Introduction to Introduction to Sensory Data Analysis - Camo

Introduction to
Sensory Data Analysis
Marion Cuny
Camo Software AS

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler

1. Why sensory data analysis
What can sensory data analysis provide us
• Describing product characteristics / Quality Control
– Sensory panel of experts sensory profile
– Chemical / industrial process measurements
Multivariate regression analysis
cheaper quality control
• Understanding of the behaviour and liking of the
consumers
– Consumer studies preferences mapping
PCA / Multivariate regression analysis
Relating product characteristics to the needs
of the consumers / Prediction of market response
to a new product
• Investigation of competitive products / new recipes
PCA
Positionning

1. Why sensory data analysis
Can we trust the sensory panel
Assessors consistently give variable results, due to differences in
motivation, sensitivity, and psychological response behaviors.
In a sensory lab:
• assessors come and go
• time for training is short,
measuring and tracking each assessor’s performance is essential.

1. Why sensory data analysis
Focus of today
Check the performance of the panel
– Seeking the attributes that are the most reliable
– Finding the panelists that need more training
Modeling
– Behaviour of the attributes, grouping of samples (PCA)
– Regression over the preference (PLS)

1. Why sensory data analysis
Sensory Analysis workflow
DoE
Selection of
the Products
Analysis of
the Products
Selection of
the judges
Check the data
CHEMICAL
data on the
products
Statistics
ANOVA
DATA
PRODUCT
Statistics
ANOVA
DATA
Judge
Check the
model & results
Relation
between
chemical and
sensory data
MVA
Sensory
Profile
MVA
Preference
mapping

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler

2. Data collection and experimental design
Data collection and experimental design
in Sensory
Depending on objectives:
• Positionning
Samples from the market
• Products (new recipe, QC) /
reference
Experimental design
•Maximum acceptance
Experimental ldesign

2. Data collection and experimental design
Requirements to input data
Sampling 1
• Representative: Samples must be
Population
representative with respect to:
– Average values
– Variability
– Levels
• Accurate/Reproducible: The grades must be the same for the same
product independently of the panelist and time
Sampling 2
Garbage in gives garbage out:
No software program should find information where none exists.

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler

3. Inspection and preparation of the data / a) Theory
Inspecting the data
•Data that are different from
the others
(Typing error, missing values...)
• Distribution of the samples
for different attributes:
– uniform,
– groupings...
g

3. Inspection and preparation of the data / a) Theory
Judging panel performance
1. Assessor
– Sensitivity
– Reproducibility
2. Panel Agreement
Checking for Crossover and
Checking for Crossover and
ranking (Eggshell Correlation)

3. Inspection and preparation of the data / a) Theory
Example data set: Tomatoes
17 tomato varieties (products)
11 descriptive evaluations (attributes) grade: 0 to 10
14 trained assessors (panelists)

3. Inspection and preparation of the data / a) Theory
Quali-Sense

3. Inspection and preparation of the data / a) Theory
Importing the data in Quali‐Sense
• Select the columns
corresponding to
the products and
panelists
• Exclude the
colums that are
not attributes
• Adjust the score
range

3. Inspection and preparation of the data / a) Theory
Preview of the Data
Spider plot
Overview by
product of the
judges’ grades
on the diferent
attributes
Branch= Attribute
Line= Panelist

3. Inspection and preparation of the data / a) Theory
Sensitivity
• Measures the ability of a single assessor to identify product differences.
• A low p‐value shows a significant difference between products, and is thus good.
Panelist needing training
Attribute not
discriminative

3. Inspection and preparation of the data / a) Theory
Reproducibility
Monitors the ability of a single assessor to reproduce its
y g p
results with respect to the rest of the panel.

3. Inspection and preparation of the data / a) Theory
Reproducibility
Size of the spot = mean
difference in repeated
scores for all products
Color of the spot =
frequency of bad
replication

3. Inspection and preparation of the data / a) Theory
Assessor agreement
The agreement test measures each individual assessor's agreement
compared to the rest of the panel.

3. Inspection and preparation of the data / a) Theory
Cross‐overover
Crossover effects occur when an assessor scores products opposite in intensity
to the rest of the panel.
Bad agreement
and high crossover
probability
indicate
misused of the
scale

3. Inspection and preparation of the data / a) Theory
Test 5: Rank Correlation
• Rank correlation is also a form of agreement test.
• Here, the ranking instead of score values are used and compared between
assessors.
• Rank correlation measures the correlation between an assessor and the
panel consensus ranking of products.
• Rank correlation values can be used to form so called ”eggshell” plots.

3. Inspection and preparation of the data / a) Theory
Rank correlation table
In this test, the assessor differences are found using the assessors'
cumulative product ranks instead of the assessor scores directly.

3. Inspection and preparation of the data / a) Theory
Select the trusted data
Exclusion of panelist, samples, attributes

3. Inspection and preparation of the data / a) Theory
Make an average of the trusted data for
multivariate analysis

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler

3. Inspection and preparation of the data / b) Demo in Quali-Sense
Quali-Sense

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler

4. Principal Component Analysis: PCA / a) Theory
Principal Component Analysis (PCA)
•Exploratory data analysis
•Extract information
• Noise removal
• Dimensionality reduction
Data structure in PCA:
• Each row represents an observation
• Each column represents a variable
Variable 1 Variable 2 Variable 3
Object 1
Object 2
Object 3
Object 4
X Model Error
Data Structure Noise

4. Principal Component Analysis: PCA / a) Theory
Principal Component Analysis (PCA)
New latent variables that are linear combinations of the
original variables.
PC1 = a 1 V1 + a 2 V2 + a 3 V3
X = Mean + b 1 PC1 + b 2 PC2 + Error
Constraints :
• Maximise the dispersion of samples along the
latent variables (the variance)
• Orthogonality
PCA = A change of variable space

4. Principal Component Analysis: PCA / a) Theory
Principal Component Analysis (PCA)
Adhesive e
Average =
most typical
example
Principal
Component 1
(PC 1)
Adhesive e
PC2
PC1

4. Principal Component Analysis: PCA / a) Theory
Varimax Rotation
The aim is to enhance interpretation
Rotation works on the structured part of the data only (depends on the selected number of
PCs)
Total explained variance is not changed (But more evenly distributed among PCs)
Scores PC2
Loadings PC2
1 4
PC1
2 3
PC1
Scores
Loadings

4. Principal Component Analysis: PCA / a) Theory
Data preprocessing before PCA
• In practice, there is often a need to slightly modify
the shape of the data to better suit an analysis.
•Such a modification is called preprocessing or
pretreatment. (centering, scalling, derivative...)
•But when we use a trained panel it is not necessary

4. Principal Component Analysis: PCA / a) Theory
Example data set: Tomatoes
17 tomato varieties (products)
11 descriptive evaluations (attributes) grade: 0 to 10
14 trained assessors (panelists)

4. Principal Component Analysis: PCA / a) Theory
PCA vocabulary
Principal components
Main data variations, also known as ”latent variables” , ”factors” and ”eigenvectors”
.
Scores, T
Map of samples: Projected locations of objects onto the principal components
Loadings, P
Map of variables: Correlation between variables (regression of X on T)
Residuals, E
Error. The data can be divided into structure and residual: X = Xstruct + E
Variance
Residual variance
– variance remaining in E
Explained variance –The% variance explained by Xstruct
Model Equation:
X = TP T + E
structure residual

4. Principal Component Analysis: PCA / a) Theory
Example data set: Tomatoes
External color
Acidity
The scale has been used with good
variation
3 groups appeared
The scale has been used with a
small variation range
Almost uniform distribution

4. Principal Component Analysis: PCA / a) Theory
Data overview
Check the range of
value... No outlier

4. Principal Component Analysis: PCA / a) Theory
Do a PCA

4. Principal Component Analysis: PCA / a) Theory
Number of component to take into account
Explained variance
in cross‐validation
With the explained variance in validation we decide to
take into account 5 PCs

4. Principal Component Analysis: PCA / a) Theory
Map of samples
Average
sample
• Tomatoes displayed as a
score plot.
• The purpose is to
describe products
according to their
sensory characteristics.
• The relative positions of
products reflect their
similarities or
differences.

4. Principal Component Analysis: PCA / a) Theory
Map of variables
High contribution on PC 2
Firmness and Firmness inside are
correlated
Anticorrelated with Meltyness
Not contributing
to PC1 & 2
High contribution on PC 1
Tomato odor/flavor, Juciness, Sweetness, External color
are anti‐correlated with Mealyness
• Loadings can be
visualized to map
which variables
have contributed
to the score plot.
• Variables far away
from the center
are well described
and important
• Variables near the
center are less
important.

4. Principal Component Analysis: PCA / a) Theory
Bi‐Plot

4. Principal Component Analysis: PCA / a) Theory
Bi‐Plot with Varimax rotation

4. Principal Component Analysis: PCA / a) Theory
• Some variables are correlated :
Conclusions on PCA
– ”Firm” and ”Firm inside” //// ”Meltiness”
– ”Tomato odor”, ”Tomato flavor”, ”Juiciness”, ”Sweetness”, ”External color”
//// ”Mealyness”
Some of those variables can be selected if we want to save on time of
sensory analyses
• Some variables are not descriminative: ”Acidity” and ”Skin width”
They don’t have to be tested in the future.
• Some tomato varieties are presenting similar characteristics:
– F and K are ”Firm”
”
– G and H are ”Firm inside”
– A, O and C are ”Juicy”
– Q and D are ”Melty”
Some can be dropped in a consumer study

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler

4.Principal Component Analysis: PCA / b) Demo in the Unscrambler

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler

5. PLS regression / a) Theory
Regression methods
Find a linear relationship between Y (variables to
predict) and the x‐variables (variables explaining the
data)
Y=B0+B1X1+ B2X2+…+ BNXN+ F
Y
Fitted value
With PLS: the new variables are called
“latent variables” (linear combination
from the former variables)
Y=B 0 +B 1 LV 1 + B 2 LV 2 +…+ B N LV N + F
LV i = a 1 X 1 + a 2 X 2 +…+ +a p X p
f
Observation
X

5. PLS regression / a) Theory
PLS terminology
Scores: (X‐scores: T, Y‐scores: : T (or U)) Map of samples. Projected dlocations
of objects onto the model components.
Loadings: (X‐loadings:( P, Y‐loadings: Q) Map of variables. Describes
relationships between either X or Y variables.
Loading weights: (X‐loading weights: W) Describes relationships between X
and Y variables. ibl
Residuals: (X‐residuals: E, y‐residuals: F) Error.
Variance: Mean squares of residuals / degrees of freedom = residual variance
Model equations:
X = TP T + E and Y = TQ T + F
Regression coefficients: i Y = B 0 + X 1 *B 1 + X 2 *B 2 + ... + X N *B N

5. PLS regression / a) Theory
Example data set: Tomatoes
17 tomato varieties (products)
11 descriptive evaluations (attributes) grade: 0 to 10
14 trained assessors (panelists)

5. PLS regression / a) Theory
Distribution of Y = Preference

5. PLS regression / a) Theory
Do a PLS 1

5. PLS regression / a) Theory
Selecting the number of latent variables
Model with 1 latent variable

5. PLS regression / a) Theory
Sample mapping
Preference

5. PLS regression / a) Theory
Attributes explaining the preference
Not important
Important variables
Preference is strongly correlated
with ”External
color” , ”Sweetness” , ”Tomato
flavor” and ”Juiciness”
And strongly anti‐correlated with
”M li ”

5. PLS regression / a) Theory
Prediction quality
Good R2 good correlation between
prediction and measurement
Good validation error small error
(0.3) when predicting the preference:
from 3 to 10

5. PLS regression / a) Theory
What did I earn...
• A new Tomato variety could be tested by a sensory
panel on a restraint number of attributes:
– Mealiness
– External color
– Tomato flavor
– Juciness Gain of time and money
– Sweetness
•To predict the consumer liking

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler
6. Summary

5. PLS regression / b) Demo in The Unscrambler

Sensory Data Analysis:
Course outline:
1. Why sensory data analysis
2. Data collection and experimental design
3. Inspection and preparation of the data
a. Theory
b. Demo in Quali‐Sense
4. Principal Component Analysis: PCA
a. Theory
b. Demo in The Unscrambler
5. Partial‐Least Square Regression: PLS
a. Theory
b. Demo in The Unscrambler
6. Summary

6. Summary
Summary
1. Managing data from panelists - Evaluation of panel performance
2. Univariate i statistics
i
3. Principal component analysis (PCA)
4. Varimax rotation
5. Regression analysis (PCR, PLS, MLR)
6. Preference mapping
7. L-PLS regression
8. Cluster Analysis
9. Classification (SIMCA, PLS-DA)
10. 3-way PLS regression
11. Design of Experiment

6. Summary
CAMO Products
Product Optimizer
The Unscrambler
A complete Multivariate
Analysis and Experimental
Design software.
A powerful product formulation
and process optimization tool.
On‐line applications:
•The Unscrambler on‐line
•OLUC
•OLUP
A plug 'n' play product designed to make effective on‐line
predictions and classifications, to monitor processes and
ensure quality control with spectroscopic measurements.
Quali‐Sense
The best companion for panel leader
detects the personal strengths and
weaknesses of each assessor in your
panel
Training and Consultancy
Designed courses and support to
help you get the best of your
experiments and data

Thank you for your attention
Marion Cuny for technical questions: mc@camo.no
Maria Falcão for sales: maria@camo.no