Short communications

The SIFT-MS fingerprint of Vitis vinifera L. cv. Syrah berries is stable over the second part of maturation under warm conditions of climate

Abstract

Wine grape breeding for fungal resistance has been very dynamic worldwide over the last decade. The quick phenotyping of genotype quality traits, including aroma composition, remains challenging. Selected ion flow tube mass spectrometry (SIFT-MS) could be particularly valuable for this usage. Due to the high number of seedlings to phenotype and the low availability of berries, the sampling strategy can hardly rely on time-consuming destructive methods such as the measurement of classical maturity parameters (i.e., sugar concentration). To investigate the impact of the sampling time on the SIFT-MS fingerprint, berries from Vitis vinifera L. Syrah were collected in 2020, a season characterised by warm climatic conditions, at seven times during maturation and analysed by SIFT-MS using O2+ as reagent ion. This fingerprint has proved to be stable from 28 days after mid-veraison. This finding greatly simplifies the sampling procedure for future berry phenotyping, which can only rely on non-destructive data (lapse of time after mid-veraison date). For most m/z, a decrease in abundance was observed during the maturation, which could be the consequence of volatile emission or an increase in non-detectable bound compounds. Further studies would be necessary to assess the full grape aroma potential, to better understand the mechanisms involved, and to evaluate our approach over more than one season.

Introduction

Vitis vinifera L. is one of the most widely grown and economically important grapevine species in the world, mainly as a consequence of the high quality of its wines (Vivier and Pretorius, 2002). However, it is susceptible to abiotic and biotic stresses such as pests or fungal diseases. These pathogens can be chemically fought or through crossbreeding with resistant or tolerant genotypes (Töpfer et al., 2011). Crossbreeding for wine grape biotic resistance has been very dynamic across Europe in the second part of the 19th century due to the introduction of phylloxera (Daktulosphaira vitifoliae). It led to the development of hybrid direct producers (HDPs) that interestingly also exhibited cross-tolerance to downy mildew (Plasmopara viticola) and powdery mildew (Erysiphe necator) (Alleweldt and Possingham, 1988). However, these genotypes were associated with poor quality wines, which prevented the continuation of related breeding programs in most countries, with the exception of Germany and Hungary (Alleweldt and Possingham, 1988). Nowadays, grape breeding for fungal resistance is experiencing a resurgence of interest worldwide because of the societal pressure towards the reduction of pesticide use. Breeding programs based on controlled sexual reproduction typically generate 50,000 seedlings a year and last between 25 to 30 years from the initial crossing to the release of new cultivars on the market. The use of marker-assisted selection (MAS), notably markers related to mildew resistance, can help the screening and accelerate the process for up to 10 years by quickly reducing to about 5000 the number of plants to phenotype (Töpfer et al., 2011).

Despite the identification of quantitative trait loci (QTLs) for berry and wine quality (Doligez et al., 2006; Eibach et al., 2002; Hausmann et al., 2018), the evaluation of quality traits remains by far one of the most time-consuming steps. Grape aroma compounds imparting wine typicity are considered some of the most important molecules driving wine quality and appreciation (Zhu et al., 2016). In this context, there is a high demand within the grape research community for high throughput technology to quickly assess the varietal aroma composition of a large amount of genotypes.

Selected ion flow tube mass spectrometry (SIFT-MS) is a technology commercially available since 2008 that has the advantage of offering real-time headspace analysis and high sensitivity (Smith and Španěl, 2005). This device based on soft ionisation using eight different reagent ions for the most recent equipment (H3O+, NO+, O2+, NO3-, NO2-, O-, O2- and OH-) can analyse a sample headspace and determine relative abundances in Selected Ion Monitoring (SIM) or scan mode (Hera et al., 2017).

A recent study highlighted that SIFT-MS could be valuable for discriminating the volatile composition of Vitis vinifera berries and, therefore, for the quick phenotyping of grape varieties (Baerenzung dit Baron et al., 2022). Cultivars could be easily distinguished based on their SIFT-MS fingerprint scan, notably with O2+. The use of this latter single reagent ion which has the highest ability to ionise most organic compounds was particularly relevant to reduce the time of analysis to 3 minutes. The SIFT-MS technology enabled discrimination of low and high aroma producers and to connect cultivars, in most cases, according to their parentage relationship. In this former research, grape varieties were sampled at three different dates according to their theoretical timing of veraison (Baerenzung dit Baron et al., 2022). One cultivar was collected at the three sampling dates to investigate the impact of maturity on the whole volatile fingerprint. These three samples were all included in the same cluster of varieties, supporting the hypothesis of a higher impact of the cultivar on SIFT-MS grape fingerprint than at the time of sampling.

However, in the perspective of further use of this methodology for the quick phenotyping of new varieties, the impact of the sampling date on the SIFT-MS volatilome would deserve to be investigated deeper. This would enable one to establish a reliable berry sampling strategy based on non-destructive phenological data (lapse of time after mid-veraison date). Indeed, the adaptation of the date of sampling and measurement to typical maturity parameters such as sugar concentration is hardly implementable due to the high number of individuals to phenotype and the low quantity of grapes available for each genotype, at best a couple of clusters borne by one single plant. The aim of this research work was to study the impact of seven sampling times over maturation on the SIFT-MS fingerprint of Vitis vinifera L. cv. Syrah.

Materials and methods

1. Vineyard site and grape sampling

The 0.51-ha vineyard from where the grapes were sourced was located in the southwest of France (lat. 43° 50′ 25′′ N; long. 01° 50′ 57′′ E) and was typical of the area with 2.20 m × 1 m vine spacing. The block was planted in 2002 with Syrah, the most widely grown cultivar in the vineyards of Occitanie, according to FranceAgriMer (www.franceagrimer.fr). It was grafted on Gravesac rootstock and was trained with vertical shoot positioning on a single Guyot pruning system. The orientation of the vine rows was north-east to south-west. The soil was mechanically managed under the vines and by grass cover in the inter-row area. Samples composed of 100 berries were first collected every third day from the end of July to the beginning of August 2020 to determine mid-veraison (50 % of soft berries), and then in triplicate at seven times during maturation to investigate the impact of sampling time on SIFT-MS fingerprint. Grapes were sampled on 6, 20 and 28 August 2020, 3, 10, 17 and 25 September 2020 which corresponds to mid-veraison (50 % ver.), 14 days after mid-veraison (50 % ver.+14d), 22 days after mid-veraison (50 % ver.+22d), 28 days after mid-veraison (50 % ver.+28d), 35 days after mid-veraison (50 % ver.+35d), 42 days after mid-veraison (50 % ver.+42d) and 50 days after mid-veraison (50 % ver.+50d), respectively. The commercial harvest of the vineyard took place on 15 September. Samples were always collected from the same fifty vine plants spread over three rows, from both sides of the row and several parts of the bunch (50 berries from each side of the row). Crop load was estimated at around 3 kg per vine (150 kg for the whole sampling area), which indicates that the whole amount of grapes harvested over the seven sampling dates (2100 berries) is unlikely to impact crop load or leaf area to fruit ratio for each sampled plant and therefore should not induce any bias.

2. Physico-chemical parameters and weather measurements

For each 100-berry sample, 50 g were used for SIFT-MS analysis and the rest for the determination of physico-chemical parameters. In this latter subsample, the number of berries was first counted to determine berry weight. Grape samples were then crushed, the juice was centrifuged for 1 min at 5600 g and the supernatant was used for the analyses. Sugar concentration (°Brix) was estimated with an MA885 Wine Refractometer (Milwaukee, Wisconsin, USA), and pH was measured using a PHM 210 MeterLab pH meter (Radiometer, Copenhagen, Denmark). Titratable acidity (TA) expressed as g/L of tartaric acid was determined according to the method of the Organisation Internationale de la Vigne et du Vin (OIV, 2009) using a 1 M NaOH solution.

As climatic conditions over the sampling period are likely to impact physico-chemical parameters and particularly berry weight, rainfall and mean daily air temperature were also monitored daily since 2005 by a CimAGRO weather station (Cimel Electronique, Paris, France) placed within 200 m of the experimental site. These data were used to calculate the average mean temperature and cumulative rainfall between 6 August and 25 September for 2020 and for the 2005-2020 period.

3. Sample preparation and SIFT-MS measurements

Sample preparation and SIFT-MS measurements were performed according to the protocol proposed by Baerenzung dit Baron et al. (2022), which can be summarised briefly here.

After crushing, 50 g of grapes were transferred into a 1 L Schott bottle (Verres Vagner, Toulouse, France) sealed with a Teflon-secured screw cap. Then, it was kept for 6 h at room temperature and transferred to a water bath for 40 min at 40 °C. These conditions that did not saturate the device analysis potential were determined in previous research (Baerenzung dit Baron et al., 2022).

SIFT-MS measurements were conducted using a Voice 200 Ultra model (Syft Technologies, Christchurch, NZ) in full scan mode (from m/z 15 to 250) using O2+ as a reagent ion. The injection was conducted using N2 flow as a carrier gas (Alphagaz, Air Liquide, 99.9999 %, Paris, France) with a nitrogen flow rate set at 2.0 TorrL/s. The sample headspace was introduced by a calibrated capillary at a sampling flow rate of 0.3 TorrL/s. The analytes reacted with the selected precursor in the flow tube kept at 119 °C and 0.06 kPa

Instrumental repeatability was estimated at 7 % and reproducibility at 10 %. LabSyft 1.6.2. software (Syft Technologies) was used for data acquisition and analysis.

4. Data treatment

SIFT-MS data were pre-treated by removing masses with an m/z ratio below 100 and abundance below noise following the procedure proposed by Baerenzung dit Baron et al. (2022).

Then SIFT-MS data, together with physico-chemical parameters, were subjected to a one-way analysis of variance (ANOVA) treatment using XLSTAT software (Addinsoft, Paris, France). Fisher’s least significant difference (LSD) test was used as a post-hoc.

A principal component analysis (PCA) was performed on SIFT-MS significant variables (P < 0.05) using ClustVis online software (http://biit.cs.ut.ee/clustvis).

Results and discussion

1. Berry maturity and weather conditions

Results show a steady evolution of the measured physico-chemical parameters over the sampling period (Table 1). As could be expected, sugar concentration increased through accumulation in hexoses while TA decreased through malate catabolism (Coombe, 1992). Trivially, this latter phenomenon was also accompanied by an increase in pH. It is worth mentioning that the changes in sugar content and TA level were particularly marked between 50 % ver.+35d and 50 % ver.+42d. This could be the consequence of the warmer and dryer conditions of climate experienced over this lapse of time (Figure 1) that might have enhanced the speed of maturation (Scholasch and Rienth, 2019). It can be noticed that the climatic conditions were generally warm over the studied period, with mean temperatures surpassing 20 °C in most cases and even approaching 30 °C just after veraison. Indeed, the average temperature that reached 22.3 °C for the sampling period was warmer than those recorded for the 2005-2020 period (20.1 ± 1.2 °C), while cumulative rainfall was in the same range (57.7 mm for 2020; 78.7 ± 43.8 mm for 2005-2020). The 2020 season was an early vintage with a 15-day advance in phenology, and these data might not completely reflect the differences in temperature observed during maturation between the studied season and the other vintages, during which maturation occurs later and under likely cooler conditions.

Table 1. Results (mean and standard deviation of three observations) and significance of physico-chemical parameters analysed on berries sampled at seven times during maturation, from mid-veraison (50 % ver.). Different letters within a column indicate significantly different means at P < 0.05 by the Fisher test.


Time of sampling

Sugar concentration
(°Brix)

Titratable acidity (g/L as tartaric acid)

pH

Berry weight (g)

50 % ver.

13.0 ± 0.1 e

16.95 ± 0.36 a

2.80 ± 0.02 e

1.31 ± 0.11 bc

50 % ver.+14d

18.3 ± 0.6 d

15.90 ± 1.31 a

3.12 ± 0.03 d

1.57 ± 0.17 a

50 % ver.+22d

20.0 ± 0.9 c

12.75 ± 1.40 b

3.25 ± 0.02 c

1.21 ± 0.10 c

50 % ver.+28d

21.9 ± 0.6 b

11.25 ± 0.90 b

3.25 ± 0.02 c

1.31 ± 0.12 bc

50 % ver.+35d

22.5 ± 0.4 b

8.44 ± 0.15 c

3.32 ± 0.03 b

1.40 ± 0.08 ab

50 % ver.+42d

24.9 ± 0.2 a

4.77 ± 0.16 d

3.46 ± 0.04 a

1.37 ± 0.06 bc

50 % ver.+50d

25.1 ± 0.7 a

4.90 ± 0.28 d

3.49 ± 0.06 a

1.27 ± 0.11 bc

P

< 0.0001

< 0.0001

< 0.0001

< 0.05

Figure 1. Daily mean temperature (red line) and rainfall (blue bar) over the sampling period. Sampling dates are symbolised by arrows.

As a consequence of these warm conditions, the sugar concentration was already high at 50 % ver.+42d, reaching 24.9 ± 0.2 °Brix. Between 50 % ver. and 50 % ver.+14d, and to a lesser extent, between 50 % ver.+28d and 50 % ver.+35d, an increase in berry weight was noticed. Despite that xylem is known to be dysfunctional from veraison and that berries become less sensitive to soil moisture (Scholasch and Rienth, 2019), this could be related to some rainfall events that provoked a significant water inflow.

2. SIFT-MS fingerprint

Among the 150 ions monitored by SIFT-MS with m/z between 100 and 250, 61 showed an abundance above noise and 59 were significantly impacted by the sampling date (Table 2).

For the seven sampling dates, several groups of masses were observed with the highest abundances around m/z 105, 119 and 147, which is in accordance with previous SIFT-MS results obtained on Vitis vinifera berries (Baerenzung dit Baron et al., 2022). Even if such masses could be related to several ions, such as C7H5O+ for m/z 105 that originates from the ionisation of benzaldehyde (Španěl et al., 1997), they are also commonly related to the fragmentation of terpenoids (Amadei and Ross, 2011).

The aroma of Syrah grapes and wines has been the subject of much research worldwide (Geffroy et al., 2020b; Morère, et al., 2020; Mayr et al., 2014; Segurel, 2005). These works highlighted that rotundone, 3-mercaptohexanol, dimethyl sulfide (DMS), β-damascenone and other glycosidic precursors were the key compounds involved in the varietal aroma of this cultivar. 3-Mercaptohexanol, which is found in grapes in a non-volatile form bound to amino acids or glutathione, is a priori not detectable through SIFT measurements (Roland et al., 2011). The same remark can be made for the largest part of DMS, which is mainly produced in wine by degradation of S-methylmethionine (Segurel, 2005) and for most glycosidic precursors, including β-damascenone although a minority of these latter compounds can also be present under a free aglycone form in grapes (Ugliano and Moio, 2008). Despite the high fragmentation ability of O2+, ionisation with this reagent ion is always known to generate one molecular ion (Smith and Panel, 2005). If this were the case for β-damascenone and rotundone, a signal would have been expected at m/z 190 and 218, respectively. The absence of a signal might be the consequence of concentration levels below the limit of detection of the SIFT-MS device, from 100 ppt to 1 ppb in a gas phase (Lehnert et al., 2019). This might be particularly the case for rotundone as the warm climatic conditions experienced during the studied vintage were not favourable to the biosynthesis of this molecule (Geffroy et al., 2020a). By removing masses below 100, the data pretreatment has contributed to removing potential molecular ions of DMS whose signal would have been expected at m/z 62. On the hand, it cannot be excluded that free DMS, whose concentration in Syrah juices is known to be in the ppb range (Segurel, 2005), would have also been below the limit of detection. On the other hand, SIFT-MS is known to create molecular clusters and adducts, notably with water molecules (Lehnert et al., 2019). It cannot be discarded that a signal related to DMS could be recorded with m/z above 100. The identification of such compounds would require more work using the DMS standard.

The PCA plot shows that the volatile composition of berries determined by SIFT-MS measurements greatly varied from 50 % ver. to 50 % ver.+28d but remained stable from this latter sampling date (Figure 2). Such a finding is in accordance with previous work highlighting a high similarity in SIFT-MS fingerprint between Sémillon samples harvested at three different times from 40 days after mid-veraison (Baerenzung dit Baron et al., 2022). It is particularly interesting for future high throughput berry phenotyping as it indicates that maturity does not need to be carefully monitored for physico-chemical parameters and that berries can be harvested for SIFT-MS measurements from 50 % ver.+28d. The fact that the sample harvested at 50 % ver.+42d exhibited a slightly different fingerprint remains unclear but could be related to the sudden increase in temperature previously described.

Figure 2. Factor scores with 95 % confidence ellipse for a principal component analysis (PCA) performed on the SIFT-MS abundance data using O2+ as reagent ion for berries sampled seven times during maturation, from mid-veraison (50 % ver.).

In most cases, a decrease in abundance was noticed during maturation (Table 2). Large changes in berry volatile composition involving translocation, accumulation, or metabolism mechanisms have been previously reported during this period (Robinson et al., 2014). To our knowledge, alkyl-methoxypyrazines are one of the rare grape aroma compounds whose concentration is known to decrease over maturation (Lei et al., 2018). Such a decrease cannot be observed for 3-isobutyl-2-methoxypyrazine (IBMP), whose molecular ion is not detected at m/z 166. An abundance reduction can be noticed at m/z 152, which could be related to 3-isopropyl-2-methoxypyrazine (IPMP). However, this hypothesis is unlikely as IPMP is generally found in grapes at a lower abundance than IBMP (Lei et al., 2018). For the other masses, the decrease in abundance could be the consequence of volatile emissions (Rice et al., 2019) or an increase in non-detectable glycosidically-bound compounds, as reported for monoterpenols (Fenoll et al., 2009). Further work, including additional preparation steps which are not essential for our study objective, would be necessary to improve the existing model and to access the full aroma potential of grapes through either acid or enzymatic hydrolysis of bound compounds (Dziadas and Jeleń, 2016).

Table 2. SIFT-MS abundance results (mean of three observations) and significance of product ions using O2+ as reagent ion for berries sampled seven times during maturation, from mid-veraison (50 % ver.). Different letters within a row indicate significantly different means at P < 0.05 by the Fisher test.


m/z

50 % ver.

50 % ver.+14d

50 % ver.+22d

50 % ver.+28d

50 % ver.+35d

50 % ver.+42d

50 % ver.+50d

P

100

4090 a

2620 bc

3679 ab

1904 d

1561 cd

820 d

1061 d

< 0.0001

101

13428 a

7 752 b

11686 a

5388 bcd

5610 bc

2193 d

3673 cd

< 0.0001

102

1098 a

664 bc

832 b

400 de

456 cd

187 e

286 de

< 0.0001

103

4462 a

5441 a

2478 b

1491 bc

1882 bc

2438 bc

1297 c

< 0.0001

104

998 b

1273 a

591 c

241 d

388 cd

506 cd

310 d

< 0.0001

105

3382 b

6280 a

1648 c

779 c

1469 c

3677 b

1322 c

< 0.0001

106

213 bc

381 a

131 cd

93 d

104 cd

254 b

79 d

< 0.01

107

8024 b

13606 a

2662 c

1001 c

3658 c

8379 b

2379 c

< 0.0001

108

376 b

633 a

128 c

51 c

149 c

398 b

143 c

< 0.0001

109

170 d

1028 b

220 d

203 d

579 b

448 bc

222 cd

< 0.0001

111

139 c

1584 a

86 c

140 c

188 bc

451 b

189 bc

< 0.0001

115

298 a

242 ab

112 c

101 c

102 c

171 bc

92 c

< 0.0001

116

537 ab

391 b

600 a

200 c

222 c

132 c

174 c

< 0.0001

117

5643 a

4074 b

3631 b

1356 c

1463 c

836 c

900 c

< 0.0001

118

1933 a

1676 a

1559 a

528 b

829 b

699 b

506 b

< 0.0001

119

12938 a

8112 bc

11308 ab

4593 d

4866 cd

2529 d

3179 d

< 0.0001

120

900 a

464 bc

669 ab

260 cd

159 d

157 d

151 d

< 0.0001

121

4087 a

3602 ab

2366 cd

2590 cd

1787 cd

2684 bc

1680 d

< 0.01

122

280 a

230 ab

181 bc

197 bc

104 d

149 cd

102 d

< 0.01

123

83 ab

120 a

20 c

16 c

44 bc

77 ab

34 bc

< 0.05

127

329 a

292 a

50 b

112 b

50 b

71 b

70 b

< 0.0001

128

573 a

311 bc

426 ab

202 cd

152 cd

67 d

118 d

< 0.0001

129

453 ab

619 a

314 bc

108 d

208 cd

130 d

110 d

< 0.0001

130

256 a

196 b

170 b

57 cd

91 c

30 d

41 cd

< 0.0001

131

463 a

347 a

210 b

91 bc

111 bc

110 bc

62 c

< 0.0001

133

1389 a

1224 a

451 bc

139 c

440 bc

643 b

254 c

< 0.0001

134

132 ab

171 a

52 c

33 c

46 c

94 bc

33 c

< 0.01

135

632 bc

987 ab

239 cd

80 d

359 cd

1204 a

201 cd

< 0.01

136

112 ab

132 a

20 c

30 c

29 c

106 ab

42 bc

< 0.05

137

110 c

747 a

82 c

31 c

138 c

459 b

102 c

< 0.01

138

23 a

107 a

10 a

70 a

39 a

87 a

46 a

ns

139

177 c

2392 a

118 c

248 c

423 bc

1882 ab

337 bc

< 0.05

140

33 c

177 a

1 c

68 bc

46 c

130 ab

33 c

< 0.01

141

400 a

267 ab

218 bc

103 cd

81 cd

74 d

52 d

< 0.01

142

256 a

167 ab

147 bc

60 c

112 bc

47 c

53 c

< 0.01

143

2101 a

1378 b

1226 b

284 c

496 c

229 c

301 c

< 0.0001

144

724 a

828 a

568 a

123 b

257 b

124 b

157 b

< 0.0001

145

10311 a

11031 a

5700 b

1207 c

3060 bc

1526 c

1529 c

< 0.0001

146

1281 a

1468 a

767 b

193 c

454 bc

290 c

221 c

< 0.0001

147

12703 a

15696 a

6170 b

1552 c

4238 bc

3392 bc

2254 c

< 0.0001

148

1212 b

1517 a

494 c

148 d

384 cd

291 cd

216 cd

< 0.0001

149

3527 b

6938 a

1331 c

657 c

1559 c

3887 b

1100 c

< 0.0001

150

282 b

581 a

99 c

37 c

48 c

328 b

66 c

< 0.0001

151

47 b

101 a

14 bc

1 c

22 bc

26 bc

27 bc

< 0.01

152

31 ab

53 a

11 b

9 b

20 b

16 b

10 b

< 0.05

153

82 ab

89 a

30 b

47 b

24 b

42 b

30 c

< 0.01

155

132 a

60 b

50 b

68 b

13 b

18 b

28 b

< 0.05

156

100 a

53 bc

71 ab

30 cd

14 d

9 d

14 d

< 0.01

157

233 a

192 ab

126 b

44 c

43 c

19 c

23 c

< 0.0001

159

242 a

248 a

131 b

57 c

49 c

66 c

38 c

< 0.0001

161

516 a

369 ab

459 a

71 c

111 c

223 bc

140 c

< 0.0001

165

87 ab

130 a

61 bc

52 bc

57 bc

67 bc

22 c

< 0.05

167

42 c

331 a

42 c

82 bc

131 bc

156 b

91 bc

< 0.01

181

69 a

70 a

48 a

24 a

40 a

14 a

12 a

ns

189

114 a

71 b

14 c

6 c

7 c

32 c

6 c

< 0.0001

193

36 b

137 a

24 b

22 b

30 b

6 b

14 b

< 0.01

195

22 bc

83 a

71 a

18 bc

50 ab

26 bc

6 c

< 0.05

197

161 a

124 a

177 a

30 b

37 b

17 b

7 b

< 0.01

199

248 a

169 a

262 a

27 b

28 b

11 b

24 b

< 0.01

201

276 a

183 a

242 a

29 b

41 b

16 b

22 b

< 0.01

203

123 a

103 a

96 a

18 b

18 b

21 b

17 b

< 0.0001

ns = non significant.

Our results are only valid for a season characterised by warm climatic conditions during the maturation period. They might not be generalisable and transferable to vintages with cooler conditions. However, it must be pointed out that most of the newly developed genotypes are generally grown during the first years in greenhouses under semi-controlled environmental conditions. Under these growing conditions, temperatures are expected to be warm, and seasonality is likely to have a weaker impact in comparison with field-grown vines.

Conclusion

Our work highlighted that the SIFT-MS fingerprint of Syrah berries was stable from 50 % ver. +28 days under warm conditions of climate. This result is particularly relevant for the future high throughput phenotyping of berries under warm conditions of maturation as it enables to simplify the sampling strategy greatly. The proposed strategy only relies on phenological data and does not require accurate monitoring of physico-chemical parameters. In most cases, a decrease in abundance was observed over the maturation period, which could be the consequence of volatilisation or an increase in glycosidically-bound compounds that are not volatile and cannot be detected through SIFT-MS measurements. Additional research would be necessary to test this approach over more than one season or in greenhouses and to improve the model to get access to the full grape aroma potential through preliminary acid or enzymatic hydrolysis preparation step.

Acknowledgements

This study was carried out with financial support from the Occitanie region through the funding of Thomas Baerenzung dit Baron PhD thesis. We are grateful to Leticia Vitola Pasetto of Toulouse INP-Purpan for her assistance in the SIFT-MS analyses.

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Authors


Olivier Geffroy

olivier.geffroy@purpan.fr

ORCID iD

Affiliation : Physiologie, Pathologie et Génétique Végétale, PPGV, Université de Toulouse, INP - Purpan, 31076 Toulouse

Country : France


Thomas Baerenzung dit Baron

Affiliation : Physiologie, Pathologie et Génétique Végétale, PPGV, Université de Toulouse, INP - Purpan, 31076 Toulouse - Laboratoire de Chimie Agro-industrielle, LCA, Université de Toulouse, INRAE, 31030 Toulouse

Country : France


Olivier Yobrégat

ORCID iD

Affiliation : Institut Français de la Vigne et du Vin pôle Sud-Ouest, IFV Sud-Ouest, 81310 Peyrole

Country : France


Marie Denat

ORCID iD

Affiliation : Physiologie, Pathologie et Génétique Végétale, PPGV, Université de Toulouse, INP - Purpan, 31076 Toulouse

Country : France


Valérie Simon

ORCID iD

Affiliation : Laboratoire de Chimie Agro-industrielle, LCA, Université de Toulouse, INRAE, 31030 Toulouse

Country : France


Alban Jacques

ORCID iD

Affiliation : Physiologie, Pathologie et Génétique Végétale, PPGV, Université de Toulouse, INP - Purpan, 31076 Toulouse

Country : France

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