presentation parfum.pdf - Senstools

How reliable are the consumers? 

Comparison of sensory profiles 

from consumers and experts 

(1) OPP Product Research 

(2) AAgroCampus C OOuest t 

Project 8013 

July 2008 

WORCH Thierry (1) 

LÊ Sébastien (2) 

LÊ Sébastien ( ) 

PUNTER Pieter (1) 

mailto: thierry@opp.nl 

Senior project manager Pieter Punter 

Project manager Thierry Worch

introduction 

• in the sensory theory: 

• experts p ppanels are used for the pproducts’ description p 

• consumers should only be used for the hedonic task 

• they lack two essentials qualities for profiling (consensus 

and reproducibility) 

• there are strong halo effects (Earthy, MacFie & Hedderley, 

1997) 

• in the sensory practice: 

• consumers are sometimes used for both tasks 

8013 

• it has been proven that consumers’ consumers description show the 

required qualities (consensus and reproducibility) (Husson, Le 

Dien, Pagès, 2001) 

3

problematic 

How reliable are the consumers? 

8013 

4

presentation of the studies 

• products: 

• twelve luxurious women perfumes 

(Gazano, Ballay, Eladan & Sieffermann, 2005) 

8013 

AAngel l L’I L’Instant t t 

(Eau de Parfum) (Eau de Parfum) 

Cinéma J’Adore 

(Eau de Parfum) (Eau de Toilette) 

Pleasures 

J’Adore 


Aromatics Elixir 

(Eau de Parfum) 

Lolita Lempicka 


Pure Poison 


Shalimar 

(Eau de Toilette) 

Chanel N°5 N 5 

Coco Mademoiselle 


5


• expert panel (Agrocampus Rennes) 

8013 

• twelve persons (11 students and 1 teacher) from the Chantal Le 

Cozic school (esthetics and cosmetic school) 

• focus group per group of six, with two animators 

• generation of a list of twelve attributes 

• “Vanille”, “Notes Florales”, “Agrume”, “Boisé”, “Vert”, “Epicé”, “Capiteux”, 

“Fruité”, “Fraîcheur Marine”, “Gourmand”, “Oriental”, “Enveloppant” 

• training session for the most difficult ones 

• the twelve products were tested two times in two one-hour 

sessions 

6


• consumer panel (OP&P Product Research, Utrecht) 

8013 

• 103 naïve Dutch consumers living in the Utrecht area 

• the same twelve perfumes were rated on 21 attributes 

• “odour intensity”, “freshness”, “jasmine”, “rose”, “camomile”, “fresh lemon”, 

“vanilla”, “mandarin/orange”, “anis”, “sweet fruit/melon”, “honey”, “caramel”, 

“spicy” spicy , “woody” woody , “leather” leather , “nutty/almond” nutty/almond , “musk” musk , “animal” animal , “earthy” earthy , “incense” incense , 

“green” 

• two products (Shalimar and Pure Poison) were duplicated 

• the fourteen (12+2) products were tasted in two one-hour sessions (seven 

products in each session, presentation order was balanced) 

7

presentation route map 

• the consumer and expert data are compared in three different ways 

8013 

1.Univariate analysis 

• analyses of variance 

• correlations 

22.Multivariate Multivariate comparison 

• construction of the two products’ spaces (PCA) 

• comparison of the products products’ spaces through GPA and MFA 

3.Confidence ellipses 

• graphical confidence intervals around the products averaged 

over the two panels 

• graphical confidence intervals around the products defined 

by the different panels 

8

8013 

Performance of the two panels 

(univariate analysis) 

9

performance of the panels 

• usually, the expert panels should have many qualities: 

8013 

• discrimination: panelists should be able to detect and describe 

the differences existing between the products 

• reproducibility: panelists should describe the products in the 

same way, when they are repeated 

• agreement: panelists should give the same description of the 

products as the rest of f the panel 

• it can be measured with the correlations (usually, one 

panelist pa e s is s co compared pa ed to o the e mean ea oover e the e rest es oof the e 

panel) 

10

expert panel 

• panel performance 

• discriminate on 11 out of 12 attributes (“Agrume”, pvalue=0.08) 

• reproducible for 11 out of 12 attributes (“Notes Florales”) 

• panellist performance (discrimination (discrimination, reproducibility) 

8013 

• panellists 1, 3 and 12 are very good 

• panellists 8, 9 and 10 are not good in discrimination 

(discriminate the products on less than 6 out of 12 attributes) 

• panellist lli 9 iis also l not good d iin reproducibility d ibili ( (reproducible d ibl on 

only 3 out of 12 attributes. “Notes Florales”, “Agrume” and 

“Enveloppant”) 

11

expert panel (correlations) 

• distribution of the correlations (correlation between expert i and the 

mean over the (n-1) ( ) others) ) 

8013 

12

consumer panel 

• discrimination (on the twelve original products) 

• the consumers discriminate the products on all attributes except 

“camomile” (pvalue = 0.62) 

• NB: the consumers discriminate on “Citrus” (pvalue < 0.001) 

• reproducibility (on the two duplicated products only) 

8013 

• consumers are reproducible on all attributes except one 

(“woody”) 

13

consumer panel (reproducibility) 

Shalimar 

Shalimar 2 

8013 

14

consumer panel (correlations) 

• distribution of the correlations (correlation between a consumer i and 

the mean over the (n-1) ( ) others) ) 

8013 

15

conclusions on the panel performance 

• expert panel 

• discriminates between the products 

• are reproducible 

• high correlations 

• consumer panel 

8013 

• discriminates between the products 

• shows reproducibility reproducibility’s s qualities 

• lower but still positive correlations (consumers are untrained) 

Both panels show the same qualities 

16

8013 

Products’ spaces 

(multivariate analysis) 

17

methodology 

• products’ spaces 

• the products profiles (averaged over the panellists or consumers) 

are computed. 

• Principal Components Analysis is then run on these product x 

attribute matrices 

• comparison of the two products’ products spaces (expert and consumer) is a 

“multi-table problem” 

• comparison through the Procrustean analysis 

8013 

• comparison through Multiple Factor Analysis ( (MFA) ) 

• comparison through the confidence ellipses technique 

18

Dimennsion 

2 (21.87 %) % 

3,5 

3,0 

25 2,5 

2,0 

1,5 

1,0 

0,5 

0,0 

-0,5 

-1,0 

-1,5 

-2,0 

-2,5 

-3,0 

-3,5 

-4,0 

-4,5 

expert panel g p 

Pleasures 

JAdore_EP JAdore_ET 

CocoMelle 

PurePoison 

LInstant 

Cinema 

AromaticsElixir 

Chaneln5 

LolitaLempicka 

Shalimar 

Angel 

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

Dimension 1 (64.22 %) 

8013 

2 (21.87 %) 

Dimension 

1 

0 

Notes.florales 

Agrume 

Fraicheur.marine 

Fruité 

Vert 

-1 Gourmand 

Boisé 

Vanille 

Epicé 

Oriental 

Capiteux 

Enveloppant 

-1 0 1 


19

Dimennsion 

2 (17.97 %) % 

6,0 

5,5 

5,0 

4,5 

4,0 

3,5 

3,0 

2,5 

2,0 

15 1,5 

1,0 

0,5 

consumer panel 

JAdore_EP 

Cinema 

0,0 

-0,5 JAdore_ET 

CocoMelle 

-1,0 

-1,5 

-2,0 

-2,5 

-3,0 

-3,5 

-4,0 

Pleasures PurePoison 

-4,5 

-5,0 

-5,5 

-6,0 

-6,5 

-7,0 70 


LInstant 

Chaneln5 Shalimar 


Angel 

-5 -2.5 0 2.5 5 7.5 


8013 

Dimensionn 

2 (17.97 %) 

1 

0 

-11 

sweet_fruit 

freshness 

green 

jasmin 

rose 

citrus 

fresh_lemon 

g 

vanilla 

honey 

camomille 

caramel 

anis 

iintensity t it spicy i 

nutty 

animal 

musk 

incense 

leather 

woody 

earthy 

-1 0 1 


20

8013 

Multivariate comparison of the 

two panels (GPA and MFA) 

21

expert vs consumer: Procrustes analysis 

GPA consensus space 

(coefficient of similarity: 0.93) 

8013 

Dimm 

2 

0.4 

0.1 0.2 0 0.3 

0.0 

-0.3 -0.2 - -0.1 

JAdore_EP JAdore_ET CocoMelle 

Pleasures PurePoison 

Cinema 

LInstant 


Chaneln5 

AAngell Shalimar 


-0.2 02 00 0.0 02 0.2 04 0.4 

Dim 1 

22

expert vs consumer: Multiple Factor Analysis 

MFA partial points’ 

representation 

(RV coefficient: 0.87) 

8013 

Dim 2 (19.35 

%) 

2 

1 

0 

-1 

-2 

experts 

consommateurs 

Cinema 

LInstant 

JAdore_EP JAdore_ETCocoMelle 

PurePoison 

Pleasures 


Chaneln5 

Angel 

Shalimar 


-2 -1 0 1 2 3 

I di id lf t 

Dim 1 (64.06 %) 

23

expert vs consumer: Multiple Factor Analysis 

g 

MFA variables’ 

representation 

(RV coefficient: 0.87) 

8013 

(19.35 %) 

Dimension D 2 

1 

0 

-1 

expert 

consumer 

citrus 

Fruité 

sweet_fruit 

freshness 

green 

jasmin 

rose 

Agrume 

Notes.florales 

ffresh_lemon h l 

Fraicheur Fraicheur.marine marine 

Vert 

Gourmand 

vanilla 

honey 

Vanille 

caramel 

anis 

camomille 

intensity 

Boisé 

nutty 

Enveloppant 

animal 

musk 

woody 

leather 

incense 

earthy 

Capiteux 

Epicé 

Oriental 

spicy 

-1 0 1 


24

8013 

Comparison through the 

confidence fid ellipses lli ttechnique h i 

(Husson, ( , Lê & Pagès, g , 2005) ) 

(Lê, Pagès & Husson, 2008) 

25

confidence ellipses 

methodology 

1.Compute the product profiles (averaged by product over the judges) 

2.Create the products’ space 

33.Re-sample R l bby bbootstraping t t i new panels l 

4.For each new panel, compute new products’ profiles 

5.Project as illustrative the products on the original product space 

6.Steps 3 to 5 are repeated many times (i.e. 500 times) 

7.Confidence ellipses around the products containing 95% of the data are 

constructed 

8013 

principle 

• if ellipses are superimposed, the products are not significantly different 

• the size of the ellipses is related to the variability existing around the 

products p 

26


Dim 2 (19. .39%) 

2 

1 

0 

-1 

8013 

Confidence ellipses around the products 

JAdore_EP 

JAdore_ET 

Pleasures 

CocoMelle 

Cinema 

LInstant 

PurePoison 


Chaneln5 

Angel 

Shalimar 


-3 -2 -1 0 1 2 3 4 

Dim 1 (64.02%) 

27


• as we have two different panels, we can apply this methodology to 

both 

8013 

• creation of confidence ellipses around each product seen by 

each panel (24 ellipses are created here) 

• comparison of a given product through the two panels (same 

colour) 

• comparison of f the different ff products within a panel (same ( type 

of line) 

28

Dim 2 (199.39%) 


2 

1 

0 

-2 - -1 

8013 

cons. 

expert 

Confidence ellipses for the partial points 

Cinema 

LInstant 

JAdore_EP JAdore_ET CocoMelle 

PurePoison 

Pleasures 


Chaneln5 

Angel 

Shalimar 


-4 -2 0 2 4 

Dim 1 (64.02%) 

(64 02%) 

29


• partial points 

8013 

• within a product, the ellipses related to the two panels are 

always superimposed (no differences between the panels) 

• the sizes of the ellipses are equal 

• the higher amount of consumers compensate the higher 

variability due to the lack of training for consumers 

30

conclusions 

• although consumers don’t don t have the habit to describe perfumes 

(difficult task), they give the same information as the expert panel (and 

it’s identical to the standard description of the perfumes) 

• they also have the same qualities (discrimination and reproducibility) 

• a difference between consumers and experts panel exists in the 

variability of the results (more variability for consumers), but this is 

compensated by the larger size of the panel (here 103 vs 12) 

• with consumers, co su e s, not o oonly y intensity, e s y, bu but aalso so ideal dea aand d hedonic edo c 

questions can be asked in the same time 

8013 

31

eferences 

• Earthy P., MacFie H & Hedderlay D. (1997). Effect of question order on sensory 

perception and preference in central locations. Journal of Sensory Studies, vol.12, p215- 

237 

• Gazano G., Ballay S., Eladan N. & Sieffermann J.M. (2005). Flash Profile and flagrance 

research: using the words of the naïve consumers to better grasp the perfume’s perfume s universe universe. 

In: ESOMAR Fragrance Research Conference, 15-17 May 2005, New York, NY. 

• Husson FF., Le Dien SS. & Pagès J J. (2001) (2001). Which value can be granted to sensory 

profiles give by consumers? Methodology and results. Food Quality and Preference, 

vol.16, p291-296 

• Husson F., Lê S.& Pagès J. (2005). Confidence ellipses for the sensory profile obtained 

by principal component analysis. Food Quality and Preference, vol.16, p245-250 

• Lê S., Pagès J. & Husson F. (2008). Methodology for the comparison of sensory 

profiles provided by several panels: Application to a cross cultural study. Food Quality and 

Preference, vol.19, p179-184 

8013 

32

thank you 

• special thanks to 

• Melanie COUSIN 

• Maëlle PENVEN 

• Mathilde PHILIPPE 

• Marie TOULARHOAT 

students from AgroCampus-Rennes, who took care of the whole 

expert panel data. 

8013 

33

Thank you for your attention!

presentation parfum.pdf - Senstools

Create successful ePaper yourself

Delete template?

Save as template?