Mean of Liking for JAR
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Transcript Mean of Liking for JAR
Key
Features
and Results
Benefits
XLSTAT-MX functions
Preference Mapping (PREFMAP)
Build decision making maps to:
• Improve or develop products
• Position products in comparison with competitors’
products
• Reach a target market
Preference mapping = a powerful tool to
optimize product acceptability.
XLSTAT-MX offers several regression
models to project complementary data on
the objects maps:
• Vector model,
• Circular ideal point model,
• Elliptical ideal point model,
• Quadratic ideal point model.
Preference Mapping (PREFMAP)
10 commercial samples of potato chips
99 consumers satisfaction from 1 to
30
Consumers are segmented into 9
clusters
Preference Mapping (PREFMAP)
Generalized Procrustes Analysis (GPA)
GPA is pretreatment used to reduce the
scale effects and to obtain a consensual
configuration.
Generalized Procrustes Analysis (GPA)
GPA compares the proximity between
the terms that are used by different
experts to describe products.
Multiple Factor Analysis (MFA)
MFA is a generalization of PCA
(Principal Component Analysis) and
MCA (Multiple Correspondence
Analysis).
MFA makes it possible to:
• Analyze several tables of variables
simultaneously,
• Obtain results that allow studying the
relationship between the observations,
the variables and tables.
Multiple Factor Analysis (MFA)
36 experts have graded 21 wines
analysed on several criteria:
• Olfactory (5 variables)
• Visual (3 variables)
• Taste (9 variables)
• Quality (2 variables)
Multiple Factor Analysis (MFA)
MFA groups the information on one chart
Multiple Factor Analysis (MFA)
MFA groups the information on one chart
Multiple Factor Analysis (MFA)
Wine 13 is in the direction
of the two quality variables
and is therefore the wine of
preference.
Multiple Factor Analysis (MFA)
The olfactory criteria are
often increasing the
distance between the
wines.
Penalty analysis
Identify potential directions for the
improvement of products, on the basis of
surveys performed on consumers or
experts.
Two types of data are used:
• Preference data (or liking scores) for a
product or for a characteristic of a product
• Data collected on a JAR (Just About
Right) scale
Penalty analysis
A type of potato chips is evaluated:
By 150 consumers
On a JAR scale (1 to 5) for 4 attributes:
• Saltiness,
• Sweetness,
• Acidity,
• Crunchiness.
And on an overall liking (1 to 10) score
scale
Penalty analysis
Mean of Liking for JAR – Mean of Liking for too little
and too much
Semantic differential charts
The semantic differential method is a
visualization method to plot the
differences between individuals'
connotations for a given word.
This method can be used for:
• Analyzing experts’ agreement on the
perceptions of a product described by a
series of criteria on similar scales
• Analyzing customer satisfaction surveys
and segmentation
• Profiling products
Semantic differential charts
1 yoghurt
5 experts
6 attributes:
• Color
• Fruitiness
• Sweetness
• Unctuousness
• Taste
• Smell
Semantic differential charts
TURF analysis
TURF = Total Unduplicated Reach and
Frequency method
Highlight a line of products from a
complete range of products in order to
have the highest market share.
XLSTAT offers three algorithms to find
the best combination of products
TURF analysis
27 possible dishes
185 customers
"Would you buy this
product?" (1: No, not
at all to 5: Yes, quite
sure).
The goal is to obtain
a product line of 5
dishes maximizing
the reach
TURF analysis
Product characterization
Find which descriptors are discriminating
well a set of products and which the most
important characteristics of each product
are.
Check the influence on the scores of
attributes of:
• Session
• Product
• Judge
• Judge*Product
All computations are based on the
analysis of variance (ANOVA) model.
Product characterization
29 assessors
6 chocolate drinks
14 characteristics:
• Cocoa and milk taste and flavor
• Other flavors: Vanilla, Caramel
• Tastes: bitterness, astringency,
acidity, sweetness
• Texture: granular, crunchy, sticky,
melting
Product characterization
DOE for sensory data analysis
Designing an experiment is a
fundamental step to ensure that the
collected data will be statistically usable
in the best possible way.
DOE for sensory data analysis
Prepare a sensory evaluation where
judges (experts and/or consumers)
evaluate a set of products taking into
account:
• Number of judges to involve
• Maximum number of products that a judge
can evaluate during each session
• Which products will be evaluated by each of
the consumers in each session, and in what
order (carry-over)
Complete plans or incomplete block
designs, balanced or not.
Search optimal designs with A- or Defficiency
DOE for sensory data analysis
60 judges
8 products
Saturation: 3 products / judge
DOE for sensory data analysis
DOE for sensory data analysis
Let XLSTAT-MX be part of your product development
strategy.
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