Analysis of Odourant Compounds in Wine - with Headspace Solid-Phase Microextraction and Gas Chromatography- Mass Spectrometry

Analysis of odourant compounds in wine - With headspace solid-phase microextraction and gas chromatography- mass spectrometry

Emma Ödmar

Analytical science program in chemistry with focus on forensics

Candidate for Degree of Bachelor of Science School of Science and Technology Örebro university Spring term 2018 Table of contents Abstract ...... 3 1. Introduction...... 4 1.1 Aim ...... 4 1.2 Scope ...... 4 1.3 Background ...... 4 1.3.1 Wine ...... 4 1.3.2 Headspace solid-phase microextraction (HS-SPME)...... 5 1.3.3 Gas chromatography-mass spectrometry (GC-MS) ...... 6 2. Materials and methods...... 7 2.1 Materials...... 7 2.1.1 Chemicals ...... 7 2.1.2 Samples ...... 8 2.1.3 Sample preparation ...... 9 2.1.4 Standard compounds ...... 10 2.2 Methods ...... 11 2.2.1 Headspace solid-phase microextraction ...... 11 2.2.2 Gas chromatography-mass spectrometry...... 12 2.2.3 Method issues ...... 13 3. Results ...... 14 3.1 Artificial wine standard ...... 14 3.2 Wine analysis result ...... 15 3.2.1 White wines ...... 15 3.2.2 Orange wines ...... 18 3.2.3 Red wines ...... 19 3.2.4 Sparkling wines ...... 24 3.3 Quantification ...... 25 3.4 Identified compounds ...... 25 3.5 Statistics ...... 26 4. Discussion ...... 27 4.1 Limitations ...... 27 4.2 Quantitative analysis ...... 27

1 4.3 Aroma compounds ...... 28 4.5 Comparison of wines ...... 29 4.5.1 White wines ...... 29 4.5.2 Orange wines ...... 30 4.5.3 Red wines ...... 30 4.5.4 Sparkling wines ...... 30 5. Conclusion ...... 30 6. References ...... 32 7. Appendix ...... 34

Abstract Wine is a drink that can enhance the flavour experience of food, which is why it is important that the wine’s sensory profile is explained correctly to the consumers. In this study, headspace solid-phase microextraction gas chromatography mass spectrometry was used to characterise odourant compounds in wine to find chemical markers to explain wine sensory profiles instead of sensory analysis.

The study included 16 different wines, red, white, orange and sparkling, where the nine most abundant peaks in each wine sample were evaluated. Homologue patterns based on areas were used to compare profiles between different wines. When studying homologue patterns for each wine and comparing within wine groups, differences and similarities can be seen. All wine samples contained isoamyl alcohol and the majority of them also contained ethyl decanoate, octanoic acid and decanoic acid. Six out of eight red wines contained ethyl succinate and five of them also contained ethyl hexanoate. All white wine samples showed presence of ethyl octanoate and ethyl hexanoate. The orange wines also contained ethyl octanoate and ethyl hexanoate, along with pentanoic acid. Both sparkling wines contained ethyl octanoate and ethyl hexanoate in addition with phenylethyl alcohol.

However, a more thorough study covering more compounds to identify the less obvious differences of wine would have to be performed for a more precise explanation of the wine’s characterisation and sensory profile. It should be noted that the method of this study does leave room for improvements to improve the quality of the results. For example, since the most abundant compounds are not necessarily the ones with the most powerful odours, quantification based on response of an internal standard would strengthen the study. Additional compounds in the samples could also be further investigated. Statistically the method would also need improvement for satisfactory results regarding reproducibility of the samples.

3 1. Introduction

1.1 Aim A current problem in Sweden is the large volume of destruction of wine that has a deviant sensory profile than what was intended. By combining chemical characterisation and sensory analysis it would be possible to re-label these wines with an appropriate label. Combining this with recommendations of suitable food for the new correct profile the wines could be sold in the stores instead of being sent to destruction.

The aim in this project is to find chemical markers that can be connected to certain sensory profiles, so that chemical profiling could be used instead of sensory analysis for new profiling of wines, with the goal to reduce the large volumes of wine sent to destruction.

1.2 Scope This project is a part of a larger study and covers the gas chromatography analysis of volatile odourant compounds in wine samples. Liquid chromatography of the same samples will also be performed but will not be included in this project. This and sensory analysis could be used for comparison of results within the study.

1.3 Background

1.3.1 Wine Wine is a very popular drink that has been produced for thousands of years (1). Wine is obtained when fermenting grape must and reflects a distinguishable flavour. Originally, wine was only produced in Europe, by the Romans and the Greeks, but now the production has spread all over the world where the climate is suitable. Wine is usually appreciated combined with food as it enhances the flavour experience and is also a social phenomenon.

Chemically, wine consists of non-volatile tastants, such as phenolic compounds, and volatile aroma compound, such as phenolic acids (1, 2). The aroma is complex because the aroma compounds have different origins (1). The compounds can be found in different places such as the grape itself, produced during grape processing, alcoholic fermentation and during maturation of the wine.

Consumers need correct description of the product that they are purchasing, but traditional descriptive methods are time demanding (3). There is also issues with vocabulary use to really explain characterisation of products. Multiple studies, using methods such as headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME- GC-MS) and solid phase extraction high performance liquid chromatography (SPE-HPLC),

4 to characterise wine by chemical analysis instead of sensory analysis have been performed to create a more time effective and more easily understandable characterisation of the available products (3, 4, 5).

1.3.2 Headspace solid-phase microextraction (HS-SPME) Solid-phase microextraction is a solvent-free, rapid extraction technique that can be used within different fields such as food analysis, environmental analysis and drug analysis (6). The technique is popular because of its speed, no solvents, extraction and concentration of compounds occur simultaneously, and it is inexpensive (7). SPME is a popular technique for flavour analysis since it is sensitive enough for the volatile compounds with varying boiling points (8). It can either be performed to extract compounds directly out of liquid or from the headspace of the sample, which can be either liquid or solid.

The device consists of a fiber optic rod of fused silica coated with a polymer film, shown in Figure 1 (6, 7). Depending on what fiber and extraction conditions are used, the sensitivity and accuracy can be affected (8). Headspace-SPME (HS-SPME) is depending on the equilibrium of experimental parameters such as sample volume, extraction time, extraction temperature, sample matrix and concentration of volatiles. The headspace mechanism is based on equilibrium of analytes between fiber coating, headspace and sample. This means that the technique is highly dependent on the vapour pressure of the targeted volatile compounds.

Figure 1: The composition of a solid-phase microextraction device (7).

What fiber to use depends on the analyte’s attributes, such as molecular weight and polarity (9). The different attributes and what fibers are recommended are presented in Table 1.

5 Table 1: Analyte’s attributes regarding molecular weight, polarity and volatility and what solid-phase microextraction, SPME, fiber is recommended. Adopted from Sigma-Aldrich (9). Analyte type Recommended fiber

Gases and low molecular weight compounds (MW 75 µm/85 µm Carboxen/polydimethylsiloxane 30-225)

Volatiles (MW 60-275) 100 µm polydimethylsiloxane

Volatiles, amines and nitro-aromatic compounds 65 µm polydimethylsiloxane/divinylbenzene (MW 50-300)

Polar semi-volatiles (MW 80-300) 85 µm polyacrylate

Non-polar high molecular weight compounds (MW 7 µm polydimethylsiloxane 125-600)

Non-polar semi-volatiles (MW 80-500) 30 µm polydimethylsiloxane

Alcohols and polar compounds (MW 40-275) 60 µm Carbowax (PEG)

Flavor compounds.: volatiles and semi-volatiles, C3- 50/30 µm divinylbenzene/Carboxen on C20 (MW 40-275) polydimethylsiloxane on a StableFlex fiber

Trace compound analysis (MW 40-275) 50/30 µm divinylbenzene/Carboxen on polydimethylsiloxane on a 2cm StableFlex fiber

Amines and polar compounds (HPLC use only) 60 µm polydimethylsiloxane/divinylbenzene

1.3.3 Gas chromatography-mass spectrometry (GC-MS) In gas chromatography the mobile phase is an inert carrier gas such as nitrogen or helium (10). The stationary phase consists of a thin layer of polymer or liquid on an inert solid support inside a column made out of metal or glass. Samples to be analysed are carried through the column by the carrier gas and the analytes are separated from each other due to different interaction with the stationary phase that makes different components stay longer in the column than others.

Gas chromatography can be coupled with mass spectrometry detection (10). When the analytes exit the GC column they are ionized by either positive or negative ionization with subsequent fragmentation. Ions of different fragments are sorted by mass to charge ratios, m/z, to form a fragmentation pattern. Each analyte has a unique fragmentation pattern which makes it possible to identify the compounds with software with mass spectral libraries such as National Institute of Standard and Technology, NIST. GC alone can only separate semivolatile and volatile compounds with great resolution but no further information is achieved, while MS alone can only provide detailed structural information and fragmentation patterns. Together these two methods can give molecular fingerprints for different compounds to be

6 identified in samples.

2. Materials and methods

2.1 Materials

2.1.1 Chemicals Table 2: All the chemicals used in the study, purity and where they were bought. Chemical Purity Distributo Chemical Purity Distributor r

Methanol 98/100% Fisher Tartaric acid Kebo Analytical scientific AB, reagent UK stockhol grade m Ethanol 99,7% Solveco, Decanoic acid >98% Sigma- Sweden Aldrich (St.Louis MO) L-carvone 99% Sigma- Octanoic acid >99% Sigma- Aldrich Aldrich (St.Louis (St.Louis MO) MO) Gamma- 97% Sigma- alpha-Pinene 98% Sigma- Terpinen Aldrich Aldrich e (St.Louis (St.Louis MO) MO) p-Cymene analytica Sigma- Citral analytica Sigma- l Aldrich l Aldrich standard (St.Louis standard (St.Louis MO) MO) alpha- >85% Sigma- 3-Carene analytica Sigma- Phellandren Aldrich l Aldrich e (St.Louis standard (St.Louis MO) MO) Limonene analytica Sigma- Isoamy >97% Sigma- l Aldrich l Aldrich standard (St.Louis acetate (St.Louis MO) MO) Eucalyptol 99% Sigma- 4- >97% Sigma- Aldrich isopropylbenz Aldrich (St.Louis y l alcohol (St.Louis MO) MO) β- analytica Sigma- Farnesene analytica Sigma- Damascenone l Aldrich l Aldrich standard (St.Louis standard (St.Louis

7 MO) MO) Estragole 98.8% Sigma- D-Camphor >97% Sigma- Aldrich Aldrich (St.Louis (St.Louis MO) MO) alpha- analytica Sigma- Syringol 99% Sigma- Terpineo l Aldrich Aldrich l standard (St.Louis (St.Louis MO) MO) Furfury 99% Sigma- 5- 99% Sigma- l Aldrich methyl Aldrich alcohol (St.Louis furfural (St.Louis MO) MO) Guaiacol >99% Sigma- Eugenol 99% Sigma- Aldrich Aldrich (St.Louis (St.Louis MO) MO) Vanillin 99% Sigma- Methyl 99% Sigma- Aldrich guaiacol Aldrich (St.Louis (St.Louis MO) MO) Furfural 99% Sigma- Hydroxymeth 99% Sigma- Aldrich y lfurfural Aldrich (St.Louis (St.Louis MO) MO) Ethyl >98% Sigma- β-Citronellol analytica Sigma- dodecanoate Aldrich l Aldrich (St.Louis standard (St.Louis MO) MO) 1-heptanol >99.5% Sigma- Ethyl >98% Sigma- Aldrich decanoat Aldrich (St.Louis e (St.Louis MO) MO) 1-octanol >99% Sigma- Ethyl lactate >98% Sigma- Aldrich Aldrich (St.Louis (St.Louis MO) MO)

1-propanol >99% Sigma- Aldrich (St.Louis MO)

2.1.2 Samples The study comprises a total of 16 wines including four white wines, two orange wines, eight red wines and two sparkling wines as shown in Table 3. Capped eight ml vials were filled with 0.9g NaCl and three ml of each sample and then filled with nitrogen gas until analysis.

Table 3: Shows the type of wine, producer, name, country, region, grape and year of production for each wine included in the study. Type of wine Producer and Country/region Grape Year name White Pellerin France/Bugey Chardonnay 2014 (Sample 1) Chardonnay and 2016 (Sample 2) White Causse-Marines France/Gaillac Mauzac, Loin 2014 (Sample 3) Greilles the L’oeil, and 2015 (Sample Muscadelle 4) Orange Causse-Marines France/Gaillac Mauzac 2015 (Sample 5) Zacm’orange and 2016 (Sample 6) Red Pellerin Gamay France/Bugey Gamay 2014 (Sample 7) and 2015 (Sample 8) Red Causse-Marines France/Gaillac Braucol, 2014 (Sample 9) Peyrouzelles Syrah, Duras and 2016 (Sample 10) Red Karim Vionnet France/Beaujolais Gamay 2016 /Sample 11) Beaujolais Nouveau and 2017 (Sample 12) Red Domaine la France/Saint Grenache, 2015 (Sample 13) Rabidote Chinian Carignan, and 2016 (Sample Syrah 14) Sparkling Balviet Cordon du France/Bugey Gamay, Poulsard 2013 (Sample 15) Bugey Sparkling Labarthe France/Gaillac Mauzac 2014 (Sample 16) Ancestrale

All samples were analysed in duplicate, and one sample of each type of wine was analysed in quadruplicate to evaluate the method statistically. The samples were chosen at random and ended up being samples 1, 5, 9 and 16.

2.1.3 Sample preparation To perform qualitative and quantitative analysis of the chosen compounds (Table 4) an artificial wine was prepared. A standard solution of each compound with a concentration around 1000 µg/ml was made. The exact achieved concentrations are presented in table 3.

The artificial wine was prepared to be 13% ethanol and pH ~3.5 (11). An alcohol content of 13% was chosen instead of 11% since most of the samples are red wines which generally has higher percentage of ethanol. This was prepared by mixing ethanol with water and then adjusting the pH with tartaric acid. This solution was used to make an artificial wine blank and an artificial wine standard. The artificial wine standard is the artificial wine mixed with

9 all compounds and since the analytes of interest were dissolved in methanol, the artificial wine blank is the artificial wine mixed with methanol instead of the compound mastermix.

All compounds were prepared in a mastermix to be added into the artificial wine. The final concentration of the mastermix was 29 ppm. The target concentration for the artificial standard was 10 ppm in 20 ml artificial wine. The artificial wine standard had 34.5% of master mix in the final volume which was compensated for in the blank by preparing 34.5% methanol with the artificial wine.

In all samples, artificial wine blank and artificial wine standard, 10 ppm internal standard was added. The internal standard used was hexanoic acid labeled with deuterium in three places.

2.1.4 Standard compounds Compounds included in the standard solution were chosen based on other studies (4, 5) and what chemicals were available in the MTM laboratory at Örebro university.

Table 4: Compounds included in the study, solvents used for dilution and final concentration of the prepared standard. Compound Solvent Molecular Concentration weight (µg/ml) 3-Carene Methanol 136.23 1149 4-isopropylbenzyl Methanol 150.22 985 alcohol alpha Terpineol Methanol 154.25 1037 alpha-Phellandrene Methanol 136.23 1054 alpha-Pinene Methanol 136.23 1121 Citral Methanol 152.23 1156 D-Camphor Methanol 152.23 1063 Estragole Methanol 148.20 985 Eucalyptol Methanol 154.25 1019 Farnesene Methanol 204.35 995 gamma-Terpinene Methanol 136.23 1118 L-Carvone Methanol 150.22 1092 Limonene Methanol 136.238 1130 p-Cymene Methanol 134.22 1057 Eugenol Methanol 164.2 1077 Furfural Methanol 96.09 1028 Furfuryl alcohol Methanol 98.101 996 Guaiacol Methanol 124.14 1051 5- Methanol 126.11 1006 HyrdoxymethylFurf

10 ural Methyl Furfural Methanol 110.112 1135 Methyl Guaiacol Methanol 138.166 1040 Syringol Methanol 154.162 1059 Vanillin Methanol 152.15 1064 3-Methylbutyl Methanol 130.18 1066 acetate/isoamyl acetate β-Citronellol Methanol 156.27 1069 β-damascenone Methanol 190.28 1041 Ethyl decanoate Methanol 200.32 1063 Ethyl dodecanoate Methanol 228.37 1096 Ethyl lactate Methanol 118.13 1040 1-heptanol Methanol 116.20 1025 1-propanol Methanol 60.10 1163 1-octanol Methanol 130.23 1022 Octanoic acid Methanol 144.21 1013 Decanoic acid Methanol 172.26 1070

2.2 Methods

2.2.1 Headspace solid-phase microextraction The fiber chosen for the HS-SPME was 50/30µm DVB/CAR/PDMS StableFlex by Supelco, USA, which is a combination of three stationary phases: divinylbenzene, carboxen and polydimethylsiloxane (11). This fiber allows extraction of volatile and semivolatile compounds with molecular weights between 40 and 275 Da. The fiber was assembled, conditioned and cleaned according to instructions by the manufacturer, Supelco/Sigma- Aldrich (12).

A previous study using the same fiber showed that analytes in white wines reach equilibrium after 20 minutes and analytes in red wines after 30 minutes (13). Different sample volumes were tested, 70 ml and 3 ml, and it showed no significant change in results. The extraction temperature had no crucial effect when extracting volatile analytes, but the condensation of ethanol that occurs above 30°C causes non-repeatable extraction as experienced by Hyötyläinen et al. (13) which is why the extraction was kept at a temperature below 30°C. For this study, the sample volume chosen was 3 ml mixed with 0.9g NaCl and the extraction time was 30 minutes for all types of wines. Extraction was done at ~25°C while stirring with a magnetic stirrer. The SPME fiber was injected manually into the GC-MS and left in the injector for 5 minutes to make sure that it is clean. This was tested with the artificial wine standard and it showed that 5 minutes was enough to clean the fiber and use again directly

11 afterwards.

At the start of each day of HS-SPME extractions, the artificial wine blank was analysed, followed by the artificial wine standard and then the samples. Tests were performed with the artificial wine standard to confirm that the same vial could be analysed multiple times with similar result.

2.2.2 Gas chromatography-mass spectrometry For gas chromatographic separation a 30 m long, 0.250 mm diameter and 0.25 µm film thickness DB-WAXETR column from Agilent Technologies, USA, was used. The instruments used were a HP 6890 series GC system and a HP 5973 mass selective detector from Agilent Technologies, USA. Three different GC-MS methods were tested to evaluate which one would be the best to use. Scan was mode was used between 30 and 300 m/z and NIST 11, database was used to identify the compounds in the standard. The tested methods were the following:

Method 1 and 3, was adopted from (7), with changed quadrupole temperature for method 3. Ion source, quadrupole and injector temperatures were set to 230°C, 150°C and 250°C, respectively, and the injector was equipped with a SPME liner. The flow rate of the carrier gas (helium) was set to 1.1 ml/min. Splitless mode and positive electron ionization was used and the scan range went from 30 to 300 m/z. The oven program was run according to Table 5 and gave a total run time of 33 minutes. The same settings and oven program, with a change of the quadrupole temperature to 106°C was used for method 3.

Table 5: The Gas chromatography-mass spectrometry, GC-MS, oven program settings for method 1 and 3. Step °C/min Temperature (°C) Hold time (min)

1 0 50 2

2 7 110 10

3 10 230 0

Method 2, adopted from (4). Ion source, quadrupole and injector temperatures were set to 230°C, 150°C and 250°C, respectively, and the injector was equipped with a SPME liner. The flow rate of the carrier gas (helium) was set to 1.0 ml/min. Splitless mode and positive electron ionization was used and the scan range went from 30 to 300 m/z. The oven program was run according to Table 6 and gave a total run time of 76 minutes.

12 Table 6: The GC-MS oven program settings for method 2. Step °C/min Temperature (°C) Hold time (min)

1 0 40 1

2 2 135 0

3 5 212 0

4 15 250 10

The chosen method was method 3 since that method resulted in the best chromatographic separation in relation to the time needed. The chromatograms obtained from each method are presented in Figures 2-4 in the Appendix The only difference between method 1 and 3 is the quadrupole temperature. The lower temperature gave an improved chromatogram with increased number of peaks present, which is why method 3 was used instead of method 1.

2.2.3 Method issues Initially an artificial wine blank with no internal standard was run, followed by a blank with internal standard. A new peak with a retention time around 23.23 was obtained and expected to be the internal standard. The same thing occurred in the artificial wine standard (Figure 5). However, after all samples were run and the peaks were integrated, this peak was identified by NIST 11 as pentanoic acid instead of labeled hexanoic acid, as was added. In the wine samples, both pentanoic acid and hexanoic acid could be identified by NIST 11, but with very similar retention times and in some samples they were co-eluted. For this reason, the internal standard could not be used to quantify compounds due to the uncertainty with possible co- elution with pentanoic acid in the wine samples.

Figure 5: Chromatograms the artificial wine standard with internal standard at the top, artificial wine blank without internal standard in the middle and artificial wine blank with internal standard at the bottom. The obtained peaks were identified by NIST 11 as pentanoic acid instead of the

13 actual mass-labeled hexanoic acid, which lead do the internal standard not being usable for quantification.

3. Results

3.1 Artificial wine standard In the artificial wine standard only 15 compounds could be identified with mass spectra comparison and NIST 11, which are presented in Table 7. The remaining compounds that could not be found could possibly be lost in background noise, not being volatile enough or being too volatile hence too low concentrations to be seen. The chromatographic result is shown in Figure 6.

Figure 6: Chromatogram from the artificial standard containing 10 ppm of the 34 compounds presented in Table 4.

Table 7: The compounds with their corresponding retention time and area in the artificial wine standard that were identified by NIST 11. Compound Retention time Area

3-Carene 4.709 1111712

4-isopropylbenzyl alcohol 27.694 3579 alpha-Phellandrene 4.989 525771 alpha-Pinene 4.014 7660

D-Camphor 11.292 18351

Estragole 15.363 167964

14 Gamma-Terpinene 6.421 2345116

Limonene 5.567 2437386 p-Cymene 6.842 1624548

Eugenol 28.460 12768

3-Methylbutyl acetate/isoamyl 4.330 28177 acetate Ethyl decanoate 15.050 1674775

Ethyl dodecanoate 23.906 4546498

Octanoic acid 27.276 37497

Decanoic acid 29.746 13686

3.2 Wine analysis result All wines were analysed in duplicate and wine sample number 1, 5, 9 and 16 were analysed in quadruplicate. Since the internal standard could not be used for calculations, the overall results in tables are presented in area units. The ten peaks with highest intensity were chosen to be included in the study. However, one of these ten were the internal standard that was excluded which means that the figures presented in the results shows the nine most abundant peaks according to their areas. The reason for choosing these peaks was due to time limitations and not being able to do proper quantitative calculations. What sample corresponds to which wine is presented in Table 3.

3.2.1 White wines

15 Figure 7: Homologue patterns based on area of the nine most abundant peaks of the wine Pellerin from 2014. 1-4 are replicates of the same wine sample.

Figure 8: Homologue patterns based on area of the nine most abundant peaks of the wine Pellerin from 2016. 1-2 are replicates of the same wine sample.

Figure 9: Homologue patterns based on area of the nine most abundant peaks of the wine Causse-Marines Greilles from 2014. 1-2 are replicates of the same wine sample.

Figure 10: Homologue patterns based on area of the nine most abundant peaks of the wine Causse-Marines Greilles from 2015. 1-2 are replicates of the same wine sample. In 4.2 the pentanoic acid area was put as zero due to co-elution with the peak for hexanoic acid.

Figure 11: Homologue patterns based on area of the nine most abundant peaks of the white wines Pellerin from 2014 (number 1-4), Pellerin from 2016 (number 5-6), Causse-Marines Greilles from 2014 (number 7-8) and Causse-Marines Greilles from 2015 (number 9-10).

17 3.2.2 Orange wines

Figure 12: Homologue patterns based on area of the nine most abundant peaks of the wine Causse-Marines Zacm’ orange from 2015. 1-4 are replicates of the same wine sample.

Figure 13: Homologue patterns based on area of the nine most abundant peaks of the wine Causse-Marines Zacm’ orange from 2016. 1-2 are replicates of the same wine sample.

Figure 14: Homologue patterns based on area of the nine most abundant peaks of the orange wines Causse-Marines Zacm’ orange from 2015 (number 1-4) and Causse-Marined Zacm’ orange from 2016 (number 5-6).

3.2.3 Red wines

Figure 15: Homologue patterns based on area of the nine most abundant peaks of the wine Pellerin Gamay from 2014. 1-2 are replicates of the same wine sample.

Figure 16: Homologue patterns based on area of the nine most abundant peaks of the wine Pellerin Gamay from 2015. 1-2 are replicates of the same wine sample.

Figure 17: Homologue patterns based on area of the nine most abundant peaks of the wine Causse-Marines Peyrouzelles from 2014. 1-4 are replicates of the same wine sample.

Figure 18: Homologue patterns based on area of the nine most abundant peaks of the wine Causse-Marines Peyrouzelles from 2016. 1-2 are replicates of the same wine sample.

Figure 19: Homologue patterns based on area of the nine most abundant peaks of the wine Karim Vionnet Beaujolais Nouveau from 2016. 1-2 are replicates of the same wine sample.

Figure 20: Homologue patterns based on area of the nine most abundant peaks of the wine Karim Vionnet Beaujolais Nouveau from 2016. 1-2 are replicates of the same wine sample. In 12.2 the pentanoic acid area was put as zero due to co-elution with the peak for hexanoic acid.

Figure 21: Homologue patterns based on area of the nine most abundant peaks of the wine Domaine la Rabidote from 2015. 1-2 are replicates of the same wine sample.

Figure 22: Homologue patterns based on area of the nine most abundant peaks of the wine Domaine la rabidote. 1-2 are replicates of the same wine sample.

Figure 23: Homologue patterns based on area of the nine most abundant peaks of the red wines Pellerin Gamay from 2014 (number 1-4), Pellerin Gamay from 2016 (number 5-6), Causse-Marines Peyrouzelles from 2014 (7-8), Causse-Marines Peyrouzelles from 2016

23 (number 9-10), Karim Vionnet Beaujolais Nouveau from 2016 (number 11-12), Karim Vionnet Beaujolais Nouveau from 2017 (number 13-14), Domaine la Rabidote from 2015 (number 15-16) and Domaine la Rabidote from 2016 (number 17-18).

3.2.4 Sparkling wines

Figure 23: Homologue patterns based on area of the nine most abundant peaks of the wine Balviet Cordon du Bugey from 2013. 1-2 are replicates of the same wine sample.

Figure 25: Homologue patterns based on area of the nine most abundant peaks of the wine Labarthe Ancestrale from 2014. 1-4 are replicates of the same wine sample.

Figure 26: Homologue patterns based on area of the nine most abundant peaks of the sparkling wines Balviet Cordon du Bugey from 2013 (number 1-2) and Labarthe Ancestrale from 2014 (number 3-6).

3.3 Quantification Tentative concentrations were calculated for the compounds found in the wine samples that also were found in the artificial wine standards. The compounds were ethyl decanoate, ethyl dodecanoate, octanoic acid and decanoic acid. The calculated concentrations are presented in Table 8 in the Appendix.

Out of the four compounds, the most abundant being found in 15 out of 16 wines was decanoic acid with concentrations ranging from 16 to 265 ppm. The second highest abundance was octanoic acid, detected in 15 out of 16 wines with concentrations ranging from 13 to 124 ppm. Ethyl decanoate was also found in 15 out of 16 wines but at much lower concentrations ranging from 0.3 to 7 ppm. Ethyl decanoate could only be found in 5 out of 16 wines at very low concentrations ranging from 0.1 to 0.3 ppm.

3.4 Identified compounds Common for all wine samples was the presence of isoamyl alcohol, and as previously mentioned the majority of the samples also contained ethyl decanoate, octanoic acid and decanoic acid. Some different trends were seen between the different colours of wines. Both sparkling wines contained ethyl hexanoate, ethyl octanoate and phenylethyl alcohol. Six out

25 of eight red wines contained ethyl succinate and five of them also contained ethyl hexanoate. Wine sample number 14 was the most different out of the red wine samples with high abundance of tetradecamethylhexasiloxane, 2,2,4,4,5,5,7,7-Octamethyl-3,6-dioxa-2,4,5,7- tetrasilaoctane and tetrakis(trimethylsiloxy)silane.

Common compounds in the orange wines apart from the previously mentioned were ethyl hexanoate, ethyl octanoate and pentanoic acid. All white wine samples also contained ethyl hexanoate and ethyl octanoate.

3.5 Statistics Statistical calculations for relative standard deviation was performed and results are shown in Table 9. Each sample was analysed in quadruplicate from four different sample vials. All replicates of sample 1 was analysed on day one, all of sample 5 on day two, one of sample 9 on day two, three of sample 9 on day three and all of sample 16 on day 4. The statistics are also not as adequate as initially planned due to not being able to quantify more compounds.

The relative standard deviation ranges from 6-34% for ethyl decanoate, 35-48% for ethyl dodecanoate, 13-21% for octanoic acid and 12-59% for decanoic acid. This indicates that none of the extractions are repeatable with good results due to the high deviation. Reasons for non-repeatable extractions could be the HS-SPME fiber performance, reactions with air despite filling the vials with nitrogen gas or not homogenous samples.

Table 9: The relative standard deviation percentage for ethyl decanoate, ethyl dodecanoate, octanoic acid and decanoic acid for samples 1, 5, 9 and 16 that were analysed in quadruplicate.

Limit of detection, LOD, and limit of quantification, LOQ, for the samples has been calculated separately according to the artificial wine standard that was analysed the same day as the samples. This resulted in four different LOD and LOQ for each compound that is presented in Table 10. The LOD was calculated as (concentration/signal to noise)*3 and the LOQ as (concentration/signal to noise)*10. The concentration was 10 ppm and the exact signal to noise ratios are presented in Table 12 in appendix.

26 Table 10: Limit of detection, LOD, for ethyl decanoate, ethyl dodecanoate, octanoic acid and decanoic acid in the different samples depending on the day they were analysed.

Table 11: Limit of quantification, LOQ, for ethyl decanoate, ethyl dodecanoate, octanoic acid and decanoic acid in the different samples depending on the day they were analysed.

The concentration for these four compounds in the wine samples were higher than LOD and LOQ which lead to possible quantification of the four compounds in Table 8 in the Appendix. The concentrations were calculated by using the areas presented in Table 13 in the Appendix.

4. Discussion

4.1 Limitations Normalised homologue area patterns to characterise wine is not the best approach, since the compounds with the largest area is not necessarily the compound with the strongest odour. In databases like PubChem, one can find the different concentrations needed for each compound to give a distinct odour, which means that quantitative analysis would be needed to properly characterise the wines. Since quantification was not possible in this study due to issues with identifying the internal standard, the homologue area patterns are used as a method to perform characterisation of the wines. However, as stated previously, this approach is not the most optimal due to the compounds differences in concentration for detectable odour and therefore, the results might not correspond to the actual odour differences of the wines.

4.2 Quantitative analysis Quantitative analysis was performed for those compounds that were present in both of the samples and the standard, presented in Table 8 in the Appendix. Normalisation against an internal standard was not possible since the molecular ion could not be seen in the mass spectrum, which made correct identification by NIST impossible. A comparison of the mass spectra of pentanoic acid and hexanoic acid from NIST is shown in Figure 27 and 28 in the Appendix. Calculations of concentrations for other compounds identified in the samples was

27 not performed. Correct calculations for these compounds would require the injection of these compounds of a known concentration for the calculation of their respective response factors.

Because of this, only four compounds could be quantified and for the remaining compounds no quantification was made. The nine most intense peaks in each sample was integrated and identified as shown in Figures 7-26. However, in these figures pentanoic acid is included which might not be the correct area to pentanoic acid alone due to the possible mix-up and/or co-elution with the internal standard hexanoic acid. Pentanoic acid is still included in the figures since in most samples, apart from 4.2 and 12.2 it was possible to see two peaks, and the software could also identify the two peaks as different compounds.

As shown in Figures 7-26, when looking at the relation between different compounds in samples, some had high reproducibility within the duplicates/quadruplicates while some did not. The obtained areas in each replicate of the same sample did differ significantly in some samples. Areas are shown in Table 13 in the Appendix. However, the varying intensities do not affect the homologue patterns since they are normalised, but quantification may not give an accurate description.

4.3 Aroma compounds A previous study performed by Capone et al. (4) and Li et al. (5) has presented characteristics for volatiles determined by HS-SPME-GC-MS. The analytes of interest were the following presented in Table 14.

Table 14: The volatile analytes with a characterised odour presented in studies by Capone et al. (4) and Li et al. (5). Compound Odour quality

Ethyl hexanoate Apple peel, fruit

Ethyl octanoate Melon, wood, pineapple

Ethyl decanoate Floral, soap, fruity

Ethyl succinate Wine, fruit

Ethyl dodecanoate Fruity, floral, sweet

Octanoic acid Butter, almond, fatty

Decanoic acid Rancid, fat

Ethyl 9-decanoate Fruity, fatty

Isoamyl alcohol (3-methyl-1-butanol) Harsh, nail polish, fusel

Phenethyl alcohol (2-Phenylethanol) Floral, rose

28 Ethyl hexadecanoate Fruity

According to the odour quality presented in Table 14, the most significant odours from volatile compounds in all wines included in the study were melon, wood, pineapple, floral, soap, fruity, butter, almond, fatty, wine, rancid, harsh and nail polish. In this study, all wine samples contained relatively large amounts of isoamyl alcohol and ethyl octanoate which would indicate that all the wines have a shared odour. While the odour of isoamyl alcohol is regarded as unpleasant, ethyl octanoate has a pleasant odour of melon, wood and pineapple. The majority of the wines, apart from sample 12 and 13, contained ethyl hexanoate which would indicate a fruity apple peel odour.

Compounds without any previously studied odour quality, such as different siloxanes, silanes and butylated hydroxytoluene, were also found in some samples. Butylated hydroxytoluene is, according to the Pubchem database, a compound that is used in foods to prevent oxidation and free-radical formation, which would explain its presence in some of the wines. However, no explanation to the presence of siloxanes and silanes has been found.

The wines Domaine la Rabidote from 2015 and 2016 were used for taste testing for the sensory analysis of the study. The wine from 2015 corresponds to sample 13 and the wine from 2016 corresponds to sample 14. According to the results, sample 13 was experienced as spicier and possessed a greater flavour of minerals and greens. Sample 14 was experienced as more fruity and floral. Comparing with Table 14, this can be explained by Sample 14 containing ethyl dodecanoate that has a fruity and floral aroma while this compound was not one of the nine most abundant in sample 13. Sample 13 contains slightly less ethyl decanoate which also indicates that it will not taste as floral as sample 14. None of the nine most abundant peaks could explain the mineral and greens flavour being stronger in sample 13 compared to sample 14.

4.5 Comparison of wines

4.5.1 White wines The homologue patterns of the white wines presented in Figure 11 shows that the four most abundant compounds, isoamyl alcohol, ethyl hexanoate, ethyl octanoate and ethyl decanoate are very similar. The most obvious difference between the patterns would be the wine Causse-Marines Greilles from 2015 containing ethyl 9-decanoate while the others do not, which would indicate that this wine is more fruity and fatty. The wine Causse-Marines Greilles from 2014 is the only wine without ethyl dodecanoate which indicates it being less fruity, floral and sweet compared to the other wine samples.

4.5.2 Orange wines The homologue patterns of the two orange wines presented in Figure 14 are generally alike. The only identified compound to separate them that has a known aroma according to Table 13 is ethyl dodecanoate. With the abundance of ethyl dodecanoate being the only difference of the analytes with known aroma in the homologue pattern, it would indicate that the wine Causse-Marines Zacm’ orange from 2015 is more fruity, floral and sweet compared with the same wine from 2016.

4.5.3 Red wines The homologue patterns for the red wines presented in Figure 23 shows that the distribution between the three most abundant compounds, isoamyl alcohol, ethyl octanoate and ethyl decanoate, are quite similar. The wines Pellerin Gamay from 2014 and 2015, Causse-Marines Peyrouzelles from 2015 and 2016 and Karim Vionnet Beaujolais Nouveau from 2016 could have more of an apple peel and fruity aroma due to higher abundance of ethyl hexanoate. The higher abundance of phenethyl alcohol in most of the samples indicate a rose aroma. The wine Domaine la Rabidote from 2016 is the only sample of red wine where ethyl dodecanoate was identified among the most abundant ones, which implies that this wine is more sweet, fruity and floral than the others.

4.5.4 Sparkling wines There is a significant difference between the two sparkling wine’s homologue patterns presented in Figure 26. This is most likely due to the Balviet Cordon du Bugey wine being rosé and Labarthe Ancestrale being white. The nine most abundant compounds are similar but the distribution of them are not. The white sparkling wine has ethyl decanoate as one of the nine most abundant compounds which could indicate that this wine is slightly more fruity and floral than the rosé sample. When comparing the white sparkling wine with the normal white wines, there is also a significant difference in the distribution of compounds, mostly regarding the pentanoic acid. The high intensity peaks of pentanoic acid in both of the sparkling wine samples compared to the other samples, could indicate co-elution with the internal standard and therefore not accurate homologue patterns.

5. Conclusion When identifying and making homologue patterns of the nine most abundant compounds in wine samples, some differences can be seen. However, to get a more thorough understanding of the chemical markers that separates each wine’s sensory profile, more compounds would

30 have to be included in the study. The chromatograms of the wine samples contained a high number of peaks that were not included in this study, but could be used for a more detailed characterisation. Quantification of all analytes included in the study could help with further characterisation of the samples, which was not possible due to the possible co-elution of pentanoic acid and the internal standard, mass-labeled hexanoic acid. To get a more exact characterisation of wine, a method with better statistical values regarding reproducibility would be needed.

6. References 1. Agerlin Petersen, M., Helgesdotter Rogsnå, G., Hersleth, M., Misje. K.E., Rathe, M. et al. (2017). From wine to wine reduction: Sensory and chemical aspects. International Journal of Gastronomy and Food science, 9, pp. 62-74. doi: https://doi.org/10.1016/j.ijgfs.2017.06.006

2. Ánfeles Pozo-Bayon, M., Esteban-Fernándes, A., Jiménes-Girón, A., Muños-González, C. et al. (2018). Aroma release in the oral cavity after wine intake is influences by wine matrix composition. Food chemistry, 243, pp.125-133. doi: https://doi.org/10.1016/j.foodchem.2017.09.101

3. Alegre, Y., Ferreira, V., García, D., Hernández-Orte, P., Razquin, I. et al. (2017). Rapid strategies for the determination of sensory and chemical differences between a wealth of similar wines. European Food Research and Technology, 243, pp. 1295-1309. doi: 10.1007/s00217-017-2857-7

4. Capone, D., Jeffery, D., Wang, J. and Wilkinson, K. (2016). Chemical and sensory profiles of rosé wines from Australia. Food Chemistry, 196, pp. 682-693. doi: https://doi.org/10.1016/j.foodchem.2015.09.111

5. Li, B., Lu, J., Sun, J., Tian, T., Wu, D. et al. (2018). Optimization of fermentation conditions and comparison of flavour compounds for three fermented greengage wines. LWT - Food Science and Technology, 89, pp.542-550. doi: https://doi.org/10.1016/j.lwt.2017.11.006

6. Coelho, G., Elias, A. and Santos, B. (2016). Use of HS-SPME for analysis of the influence of salt concentration and temperature on the activity coefficient at infinite dilution of ethanol- water-salt systems. Fluid Phase Equilibria, 429, pp. 21-26. doi: https://doi.org/10.1016/j.fluid.2016.08.030

7. Salihovic, S. (2009). Analysis and identification of volatile oak related compounds by HS- SPME-GC-MS and MEPS-GC-MS.

8. Bekhit, A.E.D., Hamid, N., Law, T.F., Ma, Q.L. and Robertson, J. (2013). Optimization of headspace solid phase microextraction (HS-SPME) for gas chromatography mass spectrometry (GC-MS) analysis of aroma compounds in cooked beef using response surface methodology. Microchemical Journal, 111, pp. 16-24. doi: https://doi.org/10.1016/j.microc.2012.10.007

9.https://www.sigmaaldrich.com/technical-documents/articles/analytical/selecting-spme- fibers.html#fiber

32 10. Maqbool, K., Zameer Hussain, S. (2014). GC-MS: Principle, Technique and its application in Food Science. International Journal of Current Science, 13, pp. 116-126. ISSN 2250-1770.

11. Caliari, V., Grützmann Arcari, S., Sganzerla, M., Tiexeira, G. (2017). Volatile composition of Merlot red wine and its contribution to the aroma: optimization and validation of analytical method. Talanta, 174, pp. 752-766. doi: https://doi.org/10.1016/j.talanta.2017.06.074

12.https://www.sigmaaldrich.com/content/dam/sigmaaldrich/docs/Sigma/General_Information/1/t794123.pdf

13. Hyötyläinen, T., Kallio, M., Kallonen, R., Lehtonen, P., Patrikainen, E. et al. (2014). Characterisation of Wines by Comprehensive Two-Dimensional Gas Chromatography and Chemometric Methods.

33 7. Appendix

Figure 2: The chromatogram obtained from method 1.

Figure 3: The chromatogram obtained from method 2.

Figure 4: The chromatogram obtained from method 3

Table 8: The tentative concentrations of each compound that could be identified in the artificial wine standard and in the wine samples. The concentrations were calculated by using the average area of the compound in all replicas of the same wine and then comparing to the area of the same compound in the artificial wine standard, where each compound was 10 ppm.

35 Figure 27: Mass spectrum of pentanoic acid from NIST.

Figure 28: Mass spectrum of hexanoic acid from NIST.

36 Table 12: The signal to noise ratios for ethyl decanoate, ethyl dodecanoate, octanoic acid and decanoic acid in the artificial wine standard analysed different days. 20180503 applies to samples 1.1-4.1, 20180507 applies to samples 4.2-9.1, 20180508 applies to samples 9.2-13.2 and 20180509 applies to samples 14.1-16.4.

Table 13: The ten most abundant peaks in every sample including retention time and compound. Sample Peak1 Peak2 Peak 3 Peak 4 Peak 5

1.1 Rt: 5.680 Rt: 6.251 Rt: 10.028 Rt: 14.970 Rt: 23.772 Name: Isoamyl Name: Ethyl Name: Ethyl Name: Ethyl Name: Ethyl alcohol hexanoate octanoate decanoate dodecanoate Area: 105709 Area: 223869 Area: 1628309 Area: 609569 Area: 137337