The use of XLSTAT in new Product development June …€¦ · The use of XLSTAT in newThe use of...

The use of XLSTAT in newThe use of XLSTAT in new product development of foods and beverages and training

sensory professionalssensory professionals.

Hal MacFieXLSTAT conference 2007XLSTAT conference 2007

OverviewOverview

S d C T ti• Sensory and Consumer Testing• Trainingg• Free choice Profiling• Segmentation mapping• Segmentation mapping• Preference Mapping• Penalty Analysis• Multiple Factor AnalysisMultiple Factor Analysis• Path Partial Least Squares

How do we do it?Step 1: Measure human perception and hedonic response to food

How do we do it?

or personal productsPerception Hedonic response•Sensory Panel

T i d

p

•Central Location test•Trained assessors

•Excellent senses•Naïve consumers

•Heavy or medium users•Objective measures •Hedonic response

St ti ti l Li kStatistical LinkageStatistical Linkage

Types of decisionsTypes of decisions• Worth Launching or further reformulation required?• Worth doing another trial with a different product?• Have we covered all the angles?• Do we need to make more than one product?• What do we need to do next to improve the product?• Does the product experience fit the brand concept?• Can we make a claim about the sensory attribute or

improvement in liking?

The Product Cycley

F G /C j i tConcept development

Prototype development

Focus Groups /Conjoint analysis/internetSensory and CLT testingPrototype development

Product test marketed

Sensory and CLT testing

Test Market/ Home Use testProduct test marketed

Product launch

Test Market/ Home Use test

Home Use test

Product improvement Sensory, Laboratory, CLT and Home Use test

Product optimisation

Home Use test

Laboratory, CLT with RSM designs

Hal MacFie TrainingHal MacFie Training

• Statistical Training for Sensory Professionals• Multivariate capabilityp y• Work inside Excel• Cheap enough to give license as part of course• Cheap enough to give license as part of course

feeCompatible with Office particularly PowerPoint• Compatible with Office, particularly PowerPoint

• Good Help• Tutorials

www halmacfie comwww.halmacfie.com

Anne Hasted

Specialised Computer TrainingSpecialised Computer Training Facilities

Cheaper than hotels

Easier to deal with easier to cancelEasier to deal with, easier to cancel

Location: MediaTek Training Facilities 90 Broad Street90 Broad Street

11th Floor New York, NY 10004

Entrance Training Room

E-mail area Break area

Hands on CoursesHands on Courses• Sensory statistics• Sensory statistics

– Anova, Non parametric, PCA, GPA cluster, Discriminant and Canonical Variates, PLS,

• Preference Mapping and Consumer Testing– Anova, PCA, Cluster, PCA, Internal Preference

M i E t l P f i PLSMapping, External Preference mapping, PLS• Since 2002, for each course

2 public offerings per year and approximately 3 in– 2 public offerings per year and approximately 3 in-house courses per year

• France, UK, Belgium, Ireland, Sweden, , , g , , ,Switzerland, USA, Israel, Mexico, Brazil, South Africa, China, Thailand, Australia

It is important to relax before you do a course in a new country!

Hands on FormulaHands on FormulaL t 45 i t 1 h• Lecture 45 mins to 1 hour

• Hands on exercising with fully worked solutions 30fully worked solutions 30 mins

• Attendees save their work on to a CD at the end of the course

• Should be able to redo• Should be able to redo exercises in 6 months to remind themselves of how to conduct and interpret the data

XLSTAT featuresXLSTAT features

• Sitting inside Excel means easy access

• Data input in many stats packages is complex

• Messages appear when you hover over


• Multiple plotting is easy with labels


60

• Coloring up the labels is easy

Royal Gala

40

50

gd

Braeburn

Top Red Pink Lady

Golden Delicious

Johnson's Red

20

30

E_Ye

llow

bac

kg

Sun Gold

Braeburn

Fuji

Granny Smith

Gibson's Green

0

10

0 10 20 30 40 50 60 70 800 10 20 30 40 50 60 70 80

E_Green backgd

XLSTAT featuresXLSTAT features• After a PCA shows three components large, it is

easy to do a quick 3d bubble plotScree plot

Factor scores:

Observation F1 F2 F3

8

10

12

14

16

enva

lue

60

80

100

e va

riabi

lity

(%)

Observation F1 F2 F3Gibson's Green 0.675 5.228 -0.905Johnson's Red 5.210 -1.449 -0.965Golden Delicious -0.854 2.352 -0.923Granny Smith -5.812 0.429 2.305Pink Lady -2.154 -3.632 -0.922

Gibson's Green

6

0

2

4

6

F1 F2 F3 F4 F5 F6 F7 F8 F9

axis

Eige

0

20

40

Cum

ulat

ive Fuji -0.807 1.414 -0.828

Top Red 2.902 0.450 4.990Braeburn -0.339 -0.278 -3.088Royal Gala 5.517 -1.942 0.154Sun Gold -4.338 -2.570 0.180

Golden Delicious

Gibson s Green

2

4

Braeburn

Fuji

Granny Smith Top Red0

2

-10 -5 0 5 10

F2

Johnson's Red

Pink Lady

Royal Gala

Sun Gold

-4

-2

F1

XLSTAT featuresXLSTAT features• Finding sensory drivers of liking

B B A B B B A A B A A B B A B A A B A A A A B B B A B A A A4 4 3 8 6 5 3 6 2 7 6 6 3 2 5 5 6 5 5 7 5 6 3 3 6 3 2 7 7 65 5 4 9 7 6 4 7 3 7 7 6 4 2 6 6 6 4 5 8 5 7 6 2 5 4 4 6 8 63 1 7 5 2 4 6 6 3 8 6 5 4 7 4 8 8 5 6 7 7 5 1 3 4 6 3 8 7 83 3 8 9 6 7 5 8 5 3 8 5 6 3 7 7 7 6 7 7 5 6 5 5 6 5 2 5 5 44 1 3 1 3 3 8 1 7 5 4 4 4 9 3 6 1 8 1 4 5 1 9 7 1 1 7 4 4 53 1 4 3 4 2 7 3 8 4 3 4 4 8 2 6 1 8 2 3 5 1 9 8 2 1 6 3 3 3

Prod Acido Coesivo Dolce Elastico Fibroso Yogurt liscia Salato latte panna Sfogliat SuccosoA - Cow Full fat A 2.87 3 5.27 4.97 7.47 3.4 5.63 4.2 5.43 4.27 5.43 5.3C - Cow Full fat C 3 3.2 4.87 5.7 7.13 2.83 6.27 3.57 5 3.73 4.5 4.66D - Cow Low fat D 2.9 3.43 5.13 4.23 6.73 3.27 5.73 3.3 5.3 3.8 5.4 5.47E - Cow Low fat E 3.6 4 3.57 4.13 3.77 2.87 8.03 3.6 3.73 2.3 4.57 4.7E Cow Low fat E 3.6 4 3.57 4.13 3.77 2.87 8.03 3.6 3.73 2.3 4.57 4.7

G - Buffalo Full fat G 4.83 4.13 3.27 4.63 6.03 5.07 6.03 4.7 3.63 3.17 6.23 6.23I - Buffalo Full fat I 5.1 3.77 2.97 4.73 5.87 5.1 6.47 6 3.83 3 6.87 7.33


B B A B B B A A B A A B B A B A A B A A A A B B B A B A A A4 4 3 8 6 5 3 6 2 7 6 6 3 2 5 5 6 5 5 7 5 6 3 3 6 3 2 7 7 65 5 4 9 7 6 4 7 3 7 7 6 4 2 6 6 6 4 5 8 5 7 6 2 5 4 4 6 8 63 1 7 5 2 4 6 6 3 8 6 5 4 7 4 8 8 5 6 7 7 5 1 3 4 6 3 8 7 83 3 8 9 6 7 5 8 5 3 8 5 6 3 7 7 7 6 7 7 5 6 5 5 6 5 2 5 5 44 1 3 1 3 3 8 1 7 5 4 4 4 9 3 6 1 8 1 4 5 1 9 7 1 1 7 4 4 53 1 4 3 4 2 7 3 8 4 3 4 4 8 2 6 1 8 2 3 5 1 9 8 2 1 6 3 3 3

Prod Acido Coesivo Dolce Elastico Fibroso Yogurt liscia Salato latte panna Sfogliat SuccosoProd Acido Coesivo Dolce Elastico Fibroso Yogurt liscia Salato latte panna Sfogliat SuccosoA - Cow Full fat A 2.87 3 5.27 4.97 7.47 3.4 5.63 4.2 5.43 4.27 5.43 5.3C - Cow Full fat C 3 3.2 4.87 5.7 7.13 2.83 6.27 3.57 5 3.73 4.5 4.66D - Cow Low fat D 2.9 3.43 5.13 4.23 6.73 3.27 5.73 3.3 5.3 3.8 5.4 5.47E - Cow Low fat E 3.6 4 3.57 4.13 3.77 2.87 8.03 3.6 3.73 2.3 4.57 4.7

G - Buffalo Full fat G 4.83 4.13 3.27 4.63 6.03 5.07 6.03 4.7 3.63 3.17 6.23 6.23I - Buffalo Full fat I 5.1 3.77 2.97 4.73 5.87 5.1 6.47 6 3.83 3 6.87 7.33


Variables (axes F1 and F2: 74.77 %)

B BB

Fibroso1

Observations (axes F1 and F2: 74.77 %)

A

B

BB

B

B

BA

B

BA

A

B

B

A

A B

B

A

BB

A

A

B

B

A

AB

AB B

B

AB

A

AAA A

AB

B

A

B

B

Sfogliat

panna

latteElastico

Dolce

0.25

0.5

0.75

%)

G - Buffalo Full fat D - Cow Low fat

C - Cow Full fat

A - Cow Full fat

0

5

B

B

B

AA

B

B

A

BB

BBB

A

BB

AA

AA

A

B

AB

B

B AB

AB

B

A

AB

B

B

AA A

AB

A

AB

A

AB

AA

B

BB

Succoso

Sfogliat

SalatoYogurt

Acido

0 5

-0.25

0

0.25

F2 (2

0.31

%

I - Buffalo Full fat

-5

0F2

(20.

31 %

)

A

B

A ABB

B

B

B BB

BB A

B

Aliscia

Coesivo

-1

-0.75

-0.5

-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1E - Cow Low fat

-10

5

F1 (54.46 %)

Active variables Supplementary variables

-15 -10 -5 0 5 10

F1 (54.46 %)

Th d i f liki f B ff l d l lThe sensory drivers of liking for Buffalo and cow samples are clear

Xlstat MX Special Sensory ModuleXlstat MX– Special Sensory Module

• Generalised Procrustes Analysis GPA• Preference mapping (External)Preference mapping (External)• Penalty Analysis

Free Choice ProfilingFree Choice Profiling• Each assessor or respondents rates the products using

their own vocabulary• May be generated from lists or using triadic elicitation

(repertory grid )Triadic ElicitationTriadic Elicitation

Which pair of products in this triad are more similar to each other?other?

In what way are they more similar to each other than the third?

Generalized Procrustes AnalysisGeneralized Procrustes Analysis• In Greek mythology Procrustes (the stretcher) also known as Damastes• In Greek mythology, Procrustes (the stretcher), also known as Damastes

(subduer) and Polypemon (harming much), was a bandit from Attica. • iron bed into which he invited every passerby to lie down. • If the guest proved too tall, he would amputate the excess length; if theIf the guest proved too tall, he would amputate the excess length; if the

victim was found too short, he was then stretched out on the rack until he fit. Nobody would ever fit in the bed because it was secretly adjustable: Procrustes would stretch or shrink it upon sizing his victims from afar.

• Procrustes continued his reign of terror until he was captured by Theseus• Procrustes continued his reign of terror until he was captured by Theseus, who "fitted" Procrustes to his own bed and cut off his head and feet (since Theseus was a stout fellow, the bed had been set on the short position). Killing Procrustes was the last adventure of Theseus on his journey from Troezen to AthensTroezen to Athens.

• [edit] Derived meanings• A Procrustean bed is an arbitrary standard to which exact conformity is

forced. Sometimes the term is applied to the pan and scan process offorced. Sometimes the term is applied to the pan and scan process of cropping motion pictures for television and home video.

Generalized Procrustes AnalysisGeneralized Procrustes Analysis

Hotel

•Procrustes continued his reign of terror until he was captured by Theseus, who "fitted" P hi b d d ff hiProcrustes to his own bed and cut off his head and feet (since Theseus was a stout fellow, the bed had been set on the short position). Killing Procrustes was the last p ) gadventure of Theseus on his journey from Troezen to Athens.

GPA analysis averages matrices of different width

DescriptorsDescriptorsAssessor 1

A 2Assessor 2

Assessor 3m

ples

Sam

Application of GPA to the beer dataFRED JOHN KATE SUEoverall opinion overall opinion overall opinion overall opiniondark appearance light appearance dark appearance dark coloramount of head amount of color amount of head light colorlight appearance constant head thick head foamy appearancecarbonated appearance thin,watery head foamy headamount of color long lasting head light appearancelong lasting head frothy headhearty appearance golden colorfizzy appearancefizzy appearance

TOM JILL ANDY SIDoverall opinion overall opinion overall opinion thick appearancelight color pale appearance dark color coats glasscreamy head dark color appearance amount of head holds head for a long timeamount of head light appearance caramel color brown light colordark color carbonated appearance yellow color dark colorgolden color golden color appearance depth of shade amount of headduration of head amount of head overall opinion

fluffy appearancegolden brown appearancegolden-brown appearanceflat appearancebubbly appearance

• First point is that each person derived a different number of attributes• Applied GPA version that has recently become available in XLSTAT which

i ll fi ti t b th irequires all assessor configurations to be the same size

Application of GPA to the rep grid beer data

• There are 8 configurations one for each respondent• There are 8 configurations – one for each respondent• We want to do scaling, rotation and calculate a principal components

analysis on the consensus configuration.

Application of GPA to the rep grid beer data

Note I have only requested two dimensions here

Application of GPA to the beer data

Objects (axes F1 and F2: 76.75 %)

10

LLC

L

L

CB

A L

L

5FREDJOHNKATESUE

GFE

DC

B

A

L

KH

F

E D

C

BA

K

GF

ED

BA

L

K

H G

F

EC

BA

JH G

F

E

DC

BA

L

KH G

ED

CB

A

G

CB

H

FEDC

BA

KH GFED

CBA

0

TOMJILLANDYSIDConsensus

K

J

I

H

J

IGF

IH

J

I

DKJ

I

E

J I

FE KJIH F

ED

K

JI

GJ I

H Consensus

Th iti f h d t th d f

J

-5-5 0 5 10

-- axis F1 (56.61 %) -->

• The position of each product on the consensus and for each respondent is then plotted

Application of GPA to the beer dataVariables (axes F1 and F2: 76.75 %)

1

fred meanfred meanamount of headoverall opinion

overall opinion

kate meankate meankate meankate meankate mean

thick head

amount of headoverall opinion

overall opinion

ld b

overall opinion

sid meansid meansid meansid mean

amount of head

light color

holds head for a long time

0 5

1

fred meanfred mean

hearty appearance

long lasting head

amount of colorlight

dark appearance

j hj hj hfrothy headlong lasting

thin,watery head

light appearance

overall opinionlight

appearancefoamy head

dark appearance

sue meansue meansue meansue meansue meansue meansue meandark colortom meantom meantom meantom mean

duration of head

dark color

amount of headcreamy head

light color

overall opinionbubbly

golden-brown appearancefluffy

appearanceamount of head

carbonated appearance

light appearance

dark color appearance

pale appearance

overall opinion

andy meanandy meanandy meanandy meanandy mean

l l

amount of head

sid meansid meansid meansid meanlight color

coats glass

overall opinion

0

0.5

FREDJOHNKATESUE

fizzy appearancecarbonated appearance

gappearance john meanjohn meanjohn mean

long lasting headconstant head

amount of colorfoamy

appearancelight color

duration of headoverall opinionappearance

flat appearance

appearanceappearance appearancep

depth of shadeyellow color

caramel color brown

dark colordark color

coats glass

thick appearance

-0 5

0TOMJILLANDYSID

golden color

light colorgolden color

golden color appearance

-1

0.5

The position of each variable on the consensus and for each

1-1 -0.5 0 0.5 1

-- axis F1 (56.61 %) -->

• The position of each variable on the consensus and for each respondent is then plotted. This is the plot we use to determine which attributes are used consistently and go in the lexicon.

Preference MappingPreference MappingThe Principles

AnalyticalSensory Profile

ConsumersStudy

A small number of trained professionals A representative

y Study

trained professionals

A multi-criteria quantified

A representative sample

of the target A multi criteria quantified description

population

An overall hedonicNo Hedonics

An overall hedonic score

Statistical link

No Description

Preference Mapping

Sensory attributes

AnalyticalSensory Profile

ConsumersStudyThe Application

Hedonic Scores

Expertucts

Sensory attributes Hedonic Scores

ucts p1

0 10

√√ 3

8p

data

Prod

u

Prod

p8

p4√

√8

2

Dataanalysis

scores givenby 1 consumer

p1p3 p c sc

ore

5

10

p4

p3

p2

p5

p6

p7

p8

Hed

onic

0

5

2 0 202

p2

Products' expert mapAxis 2 Axis 1

-2 0-2

Preference Mapping

2"Preferers"The Quadratic Model

Hedonic Scores

-2

0

2 Preferers Hedonic Scores

ucts p1

0 10

√√ 3

8

3"Dislikers"

Prod

p8

p4√

√8

2


0

1

2

3 DislikersThe consumer scores

all the products

c sc

ore

5

10

4"Eclectics"

-1

0

2

nic

scor

eH

edon

ic

0

5

2 0 202The best fit -2

0

2

4 Eclectics-2

Axis 2

Hed

on

Axis 2 Axis 1-2 0

-2-4 Axis 2 Axis 1

Preference Mapping

Sensory attributes

pp gModelising all the consumers

Consumers

Expertucts

Sensory attributes

Hedonic scores

Consumers

ucts

pdata

Prod

u Hedonic scores

Prod

Dataanalysis


3

43

24

P3 P17p1p3 p c sc

ore

5

10

45678

1

2

3

4

P3 P1

P6

6

0

1

2

3

6

66

7

P8

P4

P1

P67p4

p3

p2

p5

p6

p7

p8

Hed

onic

0

5

2 0 202

6 80123451234

-2

-1

0

1 P8

P4

P7P5

P6

-6 -4 -2 0 2 4 6

-3

-2

-1 56

3

5

6P2

P7P5

5

4

p2

Products' expert mapAxis 2 Axis 1

-2 0-2

-8 -6 -4 -2 0 2 4 6 8

-4-3-2-10

-6 -4 -2 0 2 4 6

-3 P2

Preference Mapping

Sensory attributes

The Quadratic ResultConsumers

Expertucts

Sensory attributes

Hedonic scores

Consumers

ucts

pdata

Prod

u Hedonic scores

Prod

Dataanalysis

p1p3 p

p4

p3

p2

p5

p6

p7

p8

p2

Products' expert map

Consumer preferences andanalytical sensory description

A i 2Axis 1

(34.31 %)

Axis 2(30.36 %)

Consumer preferences andanalytical sensory description

3

4

POAFPLPF

50%

50%

60%

GreasyGreasyShinyShiny

1

2

PANC PFCF20% 30% 60%

H tH t

OdorousOdorous

0

PCRC

PPCF PCOF40%

70%

Axi

s 2

(30.

56 %

)

ColorColor

HeterogeneousHeterogeneous

-2

-1PCRC

PFDFPANF

PKSF

80%

RtdPORtdPOMatMat

GrainyGrainy

FirmFirmCompactCompact

StickySticky

-3

PPDC

PKSF

SmoothSmooth

FirmFirmClearClear

-5 -4 -3 -2 -1 0 1 2 3 4 5-4

Axis 1(32.91 %)

size 359 242 605Class cluster 1 clister 2 overallGibson's Green 5.059 4.304 4.760J h ' R d 4 608 8 232 6 064

We transpose and collect the ll i t Johnson's Red 4.608 8.232 6.064

Golden Delicious 6.876 7.125 6.958Granny Smith 6.263 3.756 5.260Pink Lady 6.046 4.860 5.573Fuji 4.469 6.712 5.378Top Red 2.889 6.662 4.421

overall means into anew table

Top Red 2.889 6.662 4.421Braeburn 5.893 4.704 5.420Royal Gala 3.892 6.683 5.028Sun Gold 7.071 4.493 6.043

Select cluster meansSelect cluster means as pref data

Regress on the first 2

PC’s.

No need for preliminary transformation as wetransformation as we have already done it.

Preference map

The position of h dGibson's Green

10

each product and the best fit model of

Golden Delicious

Gibson s Green

5

significantly fitted clusters are then plotted.

Braeburn

T R d

Fujioverall

clister 2

0

F2

pRoyal Gala

Top RedGranny Smith

Johnson's Red

cluster 1

-10 -5 0 5 10

Sun GoldPink Lady-5

F1

S51

Contour plot

S31

S36

S41

S46

80%-100%

S11

S16

S21

S26F2 60%-80%

40%-60%

20%-40%

0%-20%

S1

S6

F1

The need for two products is clear in the contour plot

Mean Drop/Penalty AnalysisMean Drop/Penalty Analysis• Consumers make simple rating of product attributes p g p

using diagnostic scales

Di ti “J t b t Ri ht” JAR lDiagnostic “Just about Right” JAR scales

1 Much too Weak

2 Too Weak

3 OK Just about Right3 OK Just about Right

4 Too Strong

5 Much too Strong•If an attribute is not at its optimum level does it have anIf an attribute is not at its optimum level does it have an impact on product liking?

Mean Drop/Penalty AnalysisMean Drop/Penalty Analysis

1

Percentages for the JAR levels

1

Percentages for the JAR levels (collapsed)

0 5

0.6

0.7

0.8

0.9

% 0 5

0.6

0.7

0.8

0.9

%

0.1

0.2

0.3

0.4

0.5%

0.1

0.2

0.3

0.4

0.5%

0Flavour JAR Garlic JAR Sweetness JAR Saltiness JAR

1 2 3 4 5

0Flavour JAR Garlic JAR Sweetness JAR Saltiness JAR

Too little JAR Too much

Mean Drop for Flavour on AMean Drop for Flavour on AMean Drop

Mean(Liking)

Mean Drop

7.0008.0009.000

3 0004.0005.0006.000

15% 35% 50%

0.0001.0002.0003.000 15% 35% 50%

Too little JAR Too much

Th i th t th i t h flThe consensus is that there is too much flavour

Mean Drop for Flavour on AMean Drop for Flavour on AMean drops vs %p

Flavour JAR

2.5

3

Garlic JAR

Flavour JAR

1.5

2ro

ps

Saltiness JAR

Sweetness JAR

Sweetness JAR

Garlic JAR

0

0.5

1

Mea

n d

Saltiness JAR

-1

-0.50 10 20 30 40 50 60 70

%

Th i th t th i t h fl d t littl G li

%

Too little Too much

The consensus is that there is too much flavour and too little Garlic

Multiple Factor Analysis (MFA)Multiple Factor Analysis (MFA)

• A new method to provide a map of several tables of data on the same samples from pdifferent sources and with different numbers of variablesnumbers of variables

• The weighting of the tables makes it possible to ensure the tables with more variables do not weight too heavily in the g yanalysis.

MFA Graphicp

Table 1 Table 2 Table 3Table 1

PCA MCA PCA or MCAPCA or MCA PCA or MCA

E1 E2 E3E1 E2 E3

/E2/E1 /E2/E1/E3

APCA

A B

C D

Loire Wine dataLoire Wine dataPE

LATI

IL ST1

ST2

ST3

ST4

ST5

ION

1

ION

2

ION

3

AK

E1

AK

E2

AK

E3

AK

E4

AK

E5

AK

E6

AK

E7

AK

E8

AK

E9

AK

E10

STE1

STE2

STE3

STE4

ID AP P

SOI

RES

RES

RES

RES

RES

VIS

VIS

VIS

SHA

SHA

SHA

SHA

SHA

SHA

SHA

SHA

SHA

SHA

TAS

TAS

TAS

TAS

2EL Saumur Mil1 3.074 3 2.714 2.28 1.96 4.321 4 3.269 3.407 3.308 2.885 2.32 1.84 2 1.65 3.259 2.963 3.2 2.963 2.107 2.429 2.51CHA Saumur Mil1 2.964 2.821 2.375 2.28 1.68 3.222 3 2.808 3.37 3 2.56 2.44 1.739 2 1.381 2.962 2.808 2.926 3.036 2.107 2.179 2.6541FON Bourgueuil Mil1 2.857 2.929 2.56 1.96 2.077 3.536 3.393 3 3.25 2.929 2.769 2.192 2.25 1.75 1.25 3.077 2.8 3.077 3.222 2.179 2.25 2.6431VAU Chinon Mil2 2.808 2.593 2.417 1.913 2.16 2.893 2.786 2.538 3.16 2.88 2.391 2.083 2.167 2.304 1.476 2.542 2.583 2.478 2.704 3.179 2.185 2.51DAM Saumur Ref 3.607 3.429 3.154 2.154 2.04 4.393 4.036 3.385 3.536 3.36 3.16 2.231 2.148 1.762 1.6 3.615 3.296 3.462 3.464 2.571 2.536 2.7862BOU B il R f 2 857 3 111 2 577 2 04 2 077 4 464 4 259 3 407 3 179 3 385 2 8 2 24 2 148 1 75 1 476 3 214 3 148 3 321 3 286 2 393 2 643 2 8572BOU Bourgueuil Ref 2.857 3.111 2.577 2.04 2.077 4.464 4.259 3.407 3.179 3.385 2.8 2.24 2.148 1.75 1.476 3.214 3.148 3.321 3.286 2.393 2.643 2.8571BOI Bourgueuil Ref 3.214 3.222 2.962 2.115 2.04 4.143 3.929 3.25 3.429 3.5 3.038 2.2 2.385 1.826 1.476 3.25 3.222 3.385 3.393 2.607 2.607 2.7783EL Saumur Mil1 3.12 2.852 2.5 2.2 2.185 4.214 3.857 3.077 3.654 3.077 2.52 2.32 2.444 2.08 1.905 3.28 3.16 2.962 3.25 2.179 2.63 2.778DOM1 Chinon Mil1 2.857 2.815 2.808 1.923 2.074 4.037 3.893 3.28 3.357 3.346 3 2.04 2.125 1.875 1.524 3.148 2.893 3.308 3.286 2.286 2.407 2.7411TUR Saumur Mil2 2.893 3 2.571 1.846 1.68 3.704 3.407 3.111 3.222 3.259 2.926 2.04 2.042 2 1.773 3.077 2.704 2.778 2.893 2.357 2.25 2.7044EL Saumur Mil2 3.25 3.286 2.714 1.926 1.962 3.857 3.643 3.259 3.607 3.385 2.889 2.115 2.16 1.955 1.571 3.286 3.036 3.222 3.321 2.429 2.571 2.893PER1 Saumur Mil2 3.393 3.179 2.769 2.038 1.92 4.714 4.5 3.321 3.481 3.385 2.962 2 2.2 2.042 1.545 3.321 3.071 3.143 3.357 2.429 2.607 2.821

The data correspond to the tasting of 21 wines from the Loire region in France by 36 experts. The data set comprises 21 observations

2DAM Saumur Ref 3.179 3.286 2.778 2.231 1.76 4.222 4.071 3.462 3.481 3.423 2.963 2.269 2.154 1.957 1.571 3.481 3.259 3.269 3.393 2.286 2.5 2.8211POY Saumur Ref 3.071 3.107 2.731 2.12 1.8 4.714 4.536 3.429 3.357 3.444 2.885 2.12 2.346 1.826 1.55 3.269 3.08 3.192 3.519 2.111 2.536 2.7781ING Bourgueuil Mil1 3.107 3.143 2.846 2.185 1.962 4.071 3.893 3.462 3.357 3.37 2.846 2.24 2.28 1.75 1.524 3.333 3.037 3.37 3.185 2.286 2.643 2.9291BEN Bourgueuil Ref 2.929 3.179 2.852 2 2.037 3.889 3.429 3.143 3.286 3.308 3.115 2.269 2 1.917 1.4 3.04 2.96 3.2 3.393 2.393 2.357 2.7042BEA Chinon Ref 3.036 3.179 3.037 2.231 1.667 3.786 3.607 3.357 3.444 3.5 3.185 2.16 2.24 1.913 1.75 3.52 3.296 3.462 3.071 2.571 2.321 2.9291ROC Chinon Mil2 3.071 2.926 2.741 2 1.88 3.679 3.393 3.192 3.37 3.36 2.963 2.308 1.917 2 1.429 3.25 2.92 2.88 3.071 2.393 2.321 2.8212ING Bourgueuil Mil1 2 643 2 786 2 536 1 889 1 808 2 607 2 536 2 444 2 889 2 8 2 5 1 962 2 111 2 08 1 318 2 68 2 308 2 556 2 179 2 25 1 964 2 25

y p pand 31 dimensions. The 31 dimensions can grouped into 6 categories:- the first 2 qualitative variables are related to the geography

2ING Bourgueuil Mil1 2.643 2.786 2.536 1.889 1.808 2.607 2.536 2.444 2.889 2.8 2.5 1.962 2.111 2.08 1.318 2.68 2.308 2.556 2.179 2.25 1.964 2.25T1 Saumur Mil4 3.696 3.192 2.833 1.826 2.385 4.321 4 3.333 3.737 3.08 2.833 1.773 2.44 2.292 1.571 3.437 2.958 2.6 2.963 2.407 2.643 2.963T2 Saumur Mil4 3.708 2.926 2.52 2.04 2.667 4.321 4.107 3.259 3.727 2.885 2.6 2.083 2.609 2.174 1.65 3.095 3.136 2.545 3.333 2.571 2.667 2.704

(appellation and soil);- the next 5 quantitative variables correspond to the olfaction after rest;- the next 3 quantitative variables correspond to visual criteria;- the next 10 quantitative variables correspond to the olfaction after shaking;

th t 9 tit ti i bl d t th t t- the next 9 quantitative variables correspond to the taste;- the last 2 quantitative variables correspond to global ratings.

Loire Wine dataLoire Wine dataPE

LATI

IL ST1

ST2

ST3

ST4

ST5

ION

1

ION

2

ION

3

AK

E1

AK

E2

AK

E3

AK

E4

AK

E5

AK

E6

AK

E7

AK

E8

AK

E9

AK

E10

STE1

STE2

STE3

STE4

ID AP P

SOI

RES

RES

RES

RES

RES

VIS

VIS

VIS

SHA

SHA

SHA

SHA

SHA

SHA

SHA

SHA

SHA

SHA

TAS

TAS

TAS

TAS

2EL Saumur Mil1 3.074 3 2.714 2.28 1.96 4.321 4 3.269 3.407 3.308 2.885 2.32 1.84 2 1.65 3.259 2.963 3.2 2.963 2.107 2.429 2.51CHA Saumur Mil1 2.964 2.821 2.375 2.28 1.68 3.222 3 2.808 3.37 3 2.56 2.44 1.739 2 1.381 2.962 2.808 2.926 3.036 2.107 2.179 2.6541FON Bourgueuil Mil1 2.857 2.929 2.56 1.96 2.077 3.536 3.393 3 3.25 2.929 2.769 2.192 2.25 1.75 1.25 3.077 2.8 3.077 3.222 2.179 2.25 2.6431VAU Chinon Mil2 2.808 2.593 2.417 1.913 2.16 2.893 2.786 2.538 3.16 2.88 2.391 2.083 2.167 2.304 1.476 2.542 2.583 2.478 2.704 3.179 2.185 2.51DAM Saumur Ref 3.607 3.429 3.154 2.154 2.04 4.393 4.036 3.385 3.536 3.36 3.16 2.231 2.148 1.762 1.6 3.615 3.296 3.462 3.464 2.571 2.536 2.7862BOU B il R f 2 857 3 111 2 577 2 04 2 077 4 464 4 259 3 407 3 179 3 385 2 8 2 24 2 148 1 75 1 476 3 214 3 148 3 321 3 286 2 393 2 643 2 8572BOU Bourgueuil Ref 2.857 3.111 2.577 2.04 2.077 4.464 4.259 3.407 3.179 3.385 2.8 2.24 2.148 1.75 1.476 3.214 3.148 3.321 3.286 2.393 2.643 2.8571BOI Bourgueuil Ref 3.214 3.222 2.962 2.115 2.04 4.143 3.929 3.25 3.429 3.5 3.038 2.2 2.385 1.826 1.476 3.25 3.222 3.385 3.393 2.607 2.607 2.7783EL Saumur Mil1 3.12 2.852 2.5 2.2 2.185 4.214 3.857 3.077 3.654 3.077 2.52 2.32 2.444 2.08 1.905 3.28 3.16 2.962 3.25 2.179 2.63 2.778DOM1 Chinon Mil1 2.857 2.815 2.808 1.923 2.074 4.037 3.893 3.28 3.357 3.346 3 2.04 2.125 1.875 1.524 3.148 2.893 3.308 3.286 2.286 2.407 2.7411TUR Saumur Mil2 2.893 3 2.571 1.846 1.68 3.704 3.407 3.111 3.222 3.259 2.926 2.04 2.042 2 1.773 3.077 2.704 2.778 2.893 2.357 2.25 2.7044EL Saumur Mil2 3.25 3.286 2.714 1.926 1.962 3.857 3.643 3.259 3.607 3.385 2.889 2.115 2.16 1.955 1.571 3.286 3.036 3.222 3.321 2.429 2.571 2.893PER1 Saumur Mil2 3.393 3.179 2.769 2.038 1.92 4.714 4.5 3.321 3.481 3.385 2.962 2 2.2 2.042 1.545 3.321 3.071 3.143 3.357 2.429 2.607 2.8212DAM Saumur Ref 3.179 3.286 2.778 2.231 1.76 4.222 4.071 3.462 3.481 3.423 2.963 2.269 2.154 1.957 1.571 3.481 3.259 3.269 3.393 2.286 2.5 2.8211POY Saumur Ref 3.071 3.107 2.731 2.12 1.8 4.714 4.536 3.429 3.357 3.444 2.885 2.12 2.346 1.826 1.55 3.269 3.08 3.192 3.519 2.111 2.536 2.7781ING Bourgueuil Mil1 3.107 3.143 2.846 2.185 1.962 4.071 3.893 3.462 3.357 3.37 2.846 2.24 2.28 1.75 1.524 3.333 3.037 3.37 3.185 2.286 2.643 2.9291BEN Bourgueuil Ref 2.929 3.179 2.852 2 2.037 3.889 3.429 3.143 3.286 3.308 3.115 2.269 2 1.917 1.4 3.04 2.96 3.2 3.393 2.393 2.357 2.7042BEA Chinon Ref 3.036 3.179 3.037 2.231 1.667 3.786 3.607 3.357 3.444 3.5 3.185 2.16 2.24 1.913 1.75 3.52 3.296 3.462 3.071 2.571 2.321 2.9291ROC Chinon Mil2 3.071 2.926 2.741 2 1.88 3.679 3.393 3.192 3.37 3.36 2.963 2.308 1.917 2 1.429 3.25 2.92 2.88 3.071 2.393 2.321 2.8212ING Bourgueuil Mil1 2 643 2 786 2 536 1 889 1 808 2 607 2 536 2 444 2 889 2 8 2 5 1 962 2 111 2 08 1 318 2 68 2 308 2 556 2 179 2 25 1 964 2 25The main goal of the study is to understand how the wines relate to each other2ING Bourgueuil Mil1 2.643 2.786 2.536 1.889 1.808 2.607 2.536 2.444 2.889 2.8 2.5 1.962 2.111 2.08 1.318 2.68 2.308 2.556 2.179 2.25 1.964 2.25T1 Saumur Mil4 3.696 3.192 2.833 1.826 2.385 4.321 4 3.333 3.737 3.08 2.833 1.773 2.44 2.292 1.571 3.437 2.958 2.6 2.963 2.407 2.643 2.963T2 Saumur Mil4 3.708 2.926 2.52 2.04 2.667 4.321 4.107 3.259 3.727 2.885 2.6 2.083 2.609 2.174 1.65 3.095 3.136 2.545 3.333 2.571 2.667 2.704

The main goal of the study is to understand how the wines relate to each other, and to identify which criteria seem to agree (are redundant?) or disagree. We decide not to use the two qualitative variables and the last two quantitative variables in the first part of the study but to only use them as supplementaryvariables in the first part of the study, but to only use them as supplementary variables at the end of the study: we don't want the analysis to be based on anything else but on objective tasting criteria.

Multiple factor AnalysisMultiple factor AnalysisTables (axes F1 and F2: 68 87 %)Tables (axes F1 and F2: 68.87 %)

restgeog

0.6

0.7

The coordinates of the tables are then displayed and used to create the map of the tables We can see on the

shake

0.4

0.5

.49

%)

tables. We can see on the map the that the first factor is highly related to the four active tables (high coordinates and

tasteglob

0.2

0.3

F2 (1

9.

( ghigh contributions). The second factor is mostly related to the olfaction after rest, and to a lower extend to the

vis

0

0.1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

F1 (49 38 %)

to a lower extend, to the olfaction after shaking.

F1 (49.38 %)

Active Supplementary

Multiple factor Analysisp yThe next chart shows the observations with the centroids of the two

Observations (axes F1 and F2: 68.87 %)centroids of the two qualitative variables. We can see that the T1 and T2 wines are very close, and isolated from the other

T2

T1

4

isolated from the other wines. They are highly related to the second factor, which, as we saw earlier, is highly related to Rest5 The

T1

2

3

%)

highly related to Rest5. The 1DAM wine has the highest coordinate on the first axis.

PER14EL

3EL1VAU

0

1F2

(19.

49 %

2ING1ROC

2BEA1BEN

1ING 1POY2DAM

1TUR DOM11BOI2BOU 1DAM

1FON

2EL-1

0

1CHA

-2-5 -4 -3 -2 -1 0 1 2 3 4

F1 (49.38 %)F1 (49.38 %)

The Future Path PLSThe Future – Path PLS

• PATH PLS is now released• Enab;es complex pathways for relationsEnab;es complex pathways for relations

between sensory categories and emotional and brand image attributes toemotional and brand image attributes to be built and the goodness of fit tested.

Possible Path model for Pref mappingPossible Path model for Pref mapping

D hiT t Cl t

Appearance

sensory DemographicsTaste Clusters

Appearance

Flavour/aroma

S

Flavour/aroma

ConsumerSensory Consumer Hedonics

Texture

SummarySummary

• Easy to use and input data• Inside Excel writes results to ExcelInside Excel, writes results to Excel• Useful Plotting• Excellent Multivariate suite• MX provides a uniquely useful resource to• MX provides a uniquely useful resource to

sensory and consumer scientists• On-going innovation (Path-PLS) keeps

momentum

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