Olfactometry Profiles and Quantitation of Volatile Sulfur Compounds of

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Journal of Agricultural and Food Chemistry

Olfactometry Profiles and Quantitation of Volatile Sulfur Compounds of Swiss Tilsit cheeses

Authors: Pascal Fuchsmann, Mireille Tena Stern, Yves-Alain Brügger, and Katharina Breme

Agroscope, Institute for Food Sciences IFS, Schwarzenburgstrasse 161, CH-3003 Berne, Switzerland

Corresponding author: Phone +41 (0) 58 463 82 60, Fax +41 (0) 58 463 82 27, Email [email protected]

ACS Paragon Plus Environment


1

Abstract

2

In order to establish the odor profiles of three differently fabricated commercial Swiss

3

Tilsit cheeses, analyses were conducted using headspace solid-phase microextraction

4

gas chromatography–mass spectrometry/pulsed flame-photometric detection, and gas

5

chromatography–olfactometry to identify and quantitate volatile compounds. In

6

addition, odor quality and the impact of target sulfur compounds on the overall odor of

7

the cheeses were investigated. The odor profile was found to be mainly influenced by

8

buttery-cheesy and sulfury odor notes in all cheeses. Buttery-cheesy odor notes were

9

attributed to three main molecules: butanoic acid, 3-methylbutanoic acid, and butane-

10

2,3-dione. Over a dozen volatile sulfur compounds were detected at ppb (parts per

11

billion) levels, but only a few influenced the odor profile of the cheeses: methanethiol,

12

dimethyl disulfide, bis(methylthio)methane, dimethyl trisulfide, 3-(methylthio)propanal,

13

and 2-methyltetrahydrothiophen-3-one (tentative). In conclusion, the conducted

14

analyses allowed to differentiate the cheeses, and gas chromatography-olfactometry

15

results confirmed that partially thermized milk cheese has a more intense and more

16

multifaceted overall flavor.

17 18

KEYWORDS: GC-Olfactometry, 2W-GC-O, PFPD, Swiss Tilsit cheese, headspace

19

SPME, volatile sulfur compounds, VIDEO-Sniff, average Odor Activity Values


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INTRODUCTION

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Swiss Tilsit is a very popular semi-hard cheese manufactured only in the Eastern part

22

of Switzerland. There are five main varieties of Swiss Tilsit, but only three types are

23

generally found on the commercial market: the so-called “green” Swiss Tilsit (made

24

from pasteurized milk with at least 45 % fat in dry matter (FDM), corresponding to an

25

approximate fat content of 28 g/100 g and a ripening time of 30–60 days, sold with a

26

green label), red Tilsit (thermized milk and raw milk, at least 45 % FDM and an

27

approximate fat content of 29.5 g/100 g, 70–110 days of ripening, sold with a red

28

label), and yellow Tilsit (pasteurized milk with a higher fat content, at least 55 % FDM

29

and an approximate fat content of 33 g/100 g, 30–70 days of ripening, sold with a

30

yellow label) (www.Tilsiter.ch, 2014). Precise fabrication parameters are individual to

31

each cheese plant, but it is known that differences in raw materials lead to sensory

32

differences, especially for cheeses made from raw milk, which are said to show a more

33

intense and complex flavor than cheeses made from pasteurized milk,1, 2 possibly due

34

to the diversity of the microflora present.

35

Volatile sulfur compounds (VSCs), such as methanethiol (MeSH), hydrogen sulfide

36

(H2S), methyl thioacetate (MTA), 3-(methylthio)propanal, and methylsulfides, including

37

dimethyl sulfide (DMS), dimethyl disulfide (DMDS), and dimethyl trisulfide (DMTS), are

38

reported as key flavor compounds in a variety of cheeses.3-5 These VSCs are mainly

39

derived from the decomposition of the sulfur-containing amino acids cysteine and

40

methionine. Due to low odor thresholds, they have very pronounced sensory

41

properties, even at low concentrations. For that reason, a variation in the concentration

42

of VSCs can have a significant influence on cheese flavor. Several publications report

43

on the analysis of VSCs in semi-hard cheeses,6-8 stating that the studied cheeses are

44

characterized by important sensory differences due to variations in fat content,

45

microflora diversity, and ripening time.

46

As they are often present in low quantities only (but have a high impact on flavor), and

47

because they are highly volatile and reactive, analyzing VSCs remains a challenge.9-11 ACS Paragon Plus Environment


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Cheese and dairy products in general, just like other food products, contain many

49

aromatic molecules. However, not all these have an impact on the foodstuff, and only

50

a few of these molecules contribute to the aroma.12 Gas chromatography coupled with

51

olfactometry (GC-O) is used to identify odor-active compounds by using the human

52

nose as an analytical detector of volatile odorant compounds and helps to rapidly

53

identify so-called “odorant zones” in an aromagram. Different methodologies are

54

available, and the technique including these methods has been extensively described

55

in the literature.13, 14 The use of multiway GC-O-systems, where at least two panelists

56

work simultaneously on the same sample, represents an important gain of time,

57

especially when dealing with sensitive and perishable food samples, and also assures

58

the reliability of results.

59

The combination of headspace solid-phase microextraction gas chromatography–

60

mass spectrometry, and olfactometry (HS-SPME-GC-MS-O) with sulfur-specific pulsed

61

flame-photometric detection (PFPD) in order to specifically identify and quantitate

62

odorant sulfur compounds, even at trace levels,15 is an efficient way to determine both

63

overall odor profiles and the specific odor impact of trace VSCs.

64

Here, we report a comparative study of the odorant profiles of Swiss Tilsit cheeses,

65

with a focus on odor-impact VSCs determined by HS-GC-MS-O with a two-way-GC-O

66

system using the VIDEO-Sniff method (vocabulary-intensity-duration of elementary

67

odors by sniffing),13 a hybrid olfactometry method combining detection frequency,

68

intensity, and descriptive vocabulary of the judges, and their quantitation by HS-

69

SPME-GC-MS/PFPD.

70

To date, and to the authors’ knowledge, there is no publication reporting on the

71

comparative study of differently manufactured Swiss Tilsit cheeses using olfactometry

72

and with a focus on VSC odor impact and quantitation.

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MATERIALS AND METHODS

74

Chemicals. Chemical compounds were purchased from Sigma-Aldrich Chemie GmbH

75

(Buchs, Switzerland) and Alfa Aesar GmbH & Co KG (Karlsruhe, Germany). Acetic ACS Paragon Plus Environment

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acid (Aldrich 320099), 2-acetyl-2-thiazoline (Aldrich W381718), allyl sulfide (Aldrich

77

A35801), benzaldehyde (Aldrich W212709), benzyl alcohol (Aldrich 305197),

78

bis(methylthio)methane (Aldrich W387800), butane-2,3-dione (Aldrich B85307), butan-

79

2-one (Aldrich 360473), butanoic acid (Aldrich W222119), carbon disulfide (Aldrich

80

335266),

81

W313505), dimethyl disulfide (Aldrich W353604), 2,5-dimethylpyrazine (Aldrich

82

W327204), dimethyl sulfide (Aldrich W274623), dimethyl sulfone (Aldrich M81705),

83

dimethyl sulfoxide (Aldrich D8418), dimethyl trisulfide (Aldrich W327506), ethanol

84

(Aldrich 02860), ethyl butanoate (Aldrich W242705), ethyl hexanoate (Aldrich

85

W243906), 4-ethylguaiacol (Aldrich W243604), ethyl isobutyrate (Aldrich W242802),

86

ethyl methyl sulfide (Aldrich 238317), ethyl octanoate (Aldrich W244902), hexanal

87

(Aldrich 115606), hexanoic acid (Aldrich W255904), 3-methylbutyl acetate (Aldrich

88

W205532), methanethiol (Aldrich 742805), 3-methylbutanal (Aldrich W269204), 3-

89

methylbutanoic acid (Aldrich W310204), 3-methylbutan-2-ol (Aldrich 110949), 3-

90

methylindole (Aldrich W301912), methyl

91

methylpropanoic acid (Aldrich W222216), 5-methylnonan-5-ol (Aldrich S505021), 4-

92

methylpentanoic acid (Aldrich 277827), S-methyl thioacetate (Aldrich CDS001513), 2-

93

methyltetrahydrothiophen-3-one (Aldrich W351202), methyl thiocyanate (Aldrich

94

722197), 3-(methylthio)propanal (Aldrich W274704), nonanal (Aldrich W278203), δ-

95

octalactone (Aldrich w321405), octanal (Aldrich W279706), octanoic acid (Aldrich

96

W279900), oct-1-en-3-ol (Aldrich W280518), oct-1-en-3-one (Aldrich W351504), oct-3-

97

en-2-one (Aldrich W341606), pentanoic acid (Aldrich W310107), phenol (Aldrich

98

P1037), potassium thiocyanate (Aldrich 207799), polyethylene glycol 200 (P3015), 2-

99

phenylethanol (Aldrich 77861), pentane-2,3-dione (Aldrich W284106), α-pinene

100

(Aldrich 147524), sodium sulfide nonahydrate (Aldrich 208043), sulfuric acid 98%

101

(Aldrich

102

W309311), m-xylene (Aldrich 95670).

decan-2-one

320501),

(Aldrich

tridecan-2-one

W510637),

(Aldrich

(2E,4E)-deca-2,4-dienal

(Aldrich

isothiocyanate (Aldrich 112771),

W338818),


undecan-2-one

2-

(Aldrich


103

Carbonyl sulfide was prepared according to Paris D. N. Svoronos et al.16 The solution

104

was stored at – 80 °C during maximum 1 week.

105

Hydrogen sulfide was prepared according to Vazquez-Landaverde et al.17 The solution

106

was stored at 4 °C during maximum 1 week.

107

A stock solution of 1 % methanethiol was prepared by bubbling gas in cold (4 °C)

108

polyethylene glycol 200. The solution was stored at – 20 °C during maximum 1 week.

109

Cheese samples. Ten pieces of Swiss Tilsit cheese of each type and of similar

110

commercial size and weight (approximately 200 g) were obtained from a supermarket

111

located in Bern, Switzerland (Table 1). The cheeses were kept at 4 °C in vacuum-

112

sealed plastic bags for a maximum of 1 month until analysis.

113

Sample preparation. The samples for all analyses were obtained on the basis of a

114

procedure described by Burbank and Qian.7 In the present study, 2 cm of rind and

115

cheese were removed from all sides of the cheese to provide a sample as

116

homogeneous as possible. The sample was then finely cut with a ceramic knife to

117

avoid contact with metal, thus limiting the oxidation of sulfur compounds. Cut cheese

118

(2.00 ± 0.10 g) was placed in 20 ml HS vials (Interchim, Montluçon, France). For

119

calibration curves, cut cheese was homogenized together with 10 µl of an internal

120

standard solution containing ethyl methyl sulfide (2 mg kg-1) dissolved in polyethylene

121

glycol 200. The mixture was homogenized using a glass pestle. The vials were placed

122

in an ice bath at 0 °C to cool the exothermic reaction when 4.00 ± 0.10 g of CaCl2 was

123

added to each mixture. Three replicates per sample were prepared. To prevent

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oxidation, the vials were flushed with argon for 5 s with two needles to create an

125

“argon stream” in the vial after it being hermetically sealed with a blue silicone/Teflon®

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septum (Interchim, Montluçon, France).

127

Solid-phase micro-extraction (SPME) and chromatography

128

Identification and quantitation of VSCs by GC-MS/PFPD. Samples were extracted

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using a 1 cm carboxen/polydimethylsiloxane (CAR/PDMS) 85 µm StableFlexTM fiber

130

(Supelco, Bellefonte, PA, USA) as the manufacturer’s recommendation and the ACS Paragon Plus Environment

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literature9, 18-22 revealed that this coating is the best solution for the extraction of highly

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volatile sulfur compounds, such as carbonyl sulfide, hydrogen sulfide, or methanethiol

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from foodstuffs.

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The fiber was conditioned according to the supplier’s recommendations (300 °C for

135

60 min). The analyses were realized using an MPS2 autosampler equipped with

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Maestro1 software, V.1.4.8.14/3.5 (Gerstel, Sursee, Switzerland), a Trace GC Ultra

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GC coupled with a DSQ II mass selective detector (MSD) (Thermo Finnigan, Milan,

138

Italy), and a PFPD (OI Analytical, College Station, USA).

139

The headspace was extracted for 120 min at 60 °C with an agitation rate of 250 rpm

140

without preheating. Bound volatiles were desorbed for 1 min at 250 °C in the injector,

141

which was in the splitless mode for 30 s, and then the split valve was opened (split

142

flow: 80 mL min-1). Volatile compounds were separated on a TRB-FFAP fused silica

143

capillary column (100 % polyethylenglycol PEG with nitroterephthalic acid, bonded and

144

crosslinked, 30 m × 0.32 mm, 1.0 µm film; Teknokroma, Barcelona, Spain) with helium

145

as the carrier gas at a constant flow of 2.1 mL min-1 (37 cm sec-1).

146

The oven temperature was programmed as follows: 5 min at 35 °C, then heated to

147

150 °C at a rate of 10 °C min-1, held for 1 min, and then heated to 220 °C at a rate of

148

20 °C min-1, with a final hold time of 5 min. The settings of the PFPD were as follows:

149

250 °C, voltage at 540 V, ignitor current at 2.6 A, trigger level at 300, range at 10, and

150

attenuation at 32 with the following flow rates: air1 at 17 mL min-1, H2 at 14 mL min-1,

151

and air2 at 10 mL min-1. The MS settings were as follows: transfer line at 230 °C,

152

source temperature at 230 °C, and the analytes were monitored in SCAN mode

153

between 30 amu and 150 amu without solvent delay.

154

Calibration curves were established by the method of standard additions in the cheese

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matrix. 100 µl of calibration solution prepared in polyethylene glycol 200 were added to

156

the matrix as given in Table 3, together with 10 µl of the internal standard solution

157

containing ethyl methyl sulfide (2 mg kg-1) to correct variations during the extraction.



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The detector response signals were integrated using the Xcalibur 2.0.7 software

159

(Thermo Fisher Scientific AG, Reinach, Switzerland). The NIST/EPA/NIH mass

160

spectral library (NIST11) version 2.0g (NIST, Gaithersburg, MD, USA) was used for

161

peak identification. The PFPD signal was used to trace the VSCs in the

162

chromatogram.

163

Gas chromatography-olfactometry

164

Odor-active compounds were analyzed using a two-way odor detection port (ODP2;

165

Gerstel, Sursee, Switzerland) connected to a 6890N Agilent gas chromatograph (2W-

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GC-O) coupled with a 5973N MSD (Agilent Technology, Santa Clara, CA, USA), and a

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PFPD (OI Analytical, Texas, USA) and equipped with an MPS2 autosampler and the

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Maestro1 software, V.1.4.8.14/3.5 (Gerstel, Sursee, Switzerland).

169

For GC-O analyses, volatile organic compounds were extracted using a 2 cm

170

50/30 µm

171

StableFlexTM fiber (Supelco, Bellefonte, PA, USA). In order to obtain a representative

172

evaluation of the odor of the samples in olfactometry, a DVB/CAR/PDMS fiber is

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suitable for the extraction of volatile and semi-volatile compounds and was reported to

174

give good odor representativeness for dairy products.23 The fiber was conditioned

175

according to the supplier’s recommendations (270 °C for 60 min).

176

The cheese headspace was extracted for 16 h at 45 °C with an agitation rate of

177

250 rpm without preheating. The SPME extraction time was optimized for an optimal

178

olfactory analysis in order to obtain clearly perceivable odorant signals.9 Absence of

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artifact formation was checked using 16 h as the incubation time and only 45 min for

180

the extraction, and the results were compared with short incubation and extraction

181

conditions (5 min incubation, 45 min extraction). Bound volatiles were desorbed for

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1 min at 250 °C in the injector, which was in the splitless mode for 30 s; then the split

183

valve was opened (split flow: 80 mL min-1). Volatile compounds were separated on a

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TRB-FFAP fused silica capillary column (60 m × 0.32 mm, 0.5 µm film; Teknokroma,

divinylbenzene/carboxen/polydimethylsiloxane


(DVB/CAR/PDMS)

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Barcelona, Spain) with helium as the carrier gas at a constant flow of 2.3 mL min-1

186

(39 cm sec-1).

187

The oven temperature was programmed as follows: 5 min at 35 °C, then heated to

188

150 °C at a rate of 10 °C min-1, held for 1 min, then heated to 220 °C at a rate of

189

20 °C min-1 with a final hold time of 5 min.

190

The olfactometric ports were provided by Gerstel (Sursee, Switzerland) and are

191

designed for two panelists to detect volatile compounds simultaneously. The

192

dimensions and capillaries after the GC separation were adapted to obtain an optimal

193

and timely synchronized signal between the physical detectors and the two sniffers.24

194

The sample is split through deactivated silica capillaries into the following proportions:

195

MSD: PFPD: ODP: ODP 1: 1: 1.5: 1.5, corresponding to a flow rate of 0.46 ml min-1:

196

0.46 ml min-1: 0.69 ml min-1, and 0.69 ml min-1. The transfer line for each ODP is

197

heated at 220 °C to prevent the condensation of volatiles in the capillaries.

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Olfactory analyses were performed by a trained panel of eight sniffers. A single

199

individual response is by definition not reproducible, and a minimum of eight sniffers is

200

required to obtain a reliable aromagram.25,

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microphone and must simultaneously press a button and comment on the odor they

202

perceive while indicating its intensity on a scale from one to five, one being barely

203

perceptible and five being a very strong odor. Comments are registered and

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processed at the end of each session with AcquiSniff® version: 6.5.9 (INRA, Clermont-

205

Ferrand, France).

206

The aromagrams were computed using the VIDEO-Sniff method and the AcquiSniff®

207

software. All vocabulary employed by the judges was sorted into the 11 olfactory

208

classes (Table 2).27

209

The settings of the PFPD were as follows: 250 °C, voltage at 540 V, ignitor current at

210

2.6 A, trigger level at 300, range at 10, and attenuation at 32 with the following flow

211

rates: air1 at 17 mL min-1, H2 at 14 mL min-1, and air2 at 10 mL min-1. The MSD

212

settings were as follows: transfer line at 230 °C, source temperature at 230 °C, and the

26

Panelists are equipped with a



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213

analytes were monitored in SCAN mode between 30 amu and 150 amu without

214

solvent delay.

215

The detectors’ response signals were integrated using the Chemstation Data Analysis

216

software version E.02.00.493 (Agilent Technologies, Santa Clara, CA, United States).

217

Aromagrams were recorded and processed using the VIDEO-Sniff method and

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AcquiSniff Software®, as described in the literature.13 The NIST/EPA/NIH mass

219

spectral library (NIST11) version 2.0g (NIST, Gaithersburg, MD) was used for peak

220

identification.

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GC–Olfactometry panel training

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GC-O is used to identify odor-active compounds with the aid of the human nose.13 It is

223

not necessary to be specially trained to participate as an olfactometry panelist.

224

However, it is very convenient for data processing to work with panelists who are

225

familiar with odors and use the same language. For this, all panelists were trained

226

statically for four months to memorize 17 odorant molecules (ethyl isobutyrate, 3-

227

methylbutyl

228

phenylethanol, 3-methylindole, α-pinene, allyl sulfide, dimethyl trisulfide, butanoic acid,

229

4-ethylguaiacol, 3-(methylthio)propanal, 2-acetyl-2-thiazoline, nonanal, (2E,4E)-deca-

230

2,4-dienal, and 3-methylbutanal) of ten different odor families commonly present in

231

cheese and dairy products (buttery-cheesy, empyreumatic, floral-fruity, green-fatty,

232

malty-chemical, meaty, spicy, nutty, sulfur, and earthy-undergrowth). After successful

233

completion, they were familiarized with the GC-O-setup and trained three times using

234

a standard solution containing the 17 molecules. Panel performance was monitored

235

through regular training, and information was recorded in a database.

236

Determination of “average odor activity values by sniffing (aOAVsniff)” using gas

237

chromatography–olfactometric detection limits of the compounds (GCOt) and

238

“average absolute quantified amounts (aAQA)”

239

Gas chromatography–olfactometric detection limits of target VSCs (GCOt)28 were

240

determined by the injection of several standard solutions of pure compounds at known

acetate,

oct-1-en-3-ol,

oct-1-en-3-one,


pentane-2,3-dione,

2-

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concentrations, using dilution factor of 2, until the molecule was no longer identifiable

242

by the panelists with the same GC-O method as described above and used in all

243

applications. The procedure was repeated with two trained panelists, and the average

244

values for each compound were used. The method was successfully verified using two

245

different GC-O units to confirm the feasibility of the technique.

246

During sniffing analyses of the cheeses, “average absolute quantified amounts

247

(aAQA)” were determined by MS quantitation as described in order to ensure that

248

these data correspond to the run during which olfactometric perception was recorded.

249

Finally and according to the classical concept of OAV,29 aOAVsniff are calculated by

250

dividing the aAQA by the GCOt (Table 3).

251

Statistical analysis

252

Olfactory charts and statistics were realized using AcquiSniff® version: 6.5.9 (INRA,

253

Clermont-Ferrand, France).

254 255

RESULTS AND DISCUSSION

256

Olfactometry evaluation

257

A total of 36 odorant zones were perceived during the sniffing of the three kinds of

258

Tilsit cheese by the eight panelists. Red Tilsit showed the most odorant zones in the

259

aromagram with 31 odorant zones, followed by yellow Tilsit with 29 odorant zones and

260

green Tilsit with 25 odorant zones. Altogether, 41 odorant compounds were found to

261

be responsible for the 36 odorant zones and could be identified by combining odor,

262

LRI (Linear Retention Indices), PFPD, and qMS (quadrupole Mass Spectrometer)

263

information. The results are presented in Table 4. A minimum of 50 % of the panelists

264

detected 17 odorant zones in the red Tilsit, 15 odorant zones in the yellow Tilsit, and

265

13 odorant zones in the green Tilsit. Detection frequency less than 50 % is generally

266

considered as noise,30 but molecules detected by less than 50 % of the panel might

267

also contribute to the overall odor of a sample. The aromagrams of the three cheeses

268

are given in Figure 1. ACS Paragon Plus Environment


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A common core of odorant compounds of the three Tilsit cheeses with a detection

270

frequency of at least 50 % consists of hexanal (peak 6 in Figure 1; odor descriptors:

271

green, grass, floral; GC-O-VIDEO-Sniff characteristics in all three cheeses such as the

272

mean intensity*detection frequency value and detection frequency in percentage are

273

given in Table 4), octanal (peak 12; fruity), butane-2,3-dione (peak 4; buttery, creamy,

274

cheesy), non-8-en-2-onet(Std) (peak 10; cooked potatoes, earthy, roasted, meaty, salty

275

meat; t= tentatively identified, e.g. reference compound not injected (t(Std)) or odor

276

descriptor (t(Odor)) or reference compound not exactly matching and/or no detection by

277

MS (t(MS))) , 3-methylbutan-2-olt(Odor) (peak 8; plastic, fatty, chemical, solvent, gum),

278

butanoic acid (peak 31; cheesy, rancid), 3-methylbutanoic acid (peak 32; cheesy,

279

rancid raclette cheese), MeSH (peak 1; cheesy, old socks, cabbage, cellar, rancid,

280

putrid), DMDS (peak 7; pungent, sulfur), bis(methylthio)methane (peak 13; animalic,

281

cheesy, sulfury, garlic, onion, rancid), DMTS (peak 17; garlic, sulfury, gas, metallic),

282

and 3-(methylthio)propanal (peak 23; boiled potatoes, sulfury).

283

Two odor-active compounds, oct-1-en-3-one (peak 14; earthy, mushrooms, green) and

284

2-methylpropanoic acid (peak 26; green, fatty, fruity, rancid), were perceived in both

285

red and yellow Tilsit with a detection frequency of at least 50 %. Acetic acid (peak 21;

286

chemical, green, fresh, acid, pungent, spicy) was perceived in both yellow and green

287

Tilsit.

288

Some compounds were found exclusively in one of the cheeses with a detection

289

frequency of at least 50 % and lower than 50 % in the other ones. Red Tilsit contains a

290

wider variety of such compounds, with five specific odor-active compounds that were

291

only found in red Tilsit: 3-methylbutanal (peak 3; burned, cooked, roasted, spicy), octa-

292

3,5-dien-2-onet(Std) (peak 28; floral, earthy), ethyl butanoate (peak 5; fruity, floral), 2-

293

methyltetrahydrothiophen-3-onet(odor threshold) (peak 27; cheesy, sulfur, pungent), and m-

294

xylenet(Odor) (peak 9; roasted, fatty, old oil, bread). Two specific odor-active compounds

295

were detected exclusively in yellow Tilsit: undecan-2-one (peak 30; floral, fresh, fruity,

296

mint) and 2,3-dimethyl-5-(1-propenyl)pyrazinet(Std) (peak 29; green, cut grass, plant), ACS Paragon Plus Environment

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and one specific odor-active compound was detected exclusively in green Tilsit: 2,5-

298

dimethylpyrazine (peak 15; mushroom, green).

299

In addition, 18 compounds were perceived with a detection frequency of panelists

300

lower that 50 % but could be easily identified by LRI, qMS, or odor. Again, red Tilsit

301

shows a greater number of these odorant compounds with 14 such molecules, of

302

which three were exclusively perceived in red Tilsit. Yellow Tilsit contains 12

303

molecules, including two specific compounds, and green Tilsit contains the least with

304

only nine molecules without specific compounds. The following compounds were

305

identified with a detection frequency of less than 50 %: nonanalt(Odor) (peak 16; fruity,

306

cooked, roasted, vanilla, honey), benzaldehyde (peak 25; sesame-spicy, fatty, green,

307

oily, animalic), butan-2-one (peak 2; burned plastic, plastic), decan-2-one (peak 24;

308

cooked, roasted, boiled potatoes), oct-3-en-2-one (peak 18; mushrooms), tridecan-2-

309

onet(Odor) (peak 34; wax, fatty), δ-octalactone (peak 38; fatty, green, floral, fruity,

310

creamy, buttery), 5-methylnonan-5-ol (peak 20, green, chemical, earthy), benzyl

311

alcohol (peak 37; urine, wax, fruity, ball pen), phenol (peak 39; plastic), ethyl

312

hexanoate (peak 11; fruity, sweet), ethyl octanoatet(Odor) (peak 19; wine, cork,

313

chemical), hexanoic acid (peak 36; cheesy, sweaty), pentanoic acid (peak 33; cheesy,

314

rancid), octanoic acid (peak 40; cheesy, rancid, fatty, ham), 4-methylpentanoic acid

315

(peak 35; cheesy, rancid), and 2,6-diethylpyrazinet(LRI,

316

sulfury, plastic).

317

The co-elution of odor-active compounds complicates the identification of an odorant

318

zone. A co-elution example with hexanal (green, grass) and DMDS (pungent, sulfur)

319

described using both green and pungent descriptors illustrates the difficulty of

320

identifying the compound only using the odor perception descriptors given by the

321

panelists.

322

Distribution according to the number of citations of the odor families shows that the

323

dominant odor family contributing to the odor of the different cheeses is “cheesy-

324

buttery”, as expected (citation percentages: green Tilsit, 42 %; red Tilsit, 41 %; and

Std)


(peak 22; chemical, green,


325

yellow Tilsit, 33 %). It is followed by the “sulfur” family (green Tilsit, 24 %; yellow Tilsit,

326

20 %; and red Tilsit, 14 %), the “malty-chemical” family (red Tilsit, 13 %; yellow Tilsit,

327

12 %; and green Tilsit, 5 %), and the “fatty-green” family, especially in red and yellow

328

Tilsit (yellow Tilsit, 12 %; red Tilsit, 10 %; and green Tilsit, 8 %). Other odor families

329

also contribute to the overall flavor but to a lesser extent, especially in green Tilsit. The

330

“cheesy-buttery” and “sulfur” families together account for over 50 % of all odor

331

citations in all three cheeses (green Tilsit, 66 %; red Tilsit, 55 %; and yellow Tilsit,

332

53 %).

333

The citation percentages of the dominant class “buttery-cheesy” depicted in yellow in

334

the aromagram (Figure 1) are very similar for the green and red Tilsit (41–42 %) and

335

are a little less predominant in the yellow Tilsit (33 %). The “buttery-cheesy” odor

336

zones are mainly evoked by three compounds: butanoic acid (cheesy, rancid), 3-

337

methylbutanoic acid (cheesy, rancid, raclette cheese), and butane-2,3-dione (buttery,

338

creamy, cheesy). These odorant compounds were detected by at least 50 % of the

339

panelists (Table 4).

340

The odor class “sulfur” depicted in blue represents 24 % of total detected odor zones

341

in green Tilsit, despite a lower overall sulfur compound concentration (Table 3). This

342

might be explained by the nature of the matrix as well as the distribution and release of

343

the compounds.

344

The “fatty-green” class depicted in green represents 10–12 % in red and yellow Tilsit,

345

and is less frequently evoked in green Tilsit (8 %). However, a single strong “fatty-

346

green” odor zone evoked due to hexanal (green, grass, and floral) was more intense in

347

the green Tilsit (Table 4, peak number 6).

348

The class “malty-chemical” shown in brown is less represented in green Tilsit and is

349

mainly due to three compounds (3-methylbutanal, benzaldehyde, and 5-methylnonan-

350

5-ol), which seem to be mainly present in red and yellow Tilsit.


Page 14 of 35

Page 15 of 35


351

The “fruity-floral” class shown in pink is slightly more frequent in red Tilsit and comes

352

from the presence of esters, such as ethyl butanoate (fruity, floral) or ethyl hexanoate

353

(fruity, sweet).

354

“Spicy” notes depicted in violet are present in the form of acetic acid (chemical, green,

355

fresh, acid, pungent, spicy) in all cheeses, and benzaldehyde (spicy, fatty, green, oily,

356

animalic) is only present in red and yellow Tilsit.

357

The “earthy-undergrowth” notes shown in dark green come from the perception of

358

mushroom notes. The compounds responsible were partially identified (Table 4: non-

359

8-en-2-onet(Std), oct-1-en-3-one, oct-3-en-2-one, and octa-3,5-dien-2-onet(Std)). Two

360

additional specific aroma-active zones with a strong “earthy-undergrowth” note are

361

present only in green Tilsit between peaks 21 and 23 and between peaks 37 and 38

362

(Figure 1). Due to the very low amount in the sample, these molecules could not be

363

identified.

364

The class “nutty” shown in orange contributes to 1 % of all detected items in cheeses.

365

This odor class was described by the panelists as cooked nut, chestnut, and nutty.

366

The very low concentrations in the cheeses did not allow for identification by

367

conventional detectors in the present study.

368

Calculation of “average odor activity values by sniffing (aOAVsniff)”

369

The olfactory impact of a compound depends on both its concentration in a sample

370

and its odor threshold. In general, to account for these two parameters, the literature

371

refers to the odor activity value (OAV), which is the ratio of the concentration of a

372

compound to its odor threshold, to describe how strongly an odorant compound is

373

perceived by the human nose.29 The odor threshold values are usually taken from the

374

literature and are determined in a simple matrix, such as water, oil, or air, using a

375

static method. It is therefore difficult to apply such OAVs, especially to a complex

376

matrix such as cheese.

377

However, the odor perception threshold is the lowest concentration of a compound

378

that can be perceived in a matrix. It is different from the odor recognition threshold at ACS Paragon Plus Environment


379

which an odorant is both perceived and its odor recognized/identified/described. In

380

general, perception thresholds are two to five time lower than identification

381

thresholds.31 Sniffing analysis is a dynamic method, and the panelists are trained to

382

detect and describe odorant zones. However, for both perception and identification,

383

the compound concentration has to be higher than the respective thresholds, and the

384

time during which the thresholds are high enough might be too short for the panelists

385

to react, or the panelists might be exhaling and thus miss the stimulus. This is why it is

386

preferable to work with identification thresholds, which ensure that the determined

387

OAV is truly the one of the target compound, and not with perception thresholds.

388

Therefore, we decided not to employ the classic concept used in the literature, but to

389

determine such gas chromatography–olfactometric detection limits GCOt for target

390

odorant VSCs28 and use them for aOAVsniff calculations: aOAVsniff of target odorant

391

VSCs were calculated by dividing the average absolute quantified amount (aAQA) of a

392

VSC found during sniffing analyses in the sample by the GCOt and are given in

393

Table 3.

394

The aOAVsniff can help to estimate the potential odor impact a compound might have

395

on the overall odor profile. As it is based on the theory of the classical OAV, an

396

aOAVsniff with a high value is a strong indication that the compound contributes greatly

397

to the cheese aroma.29

398

Sulfur profiles of the three Swiss Tilsit cheeses

399

A total of 21 VSCs were detected by GC/PFPD in the studied cheeses. Twenty-one in

400

yellow Tilsit, 18 in red, and 18 in green. Of these 21 compounds only six were found to

401

be odorant by 2W-GC-O: six in green Tilsit, six in red Tilsit, and five in yellow Tilsit.

402

Fifteen selected sulfur compounds were quantified at ppb levels (Table 3).

403

MeSH, DMDS, bis(methylthio)methane, DMTS, and 3-(methylthio)propanal, all known

404

for their low odor thresholds, were found at ppb levels in all three cheeses and were

405

also detected by 2W-GC-O.


Page 16 of 35

Page 17 of 35


406

2-methyltetrahydrothiophen-3-onet(odor

threshold)

407

was confirmed by LRI, qMS, PFPD, and odor specifications. Using its absolute

408

quantified amount found during sniffing analyses in the samples and its GCOt (100 ng)

409

determined with the pure reference compound, an aOAVsniff < 1 was calculated for 2-

410

methyltetrahydrothiophen-3-onet(odor

411

detectable by the panelists. Numerous reasons could explain the fact that the

412

compound was nevertheless smelled and confirmed by odor descriptor comparison,

413

two hypotheses being:

threshold)

was also detected in all cheeses and

. Hence, it should in theory not be

414

a) the molecule has an asymmetric carbon and hence two enantiomers exist.

415

There are no stereochemistry specifications for the commercially available

416

mixture used to determine the molecule properties (LRI, qMS, and odor

417

specifications) and it might differ from the compound or enantiomer ratio found

418

in the cheeses.

419 420

b) a synergistic effect or coelution, as octa-3,5-diene-2-onet(Std) elutes at the same time. Further investigations will be needed to elucidate this question.

421

When adding the amount in µg kg-1 of all the quantified VSCs, the total amount of

422

quantified VSCs is 4377 µg kg-1 in yellow Tilsit, 2800 µg kg-1 in green Tilsit, and

423

2761 µg kg-1 in red Tilsit. When only focusing on odorant VSCs detected during 2W-

424

GC-O, the overall amounts add up to 664.5 µg kg-1 corresponding to 15.2 % of the

425

total quantified VSCs in yellow Tilsit, 327.0 µg kg-1 corresponding to 11.8 % in red

426

Tilsit, and 54.4 µg kg-1 corresponding to 1.9 % in green Tilsit. COS, H2S, CS2, DMS,

427

MeSAc, MTC, MITC, DMSO, and Me2SO2 were also quantified, but due to their high

428

odor thresholds or odorless aspect, they were not considered.

429

Calibration curves on five points show correlation coefficients R2 higher than 0.97 for

430

all quantified compounds. Of 15 compounds, ten were above the quantitation limits

431

(LOQ). DMS in green Tilsit was below LOQ. MITC was below LOQ in all Tilsits, and 3-

432

(methylthio)propanal was below LOQ in the green and yellow Tilsit.



433

The contribution of MeSH (odor description, Table 4: cheesy, old socks, cabbage,

434

cellar, rancid, putrid) as the sulfur compound with an aOAVsniff of 400 is the most

435

important in the studied cheeses. It was perceived with almost the same intensity in

436

each of the cheeses. However, the quantified concentration of MeSH is higher in

437

yellow Tilsit (15 times higher than in green Tilsit and three times higher than in red

438

Tilsit). Its olfactory impact is so important that the signal covers DMS, which has a

439

close retention time. The next most important VSC is DMTS (garlic, sulfury, gas,

440

metallic), with an aOAVsniff of 150. Although its concentration is 11 times lower in green

441

Tilsit than in yellow Tilsit, it was found to be more intense in the latter than in the other

442

cheeses. The nature of the matrix can explain this phenomenon, as can the variability

443

of the panelists. The samples were not measured on the same day, and the

444

environment and the physical state of the panelists may influence the results. 3-

445

(methylthio)propanal (boiled potatoes, sulfury) with an aOAVsniff of 25 also contributes

446

equally to the overall odor of all cheeses. It is difficult to evaluate the impact of DMDS

447

(pungent, sulfur) in this experiment as it co-eluted with hexanal (green, grass, floral)

448

and its aOAVsniff is only 3.5. Bis(methylthio)methane (animalic, cheesy, sulfury, garlic,

449

onion, rancid) with an aOAVsniff of 3 has a strong impact in yellow Tilsit despite being

450

present at the same concentration in the red Tilsit. The variability of the panelists can

451

explain this difference.

452

The other quantified VSCs are odorless, such as COS, DMSO, and Me2SO2, or the

453

aOAVsniff is less than 1 (H2S, CS2, MTC, MITC, and 2-methyltetrahydrothiophen-3-

454

onet(odor threshold)). Therefore, they do not directly affect the overall odor because their

455

concentrations are below the odor threshold. However, they might still contribute due

456

to synergistic effects, which were not evaluated in the present study.

457

Origin of sulfur compounds in a cheese matrix

458

Several VSCs were found to have an impact on the odor of the studied Swiss Tilsit

459

cheeses. Their origin in a cheese matrix is of great interest, especially as variations in

460

their concentrations may strongly influence the cheese’s odor profile. VSCs in cheese ACS Paragon Plus Environment

Page 18 of 35

Page 19 of 35


461

originate from the enzymatic or non-enzymatic degradation of the amino acids

462

cysteine and methionine by the action of microorganisms. Methionine, present in

463

larger amounts in the cheese than cysteine, is the most important source of VSCs.32, 33

464

The vast majority of complex sulfur compounds derives from two major and highly

465

reactive compounds, MeSH and H2S. H2S is formed by Strecker degradation of

466

cysteine with diketones such as butane-2,3-dione and pentane-2,3-dione or it can also

467

be formed by thermal degradation of thiamine, during which the rearrangement of a

468

thiazole group liberates H2S.34 Another metabolic pathway through the degradation of

469

cysteine by the activity of cystathionine lyases of L. casei can also explain the

470

formation of H2S in cheese.35 MeSH is formed by Strecker degradation of

471

methionine,36 and a study carried out by Bogicevic et al.35 shows the major role of

472

deamination and dethiolation by lyases in cheese. Strains of L. casei whose genes ctl1

473

and ctl2 were present exhibited cystathionine lyase activity and broke down

474

methionine. The presence and the formation of CS2 is not well defined. A high

475

concentration is correlated with the heat treatment of milk, and the concentration of

476

CS2 decreases with storage time.37 CS2 may also be an extraction artifact due to the

477

reaction of COS with the matrix.16 This hypothesis was confirmed by internal

478

laboratory tests. A large part of DMS can come from animal feed due to the non-

479

enzymatic degradation at a pH of 7.5 of the S-methylmethionine sulphonium salt

480

present in cattle feed.34 MeSAc can be formed by Brevibacterium linens, but in

481

general, the biosynthetic pathway involved in thioester production is not yet

482

elucidated.38 However, the combination of Micrococcus strains with B. linens shows a

483

vastly higher concentration in MeSAc compared to when the strains are present

484

separately in the cheese matrix. These microorganisms appear on the surface of the

485

cheese after a ripening period of two and three weeks,39, 40 and their metabolites have

486

been found to contribute to cheese flavor.41 MeSH can react with free fatty acids to

487

form S-methyl thioesters, such as MeSAc, and contribute to a cooked cauliflower

488

aroma.7 DMDS is the result of the Strecker degradation of methionine and the reaction ACS Paragon Plus Environment


Page 20 of 35

489

of methionine with riboflavin. It also originates from the oxidation of MeSH during heat

490

treatment of milk.34 DMTS is mainly due to the oxidation of DMDS, the latter being a

491

product of the oxidation of MeSH.42 Oxidative reactions are accelerated by the

492

presence of transition metals such as Cu (II) or Fe (III).4 These metals are commonly

493

present in low concentrations in cheese produced traditionally or industrially in

494

production

495

(methylthio)propanal requires the decarboxylation of 4-methylthio-2-ketobutyrate, and

496

this reaction can be carried out by Lc. lactis strains.36 DMSO is an intermediate

497

oxidation step of DMS, the final oxidation product being Me2SO2.34 MTC and MITC can

498

be produced by a few Pseudomonas strains, which are naturally present in milk before

499

heat treatment.43 Bis(methylthio)methane can be formed through condensation or the

500

aldol reaction of the MeSH with the formaldehyde. This compound has already been

501

found in Gouda and Camembert cheeses.44,

502

threshold)

503

cheese. This molecule is formed by chemical reactions or is produced by

504

microorganisms. The microbial mechanism is still unknown.46-48

facilities,

for

example

in

cheese

45

kettles.

The

formation

of

3-

2-methyltetrahydrothiophen-3-onet(odor

has been found in many foodstuffs, such as coffee, whisky, yogurt, and

505 506

To conclude, quantitation of VSCs shows a clear difference in concentration between

507

the cheeses. Red Tilsit made from partially thermized milk and yellow Tilsit made with

508

pasteurized milk and a higher fat content contain the highest amounts for most of the

509

target VSCs. This can, however, not be explained by a unique reason, but can be

510

caused by several factors such as treatment of the raw milk, a longer ripening time, fat

511

content and also by a wide variety of indigenous microflora in raw milk.

512

According to the literature, a higher fat content can affect the growth of microflora,

513

enzymatic activity, partitioning, and matrix effects, thus influencing the concentration of

514

VSCs.49 The literature showed that flavor compound retention by lipids can be

515

significant for sulfur compounds. A lipid concentration above 10 % in the matrix can

516

decrease the release of sulfur compounds into the headspace by up to 20 %.50 In the ACS Paragon Plus Environment

Page 21 of 35


517

present case, all cheeses contain more than 10 % fat (between 28 g and 33 g fat per

518

100 g of cheese), and despite this, no negative influence of fat content on VSC release

519

was observed with the employed analytical protocol. The protein content, on the other

520

hand, has not been found to influence flavor release in a cheese matrix50 and was not

521

evaluated in the present study.

522

GC-olfactometry analyses in the employed analytical conditions suggest that the

523

overall odor of the studied Tilsit cheeses is mainly determined by cheesy-buttery odor

524

notes, followed by sulfur, malty-chemical, and fatty-green odor zones. When summing

525

up all perceived odor zones in the cheeses (mean intensity*detection frequency

526

values, Table 4), green Tilsit had an overall value of 40.2, red Tilsit had an overall

527

value of 48.0, and yellow Tilsit had an overall value of 45.8. These values illustrate that

528

red Tilsit is the most flavorful cheese of the three samples, followed by yellow Tilsit.

529

Green Tilsit, as expected, shows the least intense overall odor. Numerous odorant

530

compounds were identified in all three cheeses, but the number of compounds that

531

were only found in one of the cheeses and not in the others was higher for red Tilsit,

532

which alone of the three cheeses had m-xylenet(Odor), ethyl hexanoate, oct-3-en-2-one,

533

and unknown peak 41.

534

Concerning VSCs, 2W-GC-O showed that MeSH, DMDS (co-elution with hexanal),

535

bis(methylthio)methane,

536

methyltetrahydrothiophen-3-onet(odor

537

were the only identified VSCs to have a strong impact on the odorant profile of the

538

tested cheese samples. In total, the “sulfury” odor family represents up to 14 % of the

539

total odor profile. The aromagrams of the cheeses show differences, but the

540

differences in VSCs were not as pronounced as one might expect based on the

541

quantitation results. This might either be due to the use of optimal analytical conditions

542

for each technique or to a limitation of the methodology: when the concentration of a

543

given compound greatly exceeds the odor threshold, the panelist cannot rate the

544

intensity of the perceived odor stronger than the maximum of the employed scale (1–

DMTS, threshold)

3-(methylthio)propanal,

and

2-

(co-elution with octa-3,5-dien-2-onet(Std))



545

5), and detection frequency cannot exceed 100 %. This is particularly the case for the

546

most intense peaks and might explain why, despite clear differences in quantified VSC

547

amounts, these are not directly translated in the aromagrams. However, when

548

counting the number of times a certain odor family is perceived, differences become

549

more evident but are not congruent with the quantitation results.

550 551

Our work allowed for the clear differentiation of the three commercial Tilsit cheeses in

552

terms of VSCs, odorant VSCs, and GC-O profiles. GC-O results confirm the general

553

opinion that Tilsit cheese made from raw milk has a more intense and more diverse

554

overall flavor than cheese manufactured with pasteurized milk. VSC quantitation could

555

not be directly linked to this statement, as optimal analytical conditions were employed

556

for each technique and other factors, such as limitation of GC-O methodology, but also

557

fabrication process, fat content and ripening time, are likely to have an impact. Further

558

research, including practice trials, is necessary to determine the precise causes of the

559

differences found in this study and future results should be combined with sensory

560

profiling information.

561

Acknowledgements

562

The authors gratefully acknowledge all panelists for their participation and René

563

Badertscher, Cornelia Bär, and René Imhof for revision of the manuscript.

564

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Page 29 of 35


FIGURE CAPTIONS തതതതത Figure 1. Mean olfactory signal by classes (OSC

Int x Det)

of green (A), red (B), and

yellow (C) Tilsit over eight judges. The signals are classified in 10 distinct odor families out of the 11 families represented in Table 2. The family “undefined” is not represented in the aromagram. Labels corresponding to the peak numbers are given in Table 4. The compounds were separated on a TRB-FFAP capillary column.



Page 30 of 35

Table 1 – Description of cheeses (www.Tilsiter.ch, 2014)

Thermal treatment

Fat in dry matter (FDM) Approx. fat content in 100 g Ripening time Taste perception

Green Tilsit Pasteurized milk

Red Tilsit Partially thermized milk

at least 45 % FDM

at least 45 % FDM

Yellow Tilsit Pasteurized milk with higher fat content at least 55 % FDM

28 g

29.5 g

33 g

30–60 days Mild aroma, slightly sour

70–110 days Rich, spicy, and pungent

30–75 days Creamy, slightly sour, mild aroma


Page 31 of 35


Table 2 – Distribution of vocabulary items by eight GC-O sniffers in 11 classes Odor classes Buttery-cheesy

Earthy-undergrowth Empyreumatic Floral-fruity

Green-fatty

Malty-chemical

Meaty Nutty Spicy Sulfur Undefined

Odor descriptors Acid, animalic, buttery, caramel, creamy, butyric acid, cheese, cheesy, rancid, raclette cheese, sweaty, vomit, propionic acid, cooked milk, Tilsit, ripe, pungent, cheesy surface, diacetyl, old socks, cellar Earthy, mushrooms, forest, musty, old wardrobe, pine green, champignons Bread, burned, roasted, cooked, smoked Floral, fresh gum, fruity, strawberry, sweet, mint, pineapple, rose, vanilla, artificial fruit, rubbery, citrus, honey, terpene, cooked apple, cherry, fresh fruit, wine Fatty, green, fresh vegetable, grass, plant, oil, persil, old fat, wax, colored pencil, basilica, fresh cut grass, cut grass, bee wax, vitamin supplier Chemical, malty, cleaning product, plastic, gum, solvent, urine, acetic acid, medical like a hospital, glass cleaner, alcoholic, cork Bouillon, cooked meat, ham Cooked nut, chestnut, nutty Sesame-spicy, spicy Garlic, gas, potatoes, putrid, methanethiol, onion, sulfury, pungent, rotten, decomposed, compost, moldy ?, nd, warm, unpleasant, unknown



Page 32 of 35

Table 3 – Concentration of VSCs in Swiss Tilsit cheese sample Tilsit cheese sample1

Ions 3

LOD

4

Calibration concentrations

Regression

Compounds2

(µg kg-1)

equation

COS

0.1, 1, 10, 100, and 1000

Y=4905.3x

0.9990

0.180

0.610

32, 44, 60*

119.1±21.3

191.0±80.4

H2S

0.1, 1, 10, 100, and 1000

Y=4398.9x

0.9988

2.500

8.400

33, 34*, 35

163.0±58.0

147.0±19.6

R2

LOQ

(µg kg-1)

monitored

Green

Red

aAQA

GCOt

aOAVsniff

(ng)

(ng)

(-)

311.0±29.6

NA

odorless

NA

89.0±10.6

0.7

12

Olfactometry Profiles and Quantitation of Volatile Sulfur Compounds of

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