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Sensory Analysis Flashcards

(73 cards)

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1
Q

Sensory analysis

A

A scientific method used to evoke, measure, analyze, and interpret responses to products as perceived through the senses

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2
Q

Senses

A

Vision
Gustation
Olfaction
Touch
Audition

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3
Q

Gustation

A

Flavor = taste + aroma
The sense of taste involves perception of non volatiles
Taste receptors are on the tongue and mouth

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4
Q

5 Tastes

A

Salty
Sweet
Sour
Bitter
Umami

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5
Q

Salty

A

NaCl
KCl

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6
Q

Sweet

A

Sucrose
Glucose
Sweetener replacers

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7
Q

Sour

A

Acid
(Citric, phosphoric)

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8
Q

Bitter

A

Quinine
Caffeine

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9
Q

Umami

A

MSG

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10
Q

Potentiators

A

Enhance taste sensations

Umami (meat)
Salt (sweetness)
Acids

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11
Q

Attenuators

A

Inhibit perception

Sugar (acidity)
Fat (saltiness)

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12
Q

Miraculin

A

Sugar substitute

Glycoprotein

Makes sour food taste sweet

Binds to sweet preceptors in a sour environment

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13
Q

Olfaction

A

Volatile molecules sensed by receptors

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14
Q

Orthonasal olfaction

A

Breathing
Sniffing
Through the nose

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15
Q

Retronasal olfaction

A

Via the back of the throat
Volatiles go through mouth and into nose

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16
Q

Touch

A

Evaluate the consistency, texture, viscosity of foods

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17
Q

Audition

A

Noise emitted by food
Contributes to perceived texture

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18
Q

Expectation error

A

People tend to find what they expect to find
Give as little info as possible

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19
Q

Suggestion effect

A

Comments or noises made out loud affect judgments
Biases perception
Want physical separation

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20
Q

Distraction error

A

Conversations
Time pressure
Personal preoccupations
Be mindful of sample # and time of day

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21
Q

Color + intensity

A

Products of deeper color are presumed to be more intense in flavor

Use red light to prevent them from seeing the true color

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22
Q

Habituation

A

Need to give people breaks
Vary products

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23
Q

Order effect

A

Randomize and balance the order of presentation
Lingering aromas transfer between samples

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24
Q

Central tendency

A

Panelists avoid the scale extremes
Train the assessors

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25
Other sources of error
Motivation error Brand names can bias assessors Rating of one attribute can influence rating of others
26
Adaptation
Limit the number of samples presented Allow the sensory system to recover Palate cleansers
27
Coding
Random 3 digit codes Different codes for replicates of the same sample Consistent format Positioned in the same location
28
Palette cleansers
Avoid carry over Bottled water Milk Apples Saltines
29
Parts of testing facility
Sample prep area Serving area Booths for panelists Discussion/training area Storage area Equipment
30
Sample temp
Hot foods 60-66 °C Hot tea 66-71 ° C Cold beverages 5-9 °C ` Frozen desserts -18 to -10 °C
31
Sample size
It's not lunch!! You need enough sample to measure the attributes
32
Things to keep in mind about panelists
Recruitment Confidentiality Untrained vs trained Sample demographics
33
Types of data
Nominal (labels) Continuous (any possible #) Ordinal (ranking scale)
34
Data handling
Check raw data for errors Outliers and missing values Data transformation
35
Objective tests
Numerical data Discrimination tests Descriptive analysis
36
Subjective tests
Preferences, comments, reactions Affective tests
37
Discrimination tests
Determine whether there are any sensory differences between samples
38
Discrimination tests used for
Screening and training assessors Investinging taints Determine sensitivity thresholds Quality control (consistency of materials) Preliminary assessment
39
Overall difference tests
Assessors use all available info to make decision Can be restricted to one specific characteristic Detect differences between samples
40
Triangle tests
Determine if a difference exists between 2 samples Panelists presented with 3 samples (2 are the same) Identify which sample is the odd one 24-30 assessors
41
Triangle test data analysis
Total # of responses correctly identifying the odd sample Number of correct responses compared to statistical tables Number of correct responses must > critical min value
42
Triangle test conclusion
A significant differences does or does not exist State significance level
43
Duo trio tests
Determine if a difference exists between 2 samples Panelists get 3 samples 1 is the reference sample Which one is the most similar/different to the control? Min of 32 assessors
44
Duo trio tests data analysis
Total # of correct responses counted Number of correct responses compared to statistical tables Must exceed critical value to claim a difference
45
Difference from control test
Determine if a difference exists between 1 + samples and a control Determine the magnitude of difference between the samples and control 20-25 panelists (fewer if highly trained)
46
Difference from control data analysis
Mean score calculated for each sample Difference in scores represents heterogeneity Two factor anova
47
Same different test
Presented a pair of samples Determine if the samples are the same or different 30-50 assessors
48
Attribute specific tests
Focus on one attribute or quality
48
A not A test
Two samples are presented Control / not control 10-50 panelists, trained
49
2 AFC
Determine if a difference exists between 2 samples for 1 specific attribute Present 2 coded samples Identify which of the samples has a greater intensity of xyz attribute Min of 30 panelists
50
2 AFC data analysis
Determine the total # of times each sample is selected Larger # of responses compared to statistical table Min # of response to conclude a statistical difference
51
2 AFC conclusion
One sample is more intense than the other or that there is no difference
52
Directional difference tests
Determine in which way a particular sensory attribute differs between 2 samples
53
3 AFC test
Determine if a difference exists between 3 samples with regards to a specific attribute 2 samples are the same, 1 is different
54
Ranking test
Determine if a difference exists between 3+ samples with regards to a specific attribute Forced to make a choice for each position
55
Ranking test samples
Number of samples depends on how fatiguing the assessments are order of sample prep should be balanced
56
Ranking test data analysis
Data are summarized in a table showing rank order Rank orders summed and divided by # of samples tied for that position Friedman statistic calculated
57
Descriptive analysis
Identify the nature of a sensory difference and/or the magnitude of the difference
58
Descriptive analysis key steps
Select and train assessors Generate attributes/references Agree on attributes Determine assessment protocol Rating intensity and scale design
59
Flavor profiling
Assess aroma, mouthfeel, flavor 4-6 trained panelists 5 point scale Eval by yourself, discuss after, determine a consensus score No stats!
60
Texture profiling
Texture and mouthfeel assessed 13 point scale 6-10 panelists work in consensus
61
Quantitative descriptive analysis
Analyzed statistically Ful quantitative and qualitative sensory description 8-15 trained panelists
62
Spectrum method
Extension to products outside of food/beverages Full quantitative and qualitative sensory description Sensory qualities are assessed using predefined and standardized lexicon 15 point scale 12-15 selected panelists
63
Displaying sensory data
Spider plots Sensory traces PCA
64
Spider plots
Each attribute represented Center of plot = 0 perceived intensity Attribute means are plotted and joined with continuous lines
65
Sensory traces
Attributes marked along X axis Y axis = perceived intensity Means are plotted and joined using a line
66
Affective tests
Consumer testing assesses subjective responses to a product
67
Quantitative affective tests
100 panelists Usage/non usage questionnaire or face to face
68
Focus group
Formulate a hypothesis Test the feasibility of a new products Identify attitudes, opinions, preferences 8-12 participants
69
Focus group procedure
Trained moderator produces and guides the discussion Written report Video or audio recording
70
Preference tests
Preference tests provide evidence of whether one product is preferred over another 50-100 panelists 2 products (paired test) 2 or more products (ranking test)
71
Hedonic ranking
Subjects asked to rate liking on hedonic scale Responses converted to numeric values before analysis 100 panelists
72
Attribute diagnostics
Why do consumers like/dislike products Which sample is preferred in terms of ___ How much do you like the products

Food Analysis (19 decks)

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