Characterization of Key Aroma Compounds in a Commercial Rum and

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Chemistry and Biology of Aroma and Taste

Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics Based Expert System (SEBES)- An Approach to Use Artificial Intelligence in Determining Food Odor Codes Luca Nicolotti, Veronika Mall, and Peter Schieberle J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b00708 • Publication Date (Web): 17 Mar 2019 Downloaded from http://pubs.acs.org on March 18, 2019

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Journal of Agricultural and Food Chemistry

Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics Based Expert System (SEBES)An Approach to Use Artificial Intelligence in Determining Food Odor Codes

Luca Nicolotti#, Veronika Mall# and Peter Schieberle§*

# Leibniz-Institute for Food Systems Biology at the Technical University of Munich (formerly as Deutsche Forschungsanstalt für Lebensmittelchemie), Lise-Meitner-Straße 34, D-85354 Freising, Germany § Department of Chemistry; Technical University of Munich Lichtenbergstarsse4, D-85748 Garching, Germany

*Corresponding Author Phone:

+49 871 97698160

E-mail:

[email protected] ACS Paragon Plus Environment


2 1

ABSTRACT. Although to date more than 10,000 volatile compounds have been

2

characterized in foods, a literature survey has previously shown that only 226 aroma

3

compounds, assigned as key food odorants (KFOs), have been identified to actively

4

contribute to the overall aromas of about 200 foods, such as beverages, meat

5

products, cheeses, or baked goods. Currently, a multi-step analytical procedure

6

involving the human olfactory system, assigned as Sensomics, represents a

7

reference approach to identify and quantitate key odorants, as well as to define their

8

sensory impact in the overall food aroma profile by so-called aroma recombinates.

9

Despite its proven effectiveness, the Sensomics approach is time consuming, since

10

repeated sensory analyses, e.g., by GC/olfactometry, are essential to assess the

11

odor quality and potency of each single constituent in a given food distillate.

12

Therefore, the aim of the present study was to develop a fast, but Sensomics based

13

expert system (SEBES) able to reliably predict the key aroma compounds of a given

14

food in a limited number of runs without using the human olfactory system. First, a

15

successful method for the quantitation of nearly 100 (out of the 226 known KFOs)

16

components was developed in combination with a software allowing the direct use of

17

the identification and quantitation data for the calculation of odor activity values

18

(OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples,

19

the quantitative results obtained by the new SEBES method were compared to data

20

obtained by applying an aroma extract dilution analysis and stable isotope dilution

21

assays required in the classical Sensomics approach. A good agreement of the

22

results was found with differences below 20% for most of the compounds considered.

23

By implementing the GC×GC data analysis software with the in-house odor threshold

24

database, odor activity values (ratio of concentration to odor threshold) were directly

25

displayed in the software pane. The OAVs calculated by the software were in very

26

good agreement with data manually calculated on the basis of the data obtained by

27

SIDA. Thus, it was successfully shown that it is possible to characterize key food

28

odorants with one single analytical platform and without using the human olfactory

29

system, i.e. by “artificial intelligence smelling”.

30 31 32

KEY WORDS: odor activity value; stable isotope dilution assay; key food odorants; sensomics based expert system; SEBES; Sensomics

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INTRODUCTION

35

Aroma compounds are volatile chemicals perceived by odorant receptor located

36

in the olfactory tissue.1 Although the same chemicals are meant, the compounds are

37

assigned as odorants, when an orthonasal detection is considered and are assigned

38

as aroma compounds when a retronasal perception is discussed. Odorants do have

39

a strong impact on the acceptance of foods by consumers and discriminate food

40

products according to their origin, technological treatment or storage.2-3 For this

41

reason, research on food aroma compounds has constantly increased during the last

42

decades and has become a fundamental aspect to be taken into account by food-

43

producing companies, i.e., in the development of new products or in the mitigation

44

off-odors generated during storage.

45

A recent review paper4 highlighted that only 226 volatile compounds do play a

46

role in the overall aroma profile of more than 200 food products. This group of

47

volatiles, assigned as key food odorants (KFOs), includes compounds from a variety

48

of different chemical classes such as alcohols, aldehydes, lactones, ketones,

49

terpenoids, thiols and sulfides. To get information on compounds responsible for a

50

given food aroma, today the concept of Sensomics5 represents a reference

51

procedure for the characterization of such KFOs. This approach, formerly known as

52

“Molecular Sensory Science”, consists of several steps. The first involves the volatile

53

isolation by means of careful isolation procedures such as the Solvent Assisted

54

Flavor Evaporation (SAFE).6 This technique allows a gentle isolation of volatiles from

55

different kind of matrices: aqueous foods, aqueous food suspensions and high fat

56

containing samples. The obtained distillate is following concentrated and subjected to

57

an Aroma Extract Dilution Analysis (AEDA),7 a procedure in which successive

58

dilutions of the extract are analyzed by Gas Chromatography-Olfactometry (GC-O),7



4 59

in order to assign the odor quality and potency of the different volatiles. The higher

60

the flavor dilution factor (FD; equal to the highest dilution where the odorant was

61

lastly detected by GC-O),7 the higher is the assumed potency of the odorant under

62

investigation. Odorants showing the highest FD factors are then quantitated using

63

Stable Isotope Dilution Assays (SIDA)8 to compensate workup losses. Subsequently,

64

the Odor Activity Value (OAV)7 (ratio between the concentration of a compound in the

65

food and its odor threshold in a defined matrix) is calculated for each odor-active

66

volatile. Compounds showing an OAV ≥ 1 are considered as key aroma compounds

67

but, finally, aroma recombinates are prepared by spiking model matrices with pure

68

reference compounds of the identified key odorants in the concentrations determined

69

in the food itself.9 A trained sensory panel then compares the recombinate with the

70

original food, in order to validate the correct identification and quantitation of the key

71

odorants.

72

The main disadvantage of the Sensomics approach is the time required by each

73

step as well as the overall complexity. A variety of analytical techniques and

74

instruments must be used to obtain the desired results, thus making the Sensomics

75

approach hardly suitable for routine analysis. Literature reports a number of studies

76

focusing on identification and quantitation of food aroma compounds: many of them

77

10-12

78

procedure.

follow a standard approach; but some

13-17

move away from the conventional

79

However, to the best of our knowledge, to date no study has currently aimed at

80

providing (i) correct odorant identification, (ii) a reliable quantitation and (iii) finally the

81

evaluation of the aroma contribution by means of a single analytical platform. The

82

idea of this investigation was, therefore, to develop a Sensomics based expert

83

system (SEBES) able to predict the set of key food odorants in an extract by

84

combining odor thresholds with quantitative results in one software in order to get ACS Paragon Plus Environment

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automatically calculated odor activity values. This way food odor codes should be

86

defined without using the human olfactory system, i.e. by artificial intelligence. On the

87

basis of the current knowledge on key food odorants and their odor thresholds, the

88

SEBES approach should be developed using comprehensive two-dimensional gas

89

chromatography coupled with time of flight mass spectrometry (GC×GC-TOF/MS),

90

because its enhanced sensitivity and separation power allows the separation of up to

91

1000 compounds in one analytical run.17-22 Additionally, the GC-ImageTM software

92

(Lincoln, Nebraska) was used, since it provides features for automated quantitation

93

and a variety of customizable functions, i.e. the construction of a database containing

94

the odor thresholds of the KFOs in a variety of matrices.23

95

The efficacy of the new procedure was evaluated on a high-quality rum,

96

previously analyzed by the Sensomics approach24, and a Cabernet Sauvignon wine

97

by comparing the results obtained for KFOs by a classical Sensomics approach with

98

data of the SEBES method.



6 100

MATERIALS AND METHODS

101

Samples. Rum (obtained from sugar cane molasses and aged for 15 years) was

102

purchased from an internet supplier. Cabernet Sauvignon wine from Australia

103

(vintage 2010) was purchased from a local shop. The same batch of both samples

104

were used for the Sensomics method, and for the application of the new Sensomics

105

based expert system (SEBES).

106

Chemicals. The ninety-six pure reference compounds and the seventeen internal

107

standards (Table S1) were bought from Sigma-Aldrich Chemie, (Taufkirchen,

108

Germany), Fluka, (Neu-Ulm, Germany), Lancaster (Mühlheim, Germany) and Merck-

109

VWR (Darmstadt, Germany). 1-(2,6,6-Trimethyl-1,3-cyclohexadien-3-yl)-2-buten-1-

110

one ((E)-β-damascenone) was kindly provided by Symrise (Holzminden, Germany).

111

All solvents were freshly distilled before use. Sodium chloride, sodium sulfate and

112

sodium carbonate were obtained from Merck and liquid nitrogen for GC×GC was

113

from Westfalen Gas (Münster, Germany).

114

Isotopically labeled standards for stable isotope dilution assays. The assays

115

were performed as recently described for rum by Franitza et al.24 For the quantitation

116

of the wine odorants the following standards were synthesized following the

117

procedure published in the references: [2H3]-4-hydroxy-3-methoxybenzaldehyde and

118

[2H3]-2-methoxyphenol;25 [2H3]-3-(methylthio)propanol, [2H5]-ethyl pentanoate and

119

[2H5]-ethyl 2-methylpropanoate;26 [2H3-8]-(E)-β-damascenone;27 [2H3]-ethyl butanoate

120

and [2H3]-ethyl 3-methylbutanoate, [2H3]-ethyl hexanoate, [2H3]-ethyl octanoate, [2H2]-

121

3-methylbutyl acetate and [2H2]-3-ethylphenol;28 [2H3]-ethyl 2-methylbutanoate;29

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[2H11]-3-methyl-1-butanol;30 [13C4]-2,3-butanedione and [2H2]-butanoic acid;31 [2H1-2]-

123

octanoic

124

2(5H)-furanone;34 [2H3]-3-(methylthio)propanal;35 [2H2]-cis- and [2H2]-trans-whisky

acid;32

[2H2-4]-4-ethyl-2-methoxyphenol;33


[13C2]-3-hydroxy-4,5-dimethyl-

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lactone;36

[2H3]-phenylethyl

126

propanoate.38

acetate,

[13C2]-γ-nonalactone;37

and

[2H3]-ethyl

127

The following labeled internal standards were purchased from the commercial

128

sources given in parentheses: [3H2]-acetic acid, [2H3]-decanoic acid and [13C2]-

129

phenylacetic acid (Sigma-Aldrich Chemie); [2H7]-2-methylpropanoic acid, [2H9]-2-

130

methylbutanoic acid, [2H3]-methyl-1-propanol and [2H5]-2-phenylethanol (CDN

131

Isotopes, Quebec, Canada); [13C2]-4-hydroxy-2,5-dimethyl-3(2H)-furanone was from

132

aromaLab AG, Planegg, Germany.

133 134

Selection of Key Food Odorants and Appropriate Internal Standards for the Development of the Sensomics Based Expert System (SEBES)

135

The first set of experiments addressed the selection of more than 90 key food

136

odorants (KFOs; out of the 226 listed4) and measurements to find appropriate

137

unlabeled internal standards for the further development of the simplified quantitation

138

method. The quantitation of the 96 selected KFOs was developed with a total of 17

139

easily available internal standards.

140

Comprehensive two-dimensional gas chromatography/ time of flight mass

141

spectrometry (GC×GC-TOF/MS). The analyses using the unlabeled internal

142

standards for the development of the SEBES approach were carried out by means of

143

the Leco Pegasus IV GC×GC-TOF/MS (St. Joseph, Michigan, USA) equipped with

144

an Agilent 6890 gas chromatograph and a dual stage four jet thermal modulator

145

(modulation period 4 seconds) coupled to the TOF/MS. A J&W Scientific DB-FFAP

146

column (30 m × 0.25 mm i.d., 0.25 μm film thickness) equipped with a deactivated

147

J&W Scientific silica pre-column (2 m × 0.32 mm i.d.) was used in the first dimension.

148

The second dimension column consisted of a J&W Scientific DB-5 column (2 m

149

×0.18 mm id, 0.18 μm film thickness). A constant flow of 2 mL/min was used and the

150

primary oven temperature program was 35 °C for 1 min, then raised at 6 °C/min to ACS Paragon Plus Environment


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230 °C (5 min hold). The secondary oven temperature was set at 40 °C higher than

152

the primary oven. Mass spectra by electron ionization (MS-EI) were acquired in the

153

m/z range of 35 to 350 with an acquisition frequency of 100 Hz. Samples were

154

injected using a PAL auto-sampler (CTC analytics, Zwingen, Switzerland) in a

155

Gerstel splitless-PTV injector (Mühlheim a. d. Ruhr, Germany) kept at 20 °C and

156

raised by 10 °C/min to 250 °C (injection volume: 1 µl).

157

Validation of the quantitation within the SEBES approach. The selected KFOS

158

and internal standards were submitted to a validation protocol designed to evaluate

159

the performances of the following parameters: repeatability, instrument linearity

160

range, recovery, limit of detection (L.o.D.), limit of quantitation (L.o.Q.) and

161

quantitation uncertainty. Precision (repeatability) was estimated by replicate analyses

162

of all compounds used: Ten analytical replicates were acquired over two weeks, with

163

the same instrument and operator. For linearity evaluation, etherial solutions of the

164

pure reference compounds and selected internal standards (ISt) in different

165

concentrations were analyzed (from 0.5 to 10 µg/mL) in triplicate and the

166

instrumental responses were plotted as a function of the analyzed concentration.

167

Internal standard based calibration curves were calculated by plotting the

168

response ratio (quantifier analyte/ quantifier internal standard) as a function of the

169

amount ratio (amount analyte/ amount internal standard). Six calibration levels, within

170

the working range defined in the method validation (0.5 - 10 µg/mL), were analyzed

171

and different ratios of the internal standard and the respective analyte were used

172

(ranging from 1:5 to 5:1).

173

L.o.D. and L.o.Q. were experimentally determined by analyzing decreasing

174

concentrations (from 1 to 0.01 µg/mL) of each compound. Because the SEBES

175

approach should be applicable for various food samples, the recovery was tested

176

using two model matrices, demineralized water and deodorized cocoa butter: Either ACS Paragon Plus Environment

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deionized water (50 mL) or de-odorized cocoa butter (12 g) were spiked with known

178

amounts of the internal standard and the key food odorants. Equilibration and volatile

179

extraction as well as volatile isolation by the SAFE method6 were performed using

180

the same protocol as described below for the samples.

181

Volatile isolation from Rum and Wine for the SEBES approach. Aliquots of

182

rum (80, 40, 20 and 10 mL) were each spiked with the 13 internal standards,

183

equilibrated by stirring for 20 min at room temperature (RT) and extracted in a

184

separating funnel with diethyl ether. The organic phases were collected and washed

185

three times with an aqueous sodium chloride solution (0.1 mol/L) in order to remove

186

most of the ethanol. Isolation of volatiles was achieved by means of SAFE.6 The

187

distillates were dried over sodium sulfate, filtered and concentrated to about 1 mL

188

using a Vigreux column followed by micro distillation. For wine, 150, 50,20,5 mL were

189

used for the extraction/distillation process.

190

Data processing. Raw GC×GC data files (.peg files) were processed using the

191

GC Image software (GC ImageTM, release 2.3 b4, Lincoln, NE). Raw data files were

192

imported into the software and were baseline-corrected. 2D peaks were automatically

193

integrated by the software via the Watershed algorithm.39 A minimum peak-volume

194

threshold was set to 5000. All parameters were saved in a method and used to

195

process all runs. GC Project and GC Image Investigator, features of the GC Image

196

software, were used for automatic data processing and calibration curve calculation.

197 198

Volatile isolation from Wine for Aroma Extract Dilution Analysis (AEDA). For

199

AEDA, the wine (200 mL) was extracted with diethyl ether, and the volatiles were

200

isolated by SAFE distillation6. The distillate was dried over anhydrous sodium sulfate

201

and after filtration concentrated to about 0.5 mL.



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Quantitation of odorants in wine by extraction/SAFE distillation and stable isotope dilution assays (SIDA)

204

Wine (volumes ranging from 1 to 500 mL) was spiked with known amounts of the

205

internal standards, equilibrated for 20 min and extracted twice with diethylether. The

206

extract was concentrated to 100 mL, if necessary and subjected to SAFE distillation 6

207

If necessary, the aroma extract was separated into a neutral/basic and an acidic

208

fraction as recently reported24.

209

Quantitation of Odorants in Wine by Headspace Solid Phase Micro

210

extraction (HS-SPME). HS-SPME was carried out for all esters as well as for (E)-β-

211

damascenone, 2-phenylethanol 2,3-butanedione, 2-methyl-1-butanol, 3-methyl-1-

212

butanol and methylpropanol. The wine was appropriately diluted, spiked with the

213

respective isotopically labeled standards and, after equilibration for 30 min, subjected

214

to HS-SPME and analyzed by either high resolution gas chromatography/mass

215

spectrometry

216

differences of fiber affinity and equilibration could be neglected due to the use of the

217

isotopically labeled standards with similar chemo-physical properties as the analyte.

218

40TDetails

219

summarized in Table 1.

(HRGC/MS)

or

GC×GC-TOF/MS,

respectively.

Intermolecular

on optimized fiber extraction, chromatographic and MS parameters are

220

High resolution gas chromatography/mass spectrometry (HRGC/MS). An

221

Agilent gas chromatograph 7890 B (Waldbronn, Germany) was combined with an

222

Agilent ion trap mass spectrometer detector type 240. The samples were injected

223

cool-on-column via a programmed multi-mode-injector. The choice of column and the

224

GC temperature program was optimized for each analyte (Table 1). Mass spectra

225

were generated in the chemical ionization (MS-CI) with methanol as the reactant gas.

226

For interpretation of the mass spectral data, the program MS Workstation (Agilent,

227

Waldbronn Germany) was used. This system was used for all acids as well for the ACS Paragon Plus Environment

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SPME measurements of 2-phenylethanol, 2- and 3-methyl-1-butanol, methyl-1-

229

propanol, 2,3-butanedione, ethyl propanoate and ethyl 2-methylpropanoate.

230

Two-dimensional High-Resolution Gas Chromatography/Ion Trap Mass

231

Spectrometry (TD-GC-GC/MS). For the quantitation of 3-(methylthio)propanal, 3-

232

HDMF, 4-HDMF, 4-ethyl-2-methoxyphenol and 4-ethylphenol, a Thermo instruments

233

Trace 2000 series gas chromatograph (Dreieich, Germany) equipped with a Fisons

234

Instruments moving capillary stream switching system (MCSS) (Mainz-Kastel,

235

Germany) and linked to an Agilent gas chromatograph CP 3800 with an Agilent ion

236

trap mass spectrometer Saturn 2000 was used. Samples were injected cool-on-

237

column and after chromatography on the first capillary, the respective analyte of

238

interest and the internal standard were transferred into a cold trap (-160 °C) in the

239

second oven via the MCSS. Analyte and standard ions were monitored in MS-CI with

240

methanol as reactant gas. In the first oven, a J&W Scientific fused silica capillary DB-

241

FFAP (30 m × 0.32 mm i.d.; 0.25 µm film thickness) (Folsom, USA) was installed in

242

combination with a J&W Scientific DB-1701 (30 m × 0.25 mm i.d.; 0.25 µm film

243

thickness) in the second oven. The oven temperature was held at 40 °C for 2 min

244

rised at 6 °C/min to 230 °C and then held for another 5 min. The cut time intervals in

245

the first dimension were determined by injections of the respective reference

246

compounds in preliminary experiments.

247

Odor thresholds were taken from the institute’s database which was constructed

248

based on data from our previous publications using the Sensomics approach or odor

249

activilty value calculations. The database contained odor thresholds in water,

250

vegetable oil, starch, and water/ethanol mixtures. The odor threshold were

251

determined as detection thresholds determined using the triangle test as forced

252

choice test.



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Enantiomer and structural isomer separation. Structural isomer ratio of 2- and

254

3-methyl-1-butanol in wine samples was determined using a BGB-174-E chiral

255

column (BGB Analytik, Rheinfelden, Germany; 30 m × 0.25 mm i.d.; 0.25 μm film

256

thickness); 2-methylbutanoic acid (28%) and 3-methylbutanoic acid (72%) were

257

separated on a thick film DB-5 (30 m × 0.25 mm i.d.; 1.0 µm film thickness; J&W

258

Scientific, Folsom, CA).


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RESULTS AND DISCUSSION

261

Development of the Sensomics Based Expert System (SEBES)

262

As indicated in the introduction, the first aim of the study was to establish a fast

263

method for volatile identification and quantitation as well as getting a reliable

264

information on the contribution of each single odorant to the aroma of a given food

265

using only one platform, i.e. one GC-MS run.

266

Selection and Identification of Key Food Odorants (KFOs). As up to know

267

only 226 volatiles have been established as contributors to food aromas4, the first

268

issue was to create a fast and reliable identification procedure for a selection of more

269

than 90 key food odorants related in particular to alcoholic and non-alcoholic

270

beverages. This task was achieved by exploiting both GC×GC-TOFMS performances

271

and the “template” feature included in the GC Image software. The separation power

272

and sensitivity offered by GC×GC-TOF/MS allowed high peak capacity, limited co-

273

elution and high spectral reproducibility, thus making identification more reliable.18-22

274

Additionally,

275

identification.41,42 The “template”43 is a file including the information regarding

276

retention times in the first and second dimension as well as the MS fragmentation

277

pattern for each selected peak in a 2D chromatogram. By analyzing mixtures of the

278

pure reference compounds, it is possible to create a small number of template files

279

containing the information of the total of 226 KOFs.

the

template

feature

enabled

automated

and

reliable

peak

280

As indicated above, in this study three templates containing the information for

281

more than 90 KFOs were created. These obtained templates can be “overlaid” on the

282

two-dimensional chromatograms of a given sample, and a defined matching

283

algorithm compares the information present in the templates with those in the

284

sample’s chromatogram: If the similarity is below a defined limit, analytes are



14 285

considered as positively identified.42 This method represents a fast and reliable way

286

to screen a wide range of potential odorants in a variety of samples. A representation

287

of the successful working principle was previously shown by us on a smaller number

288

of analytes42

289

Internal Standard Selection. The second step was to establish a fast, automated

290

and accurate quantitation. The method currently used in the Sensomics concept

291

relies on stable isotope dilution assays (SIDA)8 using a labeled standard for the

292

quantitation of each odorant identified by application of an aroma extract dilution

293

analysis. The use of such labeled isotopomers guarantees a unique quantitation

294

accuracy, since in particular losses during the sample work-up are compensated.

295

But, SIDA has some practical disadvantages, because often labeled standards are

296

commercially not available and have to be synthesized and, when commercially

297

available, they are usually expensive or may not comply with the chosen ionization

298

technique.44 Thus, for the SEBES approach, common internal standard based

299

quantitation methods were developed replacing the isotopically labeled internal

300

standards by a lower number of commercially available pure compounds. But, these

301

standards must (i) be absent in the food matrices under investigation, (ii) must be

302

stable and (iii) their recovery, volatility and polarity should be comparable to the

303

physico-chemical properties of the respective analyte. Thus, the 96 KOFs were

304

classified according their chemical structure, and depending on the analyte’s volatility

305

and polarity, 17 appropriate internal standards were selected. The internal standards

306

were selected on the basis of similar volatility and polarity and showing elution times

307

between the components of the chemical class considered. Calibration curves were

308

then generated using defined mixtures of all 96 KFOs and the 17 internal standards.

309

Quantifier ions were selected to result in response factors (slope) as close as

310

possible to 1 and intercept values as close as possible to zero and to be as ACS Paragon Plus Environment

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characteristic as possible for the respective compound. The calibration curves were

312

then used to determine the linear range, the slope and the y-axis intercept (Table S1;

313

supplementary information).

314

Method validation for SEBES quantitation.

315

For the current study on fermented alcoholic beverages, thirty-six key food

316

odorants out of the ninety-two KFOs were selected following the cluster for alcoholic

317

beverages reported recently.24 To get nearer to a real food sample and to determine

318

losses during work-up and to investigate the influence of the food matrix on the

319

recovery, all 36 KFOs and the thirteen internal standards where then added to either

320

an aqueous or an oily matrix, and the volatile compounds were isolated by SAFE

321

distillation. The distillates were then submitted to a validation protocol to assess

322

precision, linearity range, L.o.D., L.o.Q. and recovery. The precision (repeatability) 45

323

is expressed as relative standard deviation (RSD) of the instrumental response for

324

the target analytes, i.e. the key food odorants as well as the internal standards.

325

Good repeatability with RSD values below 15% was observed for all 36 compounds

326

(Table 2) as well as for the 13 internal standards (Table 3). Following, linearity and

327

sensitivity were evaluated in order to define a reliable range of working

328

concentrations for each analyte. Linearity is defined as “the ability of the method to

329

obtain test results proportional to the concentration of the analyte”.45 A quantifier ion

330

mass trace was chosen for KFO and internal standards as instrument response

331

(Table 2; Table 3), and each quantifier was selected to be as characteristic as

332

possible for the respective compound. The instrumental response was plotted as a

333

function of the analyzed concentration, and results were evaluated by the coefficient

334

of determination (R2). This value indicates how well the calculated regression fits to

335

experimental data. Simultaneously to linearity, sensitivity was determined through

336

L.o.Q and L.o.D. The latter is defined as “the lowest concentration of the analyte in a ACS Paragon Plus Environment


16 337

sample that can be detected, but not necessarily quantitated under the stated

338

conditions of the test”.45 In this study, the L.o.D. corresponded to the lowest

339

concentration for which analyte identity confirmation was consistent with fixed

340

acceptable parameters (Identity Spectrum Match Factor and Reverse Match Factor >

341

800).The L.o.Q. is defined as “the lowest amount of analyte that can be determined

342

with an acceptable level of repeatability precision and trueness”.45 For each

343

concentration level, 10 analytical replicates were analyzed. Quantitative data were

344

calculated using linearity regressions and the L.o.Q. was set as the lowest amount

345

showing a quantitation accuracy error (calculated vs real amount) below 20% of

346

RSD.

347

In addition, a determination of the recovery was necessary to prove that the analytes

348

and their respective internal standards showed a comparable recovery. The external

349

calibration curves as described above (obtained from linearity data) were used to

350

estimate the recovery rates and losses were determined as difference between the

351

calculated and the spiked amount. Only if the selected internal standards were able

352

to compensate analyte losses, these provide an acceptable error of quantitation. By

353

means of this approach, it was not necessary to calculate response factors for KFO

354

vs. its internal standard. Successfully, the internal standards chosen showed

355

recovery rates comparable those of the KFOs (cf. Table 2 and Table 3).

356

Measurement of the uncertainty expresses “the range of values that can be

357

reasonably attributed to the quantity being measured”45 and was evaluated by

358

comparing the quantitative results obtained with the new SEBES approach in

359

comparison to data from SIDA application as the reference method. These results will

360

be discussed as RD (relative difference) in a later chapter.

361

Construction of a Database of Internal Standard Calibration curves. After the

362

selection and validation of the internal standards, internal standard based calibration ACS Paragon Plus Environment

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17 363

curves were calculated (Table 4). Specifically, 13 internal standards were shown to

364

be appropriate for the quantitation of thirty-six key odorants in rum and wine,

365

respectively. Calibration curve calculation was automated using the so called

366

“internal standard calibration” option available in GC Project (application of GC

367

Image).46 Using this feature, calibration regressions were automatically calculated

368

and graphics, equations of the calculated regressions and coefficients of

369

determination were prepared. Calibration data were saved in a so-called “CSV” file

370

which could be recalled and uploaded from the GC Image software pane, thus

371

allowing the generation of a customized calibration database. An additional benefit of

372

this feature is that different files can be merged in a unique CSV file containing

373

calibrations of all the compounds of interest.

374 375

Application of the Newly Developed Sensomics Based Expert System (SEBES) on Odorants in Rum.

376

For quantitation, the templates containing the information on all 36 KFOs and the

377

13 internal standards were overlaid on the sample chromatograms and the KFOs

378

were located. Then, the CSV files containing the calibration curves were uploaded

379

and, finally, the amount of standard added was specified by means of a second CSV

380

file that was uploaded into the “amount table” field.46 This way, the amount of analyte

381

in the sample was automatically calculated by the software and was displayed in the

382

“blob table” pane.46

383

In a recent study the key odorants in a commercial rum were characterized by the

384

application of the Sensomics concept including the quantitation of the odorants by

385

stable isotope dilution assays, a calculation of OAVs and an aroma recombinate24.

386

Using the same sample and the same work-up procedure, the quantitation and

387

calculation of OAVs was repeated, but applying the new SEBES approach. A

388

comparison of the results should give a clear insight into the efficacy of the new ACS Paragon Plus Environment


18 389

approach in characterizing the key aroma compounds by just analyzing the volatile

390

fraction of a given food by artificial intelligence, i.e. without a human sensory

391

evaluation. Quantitative determinations were carried out in triplicate and the relative

392

difference between SIDA and SEBES results is expressed as relative difference (RD)

393

taking the SIDA data as 100 %. For 30 key odorants, the results obtained by

394

application of the SEBES approach were in good agreement with data obtained by

395

application of the stable isotope dilution assays in the classical Sensomics approach

396

(Table 5). Although the results were very similar, in general, the concentrations

397

determined by the new method were always lower differing between 1.99 % (ethyl 3-

398

methylbutanoate) and 33.3 % for 3-methylbutyl acetate (Table 5). Unexpectedly,

399

larger differences were observed for (E)-β-damascenone and trans-whisky lactone

400

(data not shown). But, it turned out that these had previously24 been quantitated in a

401

rum sample from a different batch. Thus, by repeating SIDA on the same batch, good

402

agreement between the two methods was observed (Table 5). So, the differences for

403

both compounds between the literature data and the new method was due to

404

variation in the composition of the same rum from a different production year.

405

Despite a quite large variation observed for some compounds (20%-30%), such

406

values can still be acceptable considering the aim of the study. For example, if a

407

concentration of 30 ug/kg was measured for a given compound by SIDA, a 30%

408

difference means about 21 ug/kg. But, if the compound has an odor threshold of 1

409

ug/kg, OAVs of 30 or 21 will result meaning that the compound is still a KFO.

410

Estimation of the Aroma Contribution of Single Odorants by Automated

411

Calculation of Odor Activity Values. The GC Image software was implemented in a

412

database containing the odor thresholds of the selected KFOs by using the GC-

413

Image “plugin” tool.46 This feature allowed to upload external data into the software

414

by using java-script customized scripts. Odor thresholds have been determined and ACS Paragon Plus Environment

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19 415

stored in an “in-house database” as previously described30. A further advantage of

416

the GC Image software was the option to create a variety of customized functions by

417

means of the “CLIC” function.46 As an example, the “CLIC” allowed to calculate the

418

amount of analyte per liter of matrix (μg/L) and immediately the odor activity value. A

419

visual example of the SEBES method applied to the quantitation of 2-methoxyphenol

420

and 4-allyl-2-methoxyphenol (eugenol) in rum using 4-methyl-2-methoxyphenol and

421

2-methoxy-4-vinylphenol as internal standards is shown in Figure 1.

422

A comparison of the OAVs determined by the software used in the SEBES

423

approach and the OAVs resulting from the classical Sensomics method showed that,

424

except 3-methylbutanal, all potent odorants (OAV ≥1) were also detected by the new

425

approach (Table 6). Considering both, the OAV values and the recombination

426

experiment recently performed,24 it can be concluded that the following three

427

odorants, which were not detected with the current procedure, i.e., 2-methylbutanal,

428

ethyl cyclohexanoate and (E,E)-2,4-decadienal do not play a role in the overall profile

429

of the rum aroma. Although there are still gaps in the quantitation of some odorants,

430

these results show that the new SEBES method might work in characterizing the

431

aroma profiles of foods by artificial intelligence, i.e., without human sensory

432

experiments. This, however, should be verified by predicting the yet unknown key

433

odorants of a wine using SEBES followed by controlling the data by the classical

434

Sensomics approach.

435 436 437

Application of Sensomics Based Expert System (SEBES) on Odorants in an Australian Cabernet Wine.

438

A final experiment was, therefore, undertaken to prove that the new method is

439

able to characterize the overall molecular odor code of the key odorants of a food



20 440

which has not yet been analyzed before by the Sensomics concept. Thus, both

441

approaches were applied in parallel on an Australian Cabernet wine.

442

In the classical Sensomics approach, first, the selection of aroma compounds for

443

quantitation is made on the basis of the characterization of odor-active compounds

444

by applying an aroma extract dilution analysis (AEDA) on the volatile fraction.

445

Application of the AEDA on a volatile fraction isolated from the Cabernet wine

446

resulted in the identification of thirty-three odorants in the FD factor range of 1 to 100

447

(Table 7). To save time, the GC/Olfactometry was not performed on the commonly

448

used ten to twelve 1:1 dilutions, but only three 1:10 dilutions were analyzed. This

449

procedure saves time, but needs a lot of panelist’s expertise in the use of

450

GC/Olfactometry. Among the thirty-three odor-active compounds located in the FD

451

factor range of 1 to 100, the highest FD factors were found for ethyl propanoate,

452

methyl 2-methylbutanoate, acetic acid, 2-phenylethanol and 3-methylbutanoic acid.

453

Next, all thirty-three key odorants were quantitated by stable isotope dilution

454

assays using about 25 isotopically labeled isotopomers, and also several different

455

analytical platforms had to be used. The highest concentrations were measured for

456

acetic acid followed by 3-methylbutanol, 2-methylbutanol, methylpropanol and 2-

457

phenylethanol, which are important KFOs generated by microbial fermentation in

458

many alcoholic beverages4. Using the new SEBES approach, thirty-one out of the

459

thirty-six odorants could be quantitated with only one analytical platform (Table 8).

460

Two compounds could not be quantitated, probably because these were too volatile

461

to be detected with the current instrumental set-up (ethyl propanoate and ethyl

462

methylpropanoate) It can be assumed that the other three compounds (4-hydroxy-

463

2,5-dimethyl-3(2H)furanone; sotolon and 3-(methylthio)propanal) could no. be

464

detected, because their concentrations lay beneath the limit of detection for the

465

selected work-up procedure, i.e., the amount of wine used).Interestingly, 4-hydroxyACS Paragon Plus Environment

Page 20 of 50

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21 466

2,5-dimethyl-3(2H) furanone could not be detected in either sample. This might be

467

linked to the instrument sensitivity and the low amount isolated. But, for the thirty-one

468

odorants, the newly developed SEBES method provided good accuracy (RD), e.g.,

469

for 4-allyl-2-methoxyphenol (0.5 %). Higher differences were measured only for 2-

470

phenylethanol (34 %). The RD means relative difference as compared to the

471

respective result of the SIDA.

472

A comparison of the odor activity values obtained by application of the classical

473

Sensomics approach or the software based SEBES method (Table 9) showed that in

474

both methods, 12 odorants showed OAVs below or close to their odor threshold, and

475

thus, might not contribute to the wine aroma. However, this has to be verified by an

476

aroma recombinate which must also been done in a SEBES approach. OAV data

477

below 1 are given to demonstrate the good agreement between both methods.

478

The classical Sensomics method identified 25 key wine odorants with odor activity

479

values between 2 and 930, whereas the SEBES approach resulted in 21 odorants

480

with OAVS between 1 and 950. Although the SEBES approach was not able to

481

detect 4 out of the entire set of key food odorants, the agreement between the OAVs

482

for 21 aroma compounds in both methods was very good. For compounds, which

483

could not be detected in the current approach, for future research it might be useful

484

to use labeled internal standards in the quantitation and to use higher amounts of the

485

respective food. Nevertheless, it was shown for the first time that a food odor code

486

could be characterized by an approach using artificial intelligence.

487

In conclusion, the results obtained on rum and wine proved that the newly

488

developed SEBES method shows a quite good agreement in the key odorants in a

489

given food aroma distillate. The method offers, thus, an alternative and fast method

490

for the characterization of key food odorants in any food sample and might be useful

491

in routine investigations. ACS Paragon Plus Environment


22 492

Despite its great advantages, such as low costs on internal standards, speed and

493

the use of only one analytical platform, the procedure yet shows two limitations. The

494

first limit is merely technical as the current instrumental set-up, based on liquid

495

injection, does not allow the detection of highly volatile compounds eluting during the

496

solvent delay time slot. In order to overcome this problem, future activity will be

497

focused on transferring the concept of this approach to headspace techniques.

498

Secondly, some compounds elicit odor thresholds by far lower than the

499

instrument’s sensitivity. However, these compounds can play a fundamental role in

500

the overall food aroma even though only present in very low amounts, which cannot

501

be instrumentally detected and thus OAVs cannot be calculated. Therefore,

502

recombination experiments must always be prepared on the basis of the results of

503

the SEBES, in order to verify that all key odorants were correctly identified and

504

quantitated. If some KOFs are missing, these have to be revealed by applying the

505

classical Sensomics approach, supported by sophisticated enrichment techniques,

506

such as mercurated affi gel columns for the enrichment of thiols.

507

Further attention will also be paid on transferring this approach to several food

508

matrices, by selecting and validating appropriate internal standards for the remaining

509

about 130 key food odorants. However, it should be stated that in the current state

510

the developed procedure already shows a good level of software automatization and

511

future development in this area of study might include implementation in the field of

512

machine learning and possibly machine decision making on, e.g., the variety of a

513

wine or its age and the processing conditions, if “fed” with the respective samples, i.e.

514

their odor codes. In future perspectives, another key point that would make the

515

procedure extensively used by the scientific community will consist in creating open

516

access OT databases, containing well defined guidelines for OT determinations, so

517

that different research groups can contribute. ACS Paragon Plus Environment

Page 22 of 50

Page 23 of 50


23 518 519

AUTHOR INFORMATION

520

Corresponding Authors

521

*E-mail: [email protected]

522

Notes

523

The authors declare no competing financial interest.

524

ACKNOWLEDGMENT

525

The authors would like to acknowledge the GC ImageTM team and the GC Image

526

forum team for providing the script which allowed to create the function for the odor

527

threshold upload. Moreover, authors would like to thank C. Schwieger for the

528

precious technical help provided.

529

ABBREVIATIONS

530

SAFE, solvent assisted flavor evaporation; SIDA, stable isotope dilution assay;

531

OAV, odor activity value; O.T., odor threshold; KOF, key food odorants; HS-SPME,

532

headspace solid phase micro extraction; AEDA, aroma extract dilution analysis; FD,

533

flavor dilution; RI, retention index.



24 535

REFERENCES

536

(1) Beets, M. G. J. Structure-activity relationships in human chemoreception;

537 538 539

Applied Science Publishers: London, 1978. (2) Martín-Martín, S.; Fernández-Ruiz, V.; Sánchez-Mata, M.C.; Cámara, M. Acta Hort., 2015, 1081, 339-344.

540

(3) Chetschik, I.; Granvogl, M.; Schieberle, P. Quantitation of key peanut aroma

541

compounds in raw peanuts and pan-roasted peanut meal. Aroma reconstitution and

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comparison with commercial peanut products. J. Agric. Food Chem., 2010, 58,

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11018-11026.

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(4) Dunkel, A.; Steinhaus, M.; Kotthoff, M.; Nowak, B.; Krautwurst, D.; Schieberle,

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P.; Hofmann, T. Nature‘s chemical signatures in human olfaction: a foodborne

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perspective for future biotechnology. Angew. Chem., Int. Ed., 2014, 53, 7124-7143.

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(5) Schieberle, P.; Hofmann, T. Mapping the combinatorial code of food flavors by

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means of molecular sensory science approach. In Food Flavors – Chemical, Sensory

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and Technological Properties; Jelen, H., Ed.; CRC Press: Boca Raton, FL, 2011, p.

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413 – 438

551

(6) Engel, W.; Bahr, W.; Schieberle, P. Solvent assisted flavour evaporation – a

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new and versatile technique for the careful and direct isolation of aroma compounds

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from complex food matrices. Eur. Food Res. Technol., 1999, 209, 237-241.

554

(7) Grosch, W. Determination of potent odourants in foods by aroma extract

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dilution analysis (AEDA) and calculation of odour activity values (OAVs). Flavour

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Fragrance J., 1994, 9, 147-158.

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(8) Semmelroch, P.; Laskawy, G.; Blank, I.; Grosch, W. Determination of potent

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(9) Grosch, W. Evaluation of the key odorants of foods by dilution experiments, aroma models and omission. Chem. Senses, 2001, 26, 533-545.

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(10) Siebert, T.E.; Wood, C.; Elsey, G.M.; Pollnitz, A.P. Determination of

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rotundone, the pepper aroma impact compound, in grapes and wine. J. Agric. Food

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Chem. 2008, 56, 3745-3748.

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(11) Slabiziki, P.; Legrum, C.; Wegmann-Herr, P.; Fischer, C.; Schmarr, H.G.

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Quantification of cork off-flavor compounds in natural cork stoppers and wine by

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multidimensional gas chromatography mass spectrometry. Eur. Food Res. Technol.

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2016, 242, 977-986.

569

(12) Matheis, K.; Granvogl, M. Characterisation of the key aroma compounds in

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commercial native cold-pressed rapeseed oil by means of the Sensomics approach.

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Eur. Food Res. Technol. 2016, 242, 1565-1575.

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(13) Parker, M.; Pollnitz, A.P.; Cozzolino, D.; Francis, I.L.; Herderich, M.J.

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Identification and quantification of a marker compound for ‘pepper' aroma and flavor

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in

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chromatography−mass spectrometry. J. Agric. Food Chem. 2007, 55, 5948-5955.

Shiraz

grape

berries

by

combination

of

chemometrics

and

gas

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(14) Mahatheeranont, S.; Keawsa-ard, S.; Dumri, K. Quantification of the rice

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aroma compound, 2-acetyl-1-pyrroline, in uncooked khao dawk mali 105 brown rice.

578

J. Agric. Food Chem. 2001, 49, 773-779.

579

(15) Forero, D.P.; Orrego, C.E.; Peterson, D.G.; Osorio, C.; Chemical and

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sensory comparison of fresh and dried lulo (Solanum quitoense Lam.) fruit aroma.

581

Food Chem. 2015, 169, 85-91.

582

(16) Costa, R.; Tedone, L.; De Grazia, S.; Dugo, P.; Mondello, L. Multiple

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headspace-solid-phase microextraction: an application to quantification of mushroom

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volatiles. Anal. Chim. Acta 2013, 770, 1-6.



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(17) Chin, S.; Eyres, G.T.; Marriott, P.J. Identification of potent odourants in wine

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and brewed coffee using gas chromatography-olfactometry and comprehensive two-

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dimensional gas chromatography. J. Chromatogr. A 2011, 1218, 7487–7498.

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(18) Cordero, C., Kiefl, J.; Schieberle, P.; Reichenbach, S.E.; Bicchi, C.

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Comprehensive two-dimensional gas chromatography and food sensory properties:

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potential and challenges. Anal. Bioanal Chem. 2015, 407, 169-191.

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(19) Marriott, P.J., Kinghorn, R.M. Multidimensional and comprehensive multidimensional gas chromatography; Sheffield Academic Press: Sheffield, 2001. (20)

Seeley,

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Multidimensional

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fundamental advances and new applications. Anal. Chem. 2013, 85, 557-578. (21)

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two‐dimensional gas chromatography, selected applications. J. High Resolut.

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Chromatogr. 1999, 22, 647-665.

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(22) Adahchour, M.; Van Stee, L.L.; Beens, J.; Vreuls, R.J.; Batenburg, M.A.:

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Brinkman, U.A. Comprehensive two-dimensional gas chromatography with time-of-

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flight mass spectrometric detection for the trace analysis of flavour compounds in

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food. J. Chromatogr. A 2003, 1019, 157-172.

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(23) Q. Song, A. Savant, S.E. Reichenbach, E.B. Ledford, Jr. In Visual

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Information Processing, Proceedings of the 1999 Applications of Digital Image

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Processing XXII; Denver, CO, USA, July 20-23, 1999.

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(24) Franitza, L.; Granvogl, M.; Schieberle, P.; Characterization of the key aroma

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compounds in two commercial rums by means of the Sensomics approach. J. Agric.

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Food Chem. 2016, 64, 637-645.

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(25) Guth, H.; Grosch, W. Key aroma compounds in extruded oat flour. Changes during storgae (in German). Lebensm. Unters. Forsch. 1993, 196, 22-28. ACS Paragon Plus Environment

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(26) Vocke, M. Influence of mash fermentation, distillation and ageing on changes

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in the key aroma compounds of an American Bourbon whisky (in German). Ph. D.

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thesis, Technical University Munich, 2008.

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(27) Sen, A.; Laskawy, G.; Schieberle, P.; Grosch, W. Quantitative determination

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of -damascenone in foods using a stable isotope dilution assay. J. Agric. Food

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Chem. 1991, 39, 757-759.

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(28) Guth, H. Quantitation and sensory studies of character impact odorants of different white wine varieties. J. Agric. Food Chem. 1997, 45, 3027-3032. (29) Guth, H.; Grosch, W. Quantitation of potent odorants of virgin olive oil by stable-isotope dilution assays. J. Am. Oil Chem. Soc. 1993, 70, 513-518. (30) Czerny, M.; Schieberle, P. In Flavour Science: Recent Advances and Trends, Bredie, W.L.P., Petersen, M.A., Eds., Elsevier BV, 2006, pp 89 – 92

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(31) Schieberle, P.; Gassenmeier, K.; Guth, H.; Sen, A.; Grosch, W. Character

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impact odour compounds of different kinds of butter. LWT – Food Sci. Technol. 1993,

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adsorption of odour-active compounds from UHT-milk: Eur. Food Res. Technol.

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630

odourants in roasted coffee by stable isotope dilution assays. Flavour Fragrance J.

631

1995, 10, 1-7.

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(34) Blank, I.; Schieberle, P.; Grosch, W. In: Progress in Flavour Precursor

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Studies, Proceedings of the International Conference, Schreier, P., Winterhalter, P.,

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(35) Sen, A.; Grosch, W. Synthesis of six deuterated sulfur containing odorants to

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be used as internal standards in quantification assays. Z. Lebensm. Unters. Forsch.,

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1991, 192, 541-547.

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(36) Koppmann, T. Characterization of key aroma compounds formed during

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toasting of oak chips and model studies on their influence on the aroma of red wine.

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(in German). Ph.D. Thesis, Technical University Munich, Germany, 2018

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(37) Schütt, J. On the role of lactones in the overall aroma of milk chocolate and

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their formation during processing. Ph.D. thesis, Technical University Munich,

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Germany, 2016.

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(38) Fuhrmann, E. Investigations on the aroma of apples depending on the variety

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and structure/ odor relationship of identified esters (in German). PhD. thesis,

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Technical University Munich, Germany, 1998.

647

(39) Latha, I.; Reichenbach, S.E.; Tao, Q.P. Comparative analysis of peak-

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detection techniques for comprehensive two-dimensional chromatography. J.

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Chromatogr. A 2011, 1218, 6792-6798.

650

(40) Steinhaus, M.; Fritsch, H.T.; Schieberle, P. Quantitation of (R)- and (S)-

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linalool in beer using solid phase microextraction (SPME) in combination with a

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stable isotope dilution assay (SIDA). J. Agric. Food Chem. 2003, 51, 7100–7105.

653

(41) Reichenbach, S.E.; Tian, X.; Boateng, A.A.; Mullen, C.A.; Cordero, C.; Tao,

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Q.P. Reliable peak selection for multi sample analysis with comprehensive two-

655

dimensional chromatography. Anal. Chem. 2010, 85, 4974-4981.

656

(42) Kiefl, J.; Cordero, C.; Nicolotti, L.; Schieberle, P.; Reichenbach, S.E.; Bicchi,

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C. Performance evaluation of non-targeted peak-based cross-sample analysis for

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comprehensive two-dimensional gas chromatography–mass spectrometry data and

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application to processed hazelnut profiling. J. Chromatogr. A. 2012, 1243, 81-90.


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(43) Reichenbach, S.E.; Carr, P.W.; Stoll, D.R.; Tao, Q. Smart templates for peak

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pattern matching with comprehensive two-dimensional liquid chromatography. J.

662

Chromatogr. A 2009, 1216, 3458-3466.

663 664

(44) Pickup, J.F.; McPherson, K. Theoretical considerations in stable isotope dilution mass spectrometry for organic analysis. Anal. Chem. 1976, 48, 1885-1890.

665

(45) Eurachem Guide (2014). The Fitness for Purpose of Analytical Methods. A

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Laboratory Guide to Method Validation and Related Topics (2nd ed. 2014) [Online];

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Magnusson, B.; Örnemark, U., Eds.

668

https://www.eurachem.org/images/stories/Guides/pdf/MV_guide2nd_ed_EN.pdf.

669

(accessed May 5, 2016).

670 671 672

(46) GC Image Edition Users’ Guide. http://www.gcimage.com/gcxgc/usersguide. (accessed Oct. 8, 2016). .



30 673

FIGURE CAPTIONS

674

Figure 1. Automated quantitation and OAV calculation of two potential key odorants

675

in rum. Analytes and their internal standards are highlighted in blue and orange

676

respectively; a: 2-methoxyphenol, b: 2-methoxy-4-methylphenol (ISTD), c: eugenol,

677

d: 2-methoxy-4-vinylphenol (ISTD). Amounts (CLIC1), odor thresholds (CLIC2) and

678

OAV (CLIC3), as well as information regarding calibration (amount source) are

679

displayed in the “blob table” pane


Page 30 of 50

Page 31 of 50


31 Table 1. Analytical Parameters for the SPME Analyses of selected Key Food Odorants in the Cabernet Sauvignon wine odorant

SPME-fiber

extraction desorption time time

GC/MS system

GC-column

temperature program

first dimension: DB-FFAP

first dimension: 35 °C (3) → 6 °C/min → 230 °C (5)

(E)-β-damascenone ethyl butanoate ethyl 2-methylbutanoate ethyl 3-methylbutanoate

HRGC×GCTOFMS

PDMS/DVB, 65 µm

30 min.

PDMS/DVB, 65 µm

5 min.

2 min.

HRGC-ITMS

DB-FFAP

40 °C (2) → 6 °C/min → 230 °C (5)

PDMS/DVB, 65 µm

10 min.

4 min.

HRGC-ITMS

DB-FFAP

40 °C (2) → 6 °C/min → 230 °C (5)

polyacrylate, 85 µm

10 min.

4 min.

HRGC-ITMS

DB-5 (1.0 µm film)

35 °C (20) → 40 °C/min → 230 °C (5)

methyl-1-propanol

PDMS/DVB, 65 µm

2 min.

4 min.

HRGC-ITMS

DB-5 (1.0 µm film)

35 °C (2) → 6 °C/min → 230 °C (5)

2,3-butanedione

PDMS/DVB, 65 µm

1 min.

4 min.

HRGC-ITMS

DB-5 (1,0 µm film)

35 °C (2) → 8 °C/min → 230 °C (5)

ethyl pentanoate

1 min.

ethyl hexanoate ethyl octanoate

second dimension: DB-5

second dimension: 75 °C (3) → 6 °C/min → 250 °C (5)

3-methylbutyl acetate ethyl propanoate ethyl 2-methylpropanoate 2-phenylethanol 2-methyl-1-butanol 3-methyl-1-butanol



Page 32 of 50

32 Table 2. Method Validation Results for Thirty-six Selected Key Food Odorants. quant. ionb (m/z)

RSDd

981

LRIa (FFAP)

sensitivity (pg)f

recoveryg (%)

(%)

linearity R2 e

L.o.D.

L.o.Q.

86

15.2

0.9938

0.010

0.12

42.29

68.92

fatty matrix aqueous matrix

no.

odorant

1

2,3-butanedione

2

1,1-diethoxyethane

1003

103

13.4

0.9954

0.053

0.53

40.18

56.28

3

ethyl butanoate

1017

116

10.2

0.9927

0.057

0.57

47.85

55.39

4

ethyl 2-methylbutanoate

1031

115

16.3

0.9938

0.048

0.48

47.29

77.96

5


1048

115

11.3

0.9927

0.054

0.11

49.35

76.66

6

hexanal

1066

82

4.33

0.9989

0.051

0.26

42.92

89.13

7

methylpropanol

1069

74

2.42

0.9978

0.013

0.32

85.39

93.98

8

3-methylbutyl acetate

1106

87

6.87

1.0000

0.048

0.096

58.91

69.35

9

ethyl pentanoate

1118

101

9.56

0.9995

0.059

0.59

58.86

64.56

10

2-methyl-1-butanol

1185

74

6.23

0.9969

0.0091

0.23

80.03

95.66

11

3-methyl-1-butanol

1185

70

5.37

0.9977

0.010

0.26

81.58

96.29

12

ethyl hexanoate

1212

115

4.56

0.9922

0.056

0.11

60.14

66.43

13

ethyl octanoate

1413

127

5.67

0.9992

0.046

0.46

39.12

60.64

14

acetic acid

1435

60

4.22

0.9950

0.066

0.33

27.54

65.78

15

methyl propanoic acid

1547

4.12

0.9956

0.073

0.72

17.42

42.46

16

butanoic acid

1607

73

6.81

0.9979

0.056

0.28

19.39

34.56

17

2-methylbutanoic acid

1650

87

6.32

0.9982

0.061

0.61

13.71

29.90

88


Page 33 of 50


33 Table 2. Continued LRIa (FFAP)

quant. ionb (m/z)

sensitivity (pg)f

RSDd (%)

linearity R2 e

L.o.D.

L.o.Q.

recoveryg (%) fatty matrix aqueous matrix

no.

odorant

18


1650

60

9.89

0.9930

0.054

0.54

14.69

27.88

19

2-phenylethyl acetate

1686

104

11.30

0.9999

0.052

0.52

32.65

55.90

20

3-(methylthio)propanol

1707

106

16.40

0.9954

0.048

0.48

63.35

86.45

21

(E)-β-damascenone

1804

190

12.40

0.9927

0.14

0.27

15.38

57.20

22

2-methoxyphenol

1846

138

12.00

0.9998

0.0096

0.096

56.09

75.13

23

ethyl 3-phenylpropanoate

1869

178

9.98

0.9998

0.076

0.76

40.14

62.68

24

trans-whisky lactone

1890

99

13.17

1.0000

0.057

0.57

55.03

19.64

25

2-phenylethanol

1892

122

8.74

0.9973

0.011

0.27

25.08

64.04

26

cis-whisky lactone

1950

99

13.2

0.9930

0.042

0.42

56.82

26.33

27

γ-nonalactone

2009

85

8.03

0.9896

0.055

0.55

55.43

30.66

28

4-ethyl-2-methoxyphenol

2013

152

6.56

0.9909

0.014

0.29

49.32

62.02

29

octanoic acid

2033

101

6.28

0.9994

0.011

0.11

26.83

33.26

30

4-methylphenol

2063

108

5.54

0.9993

0.051

0.26

58.19

76.06

31

2-methoxy-4-propylphenol

2092

166

6.18

0.9971

0.051

0.25

15.59

27.92

32

4-allyl-2-methoxyphenol

2148

164

7.67

0.9967

0.057

0.28

16.46

32.68

33

4-ethylphenol

2157

122

6.34

0.9956

0.046

0.23

22.73

62.27



Page 34 of 50

34 Table 2. Continued LRIa (FFAP)

sensitivity (pg)e

(%)

linearity R2 d

quant. ionb (m/z)

RSDc

L.o.D.

recoveryf (%)

L.o.Q. fatty matrix aqueous matrix

no.

odorant

34

decanoic acid

2245

129

6.72

0.9996

0.045

0.45

31.57

39.04

35

phenylacetic acid

2530

136

14.8

0.9986

0.063

0.63

21.49

28.40

36

vanillin

2545

151

12.3

0.9667

0.044

0.22

28.59

36.65

a

LRI (FFAP); linear retention index on a DB-FFAP column. b quant. ion; mass trace used as quantifier mass. c Precision expressed as relative

standard deviation (RDS) given in % among 10 replicates acquired within 1 week. d linearity expressed as coefficient of determination R2 in a concentration range of 0.5 – 10 µg/mL. e Sensitivity expressed as the absolute amount injected (pg): L.o.D., limit of detection; L.o.Q, limit of quantitation. f recovery given in percentage determined in a fatty and an aqueous matrix.


Page 35 of 50


35 Table 3. Method Validation Results for the 13 Internal Standards Selected for the Quantitation of KFOs in Rum and Red Wine. linearity

sensitivity (pg)f

recoveryg (%) fatty aqueous matrix matrix 41.22 66.97

ISTDa 3-octanone

RIb quant.Ionc (FFAP) (m/z) 1235 99/128

RSDd (%) 7.21

R2 e

L.o.D

L.o.Q

0.9939

0.053

0.26

2

heptanol

1297

83

14

0.9954

0.01

0.1

73.35

97.82

3

2-isopropyl-5-methyl-2-hexenal

1342

97

3.89

0.9968

0.051

0.51

56.76

85.87

4

heptyl acetate

1359

98

4.09

0.9981

0.053

0.53

50.09

70.24

5

2-methylpentanoic acid

1744

87

5.32

0.9987

0.048

0.24

20.90

33.43

6

methyl phenylacetate

1748

150

10

0.9967

0.055

0.55

42.36

57.26

7

γ-heptalactone

1796

85

7.72

0.9987

0.051

0.51

51.94

52.94

8

2-methoxy-4-methylphenol

1940

138

6.43

0.9997

0.047

0.23

46.75

61.15

9

m-anisaldehyde

2017

135

7.45

0.9930

0.049

0.24

36.31

50.24

10

4-methoxyacetophenone

2132

150

8.13

0.9954

0.053

0.53

20.41

60.14

11

2-methoxy-4-vinylphenol

2178

150

7.12

0.9919

0.056

0.28

14.40

32.39

12

cinnamyl alcohol

2264

134

7.46

0.9961

0.046

0.23

19.66

51.04

13

(E)-3-decenoic acid

2333

69

5.78

0.9989

0.05

0.25

19.06

25.35

no. 1

aISt;

internal standard chosen for the SEBES experiments. b RI (FFAP); linear retention index on a DB-FFAP column. c quant. ion;

mass trace used as quantifier mass.

d

Precision expressed as relative standard deviation (RSD) given in % among 10 replicates

acquired within 1 week. e linearity expressed as coefficient of determination R2 in a concentration range of 0.5 – 10 µg/mL. f Sensitivity



Page 36 of 50

36 expressed as the absolute amount injected (pg): L.o.D., limit of detection; L.o.Q, limit of quantitation. determined in a fatty and an aqueous matrix


g

recovery given in percentage

Page 37 of 50


37 Table 4. Internal Standard Based Calibration Curves for the Quantitation of 36 Selected Key Food Odorants in Rum and Red Wine by the SEBES method. Key food odorantsa

quantifier ion KFOb (m/z)

IStc

quantifier ion IStd (m/z)

2,3-butanedione

86

3-octanone

99

0.9982 0.548

0.026

1,1-diethoxyethane

103

3-octanone

128

0.9914 0.977

-0.458

ethyl butanoate

116

heptyl acetate

98

0.9971 0.454

0.036

ethyl 2- methyl-butanoate

115

heptyl acetate

98

0.9956 1.704

-0.699

ethyl 3-methyl-butanoate

115

heptyl acetate

98

0.9967 2.321

-0.509

hexanal

82

5-isopropyl-5-methyl-2-hexenal

97

0.9994 0.442

-0.042

methylpropanol

74

heptanol

83

0.9987 0.584

0.035


87

heptyl acetate

98

0.9982 1.734

-0.061

ethyl pentanoate

101

heptyl acetate

98

0.9994

1.98

-0.143

2-methyl-1-butanol

70

heptanol

83

0.997

2.022

0.399

3-methyl-1-butanol

70

heptanol

83

0.9959 1.764

0.373

ethyl hexanoate

115

heptyl acetate

98

0.9963 0.847

-0.092

ethyl octanoate

127

heptyl acetate

98

0.9982 1.469

-0.086

acetic acid

60


87

0.9936 2.469

0.105

2-methylpropanoic acid

88


87

0.9989 0.429

-0.025

butanoic acid

73


87

0.9981 2.088

-0.128


87


87

0.9989 1.074

0


60


87

0.9958 2.237

0.488

phenylethyl acetate

104

methyl phenylacetate

150

0.9954 1.849

0.622


106

heptanol

83

(E)-β-damascenone

121

4-methoxyacetophenone

150


R2

1

slope intercept

2.498

-0.053

0.9957 1.511

-0.385


Page 38 of 50

38

Table 4. Continued Key food odorantsa

quantifier ion KFOb (m/z)

IStc

quantifier ion IStd (m/z)

2-methoxyphenol

124


138

0.9997 1.423

-0.145

ethyl 3-phenyl propanoate

178

methylphenyl acetate

150

0.9998 0.454

-0.088

trans-whiskey lactone

99

γ-heptalactone

85

0.9989 0.475

-0.013

2-phenylethanol

122

cinnamyl alcohol

134

0.9973 1.986

0.098

cis-whiskey lactone

99

γ-heptalactone

85

0.9993 0.394

-0.014

γ-nonalactone

85

γ-heptalactone

85

0.9934 0.873

-0.108


152


138

0.9995 0.726

-0.103

octanoic acid

101


87

0.9915

1.23

0.557

4-methylphenol

108


150

0.9993 2.024

-0.165


166


150

0.9996 0.651

-0.047


164


150

0.9995 0.861

-0.072

4-ethylphenol

122


150

0.9955 1.549

-0.26

decanoic acid

129

(E)-3-decenoic acid

69

0.9922 1.914

0.468

phenylacetic acid

136

(E)-3-decenoic acid

69

0.986

-0.015

vanillin

151

m-anisaldehyde

135

0.9935 1.265

a

Key odorant selected for application of the SEBES approach on rum and a Cabernet Sauvignon wine.

b

R2

slope intercept

3.399

0.03

quantifier ion KFO; characteristic

mass trace (m/z) selected for MS monitoring for the respective analyte. c ISt; internal standard. d quantifier ion ISt; characteristic mass trace (m/z) selected for MS monitoring for the respective ISt.


Page 39 of 50


39 Table 5. Concentrations of the Thirty-six Key Food Odorants in Rum quantitated by means of the SEBES Method or by Stable Isotope Dilution Assays. a concentration µg/L odorant

SEBESb

Sensomicsc

RD (%)

acetic acid

39400

55000

23.2

3-methyl-1-butanol

22300

24200

methyl-1-propanol

8360

6660

1,1-diethoxyethane

5610

5310

(S)-2-methyl-1-butanol

6400

4850

19.5

vanillin

593

912

29.9

cis-whisky lactone

382

318

13.0

2-phenylethanol

265

291

decanoic acid

167

195

butanoic acid

159

172

5.66 16.0 3.85

6.60 10.8 5.41


46.8

75.6

33.3

ethyl butanoate

61.9

74.3

12.9

ethyl hexanoate

64.7

66.8

trans-whisky lactoned

61.8

53.4d

10.3

hexanal

34.3

43.5

16.8

n.d.

34.1

-


35.7

29.7

13.0

2,3-butanedione

27.7

29.7

(S)-2-methylbutanoic acid

27.6

21.5

17.6


21.9

18.3

12.6

2-methoxyphenol

17.8

16.3

ethyl (S)-2-methylbutanoate

11.4

8.78

18.1

(S)-2-methylbutanal

n.d.

8.20

-

(R)-2-methylbutanoic acid

8.72

6.90

16.5

(R)-2-methylbutanal

n.d.

6.39

-


6.19

6.37

1.99

ethyl pentanoate

7.29

6.26

10.7

(E)-β-damascenoned

4.16

5.43d

18.7

4-ethylphenol

3.59

2.48

25.8


1.30

1.78

22.0

3-methylbutanal


2.23

4.96

6.37


Page 40 of 50

40 Table 5. Continued concentration µg/L

RD (%)

odorant

SEBESb

SIDAc

sotolon

n.d.

1.53

-

4-methylphenol

1.93

1.50

17.6


0.78

0.52

28.1


0.52

0.37

24.4

(E,E)-2,4-decadienal

n.d.

0.13

-

ethyl cyclohexanoate

n.d.

0.06

-

aThe

rum contained 322 g/L of ethanol bAverage values of triplicates, differing not more than

± 20%

c

SIDA results were taken from Franitza et al.24

d

Compounds re-quantitated using

SIDA in the same rum batch as used for the SEBES experiments. n.d.; not determined. RD: Difference (%) between the reference amount determined by SIDA to the amount determined by the SEBES approach


Page 41 of 50


41 Table 6. Comparison of Odor Activity Values (OAVS) for the Key Food Odorants in Rum calculated by the software in the SEBES approach and OAVs resulting from the classical Sensomics approach

odorant vanillin

odor threshold (ug/L)b

OAVa in rum calculated by SEBES

Sensomicsc

27

42

22

ethyl (S)-2-methylbutanoate

0.22

52

40

(E)-β-damascenone

0.14

30

39

3-methylbutanal

2.8

2,3-butanedione

2.8

10

11

ethyl butanoate

9.5

7

8

n.d.

12

1,1-diethoxyethane

720

8

7

cis-whiskey lactone

67

6

5


1.6

4

4


7.1

3

3

2

2

ethyl hexanoate

30

ethyl pentanoate

3.0

2

2

2-methoxyphenol

9.2

2

2

acetic acid

76000

0.53

0.70

methylpropanol

10000

0.83

0.70

hexanal

88

0.39

0.50

2-methylbutanal

33

n.d.

0.40

56000

0.40

0.40


78

0.46

0.40


250

0.19

0.30

0.19

0.30

0.27

0.20

0.28

0.20

0.13

0.10

n.d.c

0.10

3-methyl-1-butanol

4-ethyl-2-methoxyphenol (S)-2-methylbutanol 2-methoxy-4-propylphenol butanoic acid (E,E)-2,4-decadienal

6.9 24000 1.9 1200 1.1

2-phenylethanol

2600

0.10

0.10

decanoic acid

2800

0.06

0.10

790

0.08

0.10




Page 42 of 50

42 Table 6 Continued odor threshold (ug/L)b

odorant sotolon

OAVa in rum calculated by SEBES

24

ethyl cyclohexanoate

1.6

Sensomics

n.d.

0.10

n.d.

0.04


14

0.06

0.04

4-methylphenol

82

0.02

0.02

170

0.02

0.01

3500

0.01

0.01

4-ethylphenol (S)-2-methylbutanoic acid a

OAVs were calculated by dividing calculated concentrations by the respective odor

thresholds.

b

Odor thresholds as reported previously.24 Data were taken from our recent

publication.24n.d.; not determined.


Page 43 of 50


43 Table 7. Thirty-three Odorants Detected with High FD factors in the AEDA of a SAFE Distillate Prepared from the Australian Cabernet Sauvignon Red Wine RI (FFAP)c

FD factord

odoranta

odor qualityb

ethyl propanoate

fruity

908

100

ethyl 2-methylpropanoate

fruity

958

10

2,3-butanedione

buttery

1000

10

methyl 2-methylbutanoatef

fruity

1018

100

ethyl butanoate

fruity

1041

10


fruity

1076

10

methylpropanol

malty

1094

10

2- and 3-methyl-1-butanol

malty

1205

10

ethyl hexanoate

fruity

1238

1

dimethyl trisulfided

sweaty, sulfurous

1376

1

ethyl butanoated

fruity

1400

1

2-isopropyl-2-methoxypyrazined

earthy, pea-like

1427

1

acetic acid

vinegar-like, sour

1460

100

3-(methylthio)propanal

cooked potato

1470

1

ethyl octanoate

fruity

1471

1

propionic acid

sweaty

1540

10

linalool

citrus, flowery

1545

1

methylpropionic acid

sweaty

1565

1

butanoic acid

sweaty

1628

10


sweaty

1671

100


sweaty

1686

1

pentanoic acid

sweaty

1739

1

(E)-β-damascenone

cooked apple-like

1818

10

2-methoxyphenol

smoky

1871

10

2-phenylethanol

flowery

1919

100

cis-whisky lactone

coconut-like

1963

10

2-hydroxy-3-methyl-



Page 44 of 50

44 Table 7. Continued RI

FD

odoranta

odor qualityb

(FFAP)c

factore

4-hydroxy-2,5-dimethyl-3(2H)furanone

caramel-like

2030

10


smoky

2025

10


clove-like, smoky

2160

1

4-vinyl-2-methoxyphenol

smoky

2200

10

3-hydroxy-4,5-dimethyl-2(5H)furanone

seasoning

2207

1

phenylacetic acid

honey-like

2569

1

4-hydroxy-3methoxybenzaldehyde

vanilla-like

2585

1

a

Odorants identified by means of RI on FFAP, odor quality and intensity and mass

spectral data in comparison with authentic reference compounds. sniffing port during GC/olfactometry. c Linear retention index.

d

b

Odor perceived at the

Tentative identification based

on linear retention indices and odor properties of reference compounds.


Page 45 of 50


45

Table 8. Quantitation of the Thirty-six Key Odorants in the Australian Cabernet Sauvignon Red Wine concn (µg/L) determined by odorant

SEBESa

SIDAa

RD (%)

acetic acid

612000

546000

8.11

3-methyl-1-butanol

250000

275000

6.79

2-methyl-1-butanol

71600

99800

23.3

methylpropanol

40600

59600

26.8

2-phenylethanol

49700

30400

34.2


3020

3420

8.87

2,3-butanedione

2320

2240

2.41

octanoic acid

1690

2150


2430

2116

9.81

methylpropanoic acid

1410

1370

2.03

butanoic acid

1730

1350

decanoic acid

1110

1030


685

823

12.9

ethyl hexanoate

344

480

23.3

ethyl octanoate

347

448

18.1

358

-

ethyl propanoate

n.d.

16.8

17.4 5.50

4-ethylphenol

271

268

0.66


274

267

1.89

259

21.1

ethyl 2-methylpropanoate

n.d.

ethyl butanoate

185

250

cis-whisky lactone

164

159

vanillin

93.1


124

phenylacetic acid

110

134 121

2.17 25.5 1.49

97.4

8.38 7.11


73.7

81.5


89.9

73.0

14.7

2-phenylethyl acetate

46.5

56.6

13.8


41.9

41.6

0.53

n.d.

23.5

-

γ-nonalactone

20.5

18.1

8.87


17.1

17.8

2.98

4-hydroxy, 2,5-dimethyl-3(2H) furanone



Page 46 of 50

46

Table 8. Continued concn (µg/L) determined by odorant 2-methoxyphenol

SEBES.a

SIDA a

11.8

10.8

RSD (%) 6.18

sotolon

n.d.

9.60

-

ethyl pentanoate

5.13

4.25

13.3


n.d.

2.50

-

(E)-β-damascenone 1.93 1.53 16.3 a Average values of triplicates differing not more than ± 20%. RD: Difference (%) between the reference amount determined by SIDA to the amounts determined by the SEBES approach


Page 47 of 50


47 Table 9. Comparison of the OAVs of the thirty-six key odorants in the Australian Cabernet Sauvignon wine as calculated by the SEBES and the Sensomics approach

odorant ethyl 2-methylbutanoate methylpropanol

odor threshold (ug/L)b

OAVa calculated by SEBES

0.13c 180

Sensomics

950

930

230

330

ethyl octanoate

4.5

77

100

ethyl hexanoate

4.9

70

98

ethyl propanoate

4.9

n.d.d

73

3-methyl-1-butanol

5100

49

54

2-methyl-1-butanol

1200c

60

50

3c

30

24

ethyl 3-methylbutanoate 2,3-butanedione

100

23

22


180

17

19

ethyl methylpropanoate

14

4-ethylphenol

17

16

16

41000

15

13

cis-whisky lactone

12

14

13

ethyl butanoate

20c

9

13


30c

9

9

3600

14

8

120

6

7

acetic acid

2-phenylethanol 3-methylbutanoic acid

n.d.d

19


0.52

n.d.d

5

(E)-β-damascenone

0.40

5

4

26

4

4

480

4

3

50

1

2

1300

2

2

n.d.d

2

0.95

0.95

1.1

0.87

0.47

0.67

0.72

0.60

phenylacetic acid butanoic acid trans-whisky lactone 2-methylbutanoic acid sotolon 4-allyl-2-methoxyphenol ethyl pentanoate vanillin 2-methoxyphenol

6.3 44 4.90 200 7.1



Page 48 of 50

48 Table 9. Continued OAVa calculated by odorant

odor thresholdb

octanoic acid

Sebes

Sensomics

3900

0.43

0.55

4-hydroxy-2,5-dimethyl3(2H)furanone

51

n.d.d

0.46

γ-nonalactone

42

0.49

0.43

250c

0.19

0.23

88

0.19

0.20

15000

0.07

0.07

200000c

0.007

0.007

2-phenylethyl acetate 4-ethyl-2-methoxyphenol decanoic acid methylpropanoic acid a

OAVs were calculated by dividing calculated concentrations by the odor thresholds.

b

Odor

thresholds were determined in wine matrix (12% ethanol, 5 g/L tartaric acid, pH 3.5) following a procedure previously published.

26,28. c

Odor thresholds in water/ethanol (90/10; v/v) as

reported by Guth.28 d n.d.; not determined


Page 49 of 50


49

Figure 1.



50

TOC GRAPHIC


Page 50 of 50

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