The nitrogen nutrition of vines and particularly berries has an important effect on grape and wine quality (Bell and Henschke, 2005). The management of this nutrient in the vineyard is often challenging and can impact economic, environmental and quality aspects. Excessive nitrogen levels result in vigour rather than grape quality (Wheeler and Pickering, 2003), and they increase the risk of diseases, such as grey rot (Conradie and Saayman, 1989). On the other hand, nitrogen deficiencies in the vineyard alter grape composition (Bell and Henschke, 2005; T. Garde-Cerdán et al., 2015; Perez-Alvarez et al., 2017; Gutiérrez-Gamboa et al., 2020), resulting in fewer flavour precursors (Choné et al., 2006) and a decrease in nitrogen-containing compounds (Schreiner et al., 2014), thereby influencing fermentation rate and yeast metabolism.

Yeast can only use part of the nitrogen present in the berry, commonly referred to as yeast available nitrogen (YAN), which is in the form of primary amino acids and ammonium. A YAN value of 140 mg N/L is generally considered to be the minimum level necessary to ferment a clarified must to dryness (Bell and Henschke, 2005; Jiranek et al., 1995). This value can vary with must sugar content, vine varieties and wine style. Tahim et al. (2019) reported an optimal YAN value of 130 mg N/L for cool-climate Riesling. In the case of red cultivars, which are fermented along with the skin, even lower values have been reported: 100 mg N/L for Pinot Noir (Schreiner et al., 2018) and 60 mg N/L for Merlot (Stockert et al., 2013). This can be explained by the contribution of N contained in the skin, which is about 29 % of the berry’s total YAN (Stines et al., 2000). To prevent stuck or sluggish fermentations, winemakers commonly supplement grape juice with ammonium salt. Supplementation with amino acids or with ammonium will induce different aroma profiles (Garde-Cerdán and Ancín-Azpilicueta, 2008; Miller et al., 2007). The timing of the nitrogen addition also influences the production of the aroma compounds (Seguinot et al., 2018). The complex correlation between wine flavour and YAN values was underlined by Ugliano et al. (2007), who recommended performing YAN analyses before nitrogen supplementation in the cellar. According to our unpublished study of Swiss white wines produced from nitrogen-deficient grape juice (YAN < 140 mg N/L), these practices can decrease, but not eliminate, quality defects such as insufficient aroma quality and pronounced bitterness and astringency.

Torrea and Henschke (2004) studied the impact of must nitrogen content on the sensory properties of Chardonnay wine and reported lower ratings for positive sensory descriptors, such as fruity and floral, for wines made with low-nitrogen juices. The obvious correlation between these sensory descriptors and the concentration of YAN in grapes of different vine varieties indicates the existence of chemical molecules in wine that could be markers for nitrogen deficiencies in vineyards. Previous studies have pointed to certain volatile compounds (i.e., higher alcohols and esters) that are systematically reported in studies on nitrogen nutrition. Webster et al. (1993) reported that significant differences can be found in the volatile profile and sensory analysis of Riesling wines, even after three and five years of storage, depending on the level of N supplementation in the vineyard. Fewer results have been reported for the influence of N nutrition on nonvolatile compounds, and they have often been contradictory with respect to total acidity or total phenols (Bell and Henschke, 2005). Choné et al. (2006) reported that higher N status in vines results in a diminution of total phenolic content in white Sauvignon grapes, just as it does in red varieties. Another study showed that foliar nitrogen treatment of Cabernet-Sauvignon vines decreased the flavanol concentration in the wine (Gutièrrez-Gamboa et al., 2017). However, Portu et al. (2015) reported increasing anthocyanin and flavonol content in red wine samples after foliar treatment with urea, but the flavanols were not affected by the treatment. Unfortunately, no information is available regarding the nitrogen content in the grape musts of the different variants; therefore, it is unclear whether or not the nontreated variant suffered a nitrogen deficiency. Indeed, nitrogen-deficient vineyards react differently to nitrogen supplementation than do vineyards where N is already in sufficient concentration (Moreira et al., 2011). published a review regarding the effect of nitrogen on the chemical composition of wine that demonstrated the complexity of this question. In view of this complexity, a holistic approach (i.e., studying many compounds in wine to identify the chemical markers related to nitrogen nutrition) may be interesting.

Using metabolomics, this study aims to provide an overall survey of ‘all possible’ low-molecular-weight compounds of complex molecular blends, by highlighting significant statistical variations across samples and identifying chemical markers. The markers are identified by database matches using experimental data gathered via high-resolution mass spectrometry spectra (HRMS) and MS/MS fragmentation patterns (using either liquid chromatography–mass spectrometry or gas chromatography–electronic impact–MS). Following spectral deconvolution, the data matrix (sample feature area × m/z-RT pairs) is processed by multivariate data analysis methods to highlight major trends and identify biomarkers of interest. This approach has proven to be a valuable tool for the study of complex systems. In the past decade, the number of publications on grape and wine metabolomics has expanded dramatically. In their reviews, Cozzolino (2016) and Pinu (2018) demonstrated the importance of metabolomic approaches in grape and wine research. Such approaches have been used to compare the metabolomic profiles of different vine varieties (Arapitsas et al., 2020), as well as to study the plant metabolites’ responses to abiotic stress, such as water stress (Pinasseau et al., 2017), and to investigate oenological techniques, such as sulfite addition (Roullier-Gall et al., 2017) and storage (Gougeon et al., 2019). Moreover, untargeted techniques using MS detection have enabled the identification of new substances in wine (Arapitsas et al., 2016).

Ciampa et al. (2019) recently investigated the effect of nitrogen nutrition on the grape metabolome using the NMR technique and found evidence of significant differences in Nero di Troia grapes for valine, leucine, isoleucine, proline and malic acid. Focusing on the effect of nitrogen nutrition on the grape metabolome, other studies have reported changes in nitrogen-containing compounds (Perez-Alvarez et al., 2017). The effect of grape juice composition - particularly amino acids and fatty acids - on wine’s yeast metabolism is also a well-studied area (Fairbairn et al., 2017; Pinu et al., 2014; Pinu et al., 2019). However, these studies have often been conducted on model growing media (Rollero et al., 2015) or on grape juice with YAN values higher than 140 mg N/L, and they have often focused on volatile-compound production (Fairbairn et al., 2017; Garde-Cerdán and Ancín-Azpilicueta, 2008). To our knowledge, no study has yet examined the effect of nitrogen deficiency in the vineyard on the composition of wine’s volatile and nonvolatile metabolomes.

The purpose of the present study was to identify the chemical compounds in white wine (Vitis vinifera L. cv. Chasselas) which are related to vine and berry nitrogen nutrition, and to evaluate their relevance as markers for determining whether wine was produced from nitrogen-deficient grapes. Two groups of wine were compared, which were both produced from Chasselas grapes from the same vineyard known to give grapes with low YAN content (< 140 mg N/L). Two nitrogen management treatments were applied on the field to produce (a) nitrogen-deficient grapes (LN) with no nitrogen supplementation and (b) grapes with higher N content (YAN > 180 mg N/L) (HN) using foliar nitrogen treatment during the veraison stage. Wines were produced from these two variants of grapes and analysed. Untargeted analyses of volatile and nonvolatile compounds with the putative identification of key metabolites using MS-based platforms were carried out. To identify markers correlated with nitrogen deficiency, the results obtained were combined and analysed using statistical methods.

The metabolite profiling of nonvolatile molecules of wine was performed by high-performance liquid chromatography–time of flight mass spectrometry (UHPLC-TOFMS). All the profiles were acquired in triplicate on two different columns with different retention modes: reversed-phase in C18 and HILIC columns. These two orthogonal retention modes allowed for better separation and identification of apolar compounds in the C18 column and polar compounds in the HILIC column. The LC-MS data, recorded in positive and negative electrospray ionisation (ESI) modes, were processed as described by Marti et al. (2014). The results, characterised by their m/z ratio, retention time, and area, were analysed by principal component analyses followed by a discriminant analysis approach (OPLS-DA) to obtain a classification model and to highlight potential markers linked to nitrogen nutrition.

The number of detected features varied significantly, depending on the different chromatographic separation and ionisation modes used. Most detected features were polar, as revealed by the analyses in HILIC mode (152 features in ESI- and 262 features in ESI+). This mode detected nearly twice as many features as the C18 column (68 features in ESI- and 182 features in ESI+). Overall, 96 features overlapped between both orthogonal profiling methods. The features were putatively identified based on accurate mass and isotopic distribution scores to calculate the molecular formula (± 6 ppm) and database matching (Arapitsas et al., 2012). The Dictionary of Natural Products (DNP on DVD, CRC press v25.1) and KnapSack databases were used as entry lists to match the compounds based on HRMS data. A list of compounds was built using ‘wine or grape or vitis’ as keyword filters. After removal of adducts and other ESI artefacts, the identification procedure revealed 140 putatively annotated features, which corresponds to an annotation of level 3 according to Schymanski et al. (2014). The resulting dataset was used for the multivariate data analysis.

As a preliminary step, PCA was applied as a form of exploratory data analysis to provide an unsupervised overview of the LC–MS fingerprints (Figure 3a). Interestingly, the first principal component (PC1 axis, 27 % of total variance) clustered the nitrogen content, whereas the second component was mainly involved in vintage year separation (PC2 axis, 14 % of total variance). To focus on the most relevant biomarkers related to nitrogen content, a supervised model was set up using orthogonal projection to latent structure discriminant analysis (OPLS-DA, Figure 3b). The model predictive capability was good (R2Y = 0.99, Q2Y = 0.96). Then, an Splot was used to rank the variables according to their contribution to the OPLS-DA model. The upper-right corner encompassed metabolites upregulated in HN, whereas the lower-left corner clustered downregulated compounds (Figure 3c). Overall, 16 biomarkers from several chemical classes (amino acids, vitamins, hormones, organic acids, polysaccharides and phenolic compounds) were found to be highly correlated with nitrogen treatment (Table 4).

Table

a) Principal component analysis score plot of the concatenated UHPLC-TOFMS data table of putatively identified features. LN points are labelled in Roman and grouped by vintage with solid-line circles. HN points are labelled in italics and grouped by vintage with dashed-line circles. b) OPLS-DA score plot of the concatenated UHPLC-TOFMS data table of putatively identified features. c) S-plot of all the features. Rectangles encompass the most discriminant features (see numbers 1–16 in Table 4 for details). LN points are grouped within a solid line. HN points are grouped within a dashed line.

Most vitamins produced by plants present amino acids as precursors (Miret and Munné-Bosch, 2014); it can therefore be hypothesised that fertilisation has an influence on vitamin concentration. However, in the literature, only vitamin B1 has been reported to increase with inorganic nitrogen fertilisation in plants (Mozafar, 1993). Other studies have indicated that fertilisation has no significant effect on the vitamin content of plants (Miret and Munné-Bosch, 2014). In our study, the foliar nitrogen treatment increased the concentration of pyridoxine (vitamin B6) in wine. Pyridoxine is present in must and wine in concentrations of 0.2–0.7 µg/L (Ribéreau-Gayon et al., 2004). Wainwright (1970) reported that pyridoxine deficiency can influence sulphur-containing amino acid synthesis in the grapes and increase hydrogen sulphide release during fermentation. In this study, this effect was not observed.

Plants react to nutritional and environmental stress by regulating their hormone concentrations. In this study, the concentration of abscisic acid, a hormone implicated in the control of grape ripening and development, was lower in the LN variants. This study is the first to report this hormone in relation to nitrogen deficiency; until now, it has only been reported as a response of plants and grape berries to water stress (Chaves et al., 2010). Nitrogen fertilisation can provoke water-stress-like situations due to the stimulation of important vegetative growth, high canopy transpiration and rapid soil water depletion in the summer (Scholasch and Rienth, 2019). This effect may explain why we found higher abscisic acid concentrations in the HN variants in our study.

The results of previous studies on the effect of nitrogen nutrition on concentrations of organic acids are contradictory. In this study, we identified six organic acids by UHPLC-TOFMS as putative markers in the wine: tartaric acid, citric acid, glucuronic acid, fumaric acid, malic acid, and succinic acid. Their concentrations decreased in HN variants.

Tartaric acid is the most abundant acid in the grape and is one of the most important acids in the wine. As an important component, tartaric acid was quantified during this study in the grape must (5.5 g/L; Table 1) and in the wine (1.6 g/L; Table 2). In contrast to the results of the UHPLC-TOFMS metabolomic study, the concentrations measured in the wine by the colorimetric method did not differ between the two variants (Table 2). These results can be explained by the differences in the sample preparations. The UHPLCTOFMS analyses were performed on lyophilised samples, in contrast to the other analyses in which the wine was used directly. During lyophilisation, tartaric acid forms precipitates with calcium and potassium ions, which have poor solubility in a mixture of water and ethanol (50:50 v/v %). This precipitation phenomenon already occurs in the winemaking process and during storage, depending on the temperature and alcohol content of the wine. These uncontrollable parameters can have more influence on the tartaric acid concentrations of wines than can the nitrogen nutrition of a vineyard. This behaviour leads to the exclusion of tartaric acid from the list of markers.

Citric acid, fumaric acid and malic acid are all involved in the citric acid cycle. These acids are produced in varying quantities by the grapes and during fermentation. Fumaric acid was found only in trace amounts in the grapes, and the concentration remained low in the wine, at 1.5–8.3 mg/L; (Ding et al., 1995). In contrast citric acid can reach 1.0 g/L in the wine. A considerable amount of malic acid was present in the grapes, and the production of this acid increased with foliar nitrogen treatment (Table 1). In this study, alcoholic fermentation was followed by malolactic fermentation, in which malic acid is transformed into lactic acid. We observed a positive correlation between the lactic acid concentration in the wine and the N content in the must (Table 2). The concentration of malic acid in these wines, corresponding to the residue after malolactic fermentation, was lower than 0.3 g/L (LOD of WineScan). In practice, malolactic fermentation is stopped when malic acid can no longer be measured in the wine. We assume that the negative correlation between the malic acid concentration in the wine and the N content of the must was a result of slow fermentation due to lower nitrogen content; the end point was closer to 0.3 g/L of malic acid compared to HN variants, in which the malic acid was quickly and completely consumed.

However, even if these acids show a correlation with N nutrition in the vineyard, these components are not ideal markers for wine, because their concentrations are strongly influenced by the winemaking process, especially by malolactic fermentation. When malolactic fermentation is performed, these acids can be entirely consumed by the bacteria (Davis et al., 1986). On the other hand, citric acid can be added to the wine as an oenological additive to solubilise iron.

Glucuronic acid is a sugar acid derived from glucose. In general, the free form of this acid is quickly metabolised in the plant tissue and remains only in low concentrations in plants. The literature provides minimal information about the amount of this acid in grapes and wine (0–6 mg/L) (de Valme Garcie Moreno et al., 2002). This uronic acid is involved in glucuronidation, which produces more water-soluble products, facilitating their accumulation in vacuoles or their elimination from the cells.

Succinic acid is the main carboxylic acid produced by yeast during alcoholic fermentation. This acid is formed in the citric acid cycle, and during fermentation its concentration increases to 0.5–2.0 g/L. The concentration of succinic acid was lower in HN variants. This difference is reflected in the concentration of succinate esters, which were found to be potential markers (Table 3). In contrast to the effect of nitrogen addition to grape juice during fermentation, the effects of foliar nitrogen applications in vineyards on succinic acid formation has not yet been reported. The addition of nitrogen to grape juice during fermentation is currently practiced to prevent sluggish fermentation, resulting in a decrease in succinic acid concentration if the original YAN content is low (Rollero et al., 2015). However, the addition of certain amino acids (glutamate, asparagine, proline, glutamine, threonine and GABA) increases succinate production (Bach et al., 2009). Although several factors (yeast strains, fermentation conditions, temperature, aeration and must composition) can influence succinic acid concentration during fermentation (Rollero et al., 2015), succinic acid remains an interesting candidate as a marker. Further investigation is necessary to verify whether these factors, due to different oenological practices, mask the effect of nitrogen status on the vineyard.

Pentahydroxyflavan and kaempferol-3-glucoside were identified as potential markers from the polyphenol family. Their concentrations decreased in HN variants. Pentahydroxyflavans form a class of chemical substances that differ in the positions of hydroxyl groups. In grapes and wine, flavan-3-ols (3,5,7,3',4'pentahydroxyflavan), namely catechin and epicatechin, are present in relatively high quantities. In white wine, concentrations of between 1 and 46 mg/L for catechin and of between 0.1 and 60 mg/L for epicatechin can be found, depending on the grape variety and oenological methods. A study showed that foliar nitrogen application in Cabernet-Sauvignon vines has a considerable effect on the flavanol concentration in wine (Gutiérrez-Gamboa et al., 2017). In accordance with our results, the study’s treatments decreased catechin and epicatechin concentration.

Kaempferol-3-glucoside is the main derivative of kaempferol in grapevines. This molecule represents 6 %–28 % of the total flavonols in white grapes and is the third most abundant after quercetin-3-glucoside and quercetin-3-glucuronide (Castillo-Muñoz et al., 2010). The concentration of kaempferol-3-glucoside is low in grapes, 0.5–4.5 mg/kg for white varieties and 2.5–6.5 mg/kg in red grape skins (Zhang et al., 2015). To our knowledge, no quantitative data have been reported for white wine.

In this study, the total polyphenol index was measured to compare the polyphenol contents of the wines (Table 2). No difference was found between LN and HN in this index in the wine, in contrast to the study of Choné et al. (2006) who observed lower total phenolic content (TP) of the Sauvignon blanc grape must with high nitrogen status of vines. However, in Bell and Henschke’s (2005) review, the results regarding the effects of nitrogen nutrition on TP values are contradictory. Further studies are necessary to understand the reaction of polyphenols to nitrogen deficiency in the vineyard.

Trehalose, a natural disaccharide formed from two α-glucose units, was identified as a putative marker. The concentration of trehalose decreased in HN variants. Trehalose is absent from the grapes, but present in the wine at a concentration of between 150 mg/L and 600 mg/L (Bertrand et al., 1975). This disaccharide is well known as a protective agent produced by the yeast during fermentation under stress conditions, such as nitrogen deficiency (Novo et al., 2005). It has been proposed as a biomarker to detect nitrogen deficiency during alcoholic fermentation in wine yeast (Gutiérrez et al., 2013). Yeast produces more intracellular trehalose in cultures with low nitrogen. Even though we did not find data regarding the release of trehalose in the wine, this accumulation may explain the higher content of trehalose in the LN variants.

This untargeted differential approach, which included volatile and nonvolatile metabolites, revealed approximately 25 compounds in Chasselas wine that were influenced by nitrogen deficiency in the vineyard. These putative markers represent different chemical groups and have different biological functionalities in grape or yeast metabolism. Some of these compounds are present in the grapes and indicate changes in grape composition as a function of nitrogen nutrition (amino acids, vitamins) or as reaction to stress (hormone, phenolic compounds). Others, mainly the volatile compounds, are produced during fermentation and reflect the influence of nitrogen nutrition on yeast metabolism (higher alcohols, esters, succinic acid and disaccharide). Finally, some of the molecules are produced during storage, indicating that the vineyard’s nitrogen nutrition can affect the ageing capability of wine.

The concentrations of amino acids, pyridoxine, abscisic acid (ABA), 2-methyl-1-propanol and N(3methylbuthyl)-acetamide were positively correlated with the supplementation of the vineyard’s nitrogen nutrition. In contrast, higher alcohols, such as 2- and 3-methyl-1-butanol and 2-phenylethanol, and succinic esters, ethyl-9-decenoate, benzothiazol, catechin (epicatechin), succinic acids and trehalose decreased.

The relevance of these putative markers should be further verified by quantitative measurements of these compounds in wine samples. Other results (not reported here) showed that the effect of the vineyard’s nitrogen nutrition on the composition of wine is strongly linked to vine variety. The effects of different winemaking practices (e.g., nitrogen addition before fermentation or prolonged skin contact), different yeast strains and different vine varieties should also be studied to determine the limits of the validity of different markers.

We would like to acknowledge our Agroscope colleagues for the vineyard management (Viticulture group), the vinifications (Oenology group) and the analyses (Wine Quality group). The authors thank the Fédération Vaudois des Vignerons for their financial support.