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TM. Sensory Evaluation of Aroma Models for Flavor Characterization. Keith Cadwallader University of Illinois at Urbana-Champaign. Kenneth A. Spencer Award Symposium Kansas City Section of ACS October 27, 2008. Overview:. Rationale: why conduct sensory studies?. General approach.

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slide1

TM

Sensory Evaluation of Aroma Models for Flavor Characterization

Keith Cadwallader

University of Illinois at Urbana-Champaign

Kenneth A. Spencer Award Symposium

Kansas City Section of ACS

October 27, 2008

slide2

Overview:

  • Rationale: why conduct sensory studies?
  • General approach
  • Some important considerations
  • Some common types of model studies
  • Sensory methods (tools) used in sensory studies
  • Example of a dose-response study
  • Example of an addition study
  • Example of an omission study
  • Final thoughts
slide3

Why conduct sensory studies?

  • Cannot accurately predict the effect (sensory perception) caused by altering the chemical composition of odor mixtures based on only flavor dilution values or odor-activity values (OAVs).
  • Omission of a compound with a high OAV may not necessarily alter the sensory perception of the overall ‘flavor’ concept.
slide4

General approach for performing model studies

GCO screening

of odorants

 identification by GC-MS, RIs and odor properties

 AEDA, DHDA, GCO-H, post-peak intensity scaling

concentrations

and OAVs

 GC-MS with IS and SIDA methodology

 calculation of OAVs from threshold data

aroma model

construction

 selection of appropriate matrix

 preliminary testing/adjustments

sensory testing

of aroma model

 dose-response studies (descriptive analysis)

 omission studies (n-1) with difference testing and descriptive analysis

slide5

Some things to consider:

  • Are all key odorants accounted for?
  • Are quantitative data accurate?
  • Is an appropriate matrix available or can it be (re-)created?
  • What is the objective of study?

• Impact (cause-and-effect relationship) of a single odorant

• (Re-)creation of an aroma system (model)

• Relative impact (or influence) of all aroma components on the aroma system

  • What is an appropriate experimental approach?

• Experimental design options

• Sensory methods of analysis

slide6

Some limitations in methods used to indicate key odorants

  • Odor-activity values (OAVs) – based on quantitative data (OAV = concentration/odor detection threshold).

• Only useful for compounds of known identity

• Must have accurate concentration and odor threshold data

  • Aroma-impact based of GCO data: - (e.g. post-peak) scaling of odorant intensity - flavor dilution factors or CHARM-values (from dilution analysis).

• number of odorants detected and the their perceived intensities depend on arbitrarily selected parameters: sample size, isolation method, degree of concentration of aroma extract, etc.

slide7

Let’s assume we have all relevant or

key odorants identified and

accurately quantified, and an

appropriate matrix is available.

What’s next?

slide8

Need to consider:

  • Objective and experimental design
  • Sensory method(s) for evaluation
slide9

Common types of sensory studies . . .

 Dose response studies

- Sensory evaluation of a suitable product matrix that has been spiked with an odorant (or group of odorants) to determine if the addition causes an increase in the intensity of a specific flavor attribute.- Suitable technique to evaluate ‘cause and effect’ relationship between odorant and sensory attribute.

 Comparison of aroma model to real product (validation)

- Use of sensory difference test and/or descriptive analysis

 Omission (n – 1) studies

  • Sensory comparison of the aroma of the complete mixture against the same mixture in which an odorant (or group of odorants) have been omitted.
  • Suitable for the determination of potential impact of individual (or groups of) odorants on aroma system.
slide10

Sensory methods used in model studies . . .

Conventional Difference Tests

  • Do not require intensive training of panelists.
  • Task is easy to understand and perform.
  • Statistical analysis is straightforward (well established).
  • Sensitive to small differences provided enough observations (tests) are made.
  • Not intended to measure direction or degree of difference.
  • Use difference-from-control test if degree of difference is required.
slide11

Sensory methods used in model studies

Descriptive Analysis

  • Complement difference tests
  • provides descriptive terms for attributes and allows quantification of their perceived intensities.
  • In order to detect small differences between products, the performance level of the panel must be sufficient in terms of reproducibility (precision), discrimination power, and agreement among panelists (improved with training, use of external references and by increased number of panelists).
  • provides qualitative and quantitative comparisons of the model against the product or the omission mixture.

 Terminology (lexicon) should be developed based not only on attributes of product being studied, but also based on attributes of all n-1 combinations (attributes cannot be predicted).

slide12

Example of a Dose-Response Study

(with sensory descriptive analysis)

slide13

Farmhouse Cheddar Cheese . . .

 Results of gas chromatography-olfactometry (GCO) and Aroma Extract Dilution Analysis (AEDA) indicated

2-isopropyl-3-methoxypyrazine (3-7 ppb) and p-cresol

(200 ppb) to be “most likely” responsible for cowy/barny and earthy/bell pepper flavors, respectively.

  • Additional sensory testing was conducted to measure impact of compounds on perceived intensities of corresponding flavor descriptors (blind study).

 Compounds spiked into a bland cheese matrix across concentration found in Farmhouse cheeses.

 Evaluation by descriptive sensory panel in a blind study.

Suriyaphan, O.; Drake, M.A.; Chen, X.Q.; Cadwallader, K.R. Characteristic aroma components

of British Farmhouse Cheddar cheese. J. Agric. Food Chem. 2001, 49, 1382-1387.

slide14

e.g. Flavor Profile of British Farmhouse Cheddar Cheese

description -aromatics associated

with barns and stock trailersreference -p-cresol, Band-aid, phenol

Bitter

Whey

Brothy

6

Umami

Cooked

4

Cowy/Barny

Sweet

2

Sulfur

Diacetyl

0

Sour

Earthy/Bell Pepper (Aroma)

Salty

Earthy/Bell Pepper (Flavor)

Prickle

Free Fatty Acid

Nutty

Fruity

Lactone

slide15

Farmhouse Cheddar Cheese . . .

Linking aroma analysis results to flavor lexicon terms

Relationship between p-cresol concentration

and “cowy/barny flavor” intensity

3.5

2-isopropyl-3-methoxypyrazine

threshold = 0.002 ppb (in water)

3.0

2.5

2.0

Average Flavor

Intensity

1.5

Relationship between 2-isopropyl-3-methoxypyrazine

1.0

concentration and “earthy aroma/ flavor” intensity

0.5

0

7

0

65

100

165

300

6

earthy/bell pepper flavor

earthy/bell pepper aroma

p-Cresol (ppb)

5

4

Average Intensity

p-Cresolthreshold = 55 ppb (in water)

3

2

1

0

0

3.5

7

2-isopropyl-3-methoxypyrazine (ppb)

slide16

Example of an Addition Study

(with difference/similarity scaling)

slide17

Beefy/Brothy Cheddar Cheese . . .

 The unambiguous linking of sensory descriptors with causative chemical components permits researchers to precisely relate sensory flavour quality with the chemistry and technology of Cheddar cheese production.

  • The objective of this study was to identify volatile aroma compounds responsible for the beefy/brothy flavor note in Cheddar cheese.

 Potential beefy/brothy compounds identified by GCO.

 Compounds spiked into a bland cheese matrix across concentration found in beefy/broth cheese.

 Evaluation by similarity-to-control and descriptive sensory analysis.

Cadwallader, K.R., Drake, M.A., Carunchia-Whetstine, M.E. and Singh, T.J. 2006. Characterisation of Cheddar cheese flavour by sensory directed instrumental analysis and model studies. In Flavour Science: Recent Trends.Bredie, W.P. and Peterson, M.A. (Eds.), Developments in Food Science 43, Elsevier, New York, pp. 157-160.

slide20

Example of an Omission Study

(with R-index method)

slide21

Omission studies . . .

Four critical steps in omission studies

  • Choice of target material
  • Construction of synthetic mixture (model)
  • Sensory validation of mixture (?)
  • Choice of experimental approach and sensory method(s) for evaluating model
slide22

Omission studies . . .

Example: Evaluation of key odorants of chipotle peppers

Cadwallader, K.R.; Lorjaroenphon, Y.; Kim, H.; Lee, S-Y. Evaluation of key odorants in chipotle pepper by

quantitative analysis, calculation of odor-activity values and omission studies. In Recent Highlights in Flavor

Chemistry & Biology. Proceedings of the 8th Wartburg Symposium. Hofmann, T., Meyerhof, W. and Schieberle, P.

(eds), Deutsche Forschungsanstalt für Lebensmittelchemie, Garching, Germany.

slide23

Omission studies . . .

Predominant Odorants in chipotle peppers by GCO*

A total of 41 odorants were detected by GCO (post-peak

intensity scaling, 7 pt scale) of DSE-SAFE aroma extracts from the three dried chipotle pepper samples

 16 compounds had high odor intensities  4.0

2- and 3-methylbutanal, 2-ethyl-3,5-dimethylpyrazine, 2-isobutyl-3-methoxypyrazine, 2-(3)-methylbutanoic acid,

-damascenone, guaiacol, o-cresol, 4-hydroxy-2,5-dimethyl-3(2H)-furanone, octanoic acid, p-cresol, sotolon, syringol,

coumarin, phenyacetic acid and vanillin

 7 additional odorants had odor intensities  3

* Cadwallader, K.R.; Gnadt, T.A.; Jasso, L. Aroma components of chipotle peppers. In Hispanic Foods: Chemistry and Flavor (Tunick, M.H., González de Mejia, E., eds.); American Chemical Society: Washington, D.C., 2006, 57-66

slide24

Concentrations and Odor-Activity Values (>100)

OAV

conc. (ng/g)

no.

odorant

Threshold

(ng/mL)

27

sotolon

376

0.001

376000

10

2-ethyl-3,5-dimethylpyrazine

626

0.04

15640

3

3-methylbutanal

3069

0.2

15350

7

1-octen-3-one

75

0.005

15000

17

-damascenone

22

0.002

11000

11

2-isobutyl-3-methoxypyrazine

38

0.005

7600

8

dimethyltrisulfide

33

0.01

3300

5

ethyl 2-methylbutanoate

9

0.006

1500

13

2-methylpropanoic acid

52620

50

1052

18

guaiacol

2541

3

847

12

linalool

1688

6

281

4

2,3-butanedione

979

4

245

2

2-methylbutanal

465

3.7

126

16

2- and 3-methylbutanoic acid

5736

50

115

9

acetic acid

2253000

22000

102

slide25

Concentrations and Odor-Activity Values (<100)

conc. (ng/g)

Threshold

(ng/mL)

no.

odorant

OAV

30

skatole

291

3

97

1

methylpropanal

83

1

83

22

4-ethylguaiacol

2181

50

44

25

p-cresol

2167

55

39

32

vanillin

907

25

36

6

hexanal

167

4.5

37

23

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

790

31

25

20

4-methylguaiacol

1914

90

21

15

phenylacetaldehyde

75

4

19

14

butanoic acid

3604

240

15

19

2-phenylethanol

14330

1000

14

29

coumarin

258

25

10

26

m-cresol

5300

680

8

28

syringol

10340

1850

6

21

o-cresol

2845

650

4

31

phenylacetic acid

14000

10000

>1

24

octanoic acid

1497

3000

<1

slide26

Composition of Matrix Applied in the Sensory Experiments

composition

amount

0.1 M citrate buffer (pH 4.8)

10

mL

base

1.7

g

soybean oil

0.3

ga

base composition

Ratio

cellulose (Sigma, St. Louis, MO, USA)

2.6a

sucrose (Sigma)

2.1a

natural capsaicin (Aldrich, St. Louis, MO, USA)

722.4 μg/g (dry basis)b

a Based on dietary fiber (2.6), sugars (2.1) and total fat (0.9) in 100 g of jalapeno pepper (wet basis) (NutritionData, 2006). b Based on analysis of capsaicin and dihydrocapsaicin in chipotle pepper using method of Thomas et al. (1998).

slide27

Chipotle aroma . . .

Omission studies – some additional considerations

 Eliminating successively (n - 1) all possible components of the mixture- may not reveal much because of antagonistic effects

 Eliminating groups of compounds of the model- e.g. where each group is composed of odorants with similar odor qualities or same chemical class

slide28

Odorant groups* for omission studies

earthy(2-ethyl-3,5-dimethylpyrazine and 2-isobutyl-3-methoxypyrazine)

smoky (guaiacol, 4-methylguaiacol, o-cresol, 4-ethylguaiacol, p-cresol,

m-cresol, syringol, coumarin)

sweet aromatics(2,3-butanedione, HDMF, sotolon and vanillin)

floral/fruity(ethyl 2-methylbutanoate, linalool, phenylacetaldehyde,

-damascenone, 2-phenylethanol, phenylacetic acid)

malty (methylpropanal, 2- and 3-methylbutanal)

sour/sweaty(acetic, 2-methylproanoic, butanoic, 2/3-methylbutanoic

and octanoic acids)

sulfurous(dimethyltrisulfide)

green/plant-like(hexanal, 1-octen-3-one)

* Terms decided upon by descriptive sensory panel

slide29

Omission studies . . .

Omission studies – methodology

  • Subjects were provided with mixtures (signals) marked with 3-digit codes and the complete model (noise) coded as R. A randomized complete block design was used to randomize the samples across subjects.
  • Subjects were instructed to gently squeeze each sample container, evaluate the odor and rank the samples on how different they were from R, with 1 = least different to 9 = most different. Subjects were allowed to reevaluate samples ad libitum. Subjects were instructed to wait at least 10 seconds between evaluations to minimize adaptation effects.
  • A response matrix was constructed for the entire panel to calculate the R-indices.

O’Mahony, M. Understanding discrimination tests: A user friendly treatment of response bias, rating and ranking R-index tests and their relationship to signal detection. J. Sensory Stud. 1992, 7, 1-47.

slide31

R-index Values for Omission Test

odorant group omitteda

R-indexb

79.3

earthy (10, 11)

*

69.0

*

smoky (18, 20, 21, 22, 25, 26, 28, 29)

sweet aromatics (4, 23, 27, 32)

62.1

floral/fruity (5, 12, 15, 17, 19, 31)

62.1

malty (1, 2, 3)

55.2

sour/sweaty (9, 13, 14, 16, 24)

48.3

sulfurous (8)

44.8

green/plant-like (6, 7)

41.4

a Numbers in parentheses indicate odorant numbers omitted. Description of each group was

determined by consensus opinion of the trained sensory descriptive panel. b R-index of each

model is calculated by using John Brown computations (O’Mahony, 1992) against control

(complete model) (n=29; female=21 and male=8). *Significantly different from control at α=0.05

(critical value, expressed in percentage; R-Index = 50% for two-tailed test, α=0.05, n=29 is 17.37).

slide32

Some Final Thoughts

  • Synergistic and Antagonistic Effects

Synergistic effects are mainly observed for subthreshold concentrations,i.e. a decrease in detection threshold occurs1.

But models are build from odorants at suprathreshold concentrations - in this region antagonistic effects seem to be most common2.

In general, human subjects are unable to identify individual odorants whenthe mixture contains greater than four odorants in total3. This helps explain why omission of one or more odorants from a complex odor mixture oftenis not distinguished from the intact (complete) mixture.

  • Laska, M.; Hudson, R. A comparison of the detection thresholds of odour mixtures. Chem.Senses 1991, 16, 651-662.
  • Grosch, W. Evaluation of the key odorants of foods by dilution experiments, aroma
  • models and omission. Chem. Senses 2001, 26, 533-545.
  • 3. Liang, D.G. Perceptual odour interactions and objective mixture analyses. Food Qual. Pref.1994, 5, 75-80.
slide33

Additional References:

Brown, J. Recognition assessed by rating and ranking. Brit. J. Phychol. 1974, 65,

13-22

Czerny, M.; Mayer, F.; Grosch, W. Sensory study on the character impact odorantsof roasted arabica coffee. J. Agric. Food Chem. 1999, 47, 695-699.

Drake, M.A.; Miracle, R.E.; Caudle, A.D. ; Cadwallader, K.R. Relating sensory and instrumental analyses. In Sensory-Directed Flavor Analysis. Marsili, R. (Ed.), CRC Press/Taylor & Francis Group, LLC, Boca Raton, FL, 2007, pp. 23-54.

Engel, E.; Nicklaus, S.; Salles, C.; Le Quere, J.-L. Relevance of omission tests todetermine flavour-active compounds in food: application to cheese taste. Food Qual.Pref. 2002, 13, 505-513.

Karagul-Yuceer, Y.; Vlahovich, K.N.; Drake, M.A.; Cadwallader, K.R. Characteristicaroma components of rennet casein. J. Agric. Food Chem. 2003, 51, 6797-6801.

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