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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
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OlfactionOlfactory bulb
Olfactory membrane
Olfaction
Image courtesy of www.umds.ac.uk/physiology/jim/tasteolf.htm
X
Odor
• Requirements for odor– Molecule must be volatile
– Molecule must be adsorbed onto the olfactory hair (receptor)
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Amoore Odors
• Ethereal• Camphoraceous• Muskyy• Floral• Minty• Pungent• Putrid
Basic Tastes (APK)
• Sour -- from acids (hydrogen ion)• Salt -- metal salts (NaCl)• Sweet -- sugars, sugar alcohols, g , g ,
synthetics• Bitter -- alkaloids (caffeine)• Umami -- savory, meaty tastes
Tongue surface
Image courtesy of www.tcm.hut.fi/~mpaasiva/test/39/lhminen/kopf/kieli2.html
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Circumvallate papilla
Image courtesy of Anne LeMaistre (dpalm2.med.uth.tmc.edu/edprog/00000661.htm)
Circuvallate papillae• 1 = Circumvallate
papillae• 2 = Von Ebner’s
glandsglands
Taste buds• Arrow marks the
taste bud
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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)
Taste buds
X
Image courtesy of Tim Jacob (www.cf.ac.uk/uwcc/momed/jacob/teaching/sensory/taste.html#Anatomy)
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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)
O O
• Nucleotide monophosphates– IMP, GMP, and XMP
OH
NH2
OH Glutamic acid
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Flavor potentiators
N N
OH
HX = H, inosine monophosphate,IMP
N NO
O
O
H
OP
O
OO
-
-
X X = NH2, guanosine mono-phosphate, GMP
X = OH, xanthosine mono-phosphate, XMP
Types of Sensory Tests
• Difference tests• Rank order• Rating differencesg• 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 plusp• Within these panels it is necessary
to consider– Selection criteria (target audience)– Composition (age, sex, etc.)
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Material Evaluated• Preparation
– Method– Carrier (if used)Presentation• 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
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Environmental Conditions
• Setting (controlled sensory booth, store, State Fair, etc.)
• Lighting (color)
Paired Comparison
Which sample has more of aparticular characteristic?
512 314
The probability of guessing thesample is 0.50.
Paired Comparison Form
Judge: Date:Circle the sampleyou prefer
P i S mple No S mple NoPair Sample No. Sample No.ABCD
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Paired Comparison Form
Judge: Date:Circle the sampleyou prefer
P i S mple No S mple NoPair 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 samplessamples
• 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 < 0.050.05
• 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
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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 numberedbroccoli. 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 10 Panel 2 8 12Panel 2 8 12 Panel 3 6 14 Panel 4 5 15 Panel 5 4 16
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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 weselections 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?
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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 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 ?a e 3 6
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 ?a e 3 6
Panel 4 5 15 Seems unlikely
Panel 5 4 16 Are you kidding me?
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Example of null hypothesis and p < 0.05
• Set levels of significance• Set at p < 0.05• Examine statistical table for paired p
comparison tests at a level of p < 0.05 and utilizing 20 panelists (Lecture notes, page 27)
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 i ifi tl diff tsignificantly 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 establishanother (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
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Duo-Trio
512
Which of the samples is the sameas the reference sample?
Ref.
314Probability of guessing the rightanswer is 0.50.
Triangle Test
512
Find the odd sample, or find the twosamples that are identical.
314
711
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 (indicatep (with 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.
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Triangle Test Form
Judge: Date
Sample No. DuplicateSamples (indicateSamples (indicate
with an x)546 x
790
243 x
Problems with the Triangle Test
Suppose the triangle test was presented as shown here.
512 311 771
Further imagine that even though the panelists were toldthat one sample was different, they were, in fact, all the same!
Middle Sample Bias in the Triangle Test
Test no. 1 and 2 1 and 3 2 and 31 3 5 22 1 6 32 1 6 33 4 4 24 2 6 25 4 4 26 2 5 3
16 30 14
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Ranking/Rating
• Structured
• Unstructured
Structured Rating
Moderatelytender
Moderatelytough
Extremelytough
Extremelytender
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.
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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
Essentially a measureof how much the panelistlikes a sample. Results areindependent of other panelist rankings.
X
__ g y– __Neither like nor dislike– __Dislike slightly– __Dislike moderately– __Dislike very much– __Dislike extremely
To use, simply make a mark beside thestatement that youagree with.
Texture Evaluation
• Mechanical characteristics
• Geometrical characteristics
• Other characteristics
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Mechanical Characteristics
Primaryparameters
Hardness
Secondaryparameters
Popularterms
Soft hardHardnessCohesiveness
ViscosityElasticityAdhesiveness
BrittlenessChewinessGumminess
Soft, hardCrunchyChewy,toughPasty,gummyThin,viscousPlastic,elasticSticky,gooey
Geometrical CharacteristicsClass
Particle size,shape
Examples
Gritty,grainy,coarseshape
Particle shape,orientation
coarse
Fibrous, cellular,crystalline
Other Characteristics
Primaryparameters
M i t
Secondaryparameters
Popularterms
D i tMoisture
Fat content OilinessGreasiness
Dry, moist,wet,wateryOilyGreasy