature, 15-17°C.
Acknowledgements

This work was financially supported by grants from the Spanish Government; project AGL2010-22001-C02-02, awarded to NR. In addition, AB wants to thank the *Universitat Rovira i Virgili* for her doctoral fellowship 2009BRDI/12/13. EM wants to thank the *Universitat Rovira i Virgili* for her master fellowship DBB1AD1415.

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3. López-Martínez G, Margalef-Català M, Salinas F et al (2015) ATG18 and FAB1 are involved in dehydration stress tolerance in *Saccharomyces cerevisiae*. *PLoS ONE* **10**: e0119606.

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# Non-Saccharomyces biodiversity in Nero di Troia wine

C. GAROFALO, L. BENEDUCE, S. MASSA, P. RUSSO, G. SPANO*, V. CAPOZZI

Dipartimento di Scienze degli Alimenti, Università di Foggia, via Napoli 25, 71100 Foggia, Italy

Non-Saccharomyces – biodiversity – wine – alcoholic fermentation

## Introduction

Grape juice is a non-sterile mixture with several types of microorganisms belonging to the microbiota naturally present on grape berries. In particular, yeasts can ferment the substrate, promoting the transformation of grape sugars into ethanol, carbon dioxide and hundreds of other metabolites (alcoholic fermentation). Spontaneous fermentation is carried out through a sequence of different yeast species, in reason of their metabolic aptitudes and of alcohol tolerance: non-Saccharomyces yeasts are the most abundant at the beginning of alcoholic fermentation (AF), replaced, after 3-4 days, by Saccharomyces cerevisiae [1]. S. cerevisiae is considered the principal microorganism responsible for vinification. However, during spontaneous AF several yeast genera such as Hanseniaspora, Kloeckera Candida, Pichia, Zygosaccharomyces, Schizosaccharomyces, Torulaspora, Kluyveromyces and Metschnikowia have been isolated [1-3].

Non-Saccharomyces yeasts, generally considered spoilage yeasts, in several cases display physiological characteristics that lead to consider some strains as potential starter cultures, with a positive influence on the quality of the wine [1] and/or may be used with a specific technological purpose (e.g. to decrease volatile acidity, decrease alcohol content) [4]. In addition, several authors have recently proposed the direct application of non-Saccharomyces as biocontrol agents against moulds or spoilage microorganisms [5].

Several studies suggest the inclusion of non-Saccharomyces wine yeasts, together with Saccharomyces strains as part of mixed and multi-starter fermentations [6-9]. The study of non-Saccharomyces microbial biodiversity represents a subject of increasing interest in wine regions. It is crucial to underline that non-Saccharomyces yeasts are a very heterogeneous group of microbes, representing not only a vast source of direct applications [3], but posing also risks to the quality of wine. The aim of this work was to study, for the first time, the biodiversity of cultivable non-Saccharomyces yeasts associated with grapes collected from the North-Apulian region on the autochthonous vine variety 'Uva di Troia' ('Nero di Troia' is the corresponding wine obtained with 'Uva di Troia' as unique variety) during the vintages '2012' and '2013'. In particular we collected grapes in vineyards from four different geographical areas (San Severo, Barletta, Lucera, Ascoli Satriano) on which production of four different wines with the status of geographical indication (wine with appellation of origin) ('San Severo Rosso' DOC, 'Rosso Barletta' DOC, 'Cacc'e mitte' DOC, 'Tavoliere delle Puglie' DOC) is achieved. The common denominator of these wines is the autochthonous grape variety 'Uva di Troia', which can be used at different percentages in reason of product specification limitations.

## Materials and methods

### Spontaneous fermentations and media preparations

Spontaneous alcoholic fermentations were performed by sampling Nero di Troia grape cultivars in the North Apulia area from four vineyards located in the geographical areas of San Severo, Barletta, Lucera, and Ascoli Satriano, during vintages 2012 and 2013. Yeast sampling was accomplished at the beginning of AF on WL nutrient agar, supplemented with 10 mg/l chloramphenicol. About 25 green colonies, with different morphology, were selected for isolation and identification and stored at -80°C in YPD_medium supplemented with glycerol (30% v/v).

### Genotypic characterization

The isolates were identified by PCR-RFLP analysis of 5.8S rRNA genes and the two ribosomal internal transcribed spacers (ITS), performed according with Esteve-Zarzoso et al. [10].
<table><caption>**Table 1** - Identification of yeast isolated from grape must collected from four wineries (Barletta, San Severo, Lucera, Ascoli Satriano) by ITS-RFLP analysis and sequencing</caption><thead><tr><th rowspan="2">Species<sup>1</sup></th><th rowspan="2">ITS</th><th colspan="4">Restriction fragments</th><th colspan="2">N° isolates</th><th rowspan="2">Origin</th><th rowspan="2">Accession Number</th></tr><tr><th>HaeIII</th><th>Hinfl</th><th>CfoI</th><th>Ddel</th><th>2012</th><th>2013</th></tr></thead><tbody><tr><td>Candida boidinii</td><td>750 700</td><td>390+190+160</td><td>350+310+90</td><td>/</td><td>/</td><td>12</td><td>8</td><td>Barletta</td><td>KP241860-KP241861</td></tr><tr><td>Candida zemplinina</td><td>475 475</td><td>235+235</td><td>215+110+80+60</td><td>/</td><td>/</td><td>9</td><td>12</td><td>Barletta</td><td>KP241864-KP241865</td></tr><tr><td>Hanseniaspora guilliermondii</td><td>750 750</td><td>350+200+180</td><td>320+310+105</td><td>380+180+95+80</td><td>/</td><td>25</td><td>30</td><td>Barletta</td><td>KP241874-KP241875</td></tr><tr><td>Issatchenkia terricola</td><td>450 290+125</td><td>240+105+105</td><td>130+100+90+85+45</td><td>/</td><td>/</td><td>2</td><td>/</td><td>Barletta</td><td>KP241882-KP241883</td></tr><tr><td>Zygosaccharomyces bailii</td><td>790 690+90</td><td>340+225+160+55</td><td>320+270+95+95</td><td>/</td><td>/</td><td>2</td><td>/</td><td>Barletta</td><td>KP241898-KP241899</td></tr><tr><td>Candida boidinii</td><td>750 700</td><td>390+190+160</td><td>350+310+90</td><td>/</td><td>/</td><td>7</td><td>2</td><td>San Severo</td><td>KP2281424-KP281425</td></tr><tr><td>Candida zemplinina</td><td>475 475</td><td>235+235</td><td>215+110+80+60</td><td>/</td><td>/</td><td>12</td><td>10</td><td>San Severo</td><td>KP241866-KP241867</td></tr><tr><td>Hanseniaspora guilliermondii</td><td>750 750</td><td>350+200+180</td><td>320+310+105</td><td>380+180+95+80</td><td>/</td><td>22</td><td>24</td><td>San Severo</td><td>KP241876-KP241877</td></tr><tr><td>Issatchenkia terricola</td><td>450 290+125</td><td>240+105+105</td><td>130+100+90+85+45</td><td>/</td><td>/</td><td>6</td><td>5</td><td>San Severo</td><td>KP241884-KP241885</td></tr><tr><td>Hanseniaspora uvarum</td><td>750 750</td><td>350+200+180</td><td>320+310+105</td><td>300+180+95+90+85</td><td>/</td><td>1</td><td>8</td><td>San Severo</td><td>KP241872-KP241873</td></tr><tr><td>Zygoascus hellenicus</td><td>650 625</td><td>350+170+130</td><td>325+325</td><td>/</td><td>/</td><td>2</td><td>1</td><td>San Severo</td><td>KP241902-KP241903</td></tr><tr><td>Candida boidinii</td><td>750 700</td><td>390+190+160</td><td>350+310+90</td><td>/</td><td>/</td><td>10</td><td>4</td><td>Lucera</td><td>KP2281426-KP281427</td></tr><tr><td>Candida zemplinina</td><td>475 475</td><td>235+235</td><td>215+110+80+60</td><td>/</td><td>/</td><td>8</td><td>10</td><td>Lucera</td><td>KP2281428-KP281429</td></tr><tr><td>Hanseniaspora guilliermondii</td><td>750 750</td><td>350+200+180</td><td>320+310+105</td><td>380+180+95+80</td><td>/</td><td>26</td><td>26</td><td>Lucera</td><td>KP241878-KP241879</td></tr><tr><td>Issatchenkia terricola</td><td>450 290+125</td><td>240+105+105</td><td>130+100+90+85+45</td><td>/</td><td>/</td><td>2</td><td>1</td><td>Lucera</td><td>KP241886-KP241887</td></tr><tr><td>Zygosaccharomyces bailii</td><td>790 690+90</td><td>340+225+160+55</td><td>320+270+95+95</td><td>/</td><td>/</td><td>2</td><td>1</td><td>Lucera</td><td>KP241900-KP241901</td></tr><tr><td>Hanseniaspora uvarum</td><td>750 750</td><td>350+200+180</td><td>320+310+105</td><td>300+180+95+90+85</td><td>/</td><td>1</td><td>6</td><td>Lucera</td><td>KP241870-KP241871</td></tr><tr><td>Hanseniaspora opuntiae</td><td>750 750</td><td>350+200+180</td><td>320+310+105</td><td>/</td><td>/</td><td>1</td><td>2</td><td>Lucera</td><td>KP241868-KP241869</td></tr><tr><td>Candida boidinii</td><td>750 700</td><td>390+190+160</td><td>350+310+90</td><td>/</td><td>/</td><td>12</td><td>8</td><td>Ascoli Satriano</td><td>KP241862-KP241863</td></tr><tr><td>Candida zemplinina</td><td>475 475</td><td>235+235</td><td>215+110+80+60</td><td>/</td><td>/</td><td>6</td><td>10</td><td>Ascoli Satriano</td><td>KP2281430-KP281431</td></tr><tr><td>Hanseniaspora guilliermondii</td><td>750 750</td><td>350+200+180</td><td>320+310+105</td><td>380+180+95+80</td><td>/</td><td>30</td><td>30</td><td>Ascoli Satriano</td><td>KP241880-KP241881</td></tr><tr><td>Issatchenkia terricola</td><td>450 290+125</td><td>240+105+105</td><td>130+100+90+85+45</td><td>/</td><td>/</td><td>2</td><td>2</td><td>Ascoli Satriano</td><td>KP241888-KP241889</td></tr></tbody></table>

<footnote n="1">¹ Species assignation according to Esteve-Zarzoso et al. [10]</footnote>
and *C. zemplinina* were found as dominant species, while Ocón *et al.* [17], investigating the yeast population present in four spontaneous alcoholic fermentations in the Rioja appellation (D.O.Ca. Rioja, Spain), found *C. stellata* and *H. uvarum* in major proportions during fermentation. Our results can be correlated to specific pedoclimatic conditions, in fact both vintage studied has been characterized by high levels of precipitation.

<figure><img src="image_164.png" /><figcaption>**Fig. 2** - Percentage frequency of non-*Saccharomyces* yeasts isolated from spontaneous fermentation of vine variety "Uva di Troia", during vintages 2012 (a) and 2013 (b). Open bars, Ascoli Satriano vineyards; black bars, San Severo vineyards; light grey bars, Lucera vineyards; dark grey bars, Barletta vineyards. Different letters in superscript bars indicate statistical significance (One-way ANOVA, Tukey test P < 0.005).</figcaption></figure>

<figure><img src="image_165.png" /><figcaption>**Fig. 3** - Percentage non-*Saccharomyces* yeast frequencies during vintage 2012 (dark grey bars) and 2013 (light grey bars). Different letters in superscript bars indicate statistical significance (One-way ANOVA, Tukey test P < 0.005).</figcaption></figure>

The most represented among species isolated in this study, in both vintages and in all the locations, are *H. guilliermondii* and *C. zemplinina*, non-*Saccharomyces* yeasts of oenological interest. These findings lead us to suggest these two species as possible candidates for the design of mixed/multistrains autochthonous starter cultures for several typical Apulian wines, with the final aim to achieve a product via a representation of autochthonous virtuous microbial diversity.

In conclusion, this is the first report on the yeast microbiota during the beginning steps of spontaneous alcoholic fermentation from Apulian Nero di Troia wines. Our findings provide the basis for an improved management of non-*Saccharomyces* in typical Apulian wines important for the development of the local wine industry and to achieve an enhanced standard of safety in the final productions.
## Acknowledgements

This research was supported by the Apulian Region in the framework of the “OenoMicroManagement” project (PIF - Progetti Integrati di Filiera) and by a grant from the project PON02_00186_3417512, “S.I.Mi.S.A.”.

## Abbreviations

MLF, malolactic fermentation; AF, alcoholic fermentation; LAB, lactic acid bacteria; WL, Wallestein laboratory.

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6. Fleet GH (2003) Yeast interactions and wine flavour. *Int J Food Microbiol* **86**: 11-22.

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# Observations macroscopiques et microscopiques de préparations à base de chitosane sur le développement de *Brettanomyces bruxellensis* : application à usage curatif et préventif

B. NAZARIS<fnref n="1" />*, E. GONTIER<fnref n="2" />, L. GEAY<fnref n="2" />, C. MANSOUR<fnref n="3" />, V. MOINE<fnref n="3" />, J. COULON<fnref n="3" />

Chitosane – rémanence – *Brettanomyces bruxellensis* – microscopie électronique

## Introduction

Les moyens de lutte contre la principale levure d'altération des vins rouges, *Brettanomyces bruxellensis*, reposent essentiellement sur des démarches préventives visant à créer des conditions défavorables à son développement et à suivre analytiquement les vins. Malheureusement, ces moyens ne sont pas toujours suffisamment efficaces, et il existe encore beaucoup de situations pour lesquelles un développement excessif de cette levure entraîne des concentrations en phénols volatils bien au-delà de leur seuil limite de préférence.

Le chitosane, associé à une préparation enzymatique (OenoBrett®) est un des rares moyens permettant de lutter contre cette levure mais les mécanismes sous-tendant son action ne sont pas encore totalement élucidés. Chez certains microorganismes, une dépolarisation de la membrane plasmique est évoquée [1], associée à une chélation des métaux, empêchant ainsi la croissance cellulaire [2]. Néanmoins, peu d'études se sont intéressées à l'action du chitosane sur les microorganismes œnologiques. C'est pourquoi des observations en microscopie électronique en transmission ont été réalisées afin d'observer à l'échelle cellulaire l'action du chitosane, que ce soit en milieu modèle ou bien dans des vins naturellement contaminés.

Dans un deuxième temps, une étude sur la rémanence du chitosane a été réalisée afin de comprendre si l'application de ce produit, à action très rapide, perdurait dans le temps. Pour cela, des vins traités à l'OenoBrett® ont été artificiellement contaminés avec *B. bruxellensis* et leur population ainsi que la production de phénols volatils suivie.

## Matériel et méthodes

### Souches, vins et milieux modèles

La souche de *B. bruxellensis* IOEB 1120 (CRB Œnologie, ISVV, Bordeaux) a été utilisée et cultivée en milieu liquide YPD. Les cultures en milieu YPD ou bien des vins contaminés ont été traités à l'aide d'OenoBrett® (Laffort) à 10 g/hL.

### Analyses microbiologiques et dosage des phénols volatils

Les populations de *B. bruxellensis* sont suivies sur milieu sélectif MIL-BRETT (SARCO). Les dosages d'éthyl-4-phénol et d'éthyl-4-gaïacol sont réalisés par le laboratoire SARCO, par SBSE-GS-MS.

### Observations par microscopie électronique en transmission

Après centrifugation et obtention d'un culot, les levures sont déposées dans un support échantillon puis recouvertes d'un second support et insérées dans l'équipement afin de réaliser la cryofixation haute

<footnote n="1">¹Laffort, 126 Quai de la Souys, 33100 Bordeaux Cedex, France</footnote>

<footnote n="2">²Bordeaux Imaging Center - Pôle Imagerie Electronique - Université de Bordeaux,
33076 Bordeaux Cedex, France</footnote>

<footnote n="3">³BioLaffort, 126 Quai de la Souys, 33100 Bordeaux Cedex, France</footnote>

<footnote>*Auteur principal : bastien.nazaris@laffort.com</footnote>
pression (HPM100-Leica Microsystems, Vienne). Les échantillons sont ensuite cryo-substitués dans 2% tétroxyde d'osmium dilué dans de l'acétone anhydre à -80°C pendant 3 jours. Après plusieurs paliers de remontée en température (8h à -60°C ; 8h à -30°C puis une dernière étape à 4°C), le milieu de substitution est remplacé par de l'acétone pure. Pour l'inclusion, après un premier bain dans la résine époxy/acétone (1/3) pendant 3h, le mélange est remplacé successivement par un mélange 1/1 puis 3/1 de 3h chacun. Après une nuit en résine 100%, cette dernière est polymérisée à 60°C pendant 48 heures. Les blocs sont ensuite coupés à l'aide d'un couteau de diamant (Diatome, Biel- Bienne, Suisse) sur un ultra-microtome (EM Ultracut - UC7, Leica Microsystems, Vienne, Autriche). Les coupes Ultrafines sont alors prélevées sur des grilles de cuivre puis contrastées avec de l'acétate d'uranyle et du citrate de plomb.

Les grilles sont examinées au Bordeaux Imaging Center, à l'aide d'un microscope électronique en transmission (H7650, Hitachi, Tokyo, Japon) à 80 kV, équipé d'une caméra Orius 11Mpixel (Roper scientifique, France).

### Protocole mis en œuvre pour l'étude de la rémanence du chitosane

Un vin rouge du millésime 2013 (TAV : 12,93% vol. ; AV : 0,5 g/L H₂SO₄ ; AT : 3,21 g/L H₂SO₄ ; pH : 3,65), contenant une population initiale de 4,8.10⁴ UFC/mL de *B. bruxellensis* et une concentration en éthyl-4-phénol + éthyl-4-gaïacol de 332 µg/L est traité avec 10 g/hL d'OenoBrett® (Laffort). Une fraction est conservée non traitée. Le vin traité est régulièrement re-contaminé avec des cellules de *B. bruxellensis* (10 à 50 UFC/mL final), 8 jours, 1 mois ou 2 mois après traitement. Le vin non traité est séparé en deux autres fractions, l'une étant conservée telle que afin d'observer l'évolution des populations initiales de *B. bruxellensis*, l'autre étant filtrée sur membrane de 0,45 µm de taille de pores afin d'observer l'effet d'un traitement alternatif.

## Résultats et discussion

### Observations microscopiques de l'effet d'OenoBrett® sur des populations de B. bruxellensis en milieu modèle et dans du vin

Les photos présentées figure 1 indiquent qu'avant traitement, les cellules possèdent une bonne intégrité cellulaire, une paroi et membrane plasmique visibles, ainsi que des organites en bon état. Huit jours après traitement, de gros amas, correspondant vraisemblablement au chitosane sont visibles, associés à des structures, sans paroi ni membrane, se présentant comme des agglomérats de structures subcellulaires (figure 1). Ainsi, dans ces conditions, le chitosane a entraîné la déstructuration totale des cellules, et sur l'ensemble des lames observées, aucune structure cellulaire intacte n'a pu être visible. Dans le vin, avant traitement, les cellules présentent aussi une structure cellulaire viable (figure 2), et dès le premier jour de traitement, bien que les parois soient encore visibles, le milieu intracellulaire est fortement perturbé et les structures subcellulaires totalement désorganisées. Cet effet est renforcé après 4 et 8 jours de traitement.

<figure><img src="image_166.png" /><figcaption>**Fig. 1** - Cellules de *B. bruxellensis* cultivées en milieu YPD non traitées (photos de gauche) ou bien traitées à 10 g/hL d'OenoBrett® et observées après 8 jours (photos de droite).</figcaption></figure>
<figure><img src="image_167.png" /><figcaption>**Fig. 2** - Cellules de *B. bruxellensis* dans du vin naturellement contaminé, traité ou non à OenoBrett® (10 g/hL) et observé après 1, 4 et 8 jours de traitement.</figcaption></figure>

### Evolution des populations de B. bruxellensis dans un vin traité à l'OenoBrett : impact d'une re-contamination

La figure 3 illustre l'évolution des populations de *B. bruxellensis* au cours des 4 mois de conservation après traitement ou non à l'OenoBrett®. Le témoin non traité maintient une population au delà de $10^3$ UFC/mL et la concentration en phénols volatiles est multipliée par 7 en 4 mois. Les traitements qu'ils soient physique (filtration) ou à base de chitosane, permettent l'élimination de *B. bruxellensis* de façon rapide et pratiquement totale si bien qu'après 1 semaine, plus aucune cellule n'est détectable (inférieur à 10 UFC/mL). Néanmoins, la modalité filtrée s'est spontannément re-contaminée, indiquant que des cellules résiduelles ont pu se développer et entraîner la formation de phénols volatils, dont la concentration est multipliée par 5 durant les 4 mois de conservation (tableau 1). Le vin traité montre une augmentation transitoire des populations, correspondant peut-être à des cellules présentes dans les lies, mais qui en aucun cas n'entraînent la synthèse de phénols volatils. Dans l'essai traité et re-contaminé après deux mois, les populations s'implantent transitoirement, mais ne sont pas capables, dans ces conditions d'expérimentation, de former des phénols volatils. Des résultats similaires sont généralement obtenus pour des re-contaminations réalisées après 1 semaine et 1 mois de traitement. Dans quelques rares cas, une augmentation de population assortie d'une augmentation en phénols volatils peut être observée (mais jamais au niveau du témoin non traité ou de la modalité filtrée), indiquant que la rémanence observée du produit OenoBrett permet de limiter la synthèse de phénols volatils mais parfois pas de l'empêcher totalement (données non montrées).

<figure><img src="image_168.png" /><figcaption>**Fig. 3** - Evolution des populations de *B. bruxellensis* dans le cas d'un vin laissé sur Oenobrett® (Laffort) durant 4 mois et re-contaminé artificiellement après 2 mois</figcaption></figure>
<table><caption>**Tableau 1** - Concentration en éthyl-4-phénols et éthyl-4-gaïacol après 4 mois de conservation des vins</caption><thead><tr><td>Traitements</td><td>Somme des éthyl-4-phénol et éthyl-4-gaïacol (µg/L)</td></tr></thead><tbody><tr><td>Vin non traité (témoin)</td><td>2252</td></tr><tr><td>Vin filtré (0,45 µm)</td><td>1631</td></tr><tr><td>Vin traité avec 10 g/hL de Oenobrett®, non re-contaminé</td><td>280</td></tr><tr><td>Vin traité avec 10 g/hL de Oenobrett® et re-contaminé après 2 mois</td><td>300</td></tr></tbody></table>

## Conclusion

Les observations microscopiques réalisées indiquent que le chitosane agit rapidement sur les cellules de *B. bruxellensis*. Dès le premier jour de traitement, que ce soit en milieu modèle ou dans du vin, l'organisation intracellulaire est fortement perturbée, ne laissant plus aucun organite intact. De plus, la paroi et membrane cellulaire disparaissent progressivement, ne laissant que des "squelettes" visibles en microscopie. L'observation microscopique indique également que le chitosane forme de grosses particules, qui sédimentent rapidement. Néanmoins, l'étude sur la rémanence du produit montre que son effet est prolongé durant au moins 4 mois. Cet effet peut être lié soit à un effet direct par la présence de particules plus fines ayant aussi une activité vis-à-vis de *B. bruxellensis*, soit de façon indirecte en influençant la composition du vin, créant ainsi des conditions défavorables au développement et l'activité métabolique de cette levure. Des travaux supplémentaires sont donc nécessaires afin de comprendre plus précisemment les mécanismes sous-tendant cet effet rémanent. Ainsi, le produit OenoBrett®, jusqu'ici préconisé uniquement dans le cas d'une démarche curative, peut aussi être utilisé dans une action préventive vis-à-vis du développement de *B. bruxellensis*.

## Remerciements

Les photos en microscopie électronique en transmission ont été réalisées par le Bordeaux Imaging Center, faisant partie de la structure nationale FranceBioImaging (ANR-10-INBS-04 FranceBioImaging).

## Références

1. Raffat D, von Bargen K, Haas A et al (2008) Insights into the mode of action of chitosan as an antibacterial compound. *Appl Environ Microbiol* **74**: 3764-3773.

2. Rabea EI, Badawy ME, Stevens CV et al (2003) Chitosan as antimicrobial agent: applications and mode of action. *BioMacromolecules* **4**: 1457-1465.
# Selection and characterization of native lactic acid bacteria from ecological grapes and wines of Priorat in South Catalonia

J. FRANQUES, B. PALAHI, I. ARAQUE, C. REGUANT*, A. BORDONS

Group of Oenological Biotechnology, Department of Biochemistry and Biotechnology, Facultat d'Enologia, Universitat Rovira i Virgili (URV), c/ Marcel·lí Domingo 1, Campus Sescelades N4, 43005 Tarragona, Catalonia, Spain

Native lactic acid bacteria – malolactic fermentation – *Oenococcus oeni* – Priorat

## Introduction

Malolactic fermentation (MLF) usually occurs after alcoholic fermentation (AF), especially in red wines. MLF is the decarboxylation of L-malic acid into L-lactic acid and CO2, which contributes to an organoleptic improvement of wine and its microbiological stability. This process is carried out by lactic acid bacteria (LAB), particularly by *Oenococcus oeni*.

The trend of consumer preferences in recent years to the ecological wines represents an opportunity for traditional and peculiar terroirs. In this sense, the use of native LAB as inoculum can offer great potential. To achieve this goal, a good collection of isolated and well-typified strains is required, and an accurate assessment of their potential in winemaking too.

This work is part of a project whose main objective is to identify, select and characterize indigenous microbial diversity of vineyards and wines from the DOQ (*Denominació d'Origen Qualificada*, Qualified Appellation of Origin) Priorat (South Catalonia), and from other European wine regions.

## Materials and methods

### *Sampling*

26 samples of Grenache and Carignan grapes and 47 samples of wine made with those same grapes performing spontaneous MLF were aseptically collected from two consecutive vintages (2012 and 2013) from vineyards of Priorat. In these vineyards, minimal pesticide treatment was used and commercial LAB strains were not used. The wines had high alcohol content (13.5-16%) and a pH of 3.4-3.6.

### *Isolation and growth conditions*

Grape samples were homogenized (Stomacher-400 Circulator Seward: 2500 rpm, 2.5 min) and both the must and pulp obtained, and whole grapes were collected separately. These samples were cultured in MRSm3 medium, which is MRS [1] supplemented with L-malic acid (3 g/L), fructose (5 g/L), nystatin (100 mg/L), sodium azide (25 mg/L), L-cysteine (0.5 g/L) and tomato juice (100 mL/L), at pH 5, and incubated 7-15 days in anaerobic atmosphere at 27°C. When growth was observed, the sample was cultured on solid MRSm3 (20 g/L agar). After 7-15 days of growing in anaerobic atmosphere at 27°C, 30 colonies were picked randomly from each sample and cultured in the same medium without agar. Wine samples were directly cultured on solid MRSm3. All isolates confirmed to be LAB (morphology, Gram +, catalase -) were kept at -20°C with glycerol.

### *Strain identification and typification*

The isolates with characteristics of *Oenococcus* were confirmed to be *O. oeni* by specific PCR technique [2]. Afterwards, they were typified using *Variable Number Tandem Repeat* (VNTR) following [3]. The rest of the isolates (lactobacilli and other cocci) were identified with 16S-ARDRA method and *MseI* digestion in accordance with [4]. The DNA extraction was performed according to [5]. The PCR products were

<footnote>*Corresponding author: cristina.reguant@urv.cat</footnote>
solved by electrophoresis in 1.5% (w/v) agarose gels with TBE 1x (100V, 1h30), and were dyed with ethidium bromide. 100-pb markers were used (Roche Diagnostics). The different profiles obtained were confirmed by 16S fragment sequencing. Subsequently, they were typified using rep-PCR with GTG5 technique [6].

*L-malic acid degradation by the isolates in simulated wine*

*Wine-Like Medium* (WLM) according to [7] was used, with 2 g/L L-malic acid. The strains were grown in MRSm3 medium until A600nm = 1.6. The pellet obtained was inoculated (2%) in 50 mL of WLM 12 and 14% of ethanol, by duplicate for each strain, at 20°C. L-malic acid was measured enzymatically (Miura One, TDI S.A.) and both the L-malic acid consumption and fermentative velocity were calculated.

*Best strains phenotypic and genotypic characterization*

Resistance tests were performed with the best strains during growth in WLM combining different pHs (2.8, 3, 3.3, 3.6 and 4) and ethanol quantities (6, 12, 14 and 16%) during 3 weeks, following [8]. The detection of the histidine decarboxylase (*hdc*), tyrosine decarboxylase (*tdc*) and ornithine decarboxylase (*odc*) genes was performed by specific PCRs according to [9] with the extraction of DNA in order to discard the possible biogenic amine producer strains. The *Simple Nucleotide Polymorphism* (SNPs) technique was used to analyse these strains too, to obtain the phylogenetic relationship between them, following [8].

*MLF starter culture in cellar*

The three best *O. oeni* strains were selected regarding their L-malic acid degradation and characterization results and were used as starter culture for the inoculation of a total of four wines (two made from Grenache and two from Carignan) in cellar. For each wine, the strains were grown separately in 1.5 L of MRSm3 medium until A600nm = 1.6. The total pellet obtained was used to inoculate (2%) 225 L of wine. L-malic acid consumption was followed in the cellar. FML final samples were collected to confirm the presence of the inoculated bacteria.

## Results and discussion

The total samples analysed provided us with 1904 LAB isolates (Table 1). Most of them were confirmed to be *O. oeni* by specific PCR technique. For bacilli and other cocci, LAB common species in grapes and wine were found, such as *Lactobacillus mali*, *L. plantarum*, *L. sanfranciscensis* and *Pediococcus pentosaceus*, and other less common such as *L. lindneri* and *Fructobacillus tropaeoli* were found too. Some grapes were processed for observation with the Scanning Electronic Microscope (SEM) and probable forms of LAB were observed (Figure 1).

<table><caption>**Table 1** - Number of isolates from the grape and wine samples</caption><thead><tr><th>Lactic acid bacteria</th><th>Grape</th><th>Wine</th><th>Total</th></tr></thead><tbody><tr><td><em>Oenococcus oeni</em></td><td>111</td><td>1318</td><td>1429</td></tr><tr><td>Bacilli and other cocci</td><td>413</td><td>62</td><td>475</td></tr><tr><td>Total of isolates</td><td>524</td><td>1380</td><td>1904</td></tr></tbody></table>

*O. oeni* identified isolates were typified using VNTR, obtaining 180 different profiles. Bacilli and other cocci were typified with rep-PCR with the GTG5 technique, obtaining a wide variety of profiles. The 50 predominant strains, from *O. oeni*, bacilli and other cocci, were characterized by their efficiency of L-malic degradation and their fermentative speed in simulated wine fermentation (Table 2).

<figure><img src="image_169.png" /><figcaption>**Fig. 1** - Bacilli, probably LAB, in a sample of grape skin (SEM, *Servei de Recursos Científics i Tècnics*, URV).</figcaption></figure>
<table><caption>**Table 2** - Efficiency of L-malic degradation (2 g/L) in wine-like medium (WLM) and fermentative speed from the predominant strains from different species. Ratio strains grouped by their efficiency are shown. Assays were done in duplicate.</caption><thead><tr><th rowspan="2">Species</th><th rowspan="2">Strain ratio (%)</th><th colspan="2">% L-malic consumed</th><th colspan="2">MLF speed (mg/L/h)</th></tr><tr><th>12 g/L Ethanol</th><th>14 g/L Ethanol</th><th>12 g/L Ethanol</th><th>14 g/L Ethanol</th></tr></thead><tbody><tr><td rowspan="4">O. oeni</td><td>64</td><td>100</td><td>100</td><td>25-40</td><td>20-28</td></tr><tr><td>12</td><td>100</td><td>80-100</td><td>13-17</td><td>3-6</td></tr><tr><td>4</td><td>100</td><td>&lt;50</td><td>18</td><td>2</td></tr><tr><td>4</td><td>80-100</td><td>80-100</td><td>4</td><td>4</td></tr><tr><td>F. tropaeoli</td><td>4</td><td>100</td><td>66</td><td>15</td><td>3</td></tr><tr><td>L. mali</td><td>4</td><td>100</td><td>73</td><td>15</td><td>3</td></tr><tr><td>L. lindneri</td><td>4</td><td>100</td><td>51</td><td>15</td><td>2</td></tr><tr><td>L. plantarum</td><td>4</td><td>36</td><td>33</td><td>4</td><td>1</td></tr></tbody></table>

Based on resistance tests and fermentation speed results the best strains were selected. None of the biogenic amine genes analysed were found in the studied strains, which contribute positively to their use as MLF starter strains. On the other hand, the SNP technique results suggest that the majority of the strains are grouped into a unique phylogenetic cluster.

The inoculum prepared with the three best strains successfully performed FML in the two Grenache (1 week) and two Carignan wines (1 month). The imposition from two of the inoculated strains in those MLF was confirmed.

## Conclusion

Different species of *Lactobacillus* from grape and beginning of MLF were isolated, and an important number of *Oenococcus oeni* strains was isolated from MLF, making them an important source of study for the selection of native starters from Priorat.

The isolation of *O. oeni* directly from grape has been achieved in this project, which has not been reported before. This has been possible thanks to the exhaustive sampling and especially the use of richer media and longer incubation times.

Moreover, MLF has been carried out successfully in Grenache and Carignan wines in cellar, using the three best strains found as inoculum.

## Acknowledgements

We thank the European project funding Wildwine FP7-SME-2012-315065, involving the Council of the DOQ Priorat and Ferrer-Bobet winery. We also acknowledge the assistance of the other wineries and vineyards from which samples were obtained. Judit Franquès is a recipient of a predoctoral fellowship from the URV.

## Abbreviations

AF, alcoholic fermentation; *F., Fructobacillus*; MLF, malolactic fermentation; LAB, lactic acid bacteria; *L., Lactobacillus*; *O., Oenococcus*; SEM, scanning electronic microscope; SNP, simple nucleotide polymorphism; VNTR, variable number tandem repeat; WLM, wine-like medium.
References

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2. Zapparoli G, Torriani S, Pesente P et al (1998) Design and evaluation of malolactic enzyme gene targeted primers for rapid identification and detection of *Oenococcus oeni* in wine. *Lett Appl Microbiol* **27**: 243-246.

3. Claisse O, Lonvaud-Funel A (2014) Multiplex variable number of tandem repeats for *Oenococcus oeni* and applications. *Food Microbiol* **38**: 80-86.

4. Rodas AM, Ferrer S, Pardo I (2003) 16S-ARDRA, a tool for identification of lactic acid bacteria isolated from grape must and wine. *Syst Appl Microbiol* **26**: 412-422.

5. Ruiz-Barba JL, Maldonado-Barragán A, Jiménez-Díaz R (2005) Small-scale total DNA extraction from bacteria and yeast for PCR applications. *Anal Biochem* **347**: 333-335.

6. Hurtado A, Reguant C, Bordons A et al (2010) Evaluation of a single and combined inoculation of a *Lactobacillus pentosus* starter for processing cv. Arbequina natural green olives. *Food Microbiol* **27**: 731-740.

7. Bordas M, Araque I, Alegret JO et al (2013) Isolation, selection, and characterization of highly ethanol-tolerant strains of *Oenococcus oeni* from south Catalonia. *Int Microbiol* **16**: 113-123.

8. El Khoury M (2014) Étude de la diversité des souches d'Oenococcus oeni responsables de la fermentation malolactique des vins dans différentes régions vitivinicoles. *Thèse de doctorat*, Université de Bordeaux.

9. Nannelli F, Claisse O, Gindreau E et al (2008) Determination of lactic acid bacteria producing biogenic amines in wine by quantitative PCR methods. *Lett Appl Microbiol* **47**: 594-599.
# Selection of indigenous non-*Saccharomyces* yeast strains through fermentive potential via GC analysis

J. CALZADA<fnref n="1" />*, I. BASELGA<fnref n="1" />, D.J. GARCÍA MARTÍNEZ<fnref n="1" />, E.P. LAGO<fnref n="1" />, R. FRANCISCO-ÁLVAREZ<fnref n="1" />, L. RODRÍGUEZ<fnref n="2" />, A.M. PRIETO<fnref n="2" />, C. SANTOS<fnref n="1" />

Non-*Saccharomyces* yeasts – aromatic profile – indigenous yeast – gas chromatography

## Introduction

Interest in customized and personal vinifications has increased lately in order to differentiate high quality wines from standard ones [1]. In addition, organic wine demand has increased due to consumer concern about environmental issues [2]. Although employing commercial yeasts allows a better control on winemaking processes, it finally gives way to the homogenization of quality in the market of wine products. Nowadays, many wineries are making efforts in the development of natural and ecologic wines whose differentiated characteristics could confer market advantages [1-3]. Among different yeast genera, particularly, non-*Saccharomyces* ones are known to start fermentation and contribute metabolites responsible for particular organoleptic properties of wines [4-11].

The Research Team of Microbial Biotechnology at Francisco de Vitoria University has analyzed more than 600 grape and must samples obtained from different wineries belonging to Ribera del Duero PDO (Spain). Thus, 25 yeast strains belonging to 8 genera have been isolated and identified using AFLP molecular markers, based on the methodology previously developed by the research team. Among these, a reduced number of isolated yeast strains was selected [12, 13]. This assortment was carried out following oenological advice, sensory analysis and evaluation of microfermentations via GC-FID [14].

The aim of this work is firstly to study the contributions of these yeasts on the production of different alcohols and esters during must fermentation, and afterwards, to select aromatic effects (both positive and negative) due to differences in starting yeast population and its evolution along fermentation. Hence, our goal is to analyze the synergies or mitigated responses which could result in wine complex aromatic profiles caused by the participations of these yeasts along must fermentation.

## Materials and methods

### Chemicals

Dichloromethane was supplied by Sigma. Acetaldehyde, ethyl acetate, 1-propanol, isobutyl acetate, ethyl butyrate, isoamyl acetate, ethyl hexanoate and ethyl propionate were purchased from Dr. Ehrenstorfer GmbH. Concentrated red wine must was supplied by Mostos Españoles S.A. Glucose, peptone and yeast extract were obtained from Conda. Deionized water was used to prepare all media and stock solutions.

### Yeast strains

*Saccharomyces cerevisiae* U3c1, *Saccharomyces uvarum* M1.6.4f, *Hanseniaspora uvarum* M1.4.2fl, *Kluyveromyces thermotolerans* U3.1, *Metschnikowia pulcherrima* M1.4.5f and *Torulaspora delbrueckii* M3.7.8 were the yeast strains used in this work.

### Microfermentation assays

Experiments were carried out using 50 mL of rehydrated must (15°Bx) coming from *Tempranillo* grape variety. 10⁷ cells.mL⁻<fnref n="1" /> was employed as total initial biomass concentration. Pre-inoculums in liquid YPD

<footnote n="1">¹Department of Biotechnology, School of Biosciences, Francisco de Vitoria University. Ctra. Pozuelo-
Majadahonda Km 1,800, 28223 Pozuelo de Alarcón, Madrid, Spain</footnote>

<footnote n="2">²Gas Chromatography Service of Biological Research Center (CIB-CSIC), Ramiro de Maeztu 9,
28040 Madrid, Spain</footnote>
medium (20 g.L⁻¹ glucose, 20 g.L⁻¹ peptone and 10 g.L⁻¹ yeast extract) were prepared from frozen stocks and grown over night at 22°C. Samples were collected after 17 days of fermentation [15].

### Analysis of volatile compounds

Gas chromatography coupled with flame ionization detector (GC-FID) was employed with samples directly collected from fermentation broth. Acetaldehyde, ethyl acetate, isoamyl acetate, ethyl butyrate, ethyl hexanoate and ethyl propionate were determined by direct injection into a 7980A gas chromatographer (Agilent Technologies Inc., Palo Alto, CA) equipped with a flame ionization detector. These analyses were performed at the Gas Chromatography Service of Biological Research Center (CIB-CSIC) [16, 17].

## Results and discussion

In previous works [15], different yeast strains isolated from wineries belonging to the Ribera del Duero PDO (Spain) were employed individually in controlled must fermentations. The aromatic compounds of the fermented must were analyzed by GC-FID and the results obtained allowed the selection of specific strains showing the desired characteristics. In this work, different combinations of the yeasts previously chosen have been used in fermentation experiments carried out to analyze the aromatic profiles of the resulting wine (Table 1).

<table><caption>**Table 1** - Experimental design. Combination of indigenous yeast strains used as starters to carry out the alcoholic fermentation of grape must.</caption><thead><tr><th>Ferm. No.</th><th>S. cerevisiae U3c1, S. uvarum M1.6.4f, K. thermotolerans U3.1</th><th>M. pulcherrima M1.4.5f</th><th>H. uvarum M1.4.2fl</th><th>T. delbrueckii M3.7.8</th></tr></thead><tbody><tr><td>1</td><td rowspan="6">4·10<sup>6</sup> cells·mL<sup>-1</sup></td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td><td>-</td><td>-</td></tr><tr><td>2</td><td>-</td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td><td>-</td></tr><tr><td>3</td><td>-</td><td>-</td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td></tr><tr><td>4</td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td><td>-</td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td></tr><tr><td>5</td><td>-</td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td></tr><tr><td>6</td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td><td>2·10<sup>6</sup> cells·mL<sup>-1</sup></td><td>-</td></tr></tbody></table>

The results showing the relative production of each previously mentioned compound at the end of each fermentation is presented in Figure 1. The relative production is calculated as the ratio between the chromatographic signal of a particular compound and the maximum signal obtained.

<figure><img src="image_170.png" /><figcaption>**Fig. 1** - Relative production of volatile compounds. (a) Ethyl acetate. (b) Isobutyl acetate. (c) Isoamyl acetate. (d) Ethyl propionate. (e) Ethyl hexanoate. (f) Acetaldehyde.</figcaption></figure>
The results show that *T. delbrueckii* produce the highest levels of ethyl acetate, isobutyl acetate, ethyl propionate and acetaldehyde, when the selected strains of *S. uvarum*, *S. cerevisiae* and *K. thermotolerans* are simultaneous employed, even though it causes a big accumulation of undesirable ethyl acetate. Besides, under these conditions, *H. uvarum* causes the biggest production of ethyl hexanoate, while *M. pulcherrima* achieves the most intense isoamyl acetate build up.

Moreover, the simultaneous presence of *M. pulcherrima* and *H. uvarum* results in the best volatile profile, allowing the richest production of favorable aromatic compounds and the lowest quantity of ethyl acetate. However, despite the fact that the other combination caused a significant build up of ethyl acetate, it resulted in an interesting bouquet, according to the oenologist advice.

## Conclusion

*Hanseniaspora uvarum* M1.4.2.fl, *Kluyveromyces thermotolerans* U3.1, *Torulaspora delbrueckii* M3.7.8 and *Metschnikowia pulcherrima* M1.4.1 were selected as the best isolated yeast strains due to their particular contribution to the aromatic profile found and evaluated through oenologist advice and GC-FID analysis. For instance, *Kluyveromyces thermotolerans* U3.1 was found to produce low amounts of ethyl acetate and isoamyl acetate, but higher quantities of ethyl butyrate. These results are consistent with the result of previous work carried out using and comparing different strains of *Torulaspora delbrueckii* [18]. Other authors remark this positive effect of non-*Saccharomyces* yeasts in combination with *Saccharomyces* ones. However, sequential inoculation is preferred instead, which could be an improvement in our methodology [19]. Finally, several complex combinations of yeasts were proposed as starter cultures in order to carry out commercially outstanding fermentations. In conclusion, four of the tested complex combinations of yeasts (No. 3, 4, 5 and 6) were chosen for must fermentation in winery scale fermentation, since the results were congruent with the winemaker interests.

## Acknowledgements

This work was supported by Francisco de Vitoria University Program for Research Funding. The grant awarded to I. Baselga and D.J. García Martínez by the Foundation Universidad Francisco de Vitoria is gratefully recognized. The authors also want to thank the Gas Chromatography Service of Biological Research Center (CIB-CSIC) for help and support during this work.

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# Sequential inoculation with native *Saccharomyces cerevisiae* and non-*Saccharomyces* strains for the production of Grenache and Carignan red wines

A. MAS*, B. PADILLA, À. FERRERES, R. PASTOR, B. ESTEVE-ZARZOSO, G. BELTRAN

University Rovira i Virgili, Facultat d'Enologia, Marcel·lí Domingo 1, 43007 Tarragona, Spain

Wine yeast – Priorat – terroir – alcoholic fermentation

## Introduction

Yeast biodiversity plays a critical role in the quality of wines. In grapes, mainly non-*Saccharomyces* species are found; however, these species are gradually replaced by *Saccharomyces cerevisiae* throughout the alcoholic fermentation [1]. In order to control this process, single *S. cerevisiae* starter cultures have been used during the last decades in wineries, since non-*Saccharomyces* yeast species were associated with a negative influence and wine spoilage. Recently, several non-*Saccharomyces* species have been related with the production of favourable features such as the release of interesting volatile compounds [2]. Consequently, mixed starter cultures containing both *S. cerevisiae* and non-*Saccharomyces* species have been proposed as an approach to improve the chemical and aromatic characteristics of the final wine [3].

On the other hand, due to the increasingly competitive global market, there is a trend in which local wine producers attempt to link their products with geographical identity. Additionally, the use of the same commercial starter cultures all over the world generates uniformity in the resulting wines. To avoid this, several studies support the incorporation of locally selected microorganisms, particularly yeasts, to obtain wines exhibiting local identity as well as complexity [4].

The aim of this study is to validate at industrial level the use of a mixture of native Priorat yeast cells, composed by different non-*Saccharomyces* species as well as several *S. cerevisiae* strains. The multistarter culture was composed mimicking Priorat natural musts, and designed to sequentially inoculate Red Grenache and Carignan musts from this region.

## Materials and methods

### *Yeast strains*

In this work, different yeast strains previously isolated from QDO Priorat and selected for their oenological potential were included as starters for wine production. Sequential inoculations were performed under industrial conditions (1050 L) with Red Grenache (GR) and Carignan (CA) musts in a cellar belonging to QDO Priorat. At time 0, non-*Saccharomyces* species including *Hanseniaspora uvarum* (29.3%), *Candida zemplinina* (14.6%), *Torulaspora delbrueckii* (2.4%) and *Metschnikowia pulcherrima* (2.4%) were inoculated into the must. After 24 h, a mixture of three native strains of *S. cerevisiae* SFB1 (17.1%), SFB2 (17.1%) and SL1 (17.1%) were also inoculated. Proportions used for each non-*Saccharomyces* species reflected the biodiversity naturally found in Priorat musts. CA tank was inoculated with a total yeast population of 4·10⁶ cells/mL while GR inoculum consisted of 4·10⁵ cells/mL, since this must contained lower levels of microbial load.

### *Fermentation kinetics and yeast identification*

Fermentation kinetics and yeast population dynamics of the different species were monitored. Yeast counts were carried out in duplicate on solid YPD and agar-Lysine (LYS) media, after serial decimal dilution of the samples. Plates were incubated at 28°C during 3 days. For yeast isolation and identification, samples were taken at 4 different time points: 24 h after the inoculation of non-*Saccharomyces* (24h), 24 h after de inoculation of *S. cerevisiae* (24h Sc), and at mid (M) and final (F) fermentation. Approximately 25 colonies
from each medium and each sampling point were picked up randomly. Yeast identification was performed by RFLP-PCR of the 5.8SITS region [5] and sequencing D1/D2 of the 26S rRNA gene [6]. Additionally, typing of isolates of *H. uvarum* by RAPD-M13 [7] and *S. cerevisiae* through interdelta analysis [8] was also carried out.

## Results and discussion

In both grape must varieties, the alcoholic fermentation finished within 8 days. The delay in starting the alcoholic fermentation was due to the previous cold maceration of the must, which provoked that the low temperatures delayed this start until higher temperatures were achieved. Total yeast (YPD) and non-*Saccharomyces* (LYS) counts along GR and CA industrial fermentations are represented in Figure 1. Values at time 0 correspond to must samples before inoculation. Initial yeast population of GR and CA started at 2·10³ and 4·10⁴ cfu/mL, respectively. This very low yeast population allowed reducing the yeast inoculum to 10⁵ cells/mL in GR. In both cases, the typical growth kinetics is observed and total cell viability remains quite high until the end of the fermentation. Contrarily, there is no growth of non-*Saccharomyces* species at this end point, while counts at the mid fermentation were between 10⁵ and 10⁶ cfu/mL.

<figure><img src="image_171.png" /><figcaption>Fig. 1 - Must density and yeast growth along Grenache (GR) and Carignan (CA) fermentations.</figcaption></figure>

In the case of GR, the initial must presented high biodiversity concerning yeast composition, while in CA this diversity was much poorer than expected, although similar to other Priorat musts. In GR, isolates from nine different yeast species were found, the most abundant being *Rhodotorula mucilaginosa* (30%), *Debaryomyces hansenii* (21%) and *M. pulcherrima* (19%). Additionally, other species such as *Wickerhamomyces anomalus* and *Zygoascus hellenicus* were isolated on minor proportion. On the other hand, in the case of CA musts, only *H. uvarum*, *C. zemplinina* and *M. pulcherrima* were identified, the former being the most representative (74%).

As it can be seen from Figure 2, once the must was inoculated with non-*Saccharomyces* yeasts, the four species aforementioned were recovered from the fermenting musts, except *T. delbrueckii* which was not found in the CA fermentation. After 24 hours of fermentation, the three *S. cerevisiae* strains were also added, and gradually dominated the fermentative process, although *H. uvarum* was still found after the mid fermentation point (day 5) in both cases.
<figure><img src="image_172.png" /><figcaption>**Fig. 2** - Yeast population dynamics along Grenache and Carignan fermentations (YPD).</figcaption></figure>

Additionally, isolates from *S. cerevisiae* and *H. uvarum* species were typified in order to check the inocula imposition. As it is shown in Table 1, all *S. cerevisiae* colonies isolated at the end of the fermentation, as well as the *H. uvarum* colonies isolated at the last point recovered, showed identical electrophoretic profiles than those of the starter culture. These results indicate that the multistarter culture succeed in conducting the alcoholic fermentation in both wine grape varieties under industrial conditions.

<table><caption>**Table 1** - Percentage of strains exhibiting the same electrophoretic pattern than the inocula</caption><thead><tr><th rowspan="2">Fermentation</th><th colspan="4">S. cerevisiae</th><th>H. uvarum</th></tr><tr><th>SFB1</th><th>SFB2</th><th>SL1</th><th>Total</th><th></th></tr></thead><tbody><tr><th>Grenache</th><td>8</td><td>44</td><td>48</td><td>100</td><td>100</td></tr><tr><th>Carignan</th><td>14</td><td>18</td><td>68</td><td>100</td><td>100</td></tr></tbody></table>

The use of locally selected inocula in the alcoholic fermentation proceeded without any significant set back and followed the same kinetics than the control wines in the same cellar (inoculated with a single commercial strain of *S. cerevisiae*). It is especially relevant that the inoculated non-*Saccharomyces* species were able to survive and be recovered in plates until mid fermentation, which is not usual, due to the fast imposition of *S. cerevisiae* in these fermentations. Thus, we can presume that the characteristics due to the presence of those non-*Saccharomyces* yeast will be present in the final wines.

## Conclusion

This study highlights the applicability of locally selected non-*Saccharomyces* as well as *S. cerevisiae* strains in the production of QDO Priorat wines. Additionally, the use of the proposed multistarter culture reproduces the typical microbial footprint of wines from this geographical area. Thus, the incorporation of this yeast consortium to industrial alcoholic fermentations in QDO Priorat could enhance the *Terroir* characteristics of the resulting wines, but with controlled fermentations from the microbiological and kinetics point of view.

## Acknowledgements

This work was supported by the Wildwine EU Project (grant agreement 315065). Authors would like to thank Ferrer Bobet winery for their assistance in the industrial work.
Abbreviations

QDO, qualified designation of origin; RAPD, random amplified polymorphic DNA; RFLP, restriction
fragment length polymorphism.

References

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3. Belda I, Navascues E, Marquina D et al (2015) Dynamic analysis of physiological properties of *Torulaspora delbrueckii* in wine fermentations and its incidence on wine quality. *Appl Microbiol Biotechnol* **99**: 1911-1922.

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6. Kurtzman CP, Robnett CJ (1998) Identification and phylogeny of ascomycetous yeasts from analysis of nuclear large subunit (26S) ribosomal DNA partial sequences. *Antonie van Leeuwenhoek* **73**: 331-371.

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# The impact of oxygen on the behaviour and interaction of *S. cerevisiae* and *L. thermotolerans*

B. DIVOL¹*, N.A. LUYT¹, S. BEAUFORT², P. TAILLANDIER², F.F. BAUER¹

¹Institute for Wine Biotechnology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa

²INP-ENSIACET, 4 allée Emile Monso, BP 84234, 31432 Toulouse cedex 4, France

*L. thermotolerans* – *S. cerevisiae* – dissolved oxygen – mixed culture fermentations

## Introduction

In recent years, there has been an increasing demand for styles of wine which differ from those generated using the most common method, i.e. the use of *Saccharomyces cerevisiae* starter cultures and addition of SO₂ to eliminate indigenous yeasts [1, 2]. Furthermore, increased interest in producing wines with lower ethanol content and a more complex aromatic profile [3] has led to the re-evaluation of the role that non-*Saccharomyces* yeasts play during winemaking and their potential use in multistarter fermentations together with *S. cerevisiae* [4]. Indeed, several studies have highlighted the potential positive contributions that non-*Saccharomyces* yeasts may provide [5-10]. Although these studies are promising, some aspects remain unclear. In particular, the role that dissolved oxygen can play on the behaviour of and interaction between yeast species remains largely unknown. In this context, it is commonly believed that ethanol plays the most important role in the decline of non-*Saccharomyces* yeasts during fermentation, but recent studies have indicated that dissolved oxygen could prolong their survival, thereby enhancing their global impact on wine [11, 12]. Indeed, most wine-related non-*Saccharomyces* yeasts have higher oxygen requirements than *S. cerevisiae* and oxygen availability may increase their ability to compete with *S. cerevisiae*.

*Lachancea thermotolerans* is a good candidate for mixed culture fermentations with *S. cerevisiae* since multiple studies have shown its positive contributions [8, 13-14] and one strain is already commercialised. This study aimed at broadening our current knowledge regarding the role that dissolved oxygen may play on the behaviour and interaction of *L. thermotolerans* and *S. cerevisiae* in mixed culture fermentations.

## Materials and methods

### Strains and culture conditions

*L. thermotolerans* strain IWBT Y1240 from the yeast culture collection of the Institute for Wine Biotechnology at Stellenbosch University and *S. cerevisiae* strain Lalvin EC1118 (Lallemand Inc.) were used in this study. The strains were grown on YPD agar (20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract, 20 g/L agar) and maintained at 4°C. Viable cell counts were calculated with the use of Wallerstein (WL) Nutrient agar at 30°C.

### Fermentations

Each fermentation was inoculated to obtain a total cell count of 2 x 10⁶ cells/mL: for pure cultures 2 x 10⁶ cells/mL of either *L. thermotolerans* or *S. cerevisiae* strains and for mixed cultures, a co-inoculation strategy was used (1 x 10⁶ cells/mL of each strain). Fermentations were carried out with the use of a synthetic grape must medium (total volume of 1 L) in the BioFlo® 110 reactor manufactured by New Brunswick Scientific. All fermentations were carried out at 30°C, 250 rpm and headspace saturation with N₂ at a flow rate of 0.5 vvm (volume of air per volume of medium per minute). The latter parameter was used in an attempt to compare results with similar bioreactor fermentations. Twice a day (at 8 am and 4 pm), immediately before sampling, air was sparged through the medium for 30 min at a flow rate of 0.5 vvm. This oxygenation strategy was considered as the control. Further fermentations were conducted to test the effect of oxygen on the fermentation dynamics by sparging air for 30 min once a day at 8 am and once at the beginning of fermentation at a flow rate of 0.5 vvm. Samples of 20 mL were taken twice a day. All fermentations were carried out in duplicate.

<footnote>*Corresponding author: divol@sun.ac.za</footnote>
## Chemical analyses

Samples were used to monitor optical density at 600 nm (1mm pathway) and the rest of the sample was spun down at 7,500 rpm at 4°C. The supernatant was stored at -4°C. Dry weight was determined on the last day of fermentation by spinning down 2 mL samples at 7,500 rpm at 4°C, washing the pellet with 1 mL of distilled water, repeating the centrifugation step and drying the pellet at 100°C for 48 hours. Glycerol, acetic acid, glucose and fructose concentrations were determined with the use of enzymatic assays (Arena 20XT Photometric Analyzer obtained from Thermo Electron Oy, Finland).

## Results and discussion

A series of fermentations testing different oxygenation strategies were performed to investigate what the effect would be on the growth of *L. thermotolerans* and *S. cerevisiae* pure and mixed cultures. Air was supplied either twice a day (control), once a day or once at the beginning of fermentation. Two repeats were performed.

For all types of fermentations, *S. cerevisiae* pure cultures achieved the highest biomass measured as OD<sub>600nm</sub>, while pure cultures of *L. thermotolerans* produced the lowest, and mixed culture fermentations intermediate amounts (Figure 1). A reduction in the frequency at which oxygen was supplied led to a reduction in biomass formation for all fermentations, but this effect was somewhat less pronounced in the mixed cultures. The latter fermentations (where max OD values ranged between 10 and 15) appeared to be less affected, especially when compared to *S. cerevisiae* pure culture (where max OD ranged between 10 and 22).

<figure><img src="image_173.png" /></figure>

**Fig. 1** - Fermentation growth represented in OD<sub>600nm</sub> for *L. thermotolerans* [A] and *S. cerevisiae* [B] in pure cultures and mixed cultures [C]. Different oxygen treatments represented as: ■- (Oxygenation twice a day); ◉ (Oxygenation once a day); ▲- (Oxygenation once at beginning of fermentation).

For each fermentation, dry weight was also calculated at the end of fermentation (Figure 2). Once again, it was observed that a decrease in oxygen led to a decrease in biomass production and that *S. cerevisiae* and *L. thermotolerans* pure cultures accumulated the highest and lowest amount of biomass respectively, while the mixed culture achieved intermediate amounts. The results correlate well with those shown in Figure 1, but here a reduced effect of biomass reduction was also observed in the mixed cultures.

The reduction in the frequency of oxygen pulses also affected fermentation duration (Table 1). Here, along with Figure 1, it can be observed that a reduced oxygen availability led to an increase in the fermentation duration (i.e. time it took to ferment to dryness). This effect had the greatest impact on *L. thermotolerans* pure cultures (which all took the longest to ferment to dryness). This is somewhat unsurprising, since it has been found that *L. thermotolerans* has higher oxygen requirements compared to *S. cerevisiae* [7]. Furthermore, reduced oxygen availability had no impact on the fermentation duration of the mixed cultures.

Here, all fermentations were completed within 93 h, regardless of oxygen supply. *L. thermotolerans* died off earlier in the fermentation, likely due to 1) reduced oxygen availability and 2) the presence of a high concentration of *S. cerevisiae* cells (which has been found to lead directly to the cellular death of
*L. thermotolerans* [3]). Together, these two phenomena could have led to the death of *L. thermotolerans* and, consequently, provided an extra source of nutrients which aided *S. cerevisiae*'s ability to survive until the end of fermentation even as oxygen supply was reduced.

<figure><img src="image_174.png" /><figcaption>**Fig. 2** - Dry weight (g/L) measured on the last day of fermentation for *L. thermotolerans* (red bars) and *S. cerevisiae* (blue bars) in pure and mixed cultures (purple bars). Darker, lighter and lightest shades of red, blue and purple indicate oxygenation of twice, once a day and once at the beginning of fermentation, respectively.</figcaption></figure>

<table><caption>**Table 1** - Total sugars, glycerol and acetic acid present at the end of fermentation of *L. thermotolerans* and *S. cerevisiae* pure cultures and mixed cultures for three different oxygen treatments (oxygenation twice and once a day and once at the beginning of fermentation)</caption><thead><tr><th>Fermentation</th><th>Oxygenation</th><th>Time Point (h)</th><th>Total Sugars (g/L)</th><th>Glycerol (g/L)</th><th>Acetic Acid (g/L)</th></tr></thead><tbody><tr><td rowspan="3">L. thermotolerans Pure</td><td>2/day</td><td>144</td><td>4.4 (±0.0)</td><td>8.7 (±0.4)</td><td>0.65 (±0.1)</td></tr><tr><td>1/day</td><td>168</td><td>5.8 (±0.7)</td><td>10.6 (±1)</td><td>0.69 (±0.0)</td></tr><tr><td>Once @ beg</td><td>192</td><td>0.1 (±0.0)</td><td>10.2 (±1.2)</td><td>0.71 (±0.0)</td></tr><tr><td rowspan="3">S. cerevisiae Pure</td><td>2/day</td><td>69</td><td>0.4 (±0.1)</td><td>10.3 (±0.5)</td><td>0.91 (±0.0)</td></tr><tr><td>1/day</td><td>93</td><td>0.5 (±0.5)</td><td>11.1 (±2.4)</td><td>0.97 (±0.2)</td></tr><tr><td>Once @ beg</td><td>144</td><td>0.7 (±0.8)</td><td>9.2 (±1.8)</td><td>0.94 (±0.0)</td></tr><tr><td rowspan="3">Mixed</td><td>2/day</td><td>93</td><td>0.2 (±0.2)</td><td>9.6 (±0.4)</td><td>1.14 (±0.1)</td></tr><tr><td>1/day</td><td>93</td><td>0.2 (±0.3)</td><td>9.2 (±0.3)</td><td>0.87 (±0.1)</td></tr><tr><td>Once @ beg</td><td>93</td><td>0.5 (±0.6)</td><td>8.7 (±0.4)</td><td>1.18 (±0.3)</td></tr></tbody></table>

Overall, there was a slight reduction in glycerol for mixed cultures and *S. cerevisiae* pure cultures when less oxygen was provided, while the opposite was observed for *L. thermotolerans* pure cultures (Table 1). A significant reduction in glycerol production was previously observed for *T. delbrueckii* when less oxygen was supplied [15] and this could be attributed to the fact that respiration was favoured to generate NAD⁺. However, these authors supplied oxygen throughout the fermentations, while our study made use of oxygen pulses. Our approach (oxygen pulses) is, however, a closer representation to oxygen variation in traditional wine making practices (such as pump-overs). This method also never resulted in glycerol levels lower than the amount needed to detect an increase in wine sweetness [16]. No significant changes in acetic acid production were observed when less oxygen was provided, in agreement with previously published data [15]. It must however be stressed that some variation in the analytical data was detected and this was most likely due to the method of oxygen supply. Fermentation conditions will require further optimisation to make comparison between different data sets more feasible.

## Conclusion

Yeast interactions and the dissolved oxygen concentration have a clear impact on the fermentation dynamics, but the effect is less clear on the production of secondary compounds. Our experimental layout has not been tested before and in fact, few studies have monitored the effect of the dissolved oxygen
concentration during the wine fermentation process and how it may impact on the behaviour of the yeast and the survival of non-*Saccharomyces* yeasts in co-cultures with *S. cerevisiae*. For this reason, future work should focus on the optimisation of this method to reduce variation in data. Such efforts should find ways of effectively monitoring, measuring and controlling the application of oxygen into the system and concede to the fact that the smallest change in oxygen concentration may impact greatly on yeast behaviour.

## Acknowledgements

The authors thank Winetech and THRIP for funding this research.

## Abbreviations

SCB, single compartment bioreactor; O2 2/day, oxygen provided twice a day; O2 1/day, oxygen provided once a day; O2 once@beg, oxygen provided once at the beginning of fermentation; L.t., *Lachancea thermotolerans*; S.c., *Saccharomyces cerevisiae*.

## References

1. Fleet GH (2008) Wine yeasts for the future. *FEMS Yeast Res* 8: 979-995.

2. Moreno-Arribas MV, Polo MC (2005) Winemaking biochemistry and microbiology: current knowledge and future trends. *Crit Rev Food Sci Nutr* 45: 265-286.

3. Ciani M, Comitini F (2011) Non-*Saccharomyces* wine yeasts have a promising role in biotechnological approaches to winemaking. *Ann Microbiol* 61: 25-32.

4. Ciani M, Comitini F, Mannazzu I et al (2010) Controlled mixed culture fermentation: a new perspective on the use of non-*Saccharomyces* yeasts in winemaking. *FEMS Yeast Res* 10: 123-133.

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9. Medina K, Boido E, Fariña L et al (2013) Increased flavour diversity of Chardonnay wines by spontaneous fermentation and co-fermentation with *Hanseniaspora vineae*. *Food Chem* 141: 2513-2521.

10. Moreira N, Mendes F, Guedes de Pinho P et al (2008) Heavy sulphur compounds, higher alcohols and esters production profile of *Hanseniaspora uvarum* and *Hanseniaspora guilliermondii* grown as pure and mixed cultures in grape must. *Int J Food Microbiol* 124: 231-238.

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# Diversity and involvement of the cellar yeast in the fermentation of grape must: example of *Hanseniaspora* genus

C. GRANGETEAU¹*, D. GERHARDS², S. ROUSSEAUX¹, C. VON WALLBRUNN², H. ALEXANDRE¹, M. GUILLOUX-BENATIER¹

¹UMR PAM, Equipe VALMiS, AgroSup Dijon - Université de Bourgogne, IUVV, rue Claude Ladrey, BP 27877, 21000 Dijon, France

²Institut für Mikrobiologie und Biochemie Zentrum, Geisenheim, Germany

*Hanseniaspora* – diversity – cellar – grape berries

## Introduction

Many studies investigated the origin and diversity of *Saccharomyces cerevisiae* strains during alcoholic fermentation (AF). It has been demonstrated that a large number of *S. cerevisiae* strains participates in AF. Some strains resident of cellar can also be found in must and become dominant during fermentation at the expense of strains present on grape berries [1].

However, *S. cerevisiae* is not the only species present during AF. In particular, yeasts of the genera *Candida*, *Hanseniaspora* and *Metschnikowia* are implicated during early days of AF [2-4]. Moreover, many non-*Saccharomyces* yeast species have also been isolated in the cellar environment [4-6]. This raises the question of the origin of non-*Saccharomyces* yeasts found in grape musts during fermentation.

This study focuses on the genus *Hanseniaspora*, known to participate in the early stages of AF and described as being present both in the winery environment and in grape must [6]. The objective was to characterize strains of the genus *Hanseniaspora* present in fermenting must using FT-IR spectroscopy to determine their origin (grape and/or winery).

## Materials and methods

Yeast populations isolated from must of Chardonnay, during a spontaneous fermentation (at time T0 to T6 days of AF), were compared to those isolated on grape berries and in the cellar environment (air, soil, wine making equipment) before the arrival of the harvest (T-1). FT-IR spectroscopy coupled with an HTS-XT unit (Bruker, Ettlingen, Germany) was applied to identify yeast isolates. This spectroscopy method is based on measurement and calculation of a light absorption spectrum. This specific absorption is attributed to several cell components used for identification [7]. By comparison with reference spectra obtained with an appropriate powerful database (3000 wine relevant strains belonging to 215 species), identification is possible at the genus level and/or at species level [7]. By hierarchical cluster analysis, a differentiation at the strain level has been possible for two species of genus *Hanseniaspora*, *H. guilliermondii* and *H. uvarum*.

## Results and discussion

The populations isolated on grape berries, in the winery environment and in the must were identified by FT-IR. A total of 1375 isolates were identified. The genus *Hanseniaspora* was isolated in must and in the cellar environment before the arrival of the harvest but not on berries (Table 1). This genus represents 27% of isolates in must. The proportion of the genus *Hanseniaspora* increases during the first days of fermentation up to 0.4% (v/v) ethanol (T1). At T2 (5.1% (v/v) ethanol), this genus was isolated in low proportion (Table 1). These observations confirm literature results describing this genus as unable to resist

<footnote>*Corresponding author: cedric.grangeteau@hotmail.fr</footnote>
high ethanol concentrations [3, 8-9]. The genus *Hanseniaspora* represents 35% of isolates in cellar environment and it is the only one detected on the floor, in the equipment and in the air before the arrival of the harvest.

Two species: *H. guilliermondii* (8 strains) and *H. uvarum* (19 strains) were identified and discriminated at the strain level. An important intraspecific diversity for the two species was observed in cellar environment (7 strains for the species *H. guilliermondii* and 10 strains for the species *H. uvarum*) and in must (4 strains for the species *H. guilliermondii* and 7 strains for the species *H. uvarum*) during AF (Table 1). As observed for the isolates, the number of strains decreases with the increase of ethanol content in grape musts. Thus, at T0 (0% (v/v) ethanol), 10 strains were detected, at T1 (0.4% (v/v) ethanol) 9 strains were present and at T2 (5.1% (v/v) ethanol), 4 strains have yet been found. No yeast belonging to the genus *Hanseniaspora* was isolated in the must above this ethanol content. The resistance to ethanol of yeasts of the *Hanseniaspora* genus appears to be strain dependent.

Among the 4 strains of *H. guilliermondii* isolated into grape must or during fermentation, 3 were previously present in the cellar environment. For the species *H. uvarum*, 5 of the 7 strains isolated during the fermentation come from the cellar environment. Thus, 58% of *Hanseniaspora* strains isolated from grape must correspond to strains present in the cellar before the arrival of the harvest.

<table><caption>**Table 1** - Distribution (number of isolates and percentage in parentheses) of *Hanseniaspora* strains in different environments</caption><thead><tr><th rowspan="2">Species</th><th rowspan="2">Strain</th><th rowspan="2">Berries</th><th rowspan="2">Material T-1</th><th rowspan="2">Soil T-1</th><th rowspan="2">Air T-1</th><th colspan="4">Must during AF</th></tr><tr><th>T0</th><th>T1</th><th>T2</th><th>T3</th><th>T6</th></tr></thead><tbody><tr><td rowspan="7">Hanseniaspora guilliermondii</td><td>A</td><td>-<sup>a</sup></td><td>-</td><td>2 (2%)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>B</td><td>-</td><td>-</td><td>3 (3%)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>C</td><td>-</td><td>-</td><td>22 (21%)</td><td>-</td><td>-</td><td>1 (3%)</td><td>-</td><td>-</td></tr><tr><td>D</td><td>-</td><td>-</td><td>30 (28%)</td><td>-</td><td>2 (8%)</td><td>4 (12%)</td><td>1 (25%)</td><td>-</td></tr><tr><td>E</td><td>-</td><td>1 (2%)</td><td>8 (7%)</td><td>-</td><td>1 (4%)</td><td>-</td><td>-</td><td>-</td></tr><tr><td>F</td><td>-</td><td>-</td><td>-</td><td>-</td><td>1 (4%)</td><td>-</td><td>-</td><td>-</td></tr><tr><td>G</td><td>-</td><td>2 (4%)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>H</td><td>-</td><td>-</td><td>1 (1%)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="10">Hanseniaspora uvarum</td><td>A'</td><td>-</td><td>-</td><td>-</td><td>1 (100%)</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>B'</td><td>-</td><td>-</td><td>-</td><td>-</td><td>8 (31%)</td><td>15 (44%)</td><td>-</td><td>-</td></tr><tr><td>C'</td><td>-</td><td>-</td><td>1 (1%)</td><td>-</td><td>2 (8%)</td><td>4 (12%)</td><td>1 (25%)</td><td>-</td></tr><tr><td>D'</td><td>-</td><td>-</td><td>2 (2%)</td><td>-</td><td>1 (4%)</td><td>5 (15%)</td><td>1 (25%)</td><td>-</td></tr><tr><td>E'</td><td>-</td><td>-</td><td>4 (4%)</td><td>-</td><td>4 (15%)</td><td>2 (6%)</td><td>-</td><td>-</td></tr><tr><td>F'</td><td>-</td><td>1 (2%)</td><td>3 (3%)</td><td>-</td><td>4 (15%)</td><td>1 (3%)</td><td>1 (25%)</td><td>-</td></tr><tr><td>G'</td><td>-</td><td>-</td><td>30 (28%)</td><td>-</td><td>1 (4%)</td><td>1 (3%)</td><td>-</td><td>-</td></tr><tr><td>H'</td><td>-</td><td>-</td><td>1 (1%)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>I'</td><td>-</td><td>1 (2%)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>J'</td><td>-</td><td>28 (61%)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>K'</td><td>-</td><td>13 (28%)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>L'</td><td>-</td><td>-</td><td>-</td><td>-</td><td>2 (8%)</td><td>1 (3%)</td><td>-</td><td>-</td></tr></tbody><tfoot><tr><td colspan="2">Number of isolates<sup>b</sup></td><td>140</td><td>245</td><td>286</td><td>4</td><td>99</td><td>100</td><td>99</td><td>100</td></tr></tfoot></table>

Conclusion

The results of this study show that a significant proportion of non-*Saccharomyces* is found in the cellar environment. A wide variety of strains for *H. guilliermondii* and *H. uvarum* species is present both in the cellar environment and in grape musts during alcoholic fermentation. This work confirms also that yeasts belonging to the genus *Hanseniaspora* are not resistant to high ethanol concentration and shows that this resistance is strain dependent. Finally, for the first time, the implantation of cellar yeasts in the grape must is demonstrated for a non-*Saccharomyces* genus.

<footnote><sup>a</sup>: non detected, <sup>b</sup>: number of isolates obtained for all genera yeast per sample</footnote>
Acknowledgements

This work was funded by the Regional Council of Burgundy and the Interprofessional Office of Burgundy wines.

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# Adaptive advantage of oligopeptide transporters acquired by horizontal transfer during wine fermentation

S. MARSIT, F. BIGEY, J.L. LEGRAS, S. DEQUIN, V. GALEOTE*

UMR 1083 Sciences pour l'Oenologie, INRA, 2 Place Viala, F-34060 Montpellier, France

HGT – adaptive advantage – oligopeptide transporters – wine fermentation

## Introduction

S. cerevisiae wine yeasts display outstanding fermentative capacities during wine fermentation, where they are challenged with stressing and changing environmental conditions. The fermentation of grape juice exposes yeast cells to harsh environmental conditions: high sugar amount, increasing alcohol concentration, acidity, presence of sulfites, anaerobiosis, and progressive depletion of essential nutrients, such as nitrogen, vitamins, and lipids. These conditions, as well as unwitting selection by man for optimal winemaking traits have generated hundreds of strains that are currently used in the wine industry, characterized by unique traits (fermentation performance, tolerance to alcohol, production of low levels of SO₂, acetate or H₂S).

Deciphering the mechanisms that participate to these evolutionary processes and identifying the variations contributing to properties of wine yeast remain a major challenge. Various mechanisms contribute to the adaptive evolution of industrial strain genomes, including interspecific hybridization and introgression [1, 2], chromosomal rearrangements and duplications [3-5] and gene copy number variations [6-8]. Horizontal gene transfer (HGT) can also significantly contribute to wine yeast phenotypes [9-11]. We previously showed that three large regions (A, B and C) acquired by HGT containing 39 genes (including 5 pseudogenes) from distant yeasts have shaped the genome of wine yeasts [9]. These new genes potentially encode important metabolic functions in winemaking (sugar and nitrogen metabolism), suggesting a role in adaptation to the wine environment. To assess the adaptive value of these HGT, we carried out a functional analysis of a mutant deleted for the region C (65 kb). Here, we report that this region confers a higher biomass formation, a better cell viability and capacity to complete the fermentation in grape must. These phenotypes are associated to the presence of *FOT1-2* genes encoding oligopeptide transporters. We showed that these genes improve the utilization of nitrogen sources present in grape juice by favoring the uptake of a broader range of oligopeptides. Fot-mediated peptide uptake also substantially affects the glutamate node and the NADPH/NADP⁺ balance, resulting in decreased acetate and increased ester formation. These findings demonstrate the adaptive value of *FOT* genes which improve nutrient resource utilization in a nitrogen-limited environment and pinpoint their metabolic effects that can positively affect the organoleptic balance of wines.

## Materials and methods

### *Yeast strains*

The S. cerevisiae strain 59A used in this study is a haploid derivative of the commercial wine yeast strain EC1118. The recombinant strains used, Δ*amn1* (*MAT*a ho *AMN1*::kanMX4) and Δ*fot1-2* (*MAT*a ho *FOT1-2*::loxP *AMN1*::kanMX4) are isogenic to 59A and have been described previously [11]. We used a Chardonnay grape must (Coursan, 2012) containing 230 g/L sugars (100 g/L glucose and 130 g/L fructose) and 240 mg/L assimilable nitrogen (free amino acids and ammonium).

### *Fermentation conditions*

Fermentation experiments were performed in fermenters with a working volume of 1.1 L and inoculated at a density of 10⁶ cells/mL. Fermentations were performed at 24 °C with continuous magnetic stirring (150rpm). Population size was determined by counting yeast cells with an electronic particle counter
(Multisizer 3 coulter counter; Beckman Coulter, CA). Cell viability and GFP activity were determined by flow cytometry using a C6 cytometer (Accuri, BD Biosciences, Piscataway). Cell viability was analyzed by adding propidium iodide (PI) (Calbiochem, CA) to cell suspensions (5 µL of 0.1 mg/mL solution).

### *Grape must analysis*

Dry weight was determined gravimetrically by filtering 10 mL of sample through a Millipore filter (pore size, 0.45µm) and drying the sample for 24 h at 100 °C. Extracellular glucose, glycerol, ethanol, α-ketoglutarate, succinate, and acetate concentrations were determined by high-pressure liquid chromatography (HPLC), using an HPX-87H ion exclusion column (Bio-Rad). Dual detection was performed with refractometer and a UV detector.

### *Oligopeptide purification*

The oligopeptide fraction of the must was purified by two successive ultrafiltrations using a hollow fiber cartridge filter, UFP-10-C-4 then UFP-1-C-4, to recover the peptides under 10 kDa then 1 kDa. The oligopeptide purified fraction less than 1 kDa was then hydrolyzed by incubation for 24 h at 110 °C in 6N HCl. Amino acids were quantified using an amino acid analyzer (Biochrom 30) (liquid chromatography on an ion-exchange column).

## Results and discussion

### *Effect of FOT genes on biomass, viability and fermentation efficiency during grape must fermentation*

To investigate if region C and *FOT* genes could confer a benefit upon exposure to the selective conditions of grape must fermentation, we deleted the entire region C or the two tandem-duplicated genes *FOT1-2* in the wine yeast 59A, a haploid derivative of EC1118 strain. During wine fermentation in a synthetic medium simulating a grape must, the wild type and deletion mutants exhibited similar growth, fermentation rate and metabolite profile (data not shown). We therefore assessed the role of *FOT* genes in a Chardonnay grape must containing more diversified nitrogen resources, as for example γ-aminobutyric acid (GABA), peptides or proteins in addition to ammonium and amino acids. While the deletion mutants showed superior growth and fermentation rates during the first hours of fermentation (Fig. 1, a, b), these advantages were lost very quickly resulting in a 8% and 11.5% lower biomass production in ΔC (183 mg/L, p = 0.04) and Δ*fot1-2* (270 mg/L, p = 0.07) mutants respectively, compared to the wild type. Furthermore, the deletion mutants exhibited a higher mortality at the end of fermentation (25% and 18% higher for ΔC and Δ*fot1-2*, respectively, compared to the wild type strain) and performed incomplete fermentations, with 6 g/L of residual fructose for ΔC and 2.5 g/L for Δ*fot1-2* (Fig. 1, c, d). Thus, our data suggest that region C, and particularly *FOT1-2* genes play a key role in maintaining cell viability and fermentation efficiency during the last step of wine fermentation. Nitrogen is the main limiting nutrient during wine fermentation. Its availability is directly related to biomass production, which governs fermentation rate. As a consequence, nitrogen deficiency is the most prevalent cause of stuck and sluggish fermentations. As *FOT* genes encode transporters of a broad range of oligopeptides [12], the phenotypic differences between the wild type and Δ*fot1-2* strains could result from a higher uptake by 59A of nitrogen sources, such as small peptides.

### *Effect of FOT genes on the uptake of oligopeptides*

To evaluate the amount of oligopeptides in the grape must, we purified the oligopeptide fraction <1 kDa, including small peptides of 2 to 9 amino acids. We found that this oligopeptide fraction (<1 kDa) represents a substantial part of the total nitrogen available in the grape must (17%), and approximatively 10% of the total assimilated nitrogen. We then determined the total content in nitrogen, free amino acids and oligopeptides in the grape must and in the wine fermented by the wild type and mutant strains, at the beginning of stationary phase (40 g/L of CO2 produced, after the nitrogen source was totally exhausted). While all strains consumed the same amount of free amino acids and ammonium, 59A consumed ~35% more nitrogen derived from oligopeptides than the deletion mutants (~10 mg/L, p = 0.027 and 7.7 mg/L, p = 0.037 more than Δ*fot1-2* and ΔC, respectively). This difference linked to the higher uptake of oligopeptides is in agreement with the measured difference in biomass between the wild type strain and the deletion mutants. The analysis of amino acid composition of the oligopeptide fraction of the grape juice shows that it is mainly constituted of glutamate or glutamine (these two compounds are indistinguishable). The oligopeptide fraction specifically consumed by 59A was particularly rich in glutamate/glutamine and to a lower extent in several other amino acids (Fig. 2). Since glutathione is a tripeptide composed of
L-glutamate, L-cysteine and glycine which can be found in grape must in amounts ranging from undetectable to 100 mg/L [13, 14], we also decided to quantify this compound. In *S. cerevisiae*, glutathione is involved in many cellular processes like redox homeostasis and response to oxidative stress and can also be used as nitrogen and sulfur source. We found that most glutathione in the supernatant of the grape must is under the form of oxidized glutathione (GSSG) (16 mg/L against less than 2 mg/L for reduced glutathione, GSH). The wild type strain consumed more GSSG compared to deleted strains (Fig. 3, a). Furthermore, the wild type strain grew better on SD medium with glutathione as sole nitrogen source than the deletion mutant, which demonstrates that *Fot1-2* is also able to transport glutathione (Fig. 3, b). Therefore, consumption of glutathione contributes to the formation of a higher amount of biomass, even if the extra amount of nitrogen from glutathione consumed by 59A does not exceed 2 mg/L (corresponding to 20 % of the oligopeptides rich in glutamate/glutamine assimilated by *Fot1-2*).

<figure><img src="image_175.png" /><figcaption>**Fig. 1** - Phenotypic analysis of 59A (green), ΔC (blue) and Δfot1-2 (red) in Chardonnay must.
(a) Growth rate, (b) Fermentation rate, (c) Residual fructose measured at the end of fermentation (t=236 h), (d) Mortality at the end of fermentation (t=236 h). Data presented in a, c, d, are means ± s.d. (n=3). A representative experiment is presented for d.</figcaption></figure>

<figure><img src="image_176.png" /><figcaption>**Fig. 2** - Amino acid composition of the oligopeptide fraction of the Chardonnay must specifically assimilated by 59A in comparison with ΔC (blue) or Δfot1-2 (red) at the beginning of stationary phase (40 g/L of CO2 production). Data are presented as means ± s.d. (n=3).</figcaption></figure>
<figure><img src="image_177.png" /><figcaption>**Fig. 3** - (a) Consumption of glutathione during grape must fermentation and (b) growth in SD medium containing glutathione as sole nitrogen source. 59A (green diamond), ΔC (blue square), Δ*fot1-2* (red triangle). Data are presented as means ± s.d. (n=3).</figcaption></figure>

*The presence of FOT1-2 genes markedly affects central carbon and nitrogen metabolism*

To evaluate the impact of the assimilation of these oligopeptides on yeast metabolism, we monitored the production of the main fermentative metabolites produced by the two strains. Most by-products, including ethanol, glycerol and succinate were produced at the same level by 59A and Δ*fot1-2*. In contrast, major differences were observed for α-ketoglutarate and acetate, whose production was 3-fold higher and 2-fold lower respectively for the wild type strain compared to Δ*fot1-2* strain.

<figure><img src="image_178.png" /><figcaption>**Fig. 4** - Competition assay using 59A and Δ*fot1-2* strains in Chardonnay grape must. (a) GFP-labeled wild type strain was competed with tdimer2 labeled Δ*fot1-2* strain, (b) example of separation of GFP-labeled wild type and tdimer2 labeled Δ*fot1-2* strains by flow cytometry, (c) three successive fermentations on Chardonnay grape must were performed and the frequency of 59A-GFP (green) and Δ*fot1-2-tdimer2* cells (red) was monitored by flow cytometry throughout the fermentation. Cells were also observed by microscopy at the time points indicated by arrows.</figcaption></figure>
These metabolic changes point to a specific modification of the glutamate node as consequence of the NADP⁺/NADPH redox balance and are most likely correlated to the higher assimilation of glutamate/glutamine rich oligopeptides.

The production of volatile compounds was also markedly affected as the result of the higher assimilation of oligopeptides. The levels of higher alcohols (2-phenylethanol, isoamyl alcohol, isobutanol, propanol and methionol) and esters acetates (2-phenylethyl acetate, isobutyl acetate and amyl acetate) produced were markedly higher for the wild type strain than for the deletion mutant. By contrast, very small or no differences were observed in the amounts of ethyl esters produced by the two strains. The global activation of the synthesis of volatile compounds suggests a response to the higher availability of nitrogen.

*FOT1-2 genes increase strain fitness during wine fermentation*

To evaluate the adaptive role of *FOT* genes, we performed competition experiments under controlled wine fermentation conditions. Co-cultures of 59A expressing green fluorescent protein (GFP) and *FOT1-2* mutant expressing red fluorescent protein (t-dimer2) were achieved during three successive fermentation experiments on grape must. The relative abundance of each strain was monitored over time by flow cytometry. The competition assays revealed a considerable competitive advantage of the strain having *FOT* genes in the natural must. Despite a transient advantage of the deletion mutant in the early steps of fermentation, in line with a higher growth and fermentation rates at this stage (Fig. 1), the strain Δ*fot1-2* was quickly dominated during the stationary phase. At the beginning of the third successive co-culture, the *FOT1-2* mutant totally disappeared from the co-culture (Fig. 4). These results demonstrate the strong selective advantage provided by the gain of *FOT1-2* genes during wine fermentation.

**Conclusion**

The presence of *FOT1-2* genes and the resulting greater assimilation of oligopeptides rich in glutamate by wine strains may have several implications for winemaking. We showed that the presence of *FOT1-2* genes increases cell viability during wine fermentation, ensuring a more efficient fermentation compared to Δ*fot1-2* mutant which leaves a high amount of residual fructose (6 g/L) [11]. The higher assimilation of oligopeptides mediated by Fot1-2 also dramatically decreases acetic acid production during wine fermentation. This is of interest in oenology, since the production of high levels of acetic acid, above 0.8 g/L, is undesirable. Moreover, the production of higher alcohols and their esters was markedly increased in the presence of *FOT1-2*. These compounds, particularly esters and 2-phenylethanol have fruity and floral notes and contribute to the aromatic complexity of wine [15]. Thus, the presence of *FOT1-2* genes could become an interesting criterion for the selection of starter strains to maximize the aroma potential and acidic balance of wines while keeping good fermentation efficiency.

**Acknowledgements**

This work was supported by grants GIS IBiSA-AO 2010-2011, France (to SD, VG, JLL, FB) and AIP Bioressources from INRA, France (to SD, VG, JLL, FB). SM acknowledges the support by a doctoral grant and by a FPI fellowship from the French Government.

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5. Zimmer A, Durand C, Loira N et al (2014) QTL dissection of Lag phase in wine fermentation reveals a new translocation responsible for *Saccharomyces cerevisiae* adaptation to sulfite. *PLoS One* **9**: e86298.

6. Dunn B, Levine RP, Sherlock G (2005) Microarray karyotyping of commercial wine yeast strains reveals shared, as well as unique, genomic signatures. *BMC Genomics* **6**: 53.
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9. Novo M, Bigey F, Beyne E et al (2009) Eukaryote-to-eukaryote gene transfer events revealed by the genome sequence of the wine yeast *Saccharomyces cerevisiae* EC1118. *Proc Natl Acad Sci USA* **106**: 16333-16338.

10. League GP, Slot JC, Rokas A (2012) The ASP3 locus in *Saccharomyces cerevisiae* originated by horizontal gene transfer from Wickerhamomyces. *FEMS Yeast Res* **12**: 859-863.

11. Marsit S, Mena A, Bigey F et al (2015) Evolutionary advantage conferred by an eukaryote-to-eukaryote gene transfer event in wine yeasts. *Mol Biol Evol* **32**: 1695-1707.

12. Damon C, Vallon L, Zimmermann S et al (2011) A novel fungal family of oligopeptide transporters identified by functional metatranscriptomics of soil eukaryotes. *ISME J* **5**: 1871-1880.

13. Cheynier V, Souquet JM, Moutounet M (1989) Glutathione content and glutathione to hydroxycinnamic acid ratio in *Vitis vinifera* grapes and musts. *Am J Enol Vitic* **40**: 320-324.

14. Park SK, Boulton RB, Noble AC (2000) Automated HPLC analysis of glutathione and thiol-containing compounds in grape juice and wine using pre-column derivatization with fluorescence detection. *Food Chem* **68**: 475-480.

15. Bell S, Henschke PA (2005) Implications of nitrogen nutrition for grapes, fermentation and wine. *Aust J Grape Wine Res* **11**: 242-295.
<figure><img src="image_179.png" /></figure>
# THÈME 3

## Chimie du vin, procédés et composants du vin à effets physiologiques

*Wine chemistry, processes, and physiologically active wine components*
<figure><img src="image_180.png" /></figure>
# Grape and wine polyphenomics: progresses in flavanol and stilbenoid metabolite profiling

C. SAUCIER<fnref n="1" />,<fnref n="2" />*, R. MOSS<fnref n="2" />, Q. MAO<fnref n="3" />, D. TAYLOR<fnref n="3" />, K. PEDNEAULT<fnref n="4" />

Polyphenomics – grapes and wine – stilbenes – flavanols

## Introduction

Metabolomics can be defined as the study of metabolites in living or dynamic systems. This field has many very important applications in biology and medicine but also in food systems [1-3]. In enology, this field that we may call œnomics can involve the study of metabolite changes induced by microbiological or chemical transformations during ageing [4]. One important subfield is the study of polyphenol composition [5] and evolution from the vine to the wine. This subfield, which we have called polyphenomics [6], requires powerful analysis tools such as NMR or high resolution mass spectrometry techniques. In this paper we illustrate some applications in the field of grape and wine polyphenomics.

## Materials and methods

### Chemicals, grape and wine samples

All chemicals were of analytical reagent grade. Acetonitrile was purchased from Fisher Scientific (Waltham, MA) and formic acid from Sigma Aldrich (St. Louis, MO). Deionized water was purified with a Milli-Q water system (Millipore, Bedford, MA). Wine or grape seed samples were collected from the Okanagan Valley, BC and were at -85 °C before analysis. Wines were brought at room temperature and simply filtered with a 0.45-µm membrane (PTFE, VWR) before analysis. The complete list of samples and procedures can be found elsewhere [6-8].

### UHPLC ESI-Q-TOF conditions

The UHPLC analysis was performed on an Agilent 1290 system (Agilent Technologies, Santa Clara, CA) equipped with an autosampler, a vacuum degasser, a binary pump, a quaternary pump, a thermostated column department, and a diode-array detector. The binary mobile phase consisted of water (mobile phase A) and acetonitrile (mobile phase B), acidified with 0.1% formic acid in both cases. The specific gradient and mass spectrometry conditions for the different class of compounds can be found elsewhere [6-8].

## Results and discussion

### Identification of glycosylated flavanols in grape seed and wines

Flavonoid compounds are an important class of polyphenols in grapes and wine, which influence their colour and taste. Some of these compounds are mainly present as glycosides such as flavonols (e.g. quercetin-3-O-glucoside) or anthocyanins (e.g. malvidin-3-O-glucoside). Other compounds like flavanols are mainly present in non-glycoside forms such as flavanols (e.g. (+)-catechin) and their polymeric forms (i.e. proanthocyanidins or condensed tannins). Some recent studies have shown the

<footnote n="1">¹Université de Montpellier, Centre de Formation et de Recherche en Œnologie, UMR 1083 INRA
Sciences pour l’Œnologie, Faculté de Pharmacie, 15 Avenue Charles Flahault, BP 14491,
34093 Montpellier cedex, France</footnote>

<footnote n="2">²The University of British Columbia, Kelowna, Department of Chemistry, The Irving K. Barber School of
Arts & Sciences, BC, Canada</footnote>

<footnote n="3">³The University of Adelaide, School of Agriculture, Food and Wine, South Australia, Australia</footnote>

<footnote n="4">⁴Centre de Développement Bioalimentaire du Québec, 1642 rue de la Ferme, La Pocatière,
Qc, G0R 1Z0, Canada</footnote>
presence of catechin glycosides in other plants and they have been hemi-synthesized by enzymatic [9] and chemical strategies [10]. We used a semi-targeted LC-MS approach to successfully identify 14 glycosylated new flavanol glycosides in grape seeds and a red wine [7]. At this stage we do not know the exact nature of the hexose and the position of the linkage. We can anticipate that beta-glucosides, very common in plant and *Vitis vinifera*, are present (Figure 1).

<figure><img src="image_181.png" /><figcaption>Fig. 1 - Example of a glycosylated flavanol: (+)-catechin 7-O-glucoside.</figcaption></figure>

*Identification of polymeric stilbenes in red wines*

Stilbenoids such as resveratrol are important antioxidants in grapes and wines and are known to have potential human health-related benefits. By using a similar semi-targeted approach, 41 stilbenoid compounds including 23 new polymeric and glycosylated forms were detected [8]. A series of 41 red wines from Canada was analyzed and the new compounds were semi-quantified by using resveratrol as an internal standard (Figure 2). The new oligomeric forms of stilbenoids were present at lower concentration compared to the previously known forms of wine stilbenoid, which explains why they were not detected before.

<figure><img src="image_182.png" /><figcaption>Fig. 2 - Quantification of stilbenoids in red wines (n=41).</figcaption></figure>

**Conclusion**

Polyphenomics of wine is a new emerging field to better understand how polyphenols are biosynthesized in vine and how they are extracted and transformed in wine. Recent progresses in sample preparation and sensitivity of mass spectrometers have allowed the detection of many new compounds. As these compounds are very often not commercially available, a lot of further studies involving NMR and hemi-synthesis strategies will take place in the future to make significant progress in this field.
Acknowledgements

The authors would like to thank our sponsors for important funding: BCWGC-AAFC (Developing Innovative Products- DIAP Grant), CFI (Leader Opportunity Fund Grant), Agilent Technologies (Equipment Grant), and NSERC (Discovery Grant).

References

1. Lindon JC, Nicholson JK, Holmes E (2007) *The Handbook of Metabonomics and Metabolomics*. Elsevier: Amsterdam, The Netherlands.

2. Gibney MJ, Walsh M, Brennan L et al (2005) Metabolomics in human nutrition: opportunities and challenges. *Am J Clin Nutr* **82**: 497-503.

3. Herrero M, Simó C, García-Cañas V et al (2012) Foodomics: MS-based strategies in modern food science and nutrition. *Mass Spectrom Rev* **31**: 49-69.

4. Gougeon RD, Lucio M, Frommberger M et al (2009) The chemodiversity of wine can reveal a metabologeography expression of cooperage oak wood. *Proc Natl Acad Sci USA* **106**: 9174-9179.

5. Kennedy JA, Saucier C, Glories Y (2006) Grape and wine phenolics: history and perspective. *Am J Enol Vitic* **57**: 239-248.

6. Delcambre A, Saucier C (2013) High-throughput Œnomics: shotgun polyphenomics of wines. *Anal Chem* **85**: 9736-9741.

7. Delcambre A, Saucier C (2012) Identification of new flavan-3-ol monoglycosides by UHPLC-ESI-Q-TOF in grapes and wine. *J Mass Spectrom* **47**: 727-736.

8. Moss R, Mao Q, Taylor D et al (2013) Investigation of monomeric and oligomeric wine stilbenoids in red wines by ultra-high-performance liquid chromatography/electrospray ionization quadrupole time-of-flight mass spectrometry. *Rapid Commun Mass Spectrom* **27**: 1815-1827.

9. Cho HK, Kim HH, Seo DH et al (2011) Biosynthesis of (+)-catechin glycosides using recombinant amylosucrase from *Deinococcus geothermalis* DSM 11300. *Enzyme Microb Technol* **49**: 246-253.

10. Raab T, Barron D, Vera FA et al (2010) Catechin glucosides: occurrence, synthesis, and stability. *J Agric Food Chem* **58**: 2138-2149.
# Study of polymeric pigments in red wine: strategy for quantification via phloroglucinolysis and identification by high resolution mass spectrometry (Q-TOF)

L. ZENG<sup>1,2</sup>, P.-L. TEISSEDRE<sup>1,2</sup>, M. JOURDES<sup>1,2*</sup>

<sup>1</sup>Univ. Bordeaux, ISVV, EA 4577, Œnologie, 210 Chemin de Leysotte, F-33140 Villenave d'Ornon, France

<sup>2</sup>INRA, ISVV, USC 1366 Œnologie, F-33140 Villenave d'Ornon, France

Polymeric pigments – red wine – phloroglucinolysis – high resolution mass spectrometry

## Introduction

Anthocyanin (A) and condensed tannins (T) are two important classes of polyphenolic compounds in red wine since they are responsible for red wine color and taste. During wine making and aging, both of them undergo chemical changes leading to the formation of polymeric pigments. With the development of analytical tools such as mass spectrometry and nuclear magnetic resonance (NMR), the structures of different kinds of anthocyanin (A⁺ or A)-derived pigments [1] have been clearly revealed including B-type [2], A-type [3], aldehyde bridged type [4] and flavano-pyranoanthocyanin type [1]. Until now, the polymeric pigments in red wine have usually been quantified based on their precipitation with gelatine, their sulfite bleaching, and their high acidity resistance. However, due to the high complexity of polymeric pigments present in red wine, their respective impact of red wine color during aging cannot be elucidated with these mentioned methods. Therefore, the evolution of each family of polymeric pigments during aging is poorly known.

The objective of this study was the identification of polymeric pigment markers after phloroglucinolysis in order to establish a quantification method. Nine groups of polymeric pigments were distinguished and their corresponding quantification markers were identified by high resolution mass spectrometry (UPLC-UV-Q-TOF). A series of wines (1-19 years old) was analyzed with this new method to obtain characteristic kinetic pattern according to wine age.

## Materials and methods

### Reagents

Deionized water was purified with a Milli-Q water system (Millipore, Bedford, MA). Methanol (MeOH, analytical grade), ethanol (HPLC grade), methanol (HPLC grade), L-ascorbic acid and hydrochloric acid were purchased from Prolabo-VWR (Fontenay s/Bois, France). Water (Optimal® LC/MS), methanol (Optimal® LC/MS) and formic acid (Optimal® LC/MS) from Fisher Scientific (Geel, Belgium) were used for high resolution mass spectrometry analysis. Phloroglucinol and oenin chloride were obtained by Extrasynthese (Z.I Lyon Nord, France). Eight Bordeaux red wines from vintage 1995-2012 were obtained directly from the winery.

### Fractionation of red wines and their depolymerisation

The red wines were fractionated on a C-18 cartridge (Supelco, St Quentin Fallavier, France) as follows: 2.5 mL of wine was dried under reduced pressure, re-dissolved in 10 mL of water, and applied on the cartridge after conditioning. The cartridge was sequentially eluted with: 50 mL of distilled water, 50 mL of methanol and 50 mL of distilled water. The methanol fraction was dried under reduced pressure and dissolved in 1 mL of methanol. 200 µL of these fractions were acidified with 1% formic acid and analyzed for un-depolymerised pigment analysis. Then, the depolymerisation of polymeric pigments was conducted by phloroglucinolysis. The phloroglucinolysis reagent solution containing 0.1 N HCl in methanol, 50 g/L phloroglucinol and 10 g/L ascorbic acid was prepared. 200 µL of the MeOH fraction obtained above was
added to 200 µL of the phloroglucinolysis reagent solution. Then, the reaction mixture was placed at 50°C for 20 min and 1 ml of 40 mM acetate sodium aqueous solution was added to stop the reaction. The reaction mixture was then analyzed by UPLC-UV-Q-TOF.

## UPLC-Q-TOF

The UPLC-MS system used was an Agilent 1290 Infinity equipped with an ESI-Q-TOF-MS (Agilent 6530 Accurate Mass). Chromatographic separation was carried out on an Eclipse Plus C18 column (2.1 x 100 mm, 1.8 µm). The solvents used were: water with 0.1% formic acid for solvent A and methanol with 0.1% formic acid for solvent B at a flow rate of 0.4 mL/min. The gradient for pigment analysis was as follows: 6% of B during 0.5 min; from 6 to 95% of B in 13.5 min; 5% of B during 4 min. ESI conditions were as follows: gas temperature and flow were 300°C and 9 L/min, respectively; sheath gas temperature and flow were 350°C and 11 L/min, respectively; capillary voltage was 4000 V. The obtained data were treated by MassHunter Qualitative and Quantitative Analysis software. Calibration curve was established with oenin chloride in 0.5 M HCl in MeOH.

# Results and discussion

## Identification of the quantification markers for polymeric pigments by Q-TOF

By analyzing Bordeaux red wines, their polyphenolic fractions and the corresponding depolymerised fractions, 23 markers of polymeric pigments classified in nine groups were identified by high resolution mass spectrometry (UPLC-ESI-Q-TOF). These nine families of polymeric pigments in red wine were: A (flavene)-T, T-A⁺, A⁺-T, T-AF(A type), AF(A type)-T, A⁺-E. B.-T, A⁺-E. B.-A, anthocyanin oligomer and flavano-pyrano anthocyanin.

<figure><img src="image_183.png" /><figcaption>**Fig. 1** - Mechanism of released markers for polymeric pigment analysis after phloroglucinolysis.</figcaption></figure>
The identification of these markers and MS/MS data are not discussed in detail in this paper. Figure 1 shows the released mechanism of the markers from five groups of polymeric pigments after phloroglucinolysis. For example, compound A<sup>+</sup>-E. B.-F showed to be resistant to the phloroglucinolysis condition, so the detected marker A<sup>+</sup>-E. B.-F-phloroglucinol was used to specifically quantify the A<sup>+</sup>-E. B.-T type pigments harboring an A<sup>+</sup>-E. B.-F structure as first unit.

*Evolution kinetic pattern of polymeric pigments during red wine aging*

The release of monomeric anthocyanins after the depolymerisation reaction was observed. These released monomeric anthocyanins originated from A (flavene)-T type polymeric pigments. The released quantity indicates the concentration of such kind of polymeric pigments. The released malvidin-3-O-glucoside by relatively old red wine was 2-3 times higher than in young red wine even though the concentration of malvidin-3-O-glucoside in the young wine was 90 times higher than in the old wine (Figure 2 A).

<figure><img src="image_184.png" /><figcaption>**Fig. 2** - Released malvidin-3-O-glucoside (A) and flavano(dimer)-pyranoanthocyanin according to wine age (vintage from 1995-2012).</figcaption></figure>

After the depolymerisation, the concentration of two F-A⁺ adducts increased while the concentration of two A⁺-F adducts remained stable. The F-A⁺ adducts released during the depolymerisation derived from the depolymerisation of high molecular weight polymeric pigment with F-A⁺ sub-structure as terminal unit. This released amount decreased during aging. Concerning the A type dimer [A-(4-8, 2-O-7)-F] which are colorless, they showed a higher stability during red wine aging.

The concentration of A⁺-E. B.-F and A⁺-E. B.-A decreased dramatically during aging. A⁺-E. B.-A can only be detected in red wine less than two years old. However, polymeric pigment of A⁺-E. B.-T type decreased less dramatically over time. The flavano(dimer)-pyranoanthocyanins resisted to the phloroglucinolysis depolymerisation condition. Their concentrations decreased slightly during wine aging and some strong vintage effect (1996) was observed (Figure 2 B).

**Conclusion**

In this study, 23 markers of polymeric pigments were identified and quantified before and after depolymerisation of the polyphenolic fractions of red wine. In order to establish their concentration, a new quantification method on a UPLC-UV-Q-TOF system has been developed. Characteristic kinetic pattern of these polymeric pigments according to wine age was established for the first time.

**Acknowledgements**

The authors thank the French Ministry of High Education and Research for doctoral support and the Regional Council of Aquitaine for funding (purchase of the UPLC-UV-Q-TOF system).
Abbreviations

UPLC, ultra high performance liquid chromatography; DAD, diode array detector; ESI, electrospray; Q-TOF, quadrupole-time of flight; E. B., ethyl bridge.

References

1. Mateus N, Silva AMS, Santos-Buelga C et al (2002) Identification of anthocyanin-flavanol pigments in red wines by NMR and mass spectrometry. *J Agric Food Chem* **50**: 2110-2116.

2. Salas E, Atanasova V, Poncet-Legrand C et al (2004) Demonstration of the occurrence of flavanol-anthocyanin adducts in wine and in model solutions. *Anal Chim Acta* **513**: 325-332.

3. Remy S, Fulcrand H, Labarbe B et al (2002) First confirmation in red wine of products resulting from direct anthocyanin-tannin reactions. *J Sci Food Agric* **80**: 745-751.

4. Es-Safi N-E, Fulcrand H, Cheynier V et al (1999) Studies on the acetaldehyde-induced condensation of (-)-epicatechin and malvidin-3-O-glucoside in a model solution system. *J Agric Food Chem* **47**: 2096-2102.
# Development of a multi-way approach to non-targeted GC-MS analysis of wine volatiles

J. VESTNER¹,²,³*, S. KRIEGER-WEBER⁴, D. RAUHUT³, G. DE REVEL¹,², M. DU TOIT⁵, A. DE VILLIERS⁶

¹Univ. Bordeaux, ISVV, EA 4577, Unité de recherche Œnologie, 33882 Villenave d'Ornon, France

²INRA, ISVV, USC 1366 Œnologie, 33882 Villenave d'Ornon, France

³Department of Microbiology and Biochemistry, Hochschule Geisenheim University,
Von-Lade-Straße 1, 65366 Geisenheim, Germany

⁴Lallemand, In den Seiten 53,70825 Korntal-Münchingen, Germany

⁵Institute of Wine Biotechnology, Department of Viticulture and Oenology, Stellenbosch University,
Private Bag X1, Matieland (Stellenbosch) 7602, South Africa

⁶Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1,
Matieland (Stellenbosch) 7602, South Africa

GC-MS fingerprinting – multivariate data analysis – Cabernet Sauvignon – wine volatiles

## Introduction

Conventional targeted GC-MS analysis of wine volatile compounds is always limited to a set of *a priori* known and identified compounds. Samples can therefore only be compared in respect of these preselected compounds. In contrast to targeted analysis, non-targeted approaches take known and unknown compounds into account. The aim of non-targeted analysis such as metabolic fingerprinting is to gain as much information on as many compounds as possible from the analyzed samples. Moreover, non-targeted methods include a fast and unselective sample preparation combined with a fast chromatographic separation to facilitate a high sample throughput. Detected unknown compounds are usually identified only when they contribute to the differentiation of samples. Non-targeted analysis therefore facilitates a more holistic and unbiased exploration of samples. The extra information gained from non-targeted analysis can lead to a better understanding of chemical and biochemical processes and may help to identify compounds playing a key role in the differentiation of samples.

The gained popularity of non-targeted approaches to mass spectrometry in many different fields such as food sciences and life sciences with a special respect to all the ‘omics’ related research areas can be explained by the development and accessibility of specific software for feature selection, deconvolution, signal alignment and multivariate modeling [1-4]. However, automated feature selection such as peak integration remains troublesome due to coelution and potential erroneous peak integration and/or assignment. Often manual corrections of the results are necessary. Depending on the method and criteria for the feature selection, relevant information from the raw data can be lost [5, 6]. Moreover, the deconvolution of coeluting chromatographic peaks can sometimes be time-consuming in terms of model construction and evaluation of results [7-10].

Alternatively, more comprehensive approaches which avoid feature selection and aim at an easier extraction of more information from the data involve the modeling of the two dimensional chromatographic mass spectrometry raw data signal of each sample in entirety [11-13]. Mathematical transformation of the original raw data in a manner in which changes in elution time and peak shape are taken into account have a big potential in multivariate and multi-way modeling of chromatographic mass spectrometry data from non-targeted fingerprinting approaches. The usage of sums of squares and cross product (SSCP) matrices $XX^T$, which are the product of a matrix $X$ with its transpose, are especially suitable to either transform data prior to modeling or to be included into the algorithm of multivariate and multi-way models. This transformation however presupposes that the mass spectral profile is not shifted. SSCP matrices are utilized for instance in PARAFAC2, STATIS and the calculation of $R_V$-coefficients [12, 14-17].
The scope of this study was the development of a fast, non-targeted methodology for exploratory
multivariate data analysis of whole GC-MS chromatograms without prior retention time alignment.
The developed approach includes the segmentation of the chromatograms followed by rearrangement and
transformation of the segments to SSCP matrices. The transformed data array is then analyzed using the
three-way method PARAFAC. Particularly the transformation of segments of two dimensional
chromatograms into SSCP matrices facilitates the comparison of samples without prior alignment of peaks.
The developed data processing approach was applied to a data set obtained from a non-targeted HS-SPME-
GC-MS analysis of experimental wines which were made using different lactic acid bacteria under different
inoculation scenarios to conduct malolactic fermentation (MLF).

**Materials and methods**

Experimental wines were all produced from the same Cabernet Sauvignon grapes from the 2012 vintage (State New York, USA). Fermentations were carried out using six combinations of yeast and lactic acid bacteria, which were selected according to their organoleptic properties indicated by the manufacturer. A total of twelve experimental wines were studied here, whereas six yeast/bacteria combinations and two different inoculation strategies were used: inoculation of lactic acid bacteria 24 h after yeast inoculation (co-inoculation), and inoculation of lactic acid bacteria after the completion of alcoholic fermentation (sequential inoculation). For headspace solid phase microextraction (HS-SPME) a 100µm polydimethylsiloxane (PDMS) fiber was used. Extraction time for each sample was 30 min. Separation was carried out on a 30m HP-5 MS column with an internal diameter (i.d.) of 0.25mm and a film thickness of 0.25µm. A steep temperature ramp of 15°C/min ensured a total GC run time of 25 min. Furthermore, full mass spectra were acquired in the range of 35u to 350u. All injections were carried out fully randomized, fiber and column blanks were carried out regularly to confirm that no sample carry-over occurred.

**Results and discussion**

HS-SPME was used for sample preparation because of its simplicity for wine analysis in terms of full automation, speed, and sensitivity. The fully automated SPME procedure together with a steep GC oven program facilitated a fast GC separation in less than 25 min. A loss of resolution resulting in coeluting peaks was tolerated, as the developed data analysis approach takes the entire mass dimension into account. All chromatograms were divided into 84 segments based on visual examination of overlays of total ion chromatograms (TICs) of all samples. After the mathematical transformation using SSCP matrices, PARAFAC was used to analyze the transformed data set. A twelve component PARAFAC model was the appropriate model explaining 79.9 % of the variation in the data. Four of the twelve components contained important information on systematic differences among samples which could be observed in the PARAFAC scores. The remaining eight components reflected unsystematic variation in the chromatograms. All important segments contributing to the grouping of samples were identified by investigating the PARAFAC loadings. Single mass traces or deconvoluted signals of all peaks in each segment showing clear differences between samples were then integrated and compiled into a peak table. If possible, peaks were tentatively identified by comparing deconvoluted mass spectra with the NIST08 spectral library and linear retention indices were compared with literature values. Final PCA on the peak table was used to validate the results from PARAFAC modeling. The results show systematic differences as a function of the yeast/lactic acid bacteria inoculation strategy and highlight new avenues for future research on the effects of MLF inoculation strategies.

**Conclusion**

The new multi-way approach to non-targeted GC-MS analysis developed here is a fast method facilitating the modeling of entire two dimensional GC-MS chromatograms of multiple samples. Retention time shifts among samples and distortion of peak shapes are taken into account by transforming segments of the chromatograms into sums of squares and cross product (SSCP) matrices prior to modeling. Therefore, neither feature selection such as peak integration nor retention time alignment is needed to preprocess data. Detailed description of the approach can be found in [18].
References

1. Dixon SJ, Brereton RG, Soini HA et al (2006) An automated method for peak detection and matching in large gas chromatography-mass spectrometry data sets. *J Chemometr* **20**: 325-340.

2. Furbo S, Christensen JH (2012) Automated peak extraction and quantification in chromatography with multichannel detectors. *Anal Chem* **84**: 2211-2218.

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5. Skov T, Bro R (2005) A new approach for modelling sensor based data. *Sensor Actuat B: Chem* **106**: 719-729.

6. Ballabio D, Skov T, Leardi R et al (2008) Classification of GC-MS measurements of wines by combining data dimension reduction and variable selection techniques. *J Chemometr* **22**: 457-463.

7. Bro R (1997) PARAFAC. Tutorial and applications. *Chemometr Intell Lab Syst* **38**: 149-171.

8. Rodríguez MC, Sánchez GH, Sobrero MS et al (2013) Determination of mycotoxins (aflatoxins and ochratoxin A) using fluorescence emission-excitation matrices and multivariate calibration. *Microchem J* **110**: 480-484.

9. Behrends V, Tredwell GD, Bundy JG (2011) A software complement to AMDIS for processing GC-MS metabolomic data. *Anal Biochem* **415**: 206-208.

10. Tauler R (1995) Multivariate curve resolution applied to second order data. *Chemometr Intell Lab Syst* **30**: 133-146.

11. Sinkov NA, Harynuk JJ (2011) Cluster resolution: a metric for automated, objective and optimized feature selection in chemometric modeling. *Talanta* **83**: 1079-1087.

12. Daszykowski M, Danielsson R, Walczak B (2008) No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry. *J Chromatogr A* **1192**: 157-165.

13. Durante C, Bro R, Cocchi M (2011) A classification tool for N-way array based on SIMCA methodology. *Chemometr Intell Lab Syst* **106**: 73-85.

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15. Daszykowski M, Walczak B (2011) Methods for the exploratory analysis of two-dimensional chromatographic signals. *Talanta* **83**: 1088-1097.

16. Stanimirova I, Walczak B, Massart D et al (2004) STATIS, a three-way method for data analysis. *Application to environmental data*. *Chemometr Intell Lab Syst* **73**: 219-233.

17. Bro R, Andersson CA, Kiers HA (1999) PARAFAC2-Part II. Modeling chromatographic data with retention time shifts. *J Chemometr* **13**: 295-309.

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# DNA and type of grain: which factor better explain sensory differences between sessile and pedunculate oaks?

M. MIRABEL*, R. TEISSIER DU CROS, V. RENOUF

Chêne & Cie, Research Department of Taransaud, Canton and Kadar cooperages and Xtrachêne,
5 rue Magellan, 75008 Paris, France

Grain type – oak – cooperage – sensory and chemical analysis

## Introduction

Sessile oak and pedunculate oak have shown several differences of interest for enological purposes. Tannic and aromatic composition among sessile oak or pedunculate oak has been well studied [1, 2]. Sessile oak is generally more aromatic than pedunculate, while the latter is more tannic. This scientific point of view is rarely applied to classify oak in cooperages. Most coopers use the type of grain to distinguish wide and thin grain. While the former leads to barrels with less aromas and more tannins, often oriented to alcohols, the latter is more aromatic and convenient for wine ageing. Does the traditional cooper grading by grain size has a link with species in the chemical expression of oak? Our study is aimed to answer this question and to estimate which factor is more efficient: type of grain or DNA?

## Materials and methods

### Oak trees and species identification

Several oak trees from the same plot in the Forêt Domaniale de Saint Palais (France) were studied. Recent developments of DNA tests can identify oak species or hybrid (instead of morphological determination that can be approximate). DNA tests were performed on each tree to identify their species. The method has been developed by the INRA Institute.

### Grain type classification

After excluding hybrids, only pure sessile oak and pure pedunculate oak were considered and separated into two batches. Staves were split from each oak batch, classified according to their type of grain (tight grain is < 3 mm while wide grain is > 3mm) and put in the yard for 24 months of seasoning. For sampling, 24 staves per species were picked up from 8 pallets (5 of tight grain and 3 of wide grain as it is mainly representative of the supplying) as shown in Figure 1. This was made at the beginning (T0) and after 6 months (T6) of seasoning using the same process: shavings have been taken at the edges of each murrain, blended by pallet and stirred with hydroalcoholic solution (12% v/v), 5 g/L tartaric acid during 72 hours. Then they were filtered for chemical analyses.

### Chemical analyses

Chemical analyses (lactones, eugenol, vanillin, furfural, ellagitannins) were performed at T0 and T6 of seasoning with chromatographic methods (HPLC/UV and SBSE/GC/MS).

### Sensory analyses

Shavings have also been submitted to direct sensory analyses using a panel of experts well trained on woody aromas; microflora analyses were performed on these shavings too by numeration of different populations (yeasts, molds, bacteria) on selective nutritive media and observation of the species diversity by PCR-DGGE.
<figure><img src="image_185.png" /><figcaption>Fig. 1 - Sampling process.</figcaption></figure>

## Results and discussion

Cluster dendrograms have been made according chemical results for both samplings (Figure 2).

Among the sessile oak, two groups (in blue) can be distinguished whatever the time of sampling. The former (group G1, light blue) is extremely rich in lactones, whereas the latter (group G3, dark blue) is poorer and can be considered as a “neighbor” of pedunculate oak that is poor in lactones and eugenol but richer in ellagitannins (Figure 3). Pedunculate oak is homogeneous (group G2, red group) whether its grain is tight or wide. Sessile oak seems to be impacted by the type of grain. The richer group is related to only thin grain while the poorer is mainly made of wide grain. After 6 months of seasoning, one sessile pallet over the 8 ones considered has moved to the neighborhood of pedunculate oaks.

<figure><img src="image_186.png" /><figcaption>Fig. 2 - Cluster dendrograms of sessile and pedunculate oaks according to chemical analyses.</figcaption></figure>
<figure><img src="image_187.png" /><figcaption>Fig. 3 - Chemical analysis at the beginning (T0) and after 6 months (T6) of seasoning.</figcaption></figure>

<figure><img src="image_188.png" /><figcaption>Fig. 4 - Microflora analyses performed at the beginning (T0) of seasoning.</figcaption></figure>
The two species show already several differences at the beginning of seasoning (Figure 3). Oak compound evolution during seasoning is a result of many features but microflora is one of most important as many oak compounds can be released by enzymatic activities [3]. Then differences between species can be also explained by the kind and amount of microflora found in the staves. Analyses were performed on stave shavings blended by species. Pedunculate oak showed a more important and varied microflora as shown in Figure 4.

Tastings on oak shavings at the beginning and after 6 months of seasoning showed that sessile oak has spicy, fresh and pastry aromas whereas pedunculate oak was acetic. But statistical treatment does not lead to clusters as found with chemical analyses.

## Conclusion

These two steps at the beginning of the yard seasoning have shown that the grain has a true effect on the aromatic composition of sessile oak, especially concerning the oak lactone amount (cis and trans-whiskylactones). Indeed, sessile oaks with tight grain have significantly more oak lactones than sessile oaks with wide grain. Are these differences kept along yard seasoning barrel making and wine ageing? The sampling of the staves in the next 18 months of the seasoning will provide part of the answer.

## Abbreviations

INRA, Institut National de Recherche Agronomique.

## References

1. Doussot F, Pardon P, Dedier J et al (2000) Individual, species and geographic origin influence on cooperage oak extractible content (*Quercus robur* L. and *Quercus petraea* Liebl.). *Analusis* **28**: 960-965.

2. Prida A, Ducousso A, Petit RJ et al (2007) Variation in wood volatile compounds in a mixed oak stand: strong species and spatial differentiation in whisky-lactone content. *Ann For Sci* **64**: 313-320.

3. Bloem A, Lonvaud-Funel A, de Revel G (2008) Hydrolysis of glycosidically bound flavour compounds from oak wood by *Oenococcus oeni*. *Food Microbiol* **25**: 99-104.
# Développement d'une méthode chimique pour discriminer les bois de chêne sessile et pédonculé sur la base de leur composition triterpénique

A. MARCHAL<fnref n="1" />,<fnref n="2" />*, D. DUBOURDIEU<fnref n="1" />,<fnref n="2" />

Elevage sous bois – sucrosité – amertume – quercotriterpenosides

## Introduction

L'élevage sous bois est une étape cruciale de l'élaboration des vins, au cours de laquelle leurs caractéristiques analytiques et organoleptiques sont modifiées. A l'instar d'une tenue de soirée, le bois souligne élégamment les qualités du vin, à condition qu'il soit choisi avec discernement et utilisé à bon escient. A l'inverse, son usage excessif et mal approprié confine à la caricature et déprécie l'arôme et la saveur du vin. De nombreux paramètres influencent la composition du bois de chêne et son effet sur le goût du vin. Parmi ceux-ci, l'origine botanique a été établie comme un facteur déterminant. En effet, des travaux précédents ont démontré que les teneurs moyennes en β-methyl-γ-octalactone [1] et en ellagitannins [2] étaient respectivement plus élevées et plus faibles dans le chêne sessile (Quercus petraea Liebl.) que dans le chêne pédonculé (Quercus robur L.). Toutefois, une forte variabilité interindividuelle a été observée au sein de chaque espèce. Par conséquent, ces molécules ne peuvent servir de marqueurs pertinents pour une discrimination sans ambiguïté des deux espèces de chêne européen utilisées en tonnellerie. Récemment, des triterpènes à saveur sucrée, les quercotriterpenosides (QTT), ont été identifiés dans le bois de chêne [3, 4] et le goût amer d'un dérivé glucosylé de l'acide bartogénique (Glu-AB) a été établi [5]. La présente étude vise à déterminer l'influence de l'espèce de chêne sur la teneur en triterpènes sapides du bois de chêne (Figure 1). Une méthode de dosage en chromatographie liquide couplée à la spectrométrie de masse à haute résolution (LC-HRMS) a été développée à cette fin.

<figure><img src="image_189.png" /><figcaption>Fig. 1 - Structure chimique des triterpènes quantifiés dans le bois de chêne.</figcaption></figure>

## Matériel et méthodes

### Origine et préparation des échantillons de bois

Les QTT et le Glu-AB ont été purifiés à partir du bois de chêne par chromatographie de partage centrifuge et chromatographie liquide haute performance à l'échelle préparative, selon la procédure décrite par

<footnote n="1">¹Univ. de Bordeaux, ISVV, EA 4577, Unité de recherche Œnologie, F-33882 Villenave d'Ornon, France</footnote>

<footnote n="2">²INRA, ISVV, USC 1366 Œnologie, F-33882 Villenave d'Ornon, France</footnote>

<footnote>*Auteur principal : axel.marchal@u-bordeaux.fr</footnote>
Marchal et al. [3, 4]. La tonnellerie Seguin-Moreau a gracieusement fourni 22 échantillons de bois de chêne, prélevés dans 6 forêts françaises (Saint-Clément, Spincourt, Xures, Tronçais, Châteauroux et Liffré). Pour chaque arbre étudié, deux échantillons ont été collectés : dans le bois de cœur et dans les feuilles. Les prélèvements de feuilles ont été soumis à des analyses génétiques réalisées par le Dr Erwan Guichoux (UMR BIOGECO, INRA) afin d'identifier l'espèce de chêne [6, 7]. Le prélèvement de bois a été séché, réduit en poudre, puis macéré à 50 g/L pendant 48h dans une solution hydro-éthanolique à 12% v/v (5 g/L d'acide tartrique, pH 3.5). Après filtration dégrossissante sur papier, les macérats ont été dilués au 1/25ème, filtrés à 0.45 µm et analysés par LC-HRMS.

## Conditions chromatographiques et spectrométriques

Une méthode de quantification des QTT I, II, III et du Glu-AB a été développée [8]. La plateforme analytique était constituée d'une chaîne chromatographique U-HPLC Accela connectée à une colonne Hypersil Gold C18 (2.1 x 100 mm ; 1,9 µm ; Thermo Fisher Scientific, Les Ulis, France). De l'eau ultrapure (A) et de l'acétonitrile de qualité LC-MS (Optima, Thermo Fisher Scientific, B) ont été utilisés comme phase mobile, selon le gradient suivant : 0 min, 20%B ; 0.5 min, 20%B ; 4 min, 50%B ; 4.1 min, 98%B ; 6.1 min, 98%B ; 6.2 min, 20%B ; 7.5 min, 20%B. Le débit était de 600 µL/min, avec un volume d'injection de 5 µL. La détection était assurée par un spectromètre de masse Exactive équipé d'un analyseur Orbitrap (Thermo Fisher Scientific). L'ionisation a été réalisée en mode négatif, à l'aide d'une source par électro-nébulisation chauffée (HESI I). La quantification est basée sur la mesure des aires des pics chromatographiques construits par appel des ions (XIC) compris dans une fenêtre de 5 ppm autour de la masse monoisotopique exacte des ions déprotonés. Les résultats sont traités grâce à l'application Quanbrower du logiciel Xcalibur (Thermo Fisher Scientific), par intégration automatique.

## Analyse des données

L'expression des résultats en valeur absolue est permise par l'obtention de droites de calibration construites, pour chaque composé, par l'injection de solutions de référence (de 500 ng/L à 5 mg/L). Les résultats sont exprimés en microgrammes de composés par gramme de bois, en considérant la concentration des macérats ainsi que le facteur de dilution. Le traitement statistique a été réalisé par le logiciel XL-STAT version 2010.5.05 (Addinsoft, Paris, France), en évaluant l'incidence de l'espèce botanique sur les concentrations en triterpènes par analyse de variance à une variable, après avoir vérifié l'homogénéité de la variance (test de Levene) ainsi que la normalité de la distribution des résidus (test de Shapiro-Wilk).

## Validation de la méthode de dosage

Afin de valider la méthode de quantification, plusieurs critères analytiques ont été étudiés. La linéarité a été vérifiée dans la gamme de travail, par mesure du coefficient de corrélation des droites de calibration. La détection par spectrométrie de masse à haute résolution garantit la spécificité de l'analyse, ce qui permet par ailleurs l'injection directe des échantillons. Compte-tenu des caractéristiques de la HRMS, la méthode classique, basée sur la mesure du rapport signal/bruit n'est pas pertinente pour la détermination de la sensibilité. L'approche développée par De Paepe et al. a ainsi été utilisée pour définir les limites de détection et de quantification [9]. Des injections de 5 réplicats d'échantillons standards et d'échantillons dopés en composés dosés ont permis de calculer la répétabilité et la justesse de la méthode.

# Résultats et discussion

## Développement d'une méthode de dosage des triterpènes du bois par LC-HRMS

La LC-HRMS combine le pouvoir de séparation de la chromatographie liquide à ultra haute performance et la spécificité de détection de la spectrométrie de masse à haute résolution. Elle constitue donc une technique de choix pour le dosage de composés non-volatils dans une matrice complexe. La méthode chromatographique développée ici a permis de séparer les différents triterpènes, comme présenté à la figure 2, et de les doser à des concentrations allant de 1 µg/L à 5 mg/L, avec une linéarité supérieure à 0.996 pour tous les composés. La justesse et la répétabilité ont été mesurées respectivement supérieure à 89% et inférieure à 7% pour tous les composés. Les temps de rétention variaient très peu d'une injection à une autre et la précision de mesure de masse a toujours été inférieure à 2.8 ppm pour tous les composés. La méthode de quantification a ainsi été validée, les différents paramètres de cette validation sont reportés dans une étude récente [6].
<figure><img src="image_190.png" /><figcaption>**Fig. 2** - Chromatogrammes LC-HRMS des ions totaux et des ions extraits (en rouge correspondant aux QTT et en vert au Glu-AB) d'un macérat de bois de chêne.</figcaption></figure>

### Influence de l'espèce de chêne sur la teneur en triterpènes sapides du bois

Les analyses génétiques ont révélé que parmi les 22 échantillons analysés, 8 étaient de chêne sessile et 14 de chêne pédonculé. Les traitements statistiques ont montré une corrélation positive entre les teneurs en QTT I, II et III. La moyenne de la somme des concentrations de ces trois triterpènes sucrés (ΣQTT) a été mesurée à 1457 ± 963 µg/g dans le chêne sessile et 21 ± 8 µg/g dans le chêne pédonculé. L'espèce botanique a donc une incidence significative sur la teneur en QTT (Figure 3), mais il faut toutefois noter une forte variabilité interindividuelle traduite par l'écart-type élevé pour les échantillons de chêne sessile. A l'inverse, les teneurs moyennes en Glu-AB sont de 24 ± 7 µg/g dans le chêne sessile et 780 ± 372 µg/g dans le chêne pédonculé. En dépit de forts écarts entre individus au sein d'une même espèce, le Glu-AB est significativement plus abondant dans le chêne pédonculé que dans le chêne sessile (Figure 3). Ainsi, ces résultats démontrent que l'espèce de chêne influence la composition triterpénique du bois, puisque le chêne sessile contient davantage de triterpènes sucrés tandis que le chêne pédonculé est plus riche en triterpènes amers. Ces données analytiques peuvent être mises en parallèle de l'utilisation empiriquement privilégiée du chêne sessile pour l'élevage des vins et du chêne pédonculé pour la maturation des eaux-de-vie. Toutefois, compte tenu des fortes variabilités interindividuelles observées au sein de chaque espèce, aucun de ces composés ne peut, individuellement, permettre une identification à coup sûr de l'origine botanique d'un échantillon de bois. Des résultats similaires avaient déjà été observés pour la β-methyl-γ-octalactone [1] ou encore les ellagitannins [2].

<figure><img src="image_191.png" /><figcaption>**Fig. 3** - Concentrations moyennes en QTT (à gauche) et en Glu-AB (à droite) mesurées dans du bois de chênes sessiles et pédonculés. Les barres d'erreur correspondent aux intervalles de confiance à 5%.</figcaption></figure>
## Calcul d'un indice triterpénique permettant la détermination sans ambiguïté de l'espèce de chêne

En l'absence de marqueurs chimiques connus pouvant être dosés de façon ciblée, l'identification de l'espèce botanique d'un échantillon de chêne repose jusqu'à présent exclusivement sur des analyses génétiques. Or, l'examen des chromatogrammes obtenus précédemment a semblé montrer une influence de l'espèce sur le profil triterpénique des échantillons. Afin de traduire ce profil triterpénique en une valeur quantifiable, un indice triterpénique (IT) a été défini de la façon suivante :

$$IT = \log \frac{[QTT I] + [QTT II] + [QTT III]}{[Glu - BA]}$$

La figure 4 présente les valeurs moyennes de cet IT calculées pour les échantillons analysés.

<figure><img src="image_192.png" /><figcaption>Fig. 4 - Valeurs moyennes de l'IT calculées pour les échantillons de chênes sessiles et pédonculés.</figcaption></figure>

Les résultats montrent un effet significatif de l'espèce de chêne sur l'IT, avec une valeur moyenne de 1.63 ± 0.21 pour le chêne sessile et -1.70 ± 0.22 pour le chêne pédonculé. En outre, de faibles variations interindividuelles sont observées, tant pour les chênes sessiles (valeurs comprises entre 1.41 et 2.15) que pour les chênes pédonculés (valeurs entre -2.19 et -0.84). Tous les échantillons de chênes sessiles ont un IT supérieur à 1 tandis que tous les échantillons de chênes pédonculés ont un IT inférieur à -0.5. Ainsi, la mesure de cet index triterpénique permet une discrimination chimique de l'espèce de chêne, contrairement au dosage individuel des triterpènes, des ellagitannins [2] ou bien encore de la β-methyl-γ-octalactone [1].

## Conclusion

Ce travail décrit le développement et la validation d'une méthode de dosage de triterpènes sapides du bois de chêne par LC-HRMS. L'application de cette méthode a permis de montrer que le chêne sessile était significativement plus riche en triterpènes sucrés (QTT) que le chêne pédonculé, tandis que ce dernier contenait davantage de triterpènes amers (Glu-AB). Au-delà de cette découverte, et des futures applications œnologiques qui pourront en découler directement, cette étude a permis, grâce au calcul d'un indice triterpénique, de proposer la première méthode chimique ciblée de détermination de l'espèce de chêne. L'intérêt de cette méthode pourrait être lié à son application après séchage et après chauffe du bois, voire directement sur les vins et les spiritueux élevés au contact du bois, ce que ne permet pas la méthode génétique. Des travaux ultérieurs viseront à étudier ces hypothèses et à préciser le champ des applications possibles de cette méthode.

## Remerciements

Les auteurs remercient Andréi Prida (Seguin-Moreau) pour la collecte des échantillons ainsi que le Dr. Erwan Guichoux (UMR Biogeco) pour la réalisation des analyses génétiques. Ce travail a été soutenu financièrement par la tonnellerie Seguin-Moreau, la maison Rémy-Martin, le Conseil Interprofessionnel des Vins de Bordeaux et France AgriMer.
Références

1. Prida A, Ducousso A, Petit RJ et al (2007) Variation in wood volatile compounds in a mixed oak stand: strong species and spatial differentiation in whisky-lactone content. *Ann For Sci* **64**: 313-320.

2. Prida A, Boulet JC, Ducousso A et al (2006) Effect of species and ecological conditions on ellagitannin content in oak wood from an even-aged and mixed stand of *Quercus robur* L. and *Quercus petraea* Liebl. *Ann For Sci* **63**: 415-424.

3. Marchal A, Waffo-Téguo P, Génin E et al (2011) Identification of new natural sweet compounds in wine using centrifugal partition chromatography-gustatometry and Fourier transform mass spectrometry. *Anal Chem* **83**: 9629-9637.

4. Marchal A, Génin E, Waffo-Téguo P et al (2015) Development of an analytical methodology using Fourier transform mass spectrometry to discover new structural analogs of wine natural sweeteners. *Anal Chim Acta* **853**: 425-434.

5. Arramon G, Saucier C, Colombani D et al (2002) Identification of triterpene saponins in *Quercus robur* L. and *Q. petraea* Liebl. heartwood by LC-ESI/MS and NMR. *Phytochem Anal* **13**: 305-310.

6. Guichoux E, Lagache L, Wagner S et al (2011) Two highly validated multiplexes (12-plex and 8-plex) for species delimitation and parentage analysis in oaks (*Quercus* spp.). *Mol Ecol Resour* **11**: 578-585.

7. Guichoux E, Garnier-Géré P, Lagache L et al (2013) Outlier loci highlight the direction of introgression in oaks. *Mol Ecol* **22**: 450-462.

8. Marchal A, Prida A, Dubourdieu D (2016) New approach for differentiating sessile and pedunculate oak: development of a LC-HRMS method to quantitate triterpenoids in wood. *J Agric Food Chem* **64**: 618-626.

9. De Paepe D, Servaes K, Noten B et al (2013) An improved mass spectrometric method for identification and quantification of phenolic compounds in apple fruits. *Food Chem* **136**: 368-375.
Internal structure and gas transport through cork

A. LAGORCE-TACHON<sup>1,2,3*</sup>, T. KARBOWIAK<sup>1</sup>, C. LOUPIAC<sup>1</sup>, J.-M. SIMON<sup>2</sup>, R. GOUGEON<sup>1,3</sup>, J.-P. BELLAT<sup>2</sup>

<sup>1</sup>UMR PAM Université de Bourgogne/Agrosup Dijon, Équipe PAPC, Dijon, France

<sup>2</sup>Laboratoire Interdisciplinaire Carnot de Bourgogne, ICB UMR 6303 CNRS,
Université de Bourgogne, Dijon, France

<sup>3</sup>Institut Universitaire de la Vigne et de Vin, Université de Bourgogne, Dijon, France

Cork stopper – image analysis – oxygen transport – diffusion

Introduction

Cork is the natural material stripped from the outer bark of cork oak. It is still the most used stopper to seal wine bottles and to preserve wine during storage [1]. This material is compressible and elastic, has a low permeability to gases and liquids, but presents a high heterogeneity. Cork is generally sorted visually by hand or by optical analysis as a function of its overall outside general aspect, considering the defects viewed from the outside are a good estimation of the inside. These defects can be considered as the macroporosity of the material, named lenticular channels. The larger the quantity of lenticels, the poorer is the quality of the cork. In this way, cork qualities are divided in 7 classes, with class 0 being the best quality and class 6 the poorest quality.

During post-bottling aging of wine, oxygen transfer through the stopper occurs depending on the permeability of the sealing material used. This can lead to oxidation reactions, involving in particular phenolic compounds. A final result of such reactions is color change (browning) and off-flavor development, which modify the organoleptic properties of wine. Therefore, it is important to investigate the internal structure of cork stoppers and to better understand how the transport of gases, particularly oxygen, occurs for a better control of their barrier properties.

Materials and methods

Neutron imaging

The visual defects of cork stoppers named lenticels were investigated using two non-invasive methods: neutron and X-ray tomography. These techniques allowed to visualize, identify and quantify the pore distribution within full cork stoppers.

Oxygen transfer

Oxygen transfer through 3-mm cork wafers was measured at 25°C using a manometric permeation technique [2] (Fig. 1). The initial pressure of oxygen was fixed between 50 and 800 hPa, at one side of the cork wafer (C1), while the other side was maintained under dynamic vacuum (C2). The decrease of pressure in C1 was followed as function of time. Depending on the mechanism of mass transfer, the transport coefficients can be determined.

Results and discussion

The 3D reconstruction of the whole cork stopper revealed that there is no interconnectivity between lenticels [3] (Fig. 2). This means, in terms of permeability, that gas transport could occur through lenticels (following Darcy or Knudsen mechanism) but must go through the alveolar structure of the material to fully cross the stopper.

Increasing the initial pressure of oxygen from 50 to 800 hPa led to a change in the values of the transport coefficients determined from the manometric method. This means that oxygen transport through cork is in favor of the Fickian mechanism [4]. From these results, it can be concluded that the limiting step of oxygen
transport through cork occurs in the cell wall. From the dependence of the diffusion coefficient on pressure, an apparent activation volume of $45 \pm 4 \text{ nm}^3$ was also determined by applying the transition state theory. This large value (close to the molecular volume of the ideal gas, $41 \text{ nm}^3$) indicates that oxygen molecules also diffuse through a gas phase in large space such as lenticels, plasmodesmata (if not obstructed), or, more probably, inside the empty cells when moving from one site to another [4] (Fig. 3).

<figure><img src="image_193.png" /><figcaption>Fig. 1 - Manometric device used for the measurement of oxygen transfer through cork.</figcaption></figure>

<figure><img src="image_194.png" /><figcaption>Fig. 2 - 3D reconstructed cork stopper from X-ray tomography. (A) Stopper external surface; (B) axial and radial section; (C) lenticular channels.</figcaption></figure>

<figure><img src="image_195.png" /><figcaption>Fig. 3 - Schematic representation of oxygen diffusion through cork (o: oxygen molecule, ■: cell wall, ◐: cork cell).</figcaption></figure>
**Conclusion**

For the first time, the internal structure of cork stopper was investigated. Neutron and X-ray tomography allowed to identify and quantify cork defects. Moreover, from the 3D reconstruction, the distribution of the lenticels inside the stopper did not show any interconnectivity. According to this internal structure, gas molecules can not only transfer within lenticels but must go through a dense phase to cross the material. The limiting step of gas transfer in cork is the diffusion of gaseous molecules through the cell walls.

**Acknowledgements**

We gratefully acknowledge the Bureau Interprofessionnel des Vins de Bourgogne, the Comité Interprofessionnel du Vin de Champagne and the Regional Council of Burgundy for their financial support and the Trescases Company for providing cork stoppers. We also want to thank Frédéric Ott from Léon Brillouin Laboratory (CEA, Saclay, France) for neutron imaging.

**References**

1. Karbowiak T, Gougeon RD, Alinc JB et al (2010) Wine oxidation and the role of cork. *Crit Rev Food Sci Nutr* **50**: 20-52.

2. Lequin S, Chassagne D, Karbowiak T et al (2012) Diffusion of oxygen in cork. *J Agric Food Chem* **60**: 3348-3356.

3. Lagorce-Tachon A, Karbowiak T, Loupiac C et al (2015) The cork viewed from the inside. *J Food Eng* **149**: 214-221.

4. Lagorce-Tachon A, Karbowiak T, Simon JM et al (2014) Diffusion of oxygen through cork stopper: is it a Knudsen or a Fickian mechanism? *J Agric Food Chem* **62**: 9180-9185.
# Oxygen transfer through oak wood using an innovative permeameter

Y. QIU¹*, S. LACAMPAGNE¹, M. MIRABEL², M. MIETTON-PEUCHOT¹, R. GHIDOSSI¹

¹Univ. Bordeaux, ISVV, EA 4577 Œnologie, INRA, ISVV, USC 1366 Œnologie, F-33140 Villenave d'Ornon, France

²Chêne et Cie, Research Department of Taransaud, Canton and Kadar cooperages and Xtrachêne, 5 rue Magellan, F-75008 Paris France

## Oxygen transfer – oxygen desorption – barrel – permeameter

## Introduction

Nowadays 5 percent of wines in the world are aged in barrels [1]. Barrel aging has many beneficial effects on wine and its role as an oxygen provider is a long known advantage. However, questions such as how much oxygen is transferred into barrel during barrel aging and how oxygen molecules move through barrel's walls have not been clearly answered. A quantification of oxygen transfer during barrel aging will better guide wine-makers to adjust and control their cellar work.

Based on the hypothesis of the existence of oxygen transfer through barrel wall, Ribereau-Gayon in his earlier work estimated an annual transfer from 15 to 45 mg.L⁻¹ year⁻¹ for a new French barrel in hermetic condition; 0.7 to 3.3 mg.L⁻¹ year⁻¹ for unsealed barrels [2]. This rate is widely cited by researchers in the field. Peterson and Singleton later on questioned the possibility of oxygen transfer through barrel as they found out for sealed barrels, a vacuum could gradually formed over time, enough to cause barrel micro-deformation; this suggested a perfect air-tightness of the barrel [3, 4]. However, researchers such as del Alamo-Sanza and Nevares followed the oxygen transfer hypothesis and determined the oxygen transfer rate at 11.62 mg.L⁻¹ year⁻¹ [5]. Feuillat in 1996 pointed out the importance of oxygen desorption in wine-saturated area of barrel's inner surface [6]. But it is only recently that specific study on oxygen desorption of oak has been accomplished. Pons et al. [7] studied the oxygen desorption of oak chips using model solution in 2014. During the first 5 days of measurement, dissolved oxygen concentration increased rapidly from 0 to 1 mg.L⁻¹, which covered 80% of the total oxygen dissolved during the whole measurement period (35 days); they found out that oxygen concentration decreased from the 8th day till the end of the measurement as it is consumed by oxidative phenol compounds released from oak itself [7].

As for the subject of possible air passages in a barrel, researchers have formulated many hypotheses derived from their observations. Ribereau-Gayon [2] suggested that the zone around the bunghole is more porous to oxygen. Moutounet et al. [8] demonstrated the possibility for the barrels to have micro-deformation in the end of staves in the barrel's lower part, allowing a gas exchange. Recently, del Alamo-Sanza and Nevares [5] suggested that stave gaps may be the most effective transfer passage in a barrel.

The objective of this study is to understand barrel's oxygen supply by different oxygen desorption and oxygen transfer phenomena. A quantification of oxygen desorption during the first month of contact is studied as well as an identification of oxygen passage in barrel using an innovative permeameter.

## Materials and methods

In search for a better understanding of the subject, barrels were primarily used for a global approach; oxygen desorption phenomenon was further investigated in laboratory scale; a permeameter was developed for the study of oxygen transfer and oxygen passages; several parameters influencing oxygen transfer rate were evaluated with the permeameter.

<footnote>*Corresponding author: yang.qiu@etud.u-bordeaux2.fr</footnote>
*Oxygen desorption in barrels set in different conditions*

Barrels were set in two different conditions to differentiate the phenomenon of oxygen desorption from oak and a combination of oxygen supply from barrel and ambient air. One barrel was set in a climate-controlled room (18 ±1 °C) exposed to ambient air, the other in a closed system filled with nitrogen gas in order for the barrel to stay isolated. Barrels stayed closed during each test to avoid oxygen contamination from the bunghole. At the beginning of each experience, the barrels were filled with degassed model solution; the concentration of dissolved oxygen was followed over a month by an oxymeter (Hach HQ 30d flexi, France). The oxygen dissolution curve of the two barrels was compared.

*System of desorption for the quantification of oxygen desorption*

A special system was installed in the laboratory for the evaluation of oxygen desorption from small grain oak of different heating level (unheated, medium heat and strong heat). Oak wood was sliced into small pieces and imbibed in bottles filled with degassed model solution (104 cm² of contact surface/L model solution, the same ratio in a 225L barrel). The model solution was renewed every 5 days to make sure no oxygen consumption took place inside the bottle as a preliminary study showed that oxygen consumption by oak polyphenols starts from 15 days on. The concentration of dissolved oxygen in the solution was followed during 2 months. The influence of heating level over oxygen desorption was studied as well.

*Identification of oxygen passages using the permeameter*

Oxygen transfer through a piece of stave (dimension 4.5cm * 4.5cm * 2.3cm) was studied with a permeameter co-developed by ISVV and ENSAM. The oak samples were cut from oak stave, either in whole or sliced into two half-round pieces to imitate stave gaps. Samples were placed in the intermediate chamber of the permeameter. The upstream, intermediate and downstream chambers were equipped with an entrance and an exit of gas, permitting a preliminary step of nitrogen scanning; the volume of the model solution in contact with oak sample was calculated with the same ratio volume/surface as if in 225L barrel.

The oxygen concentration in the upstream and downstream chamber was detected using a mobile optical fiber coupled with small sensor spot. The luminescent systems selected for this were the Oxy-trace (PreSens GmbH, Germany) coupled with PSt3 type (detection limit = 15 ppb, 0 - 100% oxygen). The dissolved oxygen concentration in the solution in contact was followed during 120 hours for each test. Nitrogen gas and pure oxygen gas were injected in the upstream chamber at a fixed pressure of 0.5 bars. Oxygen desorption phenomenon could be proved to exist while alternating the type of gas applied in the upstream chamber and by analyzing the oxygen dissolution kinetics curves.

**Results and discussion**

The study of oxygen desorption in barrels showed that for barrel set to open air the quantity of oxygen supplied by barrel diminishes with the number of soaking. During the first oak/solution contact, oxygen supply achieved the highest level of 2 mg.L⁻¹ during 30 days of measurement. The third time contact brought significantly lower oxygen level to solution compared to the previous two. A dry barrel brings more oxygen than used wet barrels. The decrease of dissolved oxygen (DO) is mainly due to oxygen consumption by oak-originated polyphenols (Figure 1).

<figure><img src="image_196.png" /><figcaption>Fig. 1 - Dissolved oxygen concentration in a barrel exposed to ambient air.</figcaption></figure>
The comparison between the first repetitions of barrel set to different conditions showed that for the barrel in an isolated system, the concentration of DO decreased from 15 days on whereas for the barrel exposed to ambient air, DO decreased from 28 days on, suggesting an oxygen ingress for DO in barrel exposed to ambient air. The second repetition showed a same tendency while for both barrels DO supply was significantly decreased compared to the first two repetitions. DO's origin is thus deduced to come from the oak itself for a large proportion; oxygen can also be transferred from the exterior, possibly through stave gaps or through micro-deformations (Figures 2 & 3).

<figure><img src="image_197.png" /><figcaption>Fig. 2 - Comparison of dissolved oxygen concentration between barrels set into different conditions of first-time contact.</figcaption></figure>

<figure><img src="image_198.png" /><figcaption>Fig. 3 - Comparison of dissolved oxygen concentration in barrels set into different conditions, second repetition.</figcaption></figure>

*Quantification of oxygen desorption with a system developed in laboratory*

Quantification of oxygen desorption from oak was further studied at laboratory scale. Results show that oxygen desorption from oak is most efficient during the first month of contact and is independent of heating level. In average, 10% of oxygen in oak is released to solution in contact, corresponding to 5 mg.L⁻¹ in concentration under 0.2 bar vacuum. Oxygen desorption is thus deduced to be the first and foremost contributor to barrel's oxygen supply (Figure 4).

<figure><img src="image_199.png" /><figcaption>Fig. 4 - Percentage of oxygen desorption from oak of different heating level (5 days/cycle).</figcaption></figure>
Identification of oxygen passages and its influential factors using an innovative permeameter

Oxygen passageways were studied using the permeameter, in which a round piece of small grain oak (26 mm thick) is set in the middle, in contact with pure oxygen gas on one side, a deoxygenized model wine solution on the other side. It is observed that during 5 days of measurement, with a pressure of 0.5 bars, no pure oxygen molecule has successfully passed through the wood piece (Figure 5), neither when pure oxygen gas was applied nor when nitrogen gas was used. At the end of each measurement, DO stayed always inferior to 8 mg.L⁻¹, the saturation concentration of ambient air, instead of approaching 40 mg.L⁻¹, the saturation concentration of pure oxygen. If pure oxygen migrated under 0.5 bar pressure through oak stave, DO in downstream chamber would surpass the threshold of 8 mg.L⁻¹. The difference between two curves is thought to come from variability in oak tissues; however, further studies are required to validate this hypothesis.

<figure><img src="image_200.png" /><figcaption>Fig. 5 - Oxygen dissolution curve in the downstream chamber of the permeameter during 120 hours of measurement; in the upstream chamber: application of pure oxygen or nitrogen gas at 0.5 bars.</figcaption></figure>

Oxygen molecules were proved to be able to pass through stave gaps at different rate according to applied pressure. Several wood pairs (A and B) were made from two half-round pieces to imitate stave gaps. For both pair, A and B, a decrease in tightening force applied on stave gaps (from 14 to 8 bar) results in a release of available oxygen transfer passageways, hence the quantity of oxygen transfer to model solution over a fixed period. When applied with the same tightening force, pair B possesses more oxygen passages than pair A as the DO is far beyond 8 mg.L⁻¹ at the end of the measurement, suggesting the flow of pure oxygen molecules in solution in the downstream chamber (Figure 6). This indicates that oak surface's smoothness has an equally important impact on oxygen transfer efficiency as the tightening force.

<figure><img src="image_201.png" /><figcaption>Fig. 6 - Influence of applied pressure and influence of surface contact at stave gaps on oxygen transfer efficiency over 5 days of measurement using the permeameter.</figcaption></figure>
Conclusion

These results highlight the oxygen transfer phenomenon during barrel aging: the enrichment of oxygen in barrels is largely due to oxygen desorption from wood itself and to oxygen transferred at stave gaps under insufficient applied pressure, a transfer also influenced by stave polishing process during barrel fabrication.

References

1. Giraud W (2009) Etude physico-chimique de l'interface bois-vin pendant l'élevage en barriques. *PhD thesis*, Université de Toulouse.

2. Ribereau-Gayon J (1933) L'étude des oxydations et réductions dans les vins. *PhD thesis*, Université de Bordeaux.

3. Peterson RG (1976) Formation of reduced pressure in barrels during wine aging. *Am J Enol Vitic* **27**: 80-81.

4. Singleton VL (1995) Maturation of wines and spirits: comparisons, facts, and hypotheses. *Am J Enol Vitic* **46**: 98-115.

5. del Alamo-Sanza M, Nevares I (2014) Recent advances in the evaluation of the oxygen transfer rate in oak barrels. *Agric Food Chem* **62**: 8892-8899.

6. Feuillat F (1996) Contribution à l'étude des phénomènes d'échanges bois/vin/atmosphère à l'aide d'un fût modèle. Relations avec l'anatomie du bois de chêne (*Quercus robur* L., *Quercus petraea* Liebl.). *PhD thesis*, Ecole Nationale du Génie Rural des Eaux et des Forêts.

7. Pons A, Prida A, Benoit V et al (2014) Etude de la contribution des morceaux de bois de chêne à l'apport d'oxygène dans les vins au cours de l'élevage sous bois de chêne. *Rev Œnol Tech Vitinivic Œnol* **41**: 45-48.

8. Moutounet M, Mazauric JP, Saint Pierre B et al (1998) Gaseous exchange in wines stored in barrels. *J Sci Technol Tonnellerie* **4**: 131-145.
# Identification of new S-conjugate precursors of 3SH in Sauvignon juice

C. BOCKER<fnref n="1" />,<fnref n="2" />*, C. THIBON<fnref n="2" />,<fnref n="3" />, P. DARRIET<fnref n="3" />, V. MOINE<fnref n="1" />, D. DUBOURDIEU<fnref n="3" />

Thiol precursor – Sauvignon blanc – 3-sulfanylhexanol – identification

## Introduction

Although extensively studied, the origin of the aroma of Sauvignon blanc wines is still far from being fully understood. The compounds responsible for the varietal aroma of this wine were associated with the family of volatile thiols. 3-sulfanylhexanol (3SH, formerly named 3-mercaptohexanol or 3MH) is known to significantly contribute to the characteristic aroma of wines made from the Sauvignon blanc cultivar [1]. Usually absent in grape juice, volatile thiols are produced by *S. cerevisiae* during the fermentation process from S-3-(hexan-1-ol)-L-cysteine (Cys-3SH) [2-4] and from S-3-(hexan-1-ol)-glutathione (Glut-3SH) [5]. Another pathway of 3SH formation from corresponding C6-aldehyde was also proposed but is controversial [6]. Furthermore, Capone et al. demonstrated that the addition of labeled (E)-hex-2-enal to whole grape berries before crushing results in the formation of labeled S-3-(hexan-1-ol)-glutathione [7]. It is therefore conceivable that an intermediate compound such as S-3-(hexan-1-ol)-glutathione (Glut-3SH-Al) exists in the musts.

## Materials and methods

Juice samples were produced with Sauvignon blanc grapes. Medium Pressure Liquid Chromatography (MPLC) was carried out on the Armen Spot Prep II system with an integrated quaternary pump, detector and fraction collector. The column was a CHROMABOND® Flash RS330-C18ec column (330 g). The analysis was carried out on LC-Exactive (Thermo Fisher Scientific) mass spectrometer equipped with a heated ESI ion source. ¹H and ¹³C NMR spectra were recorded with a Bruker AC-300 FT (¹H: 300 MHz, ¹³C: 75 MHz) at +4°C on freshly prepared sample (40 mg of lyophilized compound in 0.5 mL of cold D₂O).

## Results and discussion

### Identification of the Glut-3SH-Al and Glut-3SH-SO₃ in grape juice

The aldehydic form of glutathione S-conjugate of 3SH was synthesized via the Michael-type addition of glutathione to the corresponding α,β-unsaturated aldehyde ((E)-hex-2-enal) according to protocol of Thibon et al. [8] and purified by MPLC on a C₁₈ column. The Glut-3SH-Al bisulfite adduct (Hydrogen S-3-glutathionyl-hydroxyhexanesulfonate or Glut-3SH-SO₃) could easily be obtained by bisulfitic combination (figure 1). The full-scan mass spectrum of Glut-3SH-Al recorded in positive mode exhibits a prevalent pseudo-molecular ion [M+H]⁺ at *m/z* 406.1631 corresponding to the molecular formula C₁₆H₂₈O₇N₃S, whereas the mass spectrum of Glut-3SH-SO₃ presents two prevalent ions: a peak at *m/z* 488.1366 relevant to the pseudo-molecular ion [M+H]⁺ (corresponding to the molecular formula C₁₆H₃₀N₃O₁₀S₂) and a peak at *m/z* 406.1630 [M-H₂SO₃+H]⁺.

Partial purification of the aroma precursor from Sauvignon blanc juice was achieved by applying medium pressure liquid chromatography (MPLC). We screened for the presence of target compounds in the 200 fractions obtained by fractionation of 5 L of grape juice on C18 column.

<footnote n="1">¹Biolaffort, F33100 Bordeaux, France</footnote>

<footnote n="2">²INRA, ISVV, USC 1366 Œnologie, FR-33140 Villenave d'Ornon, France</footnote>

<footnote n="3">³Univ. Bordeaux, ISVV, EA4577, FR-33140 Villenave d'Ornon, France</footnote>
<figure><img src="image_202.png" /><figcaption>**Fig. 1** - Structure of S-3-(hexan-1-al)-glutathione (Glut-3SH-Al) and Hydrogen S-3-glutathionyl-hydroxyhexanesulfonate (Glut-3SH-SO₃).</figcaption></figure>

Identification was confirmed by study of mass accuracy, fragmentation patterns and retention time. For example, the FTMS spectrum exhibits a prevalent ion peak at *m/z* [M+H]⁺ 406.16388, in agreement with the molecular formula of C16H28O7N3S, with an error less than 1 ppm in relation to the calculated mass (Table 1). Fragmentation experiments in FTMS and retention time confirmed the identification of Glut-3SH-Al and Glut-3SH-SO₃ in the sample.

<table><caption>**Table 1** - Chemical formula, diagnostic ion (Diag. ion), fragment ion, calculated and observed *m/z* in the juice extract (MPLC fraction) for each ion and compound. Mass accuracy (Mass acc.) is expressed as mass error in ppm.</caption><thead><tr><th>Chemical formula</th><th>Diag. ion</th><th>m/z observed</th><th>Mass acc.</th><th>Fragment ion</th><th>m/z observed</th><th>Mass acc.</th></tr></thead><tbody><tr><td>C<sub>16</sub>H<sub>27</sub>O<sub>7</sub>N<sub>3</sub>S</td><td>[M+H]<sup>+</sup></td><td>406.16388</td><td>0.901</td><td>[C<sub>11</sub>H<sub>19</sub>O<sub>3</sub>N<sub>2</sub>S]<sup>+</sup></td><td>259.11127</td><td>0.706</td></tr><tr><td>C<sub>16</sub>H<sub>29</sub>O<sub>10</sub>N<sub>3</sub>S<sub>2</sub></td><td>[M+H]<sup>+</sup></td><td>488.13648</td><td>0.406</td><td>[C<sub>11</sub>H<sub>19</sub>O<sub>3</sub>N<sub>2</sub>S]<sup>+</sup></td><td>259.11113</td><td>0.191</td></tr></tbody></table>

*Validation of S-3-(hexan-1-al)-glutathione as a direct precursor of 3-sulfanylhexan-1-ol*

In order to evaluate the contribution of the new putative precursors to the 3SH wine content and to investigate the capacity of *S. cerevisiae* to release 3SH from Glut-3SH-Al or Glut-3SH-SO₃ during alcoholic fermentation, Glut-3SH-Al and Glut-3SH-SO₃ were added to the model medium or must prior to fermentation at 100 or 500 µg/L. Table 2 shows the 3SH levels in the wine and model wine after total fermentation and the conversion rate in the different modalities.

The bioconversion rates were similar for Glut-3SH-Al and its bisulfite adduct when the fermentations were carried out in synthetic medium or in grape juice. The rate was approximately 0.4% for the free and combined Glut-3SH, irrespective of the added concentration. The addition of 100 µg/L of Glut-3SH-Al in the must induces an increase in 3SH release that ranged from 15 to 20% (959 vs. 810 ng/L for the control, Table 2). These results clearly indicate that Glut-3SH-Al and its bisulfite adduct are new direct precursors of 3SH with molar conversion yields close to 0.4% in enological conditions. The fact that the yeast bioconversion rate of Glut-3SH-SO₃ was higher in the absence of SO₂ in the medium may (data not shown) indicate that the bisulfitic combination is reversible and may dissociate to give the free aldehydic Glut-3SH-Al (with a higher 3SH bioconversion rate) when the free SO₂ levels diminish.

<table><caption>**Table 2** - Bioconversion of Glut-3SH-Al and Glut-3SH-SO₃ into 3SH by *S. cerevisiae*. Precursors were added to the synthetic medium or must at 100 µg/L and 500 µg/L. 3SH release was measured after complete fermentation (n = 3), expressed in ng/L and the conversion rates were estimated (%). In must, the conversion rate was estimated from the 3SH levels due to the spike (relative to the control).</caption><thead><tr><th rowspan="2">Compounds</th><th rowspan="2">Spike (µg/L)</th><th colspan="2">Synthetic medium</th><th colspan="2">Fermented must</th></tr><tr><th>3SH (ng/L)</th><th>Conversion (%)</th><th>3SH (ng/L)</th><th>Conversion (%)</th></tr></thead><tbody><tr><td>-</td><td>No Spike</td><td>-</td><td>-</td><td>810 ± 58</td><td>-</td></tr><tr><td rowspan="2">Glut-3SH-Al</td><td>100</td><td>139 ± 29</td><td>0.42</td><td>959 ± 26</td><td>0.45</td></tr><tr><td>500</td><td>645 ± 120</td><td>0.39</td><td>1470 ± 70</td><td>0.40</td></tr><tr><td rowspan="2">Glut-3SH-SO<sub>3</sub></td><td>100</td><td>126 ± 33</td><td>0.38</td><td>949 ± 36</td><td>0.42</td></tr><tr><td>500</td><td>725 ± 88</td><td>0.38</td><td>1434 ± 36</td><td>0.38</td></tr></tbody></table>
*Evaluation of Glut-3SH-Al and Glut-3SH-SO₃ contribution on 3SH release*

The detected amount of total Glut-3SH-Al in the 35-45 and 75-85 fractions seems to be of the same order of magnitude as Glut-3SH (detected in the 70-75 MPLC fraction, data not shown). As the Glut-3SH content in the must was found to be between 50 and 500 µg/L in the Sauvignon blanc musts [2, 4], we can estimate that the contribution of Glut-3SH-Al is between 50 and 500 ng/L of the resulting thiols, *i.e.* with a putative relative participation in terms of the total 3SH content between 10 and 25% in wine.

**Conclusion**

In this work, two new direct S-conjugate precursors of 3SH were identified in Sauvignon blanc juices for the first time. Further work is now required to determine the exact content of Glut-3SH-Al and its bisulfite adduct in the must in order to adjust the contribution of these new precursors to the 3SH release. The impact of SO₂ and the bisulfite dissociation constant will also be specified in order to enhance our understanding of the aromatic potential of grape juice.

**References**

1. Tominaga T, Furrer A, Henry R et al (1998) Identification of new volatile thiols in the aroma of *Vitis vinifera* L. var. Sauvignon blanc wines. *Flavour Frag J* **13**: 159-162.

2. Grant-Preece PA, Pardon KH, Capone DL et al (2010) Synthesis of wine thiol conjugates and labeled analogues: fermentation of the glutathione analogue of 3-mercaptohexan-1-ol yields the corresponding cysteine conjugate and free thiol. *J Agric Food Chem* **58**: 1383-1389.

3. Roland A, Schneider R, Razungles A et al (2010) Straightforward synthesis of deuterated precursors to demonstrate the biogenesis of aromatic thiols in wine. *J Agric Food Chem* **58**: 10684-10689.

4. Thibon C, Cluzet S, Merillon JM et al (2011) Identification of biogenesis pathways of 3-sulfanylhexanol precursors in *Vitis vinifera* cells induced by *Botrytic cinerea*, pp. 100-104. In *Proceedings of Oeno 2011*. Bordeaux, France.

5. Peyrot des Gachons C, Tominaga T, Dubourdieu D (2002) Sulfur aroma precursor present in S-conjugate form: identification of S-3-(hexan-1-ol)-glutathione in must from *Vitis vinifera* L cv Sauvignon blanc. *J Agric Food Chem* **50**: 4076-4079.

6. Harsch MJ, Benkwitz F, Frost A et al (2013) New precursor of 3-mercaptohexan-1-ol in grape juice: thiol-forming potential and kinetics during early stages of must fermentation. *J Agric Food Chem* **61**: 3703-3713.

7. Capone DL, Jeffery DW (2011) Effects of transporting and processing Sauvignon blanc grapes on 3-mercaptohexan-1-ol precursor concentrations. *J Agric Food Chem* **59**: 4659-4667.

8. Thibon C, Cluzet S, Mérillon JM et al (2011) 3-Sulfanylhexanol precursor biogenesis in grapevine cells: the stimulating effect of *Botrytis cinerea*. *J Agric Food Chem* **59**: 1344-1351.
# Le « potentiel thiol » : un nouveau concept de mesure de la qualité aromatique du vin

A. ROLAND<fnref n="1" />*, S. DELPECH<fnref n="1" />, F. CAVELIER<fnref n="2" />, L. DAGAN<fnref n="1" />, M.A. DUCASSE<fnref n="3" />, R. SCHNEIDER<fnref n="1" />

Potentiel thiol – oxydation – disulfures – 3MH et 3MHA

## Introduction

Les thiols variétaux tels que le 3-mercaptohexanol (3MH) et l'acétate de 3-mercaptohexyle (3MHA) sont des molécules clés de l'arôme du vin grâce à leur puissance olfactive. Ils confèrent aux vins des notes d'agrumes, de fruits tropicaux ou de cassis, très appréciées du consommateur.

La teneur de ces composés est réputée diminuer au cours du vieillissement des vins, entrainant une perte aromatique dont les mécanismes sous-jacent ont déjà été détaillés [1]. Cependant, contre toute attente, certaines observations montrent au contraire, que les teneurs en thiols variétaux (3MH et 3MHA) de certains vins lors de leur stockage en bouteille ont pu augmenter (jusqu'à +30% en 3MH) [2]. La réversibilité des équilibres d'oxydoréduction des thiols et disulfures pourrait expliquer la libération des thiols au cours du vieillissement.

Ainsi, il apparaît que le dosage des formes libres ne constitue qu'une information partielle de l'état aromatique d'un vin.

Le but de ce travail est de comprendre l'évolution des teneurs en thiols variétaux au cours du vieillissement du vin et de modéliser la proportion de disulfures du 3MH et du 3MHA formée en utilisant un nouvel indicateur analytique : « le potentiel thiol ». Pour cela, une méthode de dosage des thiols sous formes libre et disulfures a été développée par dilution isotopique et nanoLC-MS/MS.

## Matériel et méthodes

### Plan expérimental

3 vins blanc, rosé et rouge (respectivement Manseng-Côtes de Gascogne, Grenache-Rosé de Provence et Gamay-Beaujolais) ont subis divers traitements œnologiques : 1-témoin, 2-oxygénation, 3-CuSO₄ (10 mg/L), 4-CuSO₄ (10 mg/L)/GSH (50mg/L) et 5-GSH (50 mg/L)/oxygénation. Toutes les modalités étudiées ont été réalisées en triplicat et conditionnées dans des tubes étanches (Wine In Tube, 45 mL) équipés de capteurs à oxygène (PSt3, Nomacorc). La consommation en oxygène des vins a été suivie durant 7 à 10 jours par luminescence au moyen de la sonde NOMASense O₂. Lorsque les teneurs en oxygène dissout ont soit atteint une valeur inférieure à 1 mg/L, soit une stagnation, les vins ont été analysés.

### Analyse des thiols variétaux et des disulfures associés

Les échantillons de vin (2 mL) sont analysés différemment selon la mesure souhaitée. Pour l'analyse des thiols libres, la première étape consiste en une dérivatisation au N-phénylmaléimide (NPM) tandis que pour les disulfures, une étape de réduction avec la tris(carboxyethyl)-phosphine (TCEP) puis de dérivatisation au NPM sont réalisées. L'analyse séparée des deux types d'échantillon permet d'accéder par différence à la proportion de thiols sous formes de disulfures, quel que soit le second thiol engagé dans la liaison disulfure.

<footnote n="1">¹Nyseos, 53 Rue Claude François, Parc 2000, 34080 Montpellier, France</footnote>

<footnote n="2">²Institut des Biomolécules Max Mousseron - UMR 5247 CNRS-UM1-UM2-ENSCM, Département des Aminoacides, Peptides et Protéines (DAPP), Université de Montpellier, 34095 Montpellier cedex 05, France</footnote>

<footnote n="3">³Institut Français de la Vigne et du Vin, UMT QUALINNOV, Pôle Rhône Méditerranée, Domaine de Pech Rouge, 11430 Gruissan, France</footnote>

<footnote>*Auteur principal : aurelie@nyseos.fr</footnote>
<table><caption>**Tableau 1** - Teneurs en SO₂ libre, potentiels 3MH / 3MHA et 3MH / 3MHA libres dans des vins rouge, rosé et blanc ayant subi divers traitements œnologiques. Les teneurs en thiols sont exprimées en ng/L et sont la moyenne de triplicat d'échantillon. Les teneurs potentiel 3MHA et 3MHA libre sont non détectées dans les vins rouge et blanc (nd : non détecté).</caption><thead><tr><th rowspan="2"></th><th colspan="5">Red wine</th><th colspan="4">Rosé wine</th><th colspan="4">White wine</th></tr><tr><th>Free SO2 (mg/L)</th><th>3MH potential</th><th>3MH</th><th>3MHA</th><th>3MHA</th><th>Free SO2 (mg/L)</th><th>3MH potential</th><th>3MH</th><th>3MHA</th><th>3MHA</th><th>Free SO2 (mg/L)</th><th>3MH potential</th><th>3MH</th><th>3MHA</th><th>3MHA</th></tr></thead><tbody><tr><td>Control</td><td>10</td><td>206</td><td>189</td><td>23</td><td>19</td><td>28</td><td>2743</td><td>2632</td><td>255</td><td>228</td><td>15</td><td>5497</td><td>5070</td><td>25</td><td>12</td></tr><tr><td>O₂</td><td>4</td><td>169</td><td>144</td><td>19</td><td>15</td><td>19</td><td>2713</td><td>2311</td><td>223</td><td>180</td><td>13</td><td>5236</td><td>4688</td><td>nd</td><td>nd</td></tr><tr><td>CuSO₄</td><td>7</td><td>28</td><td>0</td><td>15</td><td>1</td><td>17</td><td>1372</td><td>10</td><td>118</td><td>13</td><td>11</td><td>1478</td><td>31</td><td>nd</td><td>nd</td></tr><tr><td>CuSO₄/GSH</td><td>5</td><td>34</td><td>9</td><td>17</td><td>11</td><td>19</td><td>1001</td><td>42</td><td>100</td><td>15</td><td>12</td><td>1180</td><td>83</td><td>nd</td><td>nd</td></tr><tr><td>GSH/O₂</td><td>11</td><td>208</td><td>188</td><td>26</td><td>18</td><td>27</td><td>2667</td><td>2365</td><td>219</td><td>187</td><td>17</td><td>5421</td><td>4737</td><td>nd</td><td>nd</td></tr></tbody></table>