introduction tointroduction to sensory data analysis - · pdf filesensory data analysis:...

Introduction toIntroduction to Sensory Data Analysis

Marion CunyfCamo Software AS

Sensory Data Analysis:Sensory Data Analysis: Course outline:

1. Why sensory data analysis?

2 Data collection and experimental design2. Data collection and experimental design

3. Inspection and preparation of the dataa. Theory

b D i Q li Sb. Demo in Quali‐Sense

4. Principal Component Analysis: PCAa. Theory

b. Demo in The Unscrambler

5. Partial‐Least Square Regression: PLSa. Theory


What can sensory data analysis provide us ?


What can sensory data analysis provide us ?• Describing product characteristics / Quality Control

– Sensory panel of experts sensory profileSensory panel of experts sensory profile– Chemical / industrial process measurements

Multivariate regression analysis

h l lcheaper 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 g pof the consumers / Prediction of market response to a new product

• Investigation of competitive products / new recipesg p p / p

PCA

Positionning

Can we trust the sensory panel?


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• assessors come and go• time for training is short,

measuring and tracking each assessor’s performance is essential.

Focus of today


Focus of today

Check the performance of the panelCheck the performance of the panel– Seeking the attributes that are the most reliable

d h l h d– Finding the panelists that need more training

Modeling– Behaviour of the attributes, grouping of samples (PCA)

– Regression over the preference (PLS)

Sensory Analysis workflow


Selection of

Sensory Analysis workflow

DoE Selection of the Products

DoE

Analysis of the Products

Selection of the judges

DATAJudge

CHEMICAL data on the products

DATAPRODUCT

Check the data StatisticsANOVA

StatisticsANOVA

Relation Sensory Profile

Preference mapping

Check the model & results

between chemical and sensory data

MVAMVA

Data collection and experimental design

2. Data collection and experimental design

Data collection and experimental designin Sensory

Depending on objectives:

• Positionning Samples from the market

• Products (new recipe, QC) / reference

Experimental design• Maximum acceptance

E i l d iExperimental design

Requirements to input data

2. Data collection and experimental design

Requirements to input data

• Representative: Samples must be Sampling 1• Representative: Samples must be representative with respect to:– Average values

Variability

Population

l– Variability– Levels

• Accurate/Reproducible: The grades must be the same for the same

Sampling 2

Accurate/Reproducible: The grades must be the same for the same product independently of the panelist and time

Garbage in gives garbage out: No software program should find information where none exists.p g

Inspecting the data

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

Inspecting the data

• Data that are different from the othersthe others

(Typing error, missing values...)

• Distribution of the samples for different attributes:for different attributes: – uniform,

– groupings...g p g


Judging panel performance

1. Assessor – Sensitivity

– ReproducibilityReproducibility

2. Panel AgreementChecking for Crossover andChecking for Crossover and ranking (Eggshell Correlation)

Example data set: Tomatoes


17 tomato varieties (products)


11 descriptive evaluations (attributes) grade: 0 to 10

14 trained assessors (panelists)


Quali-Sense

Importing the data in Quali Sense


Importing the data in Quali‐Sense

• Select the columns• Select the columns corresponding to the products andthe products and panelists

• Exclude the• Exclude the colums that are not attributesnot attributes

• Adjust the score range

Preview of the Data


Preview of the Data

Spider plot Branch= Attribute

Overview by product of the judges’ grades on the diferent attributes

Line= Panelist

Sensitivity


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 trainingAttribute not discriminative

Panelist needing training

Reproducibility


Reproducibility Monitors the ability of a single assessor to reproduce its y g presults with respect to the rest of the panel.

Reproducibility


Reproducibility

Size of the spot = mean difference in repeated pscores for all products

Color of the spot = frequency of bad replication

Assessor agreement


Assessor agreementThe agreement test measures each individual assessor's agreement compared to the rest of the panel.

Cross over


Cross‐overCrossover effects occur when an assessor scores products opposite in intensity to the rest of the panel.

Bad agreement and high cross-over probability indicate misused of the scale

Test 5: Rank Correlation


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• 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.

Rank correlation table


Rank correlation tableIn this test, the assessor differences are found using the assessors' cumulative product ranks instead of the assessor scores directly.

Select the trusted data


Select the trusted dataExclusion of panelist, samples, attributes

Make an average of the trusted data for


Make an average of the trusted data for multivariate analysis

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

Quali-Sense

Principal Component Analysis (PCA)

4. Principal Component Analysis: PCA / a) Theory


• Exploratory data analysisData structure in PCA:• Each row represents an observationp y y

• Extract information

• Noise removal Variable 1 Variable 2 Variable 3

• Each row represents an observation• Each column represents a variable

Noise removal

• Dimensionality reduction Object 1

Object 2

bjObject 3

Object 4

X Model Error

Data Structure Noise




New latent variables that are linear combinations of the original variables.

PC1 = a1 V1 + a2 V2 + a3 V3

X = Mean + b1 PC1 + b2 PC2 + Error

Constraints :

• Maximise the dispersion of samples along the ( )latent variables (the variance)

• Orthogonality

PCA = A change of variable space



Principal Component Analysis (PCA)Average =

e

Principal Component 1 (PC 1)

most typical example

e

PC1PC2

Adh

esiv

e

Adh

esiv

e PC2

Varimax Rotation


Varimax RotationThe aim is to enhance interpretationRotation works on the structured part of the data only (depends on the selected number of PCs)

PC2Scores Loadings PC2

PCs)Total explained variance is not changed (But more evenly distributed among PCs)

PC1 PC1

1 4

C

2 3

Scores Loadings

Data preprocessing before PCA


Data preprocessing before PCA

• In practice there is often a need to slightly modify• 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


Example data set: Tomatoes17 tomato varieties (products)




PCA vocabulary


PCA vocabularyPrincipal components

Main data variations also known as ”latent variables” ”factors” and ”eigenvectors”Main data variations, also known as latent variables , factors and eigenvectors .

Scores, TMap of samples: Projected locations of objects onto the principal components

L di PLoadings, PMap of variables: Correlation between variables (regression of X on T)

Residuals, EError. The data can be divided into structure and residual: X = Xstruct + E

VarianceResidual variance – variance remaining in EResidual variance variance remaining in EExplained variance – The % variance explained by Xstruct

Model Equation: X = TPT + Estructure residualstructure residual



Example data set: TomatoesExternal color Acidity

The scale has been used with good The scale has been used with a gvariation3 groups appeared

small variation rangeAlmost uniform distribution

Data overview


Data overview

Check the range of value... No outlier

Do a PCA


Do a PCA

Number of component to take into account


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 PCstake into account 5 PCs

Map of samples


Map of samples

• Tomatoes displayed as a score plot.

• The purpose is to

Average sample

describe products according to their sensory characteristics.

• The relative positions of products reflect their similarities or differencesdifferences.

Map of variables


Map of variablesHigh contribution on PC 2Firmness and Firmness inside are

• Loadings can be visualized to map

correlatedAnticorrelated with Meltyness

which variables have contributed to the score plot.

• Variables far away from the center are well described

Not contributing to PC1 & 2

and important

• Variables near the center are less

High contribution on PC 1

Tomato odor/flavor, Juciness, Sweetness, External color are anti‐correlated with Mealyness

important.

Bi Plot


Bi‐Plot

Bi Plot with Varimax rotation


Bi‐Plot with Varimax rotation

Conclusions on PCA


Conclusions on PCA• Some variables are correlated :

”Firm” and ”Firm inside” //// ”Meltiness”– Firm and Firm inside //// Meltiness

– ”Tomato odor”, ”Tomato flavor”, ”Juiciness”, ”Sweetness”, ”External color” //// ”Mealyness”

f h bl b l d f fSome 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 d K ”Fi ”– 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

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

Regression methods

5. PLS regression / a) Theory

Find a linear relationship between Y (variables to

Regression methodsp (

predict) and the x‐variables (variables explaining the data)

Fitted al e)

Y=B0+B1X1+ B2X2+…+ BNXN+ FFitted value

Y

With PLS: the new variables are called “latent variables” (linear combination from the former variables)

f

from the former variables)

Y=B0+B1LV1+ B2LV2+…+ BNLVN+ F

LV a X + a X + + a X

Observation

XLVi = a1X1+ a2X2+…+ apXp

PLS terminology


PLS terminology

Scores: (X T Y : T (or U)) M f l P j t d l tiScores: (X‐scores: T, Y‐scores: T (or U)) Map of samples. Projected locations of objects onto the model components.

Loadings: (X‐loadings: P, Y‐loadings: Q) Map of variables. Describes g ( g , g Q) prelationships between either X or Y variables.

Loading weights: (X‐loading weights: W) Describes relationships between X d Y i bland Y variables.

Residuals: (X‐residuals: E, y‐residuals: F) Error.

Variance M f id l / d f f d id l iVariance: Mean squares of residuals / degrees of freedom = residual variance

Model equations: X = TPT + E and Y = TQT + F

R i ffi i t Y B X *B X *B X *BRegression coefficients: Y = B0 + X1*B1 + X2*B2 + ... + XN*BN



17 tomato varieties (products)





Distribution of Y = Preference


Distribution of Y = Preference

Do a PLS 1


Do a PLS 1

Selecting the number of latent variables


Selecting the number of latent variables

Model with 1 latent variable

Sample mapping


Sample mapping

Preference

Attributes explaining the preference


Attributes explaining the preference

Important variables

Not important

Preference is strongly correlated with ”External color” ”Sweetness” ”Tomatocolor , Sweetness , Tomato flavor” and ”Juiciness”And strongly anti‐correlated with ”M li ”

Prediction quality


Prediction qualityGood R2 good correlation between

di ti d t

Good validation error small error

prediction and measurement

(0.3) when predicting the preference: from 3 to 10

What did I earn ?


What did I earn... ?

• A new Tomato variety could be tested by a sensory• A new Tomato variety could be tested by a sensory panel on a restraint number of attributes:

M li– Mealiness

– External color

– Tomato flavor

– Juciness Gain of time and money

– Sweetness

• To predict the consumer liking










6. Summary

5. PLS regression / b) Demo in The Unscrambler










6. Summary

Summary6. Summary

Summary1. Managing data from panelists - Evaluation of panel performance

i i i i2. Univariate statistics 3. Principal component analysis (PCA)4 Varimax rotation4. Varimax rotation5. Regression analysis (PCR, PLS, MLR)6. Preference mapping7. L-PLS regression8. Cluster Analysis9 Classification (SIMCA PLS DA)9. Classification (SIMCA, PLS-DA)10. 3-way PLS regression11. Design of Experiment

CAMO Products

6. Summary

CAMO ProductsOn‐line applications:

The UnscramblerA complete Multivariate Analysis and Experimental Design software.

pp

•The Unscrambler on‐line •OLUC •OLUP•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.

Product OptimizerA powerful product formulation ensure quality control with spectroscopic measurements.A powerful product formulation and process optimization tool.

Training and ConsultancyQuali‐SenseThe best companion for panel leader detects the personal strengths and weaknesses of each assessor in your

Training and ConsultancyDesigned courses and support to help you get the best of your experiments and data

weaknesses of each assessor in your panel

h k f iThank you for your attention

Marion Cuny for technical questions: [email protected]

Maria Falcão for sales: maria@camo noMaria Falcão for sales: [email protected]

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