GC-MS Metabolite Profiling of Extreme Southern Pinot noir Wines

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GC-MS metabolite profiling of extreme southern Pinot Noir wines: The effect of vintage, barrel maturation and fermentation dominate over vineyard site and clone selection Claudia Schueuermann, Bekzod Khakimov, Soeren Balling Engelsen, Phil J Bremer, and Patrick Joseph Silcock J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.5b05861 • Publication Date (Web): 09 Feb 2016 Downloaded from http://pubs.acs.org on February 14, 2016

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Journal of Agricultural and Food Chemistry

GC-MS metabolite profiling of extreme southern Pinot Noir wines: The effect of vintage, barrel maturation and fermentation dominate over vineyard site and clone selection

Claudia Schueuermanna, Bekzod Khakimovb, Søren B. Engelsenb, Phil Bremera and Patrick Silcocka*

a

Department of Food Science, University of Otago, P.O. Box 56, Dunedin, New Zealand

b

Spectroscopy and Chemometrics Group, Department of Food Science, University of Copenhagen,

Rolighedsvej 26, DK-1958 Fredriksberg C, Denmark

* Corresponding author (phone: +64 3 479 7564; e-mail address: [email protected]).

1 ACS Paragon Plus Environment


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Abstract

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Wine is an extremely complex beverage that contains a multitude of volatile and non-volatile

3

compounds. This study investiged the effect of vineyard site and grapevine clone on the volatile

4

profiles of commercially produced Pinot Noir wines from Central Otago, New Zealand. Volatile

5

metabolites in Pinot Noir wines produced from five grapevine clones grown on six vineyard sites in

6

close proximity, over two consecutive vintages were surveyed using gas-chromatography mass

7

spectrometry (GC-MS). The raw GC-MS data was processed using parallel factor analysis

8

(PARAFAC2) and final metabolite data was analysed by principal component analysis (PCA).

9

Winemaking conditions, vintage and barrel maturation were found to be the most dominant factors.

10

The effects of vineyard site and clone were mostly vintage dependent. Although four compounds

11

including β-citronellol, homovanillyl alcohol, N(3-methylbutyl)acetamide and N(2-phenylethyl)

12

acetamide discriminated the vineyard sites independant of vintage, Pinot Noir wines from different

13

clones were only partially discriminated by PCA and marker compound selection remained

14

challenging.

15 16

Key words

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GC-MS, PARAFAC2, vineyard site, clone, vintage, Pinot Noir, wine, VOC, volatile


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Introduction

19

Recent developments in metabolomics coupled with analytical platforms such as Gas

20

Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry

21

(LC-MS) and Nuclear Magnetic Resonance (NMR) have improved food and beverage

22

analysis and enabled the simultaneous detection of hundreds of metabolites. Thereby

23

improving the molecular understanding of food and beverages and providing new

24

opportunities to study the factors that impact on flavor, texture, aroma, color and nutritional

25

value. In this context wine metabolomics is becoming increasingly important to detect fraud,

26

identify flavor defects, to determine authenticity / origin or to assess quality. Wine is an

27

immensely complex beverage and even grape variety authentication can be a serious

28

challenge. Wine authentication becomes complex when factors such as geolocation, winery,

29

vineyard site, grapevine clone and vintage are present

30

unlikely in the near future, detailed knowledge about the compounds contributing to the wine

31

sensory properties may allow manipulation of wine character and optimization of quality.

32

An important term in wine tasting is typicity, it is used to describe the extent to which a wine

33

is typical of its style, variety, origin or vintage 5, 6. In the current project the term site-typicity

34

is defined as the expression of unique and typical vineyard site characteristics in wine that the

35

winemaker describes based on their historical knowledge. The wines from high typicity sites

36

are usually described as possessing higher levels of complexity in aroma and flavor,

37

compared to wines from low typicity sites.

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Volatile organic compounds (VOC’s) in wine originate either from the grape itself (primary

39

or varietal aroma), or are generated during the winemaking process (secondary and tertiary

40

aroma). The concentration of VOC’s in wine can range between ng L-1 to g L-1 levels 7, 8. The

41

wide concentration range of VOC’s, their properties (e.g. polarity and volatility) and the

1-4

. While wine reconstitution seems



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properties of the wine matrix (e.g. pH and ethanol content) make wine aroma very complex

43

and difficult to analyze.

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A multitude of compounds have been shown to be important in Pinot Noir wine aroma

45

including ethyl- and acetate esters, higher alcohols, volatile organic acids and mono-terpenes

46

9-12

47

alcohols, ethyl-, and acetate esters were identified by GC-MS analysis of liquid extracts of

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commercial Pinot Noir wines from the Mt. Difficulty winery in Central Otago, New Zealand

49

12

50

were present above their perception threshold. Rutan et al.

51

Mt. Difficulty Pinot Noir wine contained higher concentrations of volatile phenols, lactones,

52

varietal thiols, ethyl lactate and C13-norisoprenoids compared to the winery’s Estate wine

53

which contained higher amounts of monoterpenes, hexenols, fatty acids and ethyl esters. This

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finding suggests that these compounds could be driving the differences between the premium

55

quality and Estate wines at Mt Difficulty.

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To study VOCs present in wines at low concentrations (ng L-1), prior sample enrichment is

57

often required. Over the last 10 years there has been significant growth in the use of sorption

58

methods for the extraction of volatile compounds from wine

59

(SPE), headspace solid-phase micro-extraction (HS-SPME) and stir-bar sorptive extraction

60

(SBSE) methodologies have proven to be sensitive and quick, with the added advantage of

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avoiding the high solvent quantities required for liquid-liquid extractions

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extractions have been shown to be appropriate for volatile extraction from wine with

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polypropylene-divinylbenzene resins showing best recovery rates

64

analysis of the concentrated wine extracts by GC-MS is the method of choice due to its high

65

sensitivity and this approach has been widely applied in wine aroma studies 12, 13, 22-25.

. Previously, 51 key compounds including C13-norisoprenoids, mono-terpenes, higher

. However, no genuine impact odorants were found and only 22 of the detected compounds 12

reported that premium quality

13-16

. Solid-phase extraction

17, 18

. SPE

17, 19-21

. The subsequent


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However, the analysis of complex GC-MS data obtained on the VOCs in wine samples can

67

be challenging due to a range of non-sample related artefacts introduced to the data during

68

sample preparation and data acquisition, including co-eluting peaks, low intensity peaks,

69

retention time shifts over runs and varying baselines. In order to conduct multivariate analysis

70

and identify compounds related to the investigated factor(s), data must be correctly aligned

71

and freed from outliers and artefacts 26. In this study the raw GC-MS data was processed by

72

PARAllel FACtor Analysis 2 (PARAFAC2) which increased the information extracted from

73

the data as de-convolution of overlapped and co-eluted peaks enabled the identification of

74

underlying compounds from crowded chromatographic regions, including low signal to noise

75

(S/N) ratio peaks and baseline inconsistencies 27-29.

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The purpose of the current study was to survey the commercial Pinot Noir winemaking

77

process at the Mt. Difficulty winery in Bannockburn, Central Otago, New Zealand, in order

78

to reveal possible trends or patterns in the VOC profiles of the wines that may be responsible

79

for the site-typicity (vineyard site) and clonal characteristics that the winemaker describes.

80

The wines were sampled during the commercial winemaking process, which did not allow for

81

a balanced experimental design, but rather gave a holistic overview of the real commercial

82

winemaking process. Comprehensive GC-MS data was obtained for metabolite profiling of

83

VOCs in wine samples. Samples were analyzed by SPE-GC-MS and after PARAFAC2 data

84

processing the de-convoluted data (detected VOCs) was analyzed by principal component

85

analysis (PCA).

86

Materials and Methods

87

Vineyard sites. The vineyard sites sampled were owned and managed by Mt. Difficulty

88

Wines, located within an area of 13 km2 (Central Otago, Bannockburn, NZ) and under similar

89

viticulture practice and climatic conditions. Based on the winery’s historical knowledge, 6

90

vineyard sites were chosen (Figure 1). These included 3 vineyard sites that express high site-



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typicity (vineyard site character) in the resulting wines (TG, PT and LG) and 3 vineyard sites

92

that express less of this character in the wines (IN, F and BR). While wines from the high

93

typicity sites were usually produced as single vineyard wines, wines from the remaining low

94

typicity sites were usually blended amongst each other and with wines from the high typicity

95

sites (as stated by the winemaker). The volatile compositions of the wines originating from

96

the six sites (including 5 grapevine clones) were examined for two consecutive vintages

97

(2012 and 2013). In order to include the winemaking process as an impact factor on the

98

aroma of these wines, the wines were analyzed after fermentation (immediately pre-

99

barreling: PB) and after 4 months of barrel ageing, pre- malolactic fermentation (barreled

100

wines: BW). The VOCs of the wine samples were extracted by SPE, analyzed by GC-MS,

101

and the data extracted and analyzed using PARAFAC2 and PCA.

102

Commercial winemaking process. Grapes were hand-picked (at around 25 °Brix) and

103

delivered to the winery to be processed immediately. After 60 – 100% of the grapes for each

104

treatment were de-stemmed (Table 1) and conveyed into stainless steel tanks, approximately

105

50 mg L-1 of SO2 was added (as potassium metabisulfite). Maceration (cold-soak) for

106

approximately 9 days at 5-10 °C was followed by heating to 18-20 °C to allow the indigenous

107

yeast (no inoculation) to start alcoholic fermentation spontaneously. After fermentation and a

108

period of post-fermentation maceration the wines were racked off, pressed and filled into 12

109

to 24 French oak barrels per tank (sample batch). The barrels were of different age (used for

110

at least the two previous vintages), brand, forest and toast level.

111

Wine samples. The samples taken from the tanks (sample batches) during the winemaking

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process are presented in Table 1. At Mt. Difficulty Wines, the decision about the combination

113

of clone and whole bunch for a single fermentation tank (from one vineyard site) is made by

114

the winemaker and therefore does not necessarily match the preceding year. Samples taken

115

from the tank (pre-barreling) or from the barrel (pre-malolactic fermentation) were put in 750


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mL green glass bottles (BVS Burgundy wine bottle AG056, VinPro, NZ) which were purged

117

with oxygen free, instrument grade nitrogen (BOC, New Zealand) and sealed with press-on

118

screw caps (Novatwist, Kauri, NZ). The samples were kept at -18 °C until required for

119

analysis.

120

Materials. Pure water (18 MΩ·cm) was obtained from the water purification system Milli-Q

121

(Millipore, USA). Helium (instrument grade) and nitrogen (oxygen free, instrument grade)

122

were purchased from BOC (New Zealand). Chemicals and cartridges used in the following

123

methods were analytical grade and purchased from the following providers: Ethyl cinnamate,

124

ethyl butyrate, isoamyl acetate and whisky lactone were purchased from Sigma-Aldrich

125

(USA). β-citronellol, 3-methyl butanol, 1-hexanol and 2-phenylethanol were purchased from

126

Fluka (Sigma-Aldrich, USA). Dichlormethane, furfural and LiChrolut EN cartridges (0.5 g

127

polypropylene-divinylbenzene resin, 6 mL reservoir) were purchased from Merck

128

(Germany). 3-mercapto-1-hexanol, octanoic acid and linalool were purchased from Acros-

129

Organics (Belgium), ethanol from BDH (UK), methanol and benzaldehyde from Ajax

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Finechem (Australia) and guaiacol from J.T. Baker (USA). 2-octanol was purchased as a

131

general purpose reagent from BDH (UK), the purity was checked by GC-MS. Methyl

132

anthranilate was gifted from McCormick & Company (USA) and the purity checked by GC-

133

MS.

134

Solid Phase Extraction (SPE). Samples were analyzed in duplicate and in a randomized

135

order. LiChrolutR EN cartridges, containing 0.5 g resin were used for SPE extraction. The

136

extraction method was adapted from Culleré et al. 2003

137

manifold (VisiprepTM Supelco, Sigma Aldrich, USA) the cartridges were washed with 10 mL

138

dichlormethane, air dried for 30 s and conditioned successively with 5 mL methanol and

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10 mL 10% aqueous ethanol solution (v/v). Preceding the extraction the samples were thawed

140

over night at 4 °C, 75 mL of wine sample was subsequently mixed with 200 µL of the

13

. On a 12-port SPE vacuum



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internal standard 2-octanol (3 µg mL-1) in ethanol and the mixture added to a reservoir above

142

the LiChrolut cartridge. Loading the sample onto the resin was conducted at a flow rate of

143

2 mL min-1 (applied vacuum). After loading was completed the resins were washed with

144

10 mL of water and dried for 10 min (applied vacuum). The samples were eluted into a

145

10 mL Micro Kuderna-Danish Sample Concentrator (Supelco, Bellefonte, USA) using 7 mL

146

dichlormethane. The eluate was carefully concentrated to 500 µL under a constant nitrogen

147

flow (50 mL min-1 ± 5 mL min-1) over the head-space of the sample. An aliquot of 100 µL

148

was taken for instrumental analysis by GC-MS.

149

Gas chromatography mass spectrometry (GC-MS). GC-MS analysis was carried out using

150

an automatic liquid sampler (Agilent 7683 B) connected to a gas chromatograph (Agilent

151

6890N) and an electron impact mass selective detector (Agilent 5975 VL). The column used

152

for separation was a ZB-Wax column (60 m x 0.32 mm inner diameter x 0.5 µm film

153

thickness; Phenomenex, Torrance, CA, USA). The injector temperature was 230 °C. A

154

sample volume of 1 µL was injected using a split ratio of 10:1 with helium as carrier gas at a

155

constant flow rate of 1 mL min-1. The initial temperature of the GC oven was 50 °C followed

156

by heating at 2 °C min-1 until 230 °C was reached and then held at 230 °C for 30 min to give

157

a total running time of 120 min. After solvent delay of 7.3 min the mass selective detector

158

recorded a mass range between m/z 35-300 at a frequency of 4.86 Hz. The data was acquired

159

using the Agilent Enhanced MSD Chemstation software (version D. 03.00.611, Agilent) and

160

exported as 3-way data (.CDF) for data analysis.

161

GC-MS data processing (PARAFAC2). The data in CDF format was imported into Matlab

162

(version R.2013b, The Mathworks, Inc., USA) and retention shifts that were due to the time

163

difference of batch measurements (approx. 1 year between vintage 2012 and 2013) were

164

corrected manually. The data, arranged as a three-way array (elution time x mass spectra x

165

samples), was then manually divided into 86 low-rank intervals in elution time dimension


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with 50 to 400 scans per interval leaving up to 5 peaks in a single interval. Two main criterias

167

for data division into intervals were followed, (1) peaks corresponding to one compound must

168

be within the same interval and (2) basline must be present in cutting region. Each of the

169

intervals was individually modelled by PARAFAC2 using non-negativity constraints on the

170

spectral (mode 2) and sample (mode 3) modes of the three way array. Models with one to five

171

components were fitted to each interval where the optimal number of components was

172

determined based on four criteria, (1) the explained variance and residuals of the models, (2)

173

the core consistency of the models, (3) visual inspection of raw- and PARAFAC2 de-

174

convoluted data including elution and mass spectral profiles (must visually reflect each

175

other), (4) knowledge about the data (how many compounds were expected in the individual

176

interval, mass spectra of the individual components etc.). From the three main PARAFAC2

177

outputs (1. elution time profiles, 2. relative abundance and 3. mass spectra of the purely

178

resolved peaks of each interval), the PARAFAC2 scores that corresponded to the relative

179

abundancy of the de-convoluted compounds were extracted for subsequent analysis by PCA.

180

Retention indices of detected VOCs were calculated based on an alkane series (C10-C30) that

181

was measured using the same GC-MS method, and calculated using the van den Dool and

182

Kratz equation 30. Metabolites were identified either at level 1, comparing RI and EI-MS with

183

authentic standards, or at level 2, by comparing their RI and EI-MS against the NIST2011

184

GC-MS metabolite database (NIST, USA). Prior to PCA the metabolite data was normalized

185

to the sum of the absolute value of all variables for each sample and auto-scaled. The first

186

PCA analysis was carried out using the PARAFAC2 scores from all of the pre-barreled and

187

barreled samples collected from the 2012 and 2013 vintages. Subsequent PCAs were carried

188

out on the following sample groups, a) pre-barreled wines from vintage 2012 (PB2012), b)

189

pre-barreled wines from vintage 2013 (PB2013), c) barreled wines from vintage 2012



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(BW2012) and d) barreled wines from vintage 2013 (BW2013). All sample codes can be

191

obtained from Table 1.

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Results and Discussion

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PARAFAC2. Mass spectra of the resolved peaks from the PARAFAC2 models revealed 99

194

compounds in the pre-barreled and barreled samples collected from the 2012 and 2013

195

vintages, of which 65 were identified at level 1 and 2 using either authentic standards or

196

comparing EI-MS and RI with the metabolite database NIST11 31. In some cases the spectral

197

match of compounds against the NIST11 library increased due to the fact that PARAFAC2

198

resolved spectra were free of artificial m/z ions derived from baseline and / or co-eluting

199

peaks. However, for a few compounds, the spectral match decreased compared to the raw

200

spectral match. This might be due to the slight difference between original and de-convoluted

201

spectra when peaks have low S/N ratio. The PARAFAC2 scores (corresponding to the

202

relative abundance of each compound) were subsequently extracted for PCA. The selection

203

of compounds responsible for discriminating investigated effects was carried out using PCA

204

loadings (VOCs) plots whereby the 30 most discriminant compounds were selected by visual

205

inspection. The selected VOCs for a certain effect are encircled in the respective loadings

206

plot in the supplementary data.

207

PCA for vintage and barrel discrimination. A PCA on the extracted metabolites

208

(supplementary data, Table A1) from all samples revealed that PC1 and PC2 together

209

explained 41% of the systematic variance (Figure 2). The systematic variation observed along

210

PC1 (24.26%) was correlated with a sample maturation effect after barrel storage for 4

211

months whereby barrelled wine samples (BW, blue) were separated from the pre-barreled

212

samples (PB, pink), Figure 2-b. Samples from vintage 2012 (red) were discriminated from

213

2013 (green) along PC2 (17.18%), Figure 2-a (see supplementary data for loadings plot and

214

assigned VOCs, Figure A1). The 30 most discriminant VOCs for each effect were selected


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(15 describing vintage 2012, 15 describing vintage 2013, 15 describing the barreled wines

216

and 15 describing pre-barreled wines). The discriminant loadings (VOCs) corresponding to

217

the scores plots (Figure 2-a and 2-b) are displayed in Table 2. Samples of vintage 2012 were

218

proportionally higher in the esters: ethyl butanoate, ethyl 2-hydroxyisovalerate, ethyl

219

dodecanoate and ethyl hexadecanoate, isobutanoic acid, 2-methyl butanoic acid, phenylacetic

220

acid, the two acetamides N-(3-methylbutyl)acetamide and N-(2-phenylethyl) acetamide and

221

homovanillyl alcohol, compared to samples from vintage 2013. In contrast, vintage 2013

222

wines proportionally contained more ethyl hexanoate, 4-methyl pentanol, cis-3-hexen-1-ol, 1-

223

heptanol, 2-heptanol, 1-hexanol, 2-ethyl hexanol, 1-octanol, β-citronellol and linalool oxide

224

(pyranoid) than wines from vintage 2012. It is well known that vintage can have an effect on

225

the sensory perception and chemical composition of wine

226

between the 2012 and 2013 vintages were due to a variety of compounds, most of which were

227

formed during fermentation, which suggests that their precursor compound levels and / or

228

ratios were different between the two vintages. Minimal variation of weather conditions (e.g.

229

rain fall, wind, temperature and sunlight hours) at certain ripening stages of the grapes are

230

known to contribute to microbial and chemical composition changes in the grape

231

minor differences in climatic conditions may have led to changes in the chemical

232

composition of the grapes or in the microbial species associated with the grapes, which in

233

turn may have influenced the VOC profile of the wines. Furthermore, while winemaking

234

procedures between the vintages were similar, they were unlikely to be identical, possibly

235

also leading to changed VOC profiles in the wines.

236

Barreled wines (BW) showed increased amounts of oak and ageing related compounds

237

compared to the pre-barreled (PB) wines (Figure 2b and Table 2). BW samples had

238

proportionally higher concentrations of isoamyl acetate, ethyl 2-hydroxyisovalerate, ethyl

239

leucate, 5-methyl furfural, diethyl succinate, cis-whiskey lactone, ethyl 3-methylbutyl

4, 32-34

. In this study, differences

35, 36

. Such



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succinate, γ-carboethoxy-γ-butyrolactone, ethyl hydrogen succinate and vanillin compared to

241

the PB wines. The PB wines were higher in a range of higher alcohols and esters namely 1-

242

butanol, 1-penten-3-ol, isoamyl alcohol, 1-pentanol, trans-2-penten-1-ol, 2-heptanol, 1-

243

hexanol, methyl 4-hydroxybutanoate, methyl vanillate, ethyl vanillate and ethyl

244

hexadecanoate. These findings were not surprising as compounds extracted from oak barrels

245

include furanic compounds, phenolic aldehydes, phenolic ketones and oak lactones 37, 38.

246

In the present study, samples were only taken from barrels that had been used for wine

247

maturation in at least two preceeding vintages in order to reduce the influence of barrel

248

derived VOCs. However, a multitude of compounds were still extracted and they were in

249

agreement with the compounds previously reported as oak related and ageing compounds in

250

Nebbiolo-based wines by Bordiga et al. 2014

251

that were not reported by Bordiga et al. 37 were γ-carboethoxy-γ-butyrolactone, ethyl

252

hydrogen succinate and ethyl 3-methylbutyl succinate. These compounds occur either due to

253

ageing processes in the wines or are extracted from the oak wood. Isoamyl acetate

254

concentrations in the BW wines increased in the current study compared to pre-barreled

255

wines, while Bordiga et al. 2014

256

decreased in Nebbiolo-based wines after 6 months of maturation in barrels. In general,

257

decreased levels of isoamyl acetate during storage time are expected

258

increased level of isoamyl acetate in the current study could not be easily explained. It may

259

be due to its release from the residual lees in the barrels, as wines at this sampling stage had

260

not been filtered. Alternatively, isoamyl acetate may have been produced due to a high rate of

261

esterification reactions between isoamyl alcohol and acetic acid. Further trials will be

262

required to better understand the changes in isoamyl acetate overtime.

263

PCA for site-typicity discrimination. According to the winemaker some vineyard sites at Mt

264

Difficulty Wines Ltd, express more site-typicity in the wines they produce than others. In the

37

37

. Compounds detected in the current study

reported that concentrations of these compounds

39

. However, the


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265

present study, three sites, expressing high site-typicity (TG, PT and LG), were discriminated

266

from the three low typicity expressing sites (BR, IN and F). PCA models developed using PB

267

wines and including only vintage 2012 (PB2012) or 2013 (PB2013) depicted trends towards

268

discrimination based on site-typicity for both vintages (Figure 3-a and 3-b). A weak site-

269

typicity effect was also observed for the barreled wines of vintage 2012 (BW2012) and 2013

270

(BW2013) respectively (see supplementary data for scores and loadings plots, Figure A4-a

271

and 4-b). The PCA of PB2012 samples explained 40.5% of the total variance by PC1 and

272

PC2. No site effect was observed along PC1 (24.7%), instead PC1 discriminated samples

273

according to their fermentation conditions. Recorded data in this study, including

274

fermentation temperatures and time required for the completion of fermentation explained the

275

discrimination (data not shown). A separation between high and low site-typicity was

276

observed along PC4 (11.3%) and PC5 (8.2%) of the same PCA model, Figure 3-a. This

277

suggests that site-typicity does not represent the major proportions of the variance present in

278

the investigated data, but that it only represents a minor variation, underlying the main

279

factors, in this case fermentation conditions (PC1-3). Wine sample LG6, however, was not

280

discriminated from the low typicity sites in PB2012 wine samples. A total of the 30 most

281

discriminative metabolites for high- and low typicity of PB wines from vintage 2012 were

282

selected from the loadings plot (Figure A2-a and A2-b). The compounds describing high

283

typicity were isobutyl alcohol, isoamyl alcohol, ethyl octanoate, ethyl leucate, 1-octanol,

284

furfuryl alcohol, methionol, β-citronellol, methyl vanillate, acetovanillone and homovanillyl

285

alcohol (Table 2). It is noteworthy that the same compounds, isobutyl alcohol and isoamyl

286

alcohol were found as markers for terroir, in the 1H NMR spectroscopy based metabolomics

287

study on La Rioja wines 1. Low typicity was discriminated by relatively higher concentrations

288

of 3-methyl butanoic acid, 2-phenylethyl acetate, N-(3-methylbutyl)acetamide, N-(2-

289

phenylethyl) acetamide, ethyl hexadecanoate, phenyl acetic acid and ethyl vanillate (Table



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2), which suggests that these VOCs may suppress typicity related characteristics in wines.

291

However, this requires further studies on the sensory impact and interactions of these

292

compounds in those wines.

293

PCA on PB2013 wine samples explained 47.5% of the total variance by PC1 and PC2. While

294

the discrimination on PC1 (29.1%) was correlated with the Brix value of the grapes at harvest

295

(data not shown), PC2 (18.4%) was correlated with a site-typicity effect. The combination of

296

PC2 and PC3 (12.6%) of the same PCA model (PB2013) showed the best discrimination

297

between high and low site-typicity wines, Figure 3-b. The loadings (VOCs) responsible for

298

the discrimination between high and low site-typicity in PB2013 wines are displayed in Table

299

2 (see supplementary data for loadings plot and selected VOCs, Figure A2-b). Thirty VOCs

300

were selected for the site-typicity effect in PB2013 wine samples. The low typicity sites

301

(green) were separated to the right side of the plot and the high typicity sites (red) to the left

302

(Figure 3-b). The high typicity site wine samples contained proportionally higher

303

concentrations of isobutanoic acid, γ-butyrolactone (and its non-lactonized form), 2-

304

phenylethyl

305

dodecanoate, homovanillyl alcohol and phenylethyl alcohol. Low typicity site wines had

306

relatively higher concentrations of ethyl butanoate, ethyl lactate, 1-hexanol, 3-ethoxy-1-

307

propanol, ethyl 3-hydroxybutanoate, hexanoic acid as well as N-(3-methylbutyl)acetamide,

308

N-(2-phenylethyl) acetamide, γ-carboethoxy-γ-butyrolactone, and trans-whiskey lactone.

309

In both vintages a trend of discrimination between high and low site-typicity expressing

310

vineyard sites could be observed. In vintage 2013, PB samples of the high typicity sites were

311

more distinctly discriminated from the low typicity sites than in the PB samples of the

312

previous vintage (2012). Furthermore, in vintage 2013, BR wines that were not well

313

separated from the high typicity sites, suggested similarities between BR and the high typicity

314

wines. Subsequent interviews with the winemaker revealed that such similarities had been

acetate,

hexadecanoic

acid,

ethyl

hexadecanoate,

β-citronellol,

ethyl


Page 15 of 36


315

demonstrated in the winery before and that BR was a site believed to have the potential to

316

express high typicity in its wines. Although BR was not geographically located close to the

317

high typicity sites it had similar soil properties to the three high typicity sites, which suggests

318

that the character being measured may be influenced by the soil type.

319

In general, the VOCs that were responsible for the discrimination of typicity and the pattern

320

of discrimination varied between the PB wines of the two vintages with only a small number

321

of marker metabolites describing the same typicity in both vintages: high site-typicity was

322

consistently described by β-citronellol and homovanillyl alcohol, whereas low site-typicity

323

was consistently described by the two acetamides N-(3-methylbutyl)acetamide and N-(2-

324

phenylethyl)acetamide.. Homovanillyl alcohol and β-citronellol appear to be consistently

325

present in higher concentrations in the wines from high compared to low typicity sites. These

326

two compounds are mainly grape derived and can be formed through hydrolysis from their

327

precursors

328

nerol and geraniol

329

suggest that their precursor compounds were present in relatively higher concentrations in the

330

high typicity grapes. Alternatively, the concentration of β-citronellol may have been affected

331

by the yeast strains present 42. Interestingly, some monoterpenes, including β-citronellol, have

332

previously been reported to be significantly different in Muscat of Bornova wines from

333

different terroirs in Turkey

334

homovanillyl alcohol are possible markers for site-typicity differences in Mt. Difficulty

335

wines. However, it was beyond the scope of the current study to investigate the yeast species

336

and strains involved in the winemaking. Hence, identification of yeast strain and species at

337

commencement of fermentation and the analysis of VOC precursor compounds (in particular

338

terpene precursors) in the grapes may help to unravel the reasons for their elevated

339

concentrations in high typicity wines from Mt. Difficulty.

40-42

or by transformation reactions during fermentation, i.e. β-citronellol from 41

. The increased levels of homovanillyl alcohol and β-citronellol may

43

. The results from our study suggest that β-citronellol and



Page 16 of 36

340

The mechanism for the formation of the two acetamides is still unknown, although it is

341

assumed that they are formed during fermentation

342

reported with respect to reduced wine quality or increased grape skin contact

343

generally found at higher concentrations in wines that have mousy or vinegary characteristics

344

45, 49

345

is assumed that this did not have an effect on the acetamide concentrations. Nevertheless,

346

further studies are required in order to explore the formation of these acetamides and to

347

possibly link their presence to respective precursor compounds. Additionally, studies that

348

investigate the impact of these compounds on the wine’s sensory characteristics are required.

349

PCA for clone discrimination. For identification of clone effects the PB samples were color

350

coded by clone group. Clone group 5 consisted of all sample batches (winery tank) that

351

contained clone 5, 6 or a combination of the two. Clone group 13 consisted of all sample

352

batches that contained clone 13 or blends containing clone 13, likewise with sample batches

353

of clone group 115 and 777. Discriminations based on clone groups could be observed for all

354

sample groups. PB2012 and PB2013 wine samples are displayed in Figure 4-a and 4-b. The

355

PCA scores and loadings plots depicting the clone discriminations for the barreled wines

356

(BW2012 and BW2013) can be obtained from the supplementary data (Figure A5-a and A5-

357

b).

358

For the PB2012 wine samples, the investigated clone groups were separated along PC2

359

(15.8%) and slightly along PC6 (7.4%), Figure 4-a. Among all clone groups, group 777 was

360

the most discriminant, being separated to the right side of the plot. Groups 5, 13 and 115

361

were located in the center of the plot. Some overlap of clone group 115 with 5 and 13

362

occurred. The 15 most discriminant VOCs selected for clone group 777 along PC2 are

363

displayed in Table 2 (see supplementary data for corresponding loadings, Figure A3). The

364

main discrimination between group 777 and the remaining clone groups was due to ethyl

44, 45

. Acetamides have mainly been 46-48

and are

. As the grape skin contact time in the current study was similar between all ferments, it


Page 17 of 36


365

lactate, -octanoate, -decanoate, -dodecanoate and acetic-, hexanoic-, octanoic- and decanoic

366

acid, methionol, vanillin and ethyl-5-oxoprolinate. In contrast, clone groups 5, 13 and 115

367

were similar along PC2 due to the compounds isobutyl alcohol, isoamyl acetate, 4-methyl-1-

368

pentanol, 2-heptanol, ethyl leucate, 5-methyl furfural, γ-butyrolactone (and its non-lactonized

369

form), 3-methyl butanoic acid, 2-phenylethyl acetate, 2-phenylethanol, N-acetylglycine ethyl

370

ester and N-(2-phenylethyl) acetamide.

371

For the PB2013 wines, the discriminative pattern appeared similar to the pattern of the prior

372

vintage (PB2012). However, the discrimination of PB2013 wines was more ambiguous and

373

overlaps occurred between most of the clone groups (Figure 4-b). Hence, no marker

374

metabolites could be assigned for PB2013 wines (see supplementary data for loadings plot,

375

Figure A3-b). BR115 was very similar to the 777 group and so were LG6 and BR5. In this

376

vintage (2013), clone group 777 was less well discriminated.

377

The pattern of discrimination for the clone groups was slightly different between vintages. In

378

vintage 2012, clone group 777 was well discriminated from the remaining groups. In contrast

379

in vintage 2013 clone group 777 overlapped with BR115, BR5 and LG6. The discrimination

380

between clone groups was shown to be minimal and strongly related to vintage. The results

381

are in agreement with results found by Botelho et al. (4) who found that the discrimination

382

between cloneal Aragonez wines is extremely complex and does not solely depend on single

383

marker compounds. Furthermore it has been reported that the performance of each clone

384

differs between vintages and that wines from different clones can be similar in one vintage

385

but different in another

386

777 was well discriminated from the other clone groups in vintage 2012 but was less

387

discriminated from the other groups in vintage 2013. The differences could be due to soil and

388

climatic differences between the vineyard sites and vintages respectively, as has been

389

reported before for Nebbiolo wines in Italy (50).

4, 50

. This finding was confirmed in the current study as clone group



Page 18 of 36

390

The current study has shown that in a commercial winemaking process, vintage and barrel

391

maturation (pre-barreled vs. barreled wines) are the most dominant factors for discrimination

392

between Mt. Difficulty Pinot Noir wines. The results confirm previously reported effects of

393

vintage and winemaking process and hence help validate the current experimental approach.

394

The site-typicity effects investigated in this study were revealed as being subtle underlying

395

effects with the degree of expression being dependent on vintage. A multitude of marker

396

VOCs were identified in both vintages, of which only two compounds consistently described

397

high site-typicity in both vintages, namely β-citronellol and homovanillyl alcohol. Low site-

398

typicity was consistently described by the two acetamides N(3-methylbutyl)acetamide and

399

N(2-phenylethyl)acetamide). However, to confirm the importance of these compounds in

400

defining high and low typicity characteristics in wines, further vintages will need to be

401

investigated and the overall contribution of these compounds to the sensory profiles of the

402

wines should be explored.

403

Interestingly, an underlying clonal effect could be observed discriminating clone group 777

404

from the remaining clone groups in PB wines of vintage 2012. Patterns of clone

405

discriminations were indicated in the PCA scores plots. However, selection of VOCs

406

responsible for these descriminations was challenging. Further vintages are required for

407

marker metabolite confirmation as well as further detailed discriminative analysis. In this

408

study, for the first time, Pinot Noir clonal wines from New Zealand have been discriminated

409

based on their volatile profiles.

410

The work underlines the complexity of wine aroma, its strong influence on vintage and the

411

winemaking process and the difficulty in obtaining unambiguous volatile profiles that

412

describe the character differences between Pinot Noir wines (site-typicities and clones) even

413

within a small geographical region. It is remarkable however, that despite cofounding effects

414

of the commercial winemaking process such as yeast strain, vigor and other process related


Page 19 of 36


415

effects, it was possible to uncover hidden site-typicity and clonal differences. We believe that

416

this study shows the importance of applying research to commercial scale productions

417

(additionally to controlled laboratory approaches) to aid in the understanding of factors that

418

are inconsistent in the winery, but are very important to the wine’s character. To further

419

disentangle the complex clone and site discriminant factors, laboratory controlled

420

fermentations could be carried out, alongside with this commercial approach. Non-volatile

421

analysis should also be included for a better understanding of the complete range of flavor

422

compounds contributing to these differences. Currently, our group is investigating profiles of

423

the non-volatile compounds present in the samples analyzed in this study using 1H-NMR and

424

front-face fluorescence spectroscopy.

425

Abbreviations Used

426

SPE, solid-phase extraction; HS-SPME, head-space solid-phase micro extraction; SBSE, stir-

427

bar sorptive extraction; GC-MS, gas chromatography-mass spectrometry; PARAFAC,

428

parallel factor analysis; S/N, signal to noise ratio; VOC, volatile organic compound; PCA,

429

principal component analysis; PB, pre-barreling; BW, barreled wines; HST, high site-typicity;

430

LST, low site-typicity.

431

Acknowledgements

432

The authors thank Mt. Difficulty Wines Ltd. for sample provision. Further gratitude is

433

expressed to Michelle Leus for technical assistance in the GC-MS analysis at University of

434

Otago. The Faculty of Science (University of Copenhagen, Denmark) is acknowledged for

435

support to the elite-research area “Metabolomics and bioactive compounds”.

436

Supporting information

437

PCA scores and loadings plots referred to in this paper are displayed in the supporting

438

information allowing an overview of the selection of loadings (VOCs) used for the

439

interpretations in the paper. The complete metabolite (VOC) table including all VOCs



Page 20 of 36

440

detected (identified and unidentified) with their retention indices (RI) and EI-MS matches can

441

be obtained from the supporting information. This material is available free of charge via the

442

internet at http://pubs.acs.org

443

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444

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Page 27 of 36


Tables Table 1: Sample Batches and Their According Vineyard Site and Clone Origin. Vintage 2012 Vineyard site

PT

BR LG

Batch

F 1

Whole bunch (ratio %)

Batch

Clone (ratio %)

1

Whole bunch (ratio %)1

PT115

115 (100)

0

PT115

115 (100)

0

PT5_6_13

5+6+13 (33:33:33)

0

PT115_30%

115 (100)

30

-

-

-

PT5

5 (100)

0

-

-

-

PT6_13_30%

6+13 (50:50)

30

BR5

5 (100)

0

BR5

5 (100)

0

BR115

115 (100)

0

BR115_25%

115 (100)

25

LG6

6 (100)

0

LG6

6 (100)

0

-

-

-

LG6_30%

6 (100)

30

TG5_6_35%

5+6 (50:50)

35

TG5_6

5+6 (50:50)

0

TG777

777 (100)

0

TG777_5_6_30%

777+5+6 (70:15:15)

30

IN6_40%

6 (100)

40

IN777a

777 (100)a

0

IN777_6

777+6 (56:44)

0

IN777b

777 (100)b

0

TG

IN

Clone (ratio %)

Vintage 2013

-

-

-

IN777_23%

777 (100)

23

F6

6 (100)

0

F115

115 (100)

0

F115_5

115+5 (54:46)

0

F115_20%

115 00)

20

Whole bunch ratio: addition of complete grape clusters (not de-stemmed) to the ferment tank.



Page 28 of 36

Table 2: Metabolites (VOCs) Responsible for Vintage, Barrel Maturation, Site-typicity and Clone Effects. Metabolite Numbers Correspond to the Loadings Numbers in the Associated Loadings Plot of the PCA Models. Metabolite Number

Name [IUPAC name]

RI

EI-MS match (%)

Main m/z, relative abundance 100%

Vintage 1 effect

Barrel 2 Effect

Sitetypicity effect3

Clone effect4 2012

1

[ethyl butanoate]

1040

93.0

71

-b

-

LST

-

2

isobutyl alcohol [2-methylpropan-1-ol]

1095

93.6

43

-

-

HST

5,13,115

3

isoamyl acetate [3-methylbutyl acetate]

1124

86.0

43

-

+b

-

5,13,115

4

1-butanol [butan-1-ol]

1148

90.0

56

-

o

-

-

5

1-penten-3-ol [pent-1-en-3-ol]

1163

53.0

57

-

o

-

-

6

isoamyl alcohol [3-methyl butan-1-ol]

1216

78.0

55

-

+a

HST

-

7

[ethyl hexanoate]

1240

95.0

88

+b

-

-

-

8

1-pentanol [pentan-1-ol]

1256

60.0

42

-

+a

-

-

10

4-methyl-1-pentanol [4-methylpentan-1-ol]

1321

83.0

56

+b

-

-

5,13,115

11

trans-2-penten-1-ol [(E)-pent-2-en-1-ol]

1329

64.0

57

-

-a

-

-

12

2-heptanol [heptan-2-ol]

1334

72.0

45

+a

+a

-

5,13,115

17

ethyl 2-hydroxyisovalerate [ethyl 2-hydroxy-3-methyl butanoate]

1345

82.8

73

-a

+c

-

-

13

ethyl lactate [ethyl 2-hydroxypropanoate]

1352

78.0

45

-

-

LST

777

14

1-hexanol [hexan-1-ol]

1360

90.0

56

+a

o

LST

-

15

cis-3-hexen-1-ol [(Z)-hex-3-en-1-ol]

1371

90.0

41

+a

-

-

-

16

3-ethoxy-1-propanol [3-ethoxypropan-1-ol]

1375

95.5

59

-

-

LST

-

18

[ethyl octanoate]

1441

92.3

88

-

-

HST

777

21

[acetic acid]

1457

72.2

43

-

-

-

777

20

1-heptanol [heptan-1-ol]

1462

80.0

70

+b

-

-

-

24

2-ethyl hexanol [2-ethylhexan-1-ol]

1497

83.0

57

+c

-

-

-

25

[ethyl 3-hydroxybutanoate]

1529

97.4

43

-

-

LST

-

26

ethyl leucate [ethyl 2-hydroxy-4-methylpentanoate]

1551

86.5

69

-

+b

HST

5,13,115

27

1-octanol [octan-1-ol]

1565

83.0

56

+a

-

HST

-

31

5-methyl furfural [5-methylfuran-2-carbaldehyde]

1570

93.8

110

-

+c

-

5,13,115

28

isobutanoic acid [2-methylpropanoic acid]

1576

91.0

43

-a

-

HST

-

36

[ethyl decanoate]

1644

98.0

88

-

-

-

777

35

γ-butyrolactone [dihydrofuran-2(3H)-one] (and non-lactonised form [4-hydroxybutanoic

1646

74.0

42

-

-

HST

5,13,115


Page 29 of 36


Metabolite Number

Name [IUPAC name]

RI

EI-MS match (%)


Vintage effect1

Barrel Effect2

Sitetypicity 3 effect

Clone effect4 2012

38

[2-methyl butanoic acid]

1676

71.8

74

-a

-

-

-

39

furfuryl alcohol [2-furanmethanol]

1677

71.4

98

-

-

HST

-

37

isovaleric acid [3-methyl butanoic acid]

1678

77.4

60

-

-

LST

5,13,115

40

diethyl succinate [diethyl butanedioate]

1685

98.1

101

-

+b

-

-

41

methionol [3-methylsulfanylpropan-1-ol]

1721

97.8

106

-

-

HST

777

44

β-citronellol [3,7-Dimethyloct-6-en-1-ol]

1772

90.0

69

+a

-

HST

-

45

linalool oxide (pyranoid) [2,2,6-Trimethyl-6-vinyltetrahydro-2H-pyran-3ol]

1756

62.8

68

+a

-

-

-

46

[methyl 4-hydroxybutanoate]

1775

85.2

74

-

-a

-

-

49

[2-phenylethyl acetate]

1826

90.0

104

-

-

LST/ HST

5,13,115

53

[ethyl dodecanoate]

1848

95.0

88

-a

-

HST

777

51

[hexanoic acid]

1852

90.0

60

-

-

LST

777

54

[N-(3-methylbutyl)acetamide]

1878

95.8

73

-b

-

LST

-

57

[ethyl 3-methylbutyl succinate]

1886

75.6

101

-

+b

-

-

56

cis-whiskey lactone [(4R,5R)-5-butyl-4-methyloxolan-2-one]

1899

50.6

99

-

+c

-

-

58

[2-phenylethanol]

1921

94.0

91

-

-

HST

5,13,115

60

trans-whiskey lactone [(4S,5R)-5-butyl-4-methyloxolan-2-one]

1970

55.1

99

-

-

LST

-

65

[octanoic acid]

2064

91.0

60

-

-

-

777

2170

97.1

72

-

-

-

5,13,115

2247

72.0

85

-

+b

LST

-

68 70

N-acetylglycine ethyl ester [ethyl 2-acetamidoacetate] γ-carboethoxy-γ-butyrolactone [ethyl 5-oxooxolane-2-carboxylate ]

71

[ethyl hexadecanoate]

2258

98.0

88

-a

-a

LST/ HST

-

72

[decanoic acid]

2276

98.0

73

-

-

-

777

76

ethyl hydrogen succinate [4-ethoxy-4-oxobutanoic acid]

2385

97

101

-

+c

-

-

81

vanillin [4-hydroxy-3-methoxybenzaldehyde]

2559

45.8

151

80

[phenylacetic acid]

2563

80

91

-a

-

LST

-

82

[N-(2-phenylethyl)acetamide]

2599

91.0

104

-a

-

LST

5,13,115

2615

94.0

151

-

o

HST

-

2620

80.0

84

-

-

-

777

2637

94.0

151

-

-

HST

-

2641

91.0

151

-

+a

LST

-

2849

93

137

-a

-

HST

-

84 83 87 86 92

methyl vanillate [methyl 4-hydroxy-3-methoxybenzoate] 2-pyrrolidinecarboxylic acid-5-oxo-, ethyl ester [ethyl-5-oxoprolinate] acetovanillone [1-(4-hydroxy-3-methoxyphenyl)ethanone] ethyl vanillate [ethyl 4-hydroxy-3-methoxybenzoate] homovanillyl alcohol [4-(2-hydroxyethyl)-2-methoxyphenol]

+c

777



Metabolite Number

94 1

Name [IUPAC name]

[hexadecanoic acid]

Page 30 of 36

RI

EI-MS match (%)


Vintage effect1

Barrel Effect2

Sitetypicity 3 effect

Clone effect4 2012

2906

89

73

-

-

HST

-

Average relative change from vintage 2012 to vintage 2013 (-100 to -51%=-b; -50 to -

10%=-a; 10 to 50%=+a; 51-100%=+b; 101 to 210%=+c). Calculated by the equation: ∆%=((mean(metabolite1 2013) - mean(metabolite1 2012))/mean(metabolite1 2012)) × 100. 2

Average relative change from PB to BW samples (-55 to -1.4%=-a; -1.3 to +0.9%=o; 1 to

60%=+a; 61 to 200%=+b; 201 to 3300%=+c). calculated by the equation: ∆%=((mean(metabolite1 BW) - mean(metabolite1 PB))/mean(metabolite1 PB)) × 100. 3

Metabolites positively correlated with high site-typicity=HST and with low site-

typicity=LST (vintage 2012=black, vintage 2013=red, coinciding in both vintages=black bold. 4

Metabolites positively correlated with clone group 777=777 or correlated with clone

groups 5, 13 and 115=5,13,115 in vintage 2012.


Page 31 of 36


Figure captions Figure 1. Map of Mt. Difficulty Wines vineyard sites; red: high site-typicity expressing vineyard sites, blue: other vineyard sites; reprinted from website with permission of Mt. Difficulty Wines.

Figure 2. PCA scores plots of all samples showing A) vintage effect: vintage 2012= diamond, red; vintage 2013 = squares, green; and B) barrel maturation effect: pre-barreled= diamonds, pink; barreled= squares, blue.

Figure 3. PCA scores plots of PB wines showing the site-typicity effect A) vintage 2012; B) vintage 2013. High typicity=diamonds, red; and low typicity= squares, green.

Figure 4. PCA scores plots of PB wines showing the effect of clone groups A) vintage 2012; B) vintage 2013. Clone group 5= diamonds, red; clone group 13= squares, green; clone group 115= triangles, blue; and clone group 777= upsidedown triangles, turquoise.



Page 32 of 36

Figure graphics Figure 1.


Page 33 of 36


Figure 2. top: A; bottom: B Samples/Scores Plot 10

Scores on PC 2 (17.18%)

8 6 4 2 0 -2 -4 -6 -8 -10 -10

-5

0

5

10

Scores on PC 1 (24.26%) Samples/Scores Plot 10


8 6 4 2 0 -2 -4 -6 -8 -10 -10

-5

0

5

10




Page 34 of 36

Figure 3. top: A; bottom: B Samples/Scores Plot 8


6

F6

F115_5 F6

4

F115_5 LG6 LG6 IN777_6 IN777_6

2 0 -2

BR115

IN6_40% TG5_6_35% TG5_6_35%

BR5 BR5

-4

BR115

IN6_40%

PT5_6_13

PT115 PT115

TG777 TG777

PT5_6_13

-6 -8 -8

-6

-4

-2

0

2

4

6

8

Scores on PC 4 (11.29%) Samples/Scores Plot 10


8 6 4 2 0 -2 -4 -6

PT6_13_30%

TG777_5_6_30% TG5_6 PT115 PT115 TG5_6

TG777_5_6_30% BR115_25% BR5 PT5 LG6 BR115_25% PT5 BR5 PT6_13_30% LG6 IN777_23% F115_20% F115_20% PT6_13_30% LG6_30% IN777a IN777_23% IN777b LG6_30% IN777a IN777b F115 F115 PT6_13_30%

-8 -10 -10

-5

0

5

10



Page 35 of 36


Figure 4. top: A; bottom: B Samples/Scores Plot

8

TG5_6_35%


6

TG5_6_35% 4

IN6_40% BR5

BR5

2

F6 F6 BR115 TG777 TG777 PT115 IN777_6 BR115 LG6 IN6_40% F115_5 PT115 IN777_6 F115_5 PT5_6_13 LG6

0

-2

-4

PT5_6_13 -6

-10

-8

-6

-4

-2

0

2

4

6

8

10

Scores on PC 2 (15.82%) Samples/Scores Plot F115_20% F115_20%


6

LG6 4

F115

F115

0

-2

-4

LG6

PT5 PT5

TG5_6

PT115 PT115

TG5_6

2

PT6_13_30% LG6_30% IN777_23% BR5 IN777_23% PT6_13_30% LG6_30% BR5 PT6_13_30% TG777_5_6_30% IN777b PT6_13_30% TG777_5_6_30% IN777b BR115_25% BR115_25% IN777a IN777a

-6

-8

-6

-4

-2

0

2

4

6

8




Page 36 of 36

TOC Graphic

For table of contents only.


GC-MS Metabolite Profiling of Extreme Southern Pinot noir Wines

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