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
Olfaction Olfactory bulb Olfactory membrane
Olfaction X Image courtesy of www.umds.ac.uk/physiology/jim/tasteolf.htm
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)
Taste buds X Image courtesy of Tim Jacob (www.cf.ac.uk/uwcc/momed/jacob/ teaching/sensory/taste.html#anatomy)
Taste Requirements Molecule must be soluble in water Molecule must bind to taste receptor (protein)
Threshold concentrations Salt Sweet Sour Bitter 0.02 M 0.02 M 0.005 M 0.002 M
Flavor potentiators Monosodium glutamate (MSG) O O HO OH Glutamic acid NH 2 Nucleotide monophosphates IMP, GMP, and XMP Many of these flavor potentiators are manufactured by Ajinomoto, a Japanese company whose name means the source of flavor
Flavor potentiators X N O N H N N O O H H O O P O O - - O X = H, inosine monophosphate, IMP X = NH 2, guanosine monophosphate, GMP X = OH, xanthosine monophosphate, 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 more information 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 Which sample has more of a particular characteristic? 512 314 The probability of guessing the sample is 0.50.
Paired Comparison Form Judge: Date: Circle the sample you prefer Pair Sample No. Sample No. A B C D
Paired Comparison Form Judge: Date: Circle the sample you prefer Pair Sample No. Sample No. A 555 467 B 312 778 C 498 087 D 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 H o The hypothesis that states that there is a difference is called the alternative hypothesis (H a )
Level of significance In order to determine significant differences, a level of significance is 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 H o 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 alternative hypothesis 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< 0.05 317 512 Panel 1 10 10 Panel 2 8 12 Panel 3 6 14 Panel 4 5 15 Panel 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 H o 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 and have H o be true at the same time
Duo-Trio 512 Which of the samples is the same as the reference sample? Ref. 314 Probability of guessing the right answer is 0.50.
Triangle Test 512 Find the odd sample, or find the two samples that are identical. 711 314 Probability of guessing the right answer is 0.33. Thus, this test has more statistical power than the paired comparison or duo-trio tests.
Triangle Test Form Judge: Sample No. 546 790 243 Date Duplicate Samples (indicate with an x) 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. Duplicate Samples (indicate with an x) 546 x 790 243 x
Problems with the Triangle Test Suppose the triangle test was presented as shown here. 512 1 311 2 771 3 Further imagine that even though the panelists were told that 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 3 1 3 5 2 2 1 6 3 3 4 4 2 4 2 6 2 5 4 4 2 6 2 5 3 16 30 14
Ranking/Rating Structured Unstructured
Structured Rating Moderately tough Moderately tender Extremely tough Slightly tough Slightly tender Extremely tender 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 X moderately Like slightly Neither like nor dislike Dislike slightly Dislike moderately Dislike very much Dislike extremely Essentially a measure of how much the panelist likes a sample. Results are independent of other panelist rankings. To use, simply make a mark beside the statement that you agree with.
Texture Evaluation Mechanical characteristics Geometrical characteristics Other characteristics Szczesniak, A. S. (1963) Classification of textural characteristics. J. Food Sci., 28, 385-389
Mechanical Characteristics Primary parameters Hardness Cohesiveness Viscosity Elasticity Adhesiveness Secondary parameters Brittleness Chewiness Gumminess Popular terms Soft, hard Crunchy Chewy,tough Pasty,gummy Thin,viscous Plastic,elastic Sticky,gooey
Geometrical Characteristics Class Particle size, shape Particle shape, orientation Examples Gritty,grainy, coarse Fibrous, cellular, crystalline
Other Characteristics Primary parameters Moisture Fat content Secondary parameters Oiliness Greasiness Popular terms Dry, moist, wet,watery Oily Greasy