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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
Journal of Agricultural and Food Chemistry
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
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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
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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.
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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
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[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]-
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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
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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
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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
Journal of Agricultural and Food Chemistry
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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curves were calculated (Table 4). Specifically, 13 internal standards were shown to
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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
Journal of Agricultural and Food Chemistry
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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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
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
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
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Journal of Agricultural and Food Chemistry
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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
Journal of Agricultural and Food Chemistry
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
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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.
ACS Paragon Plus Environment
Journal of Agricultural and Food Chemistry
24 535
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536
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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
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Page 31 of 50
Journal of Agricultural and Food Chemistry
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
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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
ethyl 3-methylbutanoate
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
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Journal of Agricultural and Food Chemistry
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
3-methylbutanoic acid
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
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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.
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Journal of Agricultural and Food Chemistry
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
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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
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recovery given in percentage
Page 37 of 50
Journal of Agricultural and Food Chemistry
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
3-methylbutyl acetate
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
2-methylpentanoic acid
87
0.9936 2.469
0.105
2-methylpropanoic acid
88
2-methylpentanoic acid
87
0.9989 0.429
-0.025
butanoic acid
73
2-methylpentanoic acid
87
0.9981 2.088
-0.128
2-methylbutanoic acid
87
2-methylpentanoic acid
87
0.9989 1.074
0
3-methylbutanoic acid
60
2-methylpentanoic acid
87
0.9958 2.237
0.488
phenylethyl acetate
104
methyl phenylacetate
150
0.9954 1.849
0.622
3-(methylthio)propanol
106
heptanol
83
(E)-β-damascenone
121
4-methoxyacetophenone
150
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R2
1
slope intercept
2.498
-0.053
0.9957 1.511
-0.385
Journal of Agricultural and Food Chemistry
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
2-methoxy-4-methylphenol
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
4-ethyl-2-methoxyphenol
152
2-methoxy-4-methylphenol
138
0.9995 0.726
-0.103
octanoic acid
101
2-methylpentanoic acid
87
0.9915
1.23
0.557
4-methylphenol
108
2-methoxy-4-vinylphenol
150
0.9993 2.024
-0.165
2-methoxy-4-propylphenol
166
2-methoxy-4-vinylphenol
150
0.9996 0.651
-0.047
4-allyl-2-methoxyphenol
164
2-methoxy-4-vinylphenol
150
0.9995 0.861
-0.072
4-ethylphenol
122
2-methoxy-4-vinylphenol
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.
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Journal of Agricultural and Food Chemistry
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
3-methylbutyl acetate
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
-
3-methylbutanoic acid
35.7
29.7
13.0
2,3-butanedione
27.7
29.7
(S)-2-methylbutanoic acid
27.6
21.5
17.6
4-allyl-2-methoxyphenol
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
-
ethyl 3-methylbutanoate
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
4-ethyl-2-methoxyphenol
1.30
1.78
22.0
3-methylbutanal
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2.23
4.96
6.37
Journal of Agricultural and Food Chemistry
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
ethyl 3-phenylpropanoate
0.78
0.52
28.1
2-methoxy-4-propylphenol
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
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Journal of Agricultural and Food Chemistry
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
ethyl 3-methylbutanoate
1.6
4
4
4-allyl-2-methoxyphenol
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
3-methylbutanoic acid
78
0.46
0.40
3-methylbutyl acetate
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
trans-whisky lactone
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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
ethyl 3-phenylpropanoate
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.
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Journal of Agricultural and Food Chemistry
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
ethyl 3-methylbutanoate
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
3-methylbutanoic acid
sweaty
1671
100
2-methylbutanoic acid
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-
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44 Table 7. Continued RI
FD
odoranta
odor qualityb
(FFAP)c
factore
4-hydroxy-2,5-dimethyl-3(2H)furanone
caramel-like
2030
10
4-ethyl-2-methoxyphenol
smoky
2025
10
4-allyl-2-methoxyphenol
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.
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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
3-(methylthio)propanol
3020
3420
8.87
2,3-butanedione
2320
2240
2.41
octanoic acid
1690
2150
2-methylbutanoic acid
2430
2116
9.81
methylpropanoic acid
1410
1370
2.03
butanoic acid
1730
1350
decanoic acid
1110
1030
3-methylbutanoic acid
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
3-methylbutyl acetate
274
267
1.89
259
21.1
ethyl 2-methylpropanoate
n.d.
ethyl butanoate
185
250
cis-whisky lactone
164
159
vanillin
93.1
ethyl 2-methylbutanoate
124
phenylacetic acid
110
134 121
2.17 25.5 1.49
97.4
8.38 7.11
trans-whisky lactone
73.7
81.5
ethyl 3-methylbutanoate
89.9
73.0
14.7
2-phenylethyl acetate
46.5
56.6
13.8
4-allyl-2-methoxyphenol
41.9
41.6
0.53
n.d.
23.5
-
γ-nonalactone
20.5
18.1
8.87
4-ethyl-2-methoxyphenol
17.1
17.8
2.98
4-hydroxy, 2,5-dimethyl-3(2H) furanone
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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
3-(methylthio)propanal
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
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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
3-(methylthio)propanol
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
3-methylbutyl acetate
30c
9
9
3600
14
8
120
6
7
acetic acid
2-phenylethanol 3-methylbutanoic acid
n.d.d
19
3-(methylthio)propanal
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
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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
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Figure 1.
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TOC GRAPHIC
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