Sensory Analysis of Foods

How humans experience their food

© Copyright 2010 Umami Information Center

Olfaction

Image courtesy of www.pacsci.org/public/education/champions/smell2.html

OlfactionOlfactory bulb

Olfactory membrane

Image courtesy of www.umds.ac.uk/physiology/jim/tasteolf.htm

Olfaction

X

Odor

• Requirements for odor– Molecule must be volatile

– Molecule must be adsorbed onto the olfactory hair (receptor)

Amoore Odors

• Ethereal• Camphoraceous• Musky• Floral• Minty• Pungent• Putrid

Basic Tastes (APK)

• Sour -- from acids (hydrogen ion)• Salt -- metal salts (NaCl)• Sweet -- sugars, sugar alcohols,

synthetics• Bitter -- alkaloids (caffeine)• Umami -- savory, meaty tastes

Tongue surface

Image courtesy of www.tcm.hut.fi/~mpaasiva/test/39/lhminen/kopf/kieli2.html

Circumvallate papilla

Image courtesy of Anne LeMaistre (dpalm2.med.uth.tmc.edu/edprog/00000661.htm)

Circuvallate papillae• 1 = Circumvallate

papillae• 2 = Von Ebner’s

glands

Taste buds• Arrow marks the

taste bud

Taste buds

Image courtesy of Anne Lemaistre (dpalm2.med.uth.tmc.edu/edprog/00000661.htm)

Taste buds

Image courtesy of Anne LeMaistre (dpalm2.med.uth.tmc.edu/edprog/00000661.htm)

Image courtesy of Tim Jacob (www.cf.ac.uk/uwcc/momed/jacob/teaching/sensory/taste.html#Anatomy)

Taste buds

X

Taste Requirements

• Molecule must be soluble in water

• Molecule must bind to taste receptor (protein)

Threshold concentrations

Salt 0.02 M

Sweet 0.02 M

Sour 0.005 M

Bitter 0.002 M

Flavor potentiators

• Monosodium glutamate (MSG)

• Nucleotide monophosphates– IMP, GMP, and XMP

O O

OH

NH2

OH Glutamic acid

Many of these flavor potentiators are manufactured by Ajinomoto,a Japanese company whose name means “the source of flavor”

Flavor potentiators

N

N

N

N

O

OO

O

H

HH

OP

O

OO

-

-

X

X = H, inosine monophosphate,IMP

X = NH2, guanosine mono-phosphate, GMP

X = OH, xanthosine mono-phosphate, XMP

Types of Sensory Tests

• Difference tests• Rank order• Rating differences• Descriptive analysis• Threshold• Affective tests

Note: See pages 11-13 in your lab manual for moreinformation about this topic.

Panel Type

• Trained -- 3-10 people• Semi-trained -- 8-25 people• Untrained or consumer -- 100 plus• Within these panels it is necessary

to consider– Selection criteria (target audience)– Composition (age, sex, etc.)

Material Evaluated• Preparation

– Method– Carrier (if used)

• Presentation– Coding -- random 3 digit numbers– Order of serving -- randomization– Sample size– Temperature and method of control

Presentation (cont.)– Sample container and utensils used

– Time of day

– Special conditions (time interval between samples, mouth rinsing, etc.)

Statistical Design

• Type of experiment– Randomized block

– Factorial

Environmental Conditions

• Setting (controlled sensory booth, store, State Fair, etc.)

• Lighting (color)

Paired Comparison

512 314

Which sample has more of aparticular characteristic?

The probability of guessing thesample is 0.50.

Paired Comparison Form

Judge: Date:Circle the sampleyou prefer

Pair Sample No. Sample No.ABCD

Paired Comparison Form

Judge: Date:Circle the sampleyou prefer

Pair Sample No. Sample No.A 555 467B 312 778C 498 087D 332 714

Null hypothesis• This is the “no effect” or “no difference”

hypothesis which states that there is no difference between the test and control samples

• Usually referred to as Ho

• The hypothesis that states that there is a difference is called the alternative hypothesis (Ha )

Level of significance• In order to determine significant

differences, a level of significanceis set up, usually called p, as in p < 0.05

• What this means is that there is a less than 5% probability of getting the experimental results that you got and Ho being true at the same time

Example of null hypothesis and p< 0.05

• Suppose we were doing an experiment in which we were examining the effect of steaming time on the tenderness of broccoli. The broccolis are numbered 317 (steamed 5 minutes) and 512 (steamed 10 minutes). The alternativehypothesis is: Longer steaming time produces a more tender broccoli.

Example of null hypothesis and p < 0.05

• The null hypothesis is: Steaming time has no effect on the tenderness of broccoli

• If we have 20 panelists and we are doing a paired comparison we could get any number of responses if we asked them to choose the more tender broccoli

Example of null hypothesis and p< 0.05

317 512Panel 1 10 10Panel 2 8 12Panel 3 6 14Panel 4 5 15Panel 5 4 16

Example of null hypothesis and p < 0.05

• If the null hypothesis is really true, we might expect the results from Panel 1

• But if it is not, we might get more selections of 512 than 317. How do we tell what a significant difference is?

• How do we reject the null hypothesis?

Example of null hypothesis and p< 0.05

317 512 Ho OK?Panel 1 10 10 Yes

Example of null hypothesis and p< 0.05

317 512 Ho OK?Panel 1 10 10 Yes

Panel 2 8 12 Maybe?

Example of null hypothesis and p< 0.05

317 512 Ho OK?Panel 1 10 10 Yes

Panel 2 8 12 Maybe?

Panel 3 6 14 ?

Example of null hypothesis and p< 0.05

317 512 Ho OK?Panel 1 10 10 Yes

Panel 2 8 12 Maybe?

Panel 3 6 14 ?

Panel 4 5 15 Seems unlikely

Example of null hypothesis and p< 0.05

317 512 Ho OK?Panel 1 10 10 Yes

Panel 2 8 12 Maybe?

Panel 3 6 14 ?

Panel 4 5 15 Seems unlikely

Panel 5 4 16 Are you kidding me?

Example of null hypothesis and p < 0.05

• Set levels of significance• Set at p < 0.05• Examine statistical table for paired

comparison tests at a level of p < 0.05 and utilizing 20 panelists (Lecture notes, page 28)

Use statistical tables

Example of null hypothesis and p < 0.05

• We see that we need 15 out of 20 selections of a sample to establish that the two samples are significantly different

• When we have 15 selections out of 20 we are saying that there is less than a 5% chance of having that result and have Ho be true at the same time

Example of null hypothesis and p < 0.05

• Note that if we make the statistical test more rigorous by setting p < 0.01, it takes more choices of one sample over another (16 out of 20) to establish significant differences

• When we have 16 selections out of 20 we are saying that there is less than a 1% chance of having that result andhave Ho be true at the same time

Duo-Trio

Ref.

512

314

Which of the samples is the sameas the reference sample?

Probability of guessing the rightanswer is 0.50.

Triangle Test

512

314

711

Find the odd sample, or find the twosamples that are identical.

Probability of guessing the right answeris 0.33. Thus, this test has more statistical power than the paired comparison or duo-trio tests.

Triangle Test FormJudge: Date

Sample No. DuplicateSamples (indicatewith an x)

546

790

243

Two of these samples are identical and the other is different.Please enter all sample numbers and check the duplicate samples in the right-hand column.

Triangle Test Form

Judge: Date

Sample No. DuplicateSamples (indicate

with an x)546 x

790

243 x

Problems with the Triangle Test

Suppose the triangle test was presented as shown here.

Further imagine that even though the panelists were toldthat one sample was different, they were, in fact, all the same!

512

1

311

2

771

3

Middle Sample Bias in the Triangle Test

Test no. 1 and 2 1 and 3 2 and 31 3 5 22 1 6 33 4 4 24 2 6 25 4 4 26 2 5 3

16 30 14

Ranking/Rating

• Structured

• Unstructured

Structured Rating

Extremelytough

Extremelytender

Moderatelytender

Moderatelytough

Slightlytough

Slightlytender

Note that each point on the scale has a word anchor.To use, simply make a mark where you believe the sample falls.

Unstructured Rating

No Acidity High Acidity

Note here that only the ends of the scale are anchored.

Again to use, simply make a make where you think the sample falls.

Consumer Preference

• Form styles vary.• The important thing about

consumer panels is that you need large numbers of panelists in order to make the statistics work out.

Hedonic Ranking– __Like extremely– __Like very much– __Like moderately– __Like slightly– __Neither like nor dislike– __Dislike slightly– __Dislike moderately– __Dislike very much– __Dislike extremely

Essentially a measureof how much the panelistlikes a sample. Results areindependent of other panelist rankings.

To use, simply make a mark beside thestatement that youagree with.

X

Texture Evaluation

• Mechanical characteristics

• Geometrical characteristics

• Other characteristics

Szczesniak, A. S. (1963) Classification of textural characteristics.J. Food Sci., 28, 385-389

Mechanical Characteristics

Primaryparameters

HardnessCohesiveness

ViscosityElasticityAdhesiveness

Secondaryparameters

BrittlenessChewinessGumminess

Popularterms

Soft, hardCrunchyChewy,toughPasty,gummyThin,viscousPlastic,elasticSticky,gooey

Geometrical Characteristics

Class

Particle size,shape

Particle shape,orientation

Examples

Gritty,grainy,coarse

Fibrous, cellular,crystalline

Other Characteristics

Primaryparameters

Moisture

Fat content

Secondaryparameters

OilinessGreasiness

Popularterms

Dry, moist,wet,wateryOilyGreasy