Penalty analysis based on CATA questions to identify drivers of ...

<strong>Penalty</strong> <strong>analysis</strong> <strong>based</strong> on CATA questions to identify
drivers of liking and directions for product
reformulation
Gastón Ares Ares1 , Cecilia Dauber Dauber1 , Elisa Fernández Fernández1 , Ana Giménez Giménez1 ,
Paula Varela Varela2 1 Facultad de Química. Universidad de la República. Montevideo, Uruguay
2 Instituto de Agroquímica y Tecnología de Alimentos, Valencia, Spain.
11th Sensometrics, 10-13 July 2012, Rennes,
France
1

Introduction
During new product development
development, one of the challenges for
Sensory & Consumer Science is to provide actionable
information for specific changes in product formulation
(Moskowitz Moskowitz & Hartmann Hartmann, 2008 2008) 2008 2008).
Many strategies have been used in product optimization for
identifying drivers of liking and ideal products products: products products:
Preference mapping <strong>based</strong> on sensory
characterization of the products (van Kleef et al al., al al., 2006 2006). 2006 2006).
Consumer Consumer-<strong>based</strong> <strong>based</strong> sensory characterizations
characterizations (Dooley Dooley et al al.,
2010 2010; Ares et al al., 2010; 2010 Varela & Ares, 2012 2012). )
Consumers
Consumers’ description of the ideal product
11th Sensometrics, 10-13 July 2012, Rennes,
France 2

Just Just-about about-right right scales (JAR)
Consumers evaluate aset set of attributes as deviations from
the ideal (Lawless ( Lawless &Heymann Heymann, y , 2010 2010). )
Simple and common approach
<strong>Penalty</strong> <strong>analysis</strong> enables the identification of directions for
product reformulation (Xiong Xiong & Meullenet, Meullenet 2006). 2006
They have raised several concerns regarding their
influence on overall liking scores (Epler Epler et al al.,1998 al al.,1998 1998; Popper et al al., al al.,
2004 2004).
11th Sensometrics, 10-13 July 2012, Rennes,
France 3

Ideal profile method
Consumers rate the intensity of a set of attributes for the
samples and their ideal product using scales (Worch Worch et al., al
2010 2010; Worch et al al., 2012a,
2012 2012b) b).
Ideal product descriptions are similar to the most liked
products products. p
Provides actionable information for product reformulation
reformulation.
11th Sensometrics, 10-13 July 2012, Rennes,
France 4

Check Check-all all-that that-apply apply (CATA) questions questions
Have gained popularity for sensory characterization of
food products with consumers (Adams et al al., 2007; 2007 Dooley et al., al
2010 2010; Ares et al., al 2010; 2010 Ares et al al., 2011). 2011
Consumers are presented a list of terms and are asked to
check all the terms they consider appropriate to describe
a sample sample.
Quick, simple and easy task for consumers
2007 2007).
consumers (Adams et al al.,
It has been used to describe consumers consumers’ ideal product
2011).
(Cowden Cowden et al., al 2009; 2009 Ares et al al., 2011
<strong>Penalty</strong>/reward <strong>analysis</strong> for emotional terms (Plaehn Plaehn, 2012). 2012
11th Sensometrics, 10-13 July 2012, Rennes,
France 5

Aim of the study
Apply penalty <strong>analysis</strong> <strong>based</strong> on consumer
responses to a CATA question about a set of
samples and their ideal product to identify drivers
of liking and directions for product reformulation
reformulation.
11th Sensometrics, 10-13 July 2012, Rennes,
France
6

Materials and methods
Study Studyy 1: Yogurts
g
o 74 consumers evaluated 8 yogurts formulated
following a 23 g full full factorial design design g for for fat fat content, content
gelatin and starch. starch
o They tried the yogurts, rated their texture liking using
a 9-point point hedonic scale and answered a CATA
question composed of 16 texture terms
o Th They also l answered answeredd th the CATA question ti f for their th their i ideal id ideal l
yogurt yogurt.
Smooth Viscous Homogeneous Liquid
Lumpy Creamy Sticky Rough
Gummy Thick Gelatinous Firm
Heterogeneous Consistent Runny Mouth-coating
11th Sensometrics, 10-13 July 2012, Rennes,
France
7

Study Studyy 2:Apples Apples pp
o 119 consumers evaluated 5 commercial apple
cultivars cultivars.
o They tried the apples apples, rated their overall liking using a
9-point point hedonic scale and answered aCATA CATA question
composed of 15 odour odour, flavour and texture terms
o They also answered the CATA question for their ideal
apple. apple l
Firm Sour Odourless Juicy Crispy
Tasteless Sweet Flavoursome Mealy Bitter
Coarse Apple flavour Apple odour Soft Astringent
11th Sensometrics, 10-13 July 2012, Rennes,
France 8

Data <strong>analysis</strong> y
o Overall liking scores
• ANOVA
• Cluster <strong>analysis</strong> on data from Study 2
o CATA question
i
• Frequency of use
Cochran’s Cochran s Q test
• Cochran’s Cochran s
• Correspondence <strong>analysis</strong>
o <strong>Penalty</strong> <strong>analysis</strong>
11th Sensometrics, 10-13 July 2012, Rennes,
France 9

o <strong>Penalty</strong> <strong>analysis</strong>
• Dummy variable approach
Consumer Sample Firm Sour Odourless Juicy … Astringent
1 Crisp Pink 0 1 0 1 … 0
1 … … … … … … …
00: iindicates di t th that t th the attribute tt ib t was usedd tto
describe the sample as in the ideal product
… … … … … … … …
119 Royal gala … … … … … …
1: indicates that the attribute was used differentlyy
to describe the sample and the ideal product
11th Sensometrics, 10-13 July 2012, Rennes,
France 10

o <strong>Penalty</strong> <strong>analysis</strong>
The percentage of consumers who used an attribute
differently for describing each sample and the ideal
product
Threshold Threshold: 20% 20 (Xiong Xiong & Meullenet, Meullenet 2006; 2006 Plaehn, Plaehn 2012). 2012
• The
Mean drop associated with the deviation from the
ideal ideal.
• Mean
Kruskal Kruskal-Wallis
Kruskal Wallis test
• Partial Partial-least least squares (PLS) regression
Overall liking as dependent variable and dummy
variables as regressors (Xiong Xiong & Meullenet Meullenet, 2006). 2006
11th Sensometrics, 10-13 July 2012, Rennes,
France 11

Results
Study 1: Yogurts
Texture liking scores
Texture liking
(1‐9)
7
5
3
1
4.2 c,d
5.6 a
3.5 d
5.2 a,b
5.6 a
5.9 a
4.4 b,c
1 2 3 4 5 6 7 8
Samples
5.3 a,b
11th Sensometrics, 10-13 July 2012, Rennes,
France 12

Frequency of use of the terms (%)
Attribute
1 2 3 4
Sample
5 6 7 8 Ideal
Smooth *** 41 53 12 38 62 64 23 45 92
Lumpy *** 32 7 57 11 26 11 61 8 1
Viscous ns Homogeneous ***
Liquid i id ***
5
20
733 8
39
4
18 7 14 12
Smoothness, 8 49 26 Homogeneity 57
and 23 Creaminess 3 445 main drivers 1
7
5
22
15
43
0
12
80
3
Thick *** 3 32 of 23 texture 49 liking, 8in agreement 43 30 51 38
Gelatinous *** 1 30 with 4 31previous 0 studies 22 0 26 0
Firm *** 0 36 (Pohjanheimo ( 1 o ja e 47 47o & 1 1Sandell, Sa de , 45 452009; 009; 8 65 20
Sticky * 3 4 Bayarri 14 et al., 3 2011). 3 4 8 8 0
Creamy ** 16 35 18 36 35 38 32 38 86
Rough *** 24 5 46 16 9 7 46 11 0
Consistent *** 0 45 9 57 11 45 20 55 31
Mouth-coating * 15 11 30 16 14 19 24 16 9
Gummy ns 1 0 4 5 1 1 7 5 0
Runny *** 55 11 20 3 47 5 15 0 18
Heterogenous *** 32 19 49 4 18 7 42 0 3
11th Sensometrics, 10-13 July 2012, Rennes,
France 13

The ideal yogurt was close
to the samples with the
highest texture liking scores
and far from the least
preferred samples.
11th Sensometrics, 10-13 July 2012, Rennes,
France 14

<strong>Penalty</strong> <strong>analysis</strong>
Meean
drop inteexture
liking scores s
3
2
1
0
Sticky
Gummy
Gelatinous
Thick
Sample 1
Lumpy Consistent
Homogeneous g
Mouth‐coating
Viscous
RRoughh Heterogeneous
Runny
Thick, Homogeneous and
Liquid were the most
relevant attributes.
Smooth
Liquid
Creamy
0 10 20 30 40 50 60 70 80 90
Percentage of consumers (%)
11th Sensometrics, 10-13 July 2012, Rennes,
France 15

Recommended changes:
Increase
Thickness
in Homogeneity and
Att Attribute ib t
1 2 3 4
Sample p
5 6 7 8 Ideal
Smooth *** 41 53 12 38 62 64 23 45 92
Lumpy *** 32 7 57 11 26 11 61 8 1
Viscous ns 5 8 18 7 14 12 7 15 12
Homogeneous *** 20 39 8 49 26 57 5 43 80
Liquid *** 73 4 23 3 45 1 22 0 3
Thick *** 3 32 23 49 8 43 30 51 38
Gelatinous *** 1 30 4 31 0 22 0 26 0
Firm *** 0 36 1 47 1 45 8 65 20
Sticky * 3 4 14 3 3 4 8 8 0
Creamy ** 16 35 18 36 35 38 32 38 86
Rough *** 24 5 46 16 9 7 46 11 0
Consistent *** 0 45 9 57 11 45 20 55 31
Mouth Mouth-coating coating * 15 11 30 16 14 19 24 16 9
Gummy ns 1 0 4 5 1 1 7 5 0
Runny *** 55 11 20 3 47 5 15 0 18
Heterogenous g
*** 32 19 49 4 18 7 42 0 3
11th Sensometrics, 10-13 July 2012, Rennes,
France 16

Meean
drop in teexture
liking scores s
3
2
1
0
Gummy
Lumpy
Liquid
Sticky
Rough
Viscous
Heterogeneous
Gelatinous
Mouth‐coating
Runny
Homogeneous
Smooth
Consistent
Thick
Firm
Creamy
Sample 6
The percentage of
consumers who
considered that the
attributes deviated from
the ideal was lower than
50%. Smooth, Creamy,
and Consistent were the
most relevant attributes.
0 10 20 30 40 50 60 70 80 90
Percentage of consumers (%)
11th Sensometrics, 10-13 July 2012, Rennes,
France 17

Recommended changes: an
increase in smoothnees, and
creaminess, i andd a ddecrease consistency.
iin
Att Attribute ib t
1 2 3 4
Sample p
5 6 7 8 Ideal
Smooth *** 41 53 12 38 62 64 23 45 92
Lumpy *** 32 7 57 11 26 11 61 8 1
Viscous ns 5 8 18 7 14 12 7 15 12
Homogeneous *** 20 39 8 49 26 57 5 43 80
Liquid *** 73 4 23 3 45 1 22 0 3
Thick *** 3 32 23 49 8 43 30 51 38
Gelatinous *** 1 30 4 31 0 22 0 26 0
Firm *** 0 36 1 47 1 45 8 65 20
Sticky * 3 4 14 3 3 4 8 8 0
Creamy ** 16 35 18 36 35 38 32 38 86
Rough *** 24 5 46 16 9 7 46 11 0
Consistent *** 0 45 9 57 11 45 20 55 31
Mouth Mouth-coating coating * 15 11 30 16 14 19 24 16 9
Gummy ns 1 0 4 5 1 1 7 5 0
Runny *** 55 11 20 3 47 5 15 0 18
Heterogenous g
*** 32 19 49 4 18 7 42 0 3
11th Sensometrics, 10-13 July 2012, Rennes,
France 18

Regression coefficients from PLS model
TTerm
Sample 1
% RC
Sample 2
% RC
Sample 3
% RC
Sample 4
% RC
Sample 5
% RC
Sample 6
% RC
Sample 7
% RC
Sample 8
% RC
Smooth 62 -0.15 50 -0.24 82 -0.17 59 -0.21 41 -0.16 41 -0.20 77 -0.14 53 -0.14
Lumpy 31 -0.31 8 - 55 -0.10 12 - 27 -0.15 12 - 59 ns 9 -
Viscous 18 - 12 - 16 - 14 - 20 ns 14 - 16 - 22 -0.15
Homogeneous 65 -0.13 49 -0.18 77 -0.08 39 -0.17 59 ns 28 -0.16 74 -0.10 36 ns
Liquid 73 -0.14 4 - 26 -0.09 5 - 45 -0.18 4 - 24 ns 3 -
Thick 38 ns 32 ns 34 ns 46 ns 35 ns 41 ns 32 ns 43 ns
Gelatinous 1 - 30 ns 4 - 31 ns 0 - 22 ns 0 - 26 ns
Firm 20 ns 41 ns 22 ns 41 ns 19 - 46 ns 26 -0.14 55 -0.15
Sticky 3 - 4 - 14 ns 3 - 3 - 4 - 8 ns 8 -
Creamy 73 ns 57 -0.18 69 -0.10 58 -0.32 59 ns 51 -0.16 57 -0.19 57 -0.35
Rough 24 -0.17 5 - 46 -0.09 16 - 9 - 7 - 46 -0.14 11 -
Consistent 41 ns 45 ns 39 ns 41 ns 38 ns 39 -0.17 39 ns 45 -0.18
Mouth-coating 22 -0.13 12 - 34 -0.10 20 ns 18 - 15 - 28 -0.11 18 -
Gummy 1 - 0 - 4 - 5 - 1 - 1 - 7 - 5 -
Runny 51 ns 23 ns 30 -0.09 18 - 35 -0.13 23 ns 27 -0.11 18 -
Heterogenous 35 -0.15 22 ns 49 -0.12 7 - 18 - 9 - 45 -0.20 3 -
Intercept 7.2 7.2 6.3 7.0 6.9 7.3 7.3 7.4
Mean drop (*) 3.0 1.8 2.8 1.8 1.0 1.4 2.9 2.1
11th Sensometrics, 10-13 July 2012, Rennes,
France 19

Study Studyy 2
Frequency of use of the terms (%) for the whole consumer
sample p
Attribute
Sample
Crisp pink Fuji Granny smith Royal gala Red delicious Ideal
Firm *** 68 70 66 19 18 79
Juicy *** 63 76 49 51 48 92
Sweet *** 32 39 5 31 61 77
Bitt Bitter *** Apple odour ***
Sour ***
Crispy py ***
5
13
52
66
10
8
12
55
18 6 3
Firmness, Juiciness, Sweetness,
8 5 8
Crispiness and Apple flavour were
80 7 3
the main 46 drivers of liking. 16 11
2
39
22
64
Flavoursome *** 43 44 25 25 31 76
Coarse *** 3 1 2 15 24 3
Soft *** 1 2 2 49 45 6
Od Odourless l *** 13 14 14 22 14 1
Tasteless *** 4 9 8 31 10 0
Mealy *** 1 0 1 36 58 5
Apple pp flavour *** 45 40 14 25 37 69
Astringent *** 8 7 16 3 1 7
11th Sensometrics, 10-13 July 2012, Rennes,
France 20

Overall liking scores
liking scorres
(1‐9)
Overall
9
7
5
3
1
6.4 b
4.2 a
Granny
Smith
7.7 c
6.3 b
6.7 b
7.4 c
6.1 b
5.2 a 5.2 a
8.2 c
Crisp Pink Royal gala Fuji Red
Delicious
Cluster 1 (n=79)
Cluster 2 (n=40) (n 40)
Cluster 1 preferred Crisp Pink and
Fuji apples, whereas Cluster 2
preferred Red Delicious apples
11th Sensometrics, 10-13 July 2012, Rennes,
France 21

Dim 2 ( 14.8%)
Sour
GGranny smith ith
Astringent
Bitter
1
Odourless
Cluster 1 (n=79)
Soft
Tasteless Mealy
Coarse
Royal gala
Red delicious
0
Firm Apple odour
‐1 0 1 2
Crispy
Juicy
Crisp pink Flavoursome
The ideal apple was
Ideal Fuji
Apple flavour
Sweet located close to Crisp
‐1
Dim 1 (76 (76.8%)
8%)
Pink and Fuji apples.
Firmness, Crispiness
and Apple flavour were
the main drivers of liking.
11th Sensometrics, 10-13 July 2012, Rennes,
France 22

Dim 2 (144.6%)
1
Sour
CCoarse
Bitter Granny smith Tasteless
Royal gala
Astringent
Odourless
Apple odour
Soft
Mealy
Cluster 1 (n=79)
0
‐1 0 1 2
Crisp pink
Crispy
Firm
Fuji
‐1
Juicy
Red delicious
Sweet
Apple flavour
Flavoursome
Ideal
Dim 1 (75.1%)
The ideal apple was located
close to Red delicious and
Fuji apples.
Sweetness and Apple
flavour were the main
drivers of liking.
11th Sensometrics, 10-13 July 2012, Rennes,
France 23

Percentaage
of consumeers
(%)
100
80
60
40
20
0
89
60
92 93
80
76 75
41
38
29
8
5
1 3
1 0
Firm Juicy Sweet Bitter Apple odour Sour Crispy Flavoursome Coarse Soft
The clusters differred in their description of the ideal apple,
particularly in the frequency of mention of the terms Firm,
Sour Sour, Crispy and Soft
11th Sensometrics, 10-13 July 2012, Rennes,
France
43
80
68
18
Cluster 1 (n=79)
Cluster 2 (n=40)
24

<strong>Penalty</strong> <strong>analysis</strong> at the aggregate level
Cluster 1: Tasteless, Coarse, Soft,
MMealy, l JJuicy, i Fi Firm, Fl Flavoursome
Cluster 2: Tasteless, Sweet, Bitter,
JJuicy, ic So Sour, r Tasteless
11th Sensometrics, 10-13 July 2012, Rennes,
France 25

Regression coefficients from PLS model
Term
Crisp pink Fuji Red delicious
Cluster 1 Cluster 2 Cluster 1 Cluster 2 Cluster 1 Cluster 2
% RC % RC % RC % RC % RC % RC
Firm 42 -0.13 35 ns 38 ns 30 ns 84 -0.09 53 ns
Juicy 45 -0.31 53 -0.23 37 -0.16 35 ns 65 -0.14 38 ns
Sweet 59 -0.16 70 -0.23 50 -0.13 70 -0.19 59 -0.09 23 -0.36
Bitter 23 ns 10 - 27 -0.18 10 - 26 ns 3 -
Apple odour 47 ns 40 ns 48 ns 33 ns 49 ns 30 ns
Sour 49 ns 65 -0.17 43 ns 15 - 43 ns 10 -
Crispy 36 ns 40 ns 49 -0.13 40 -0.19 75 ns 33 ns
Flavoursome 49 ns 53 -0.14 54 ns 58 -0.22 70 -0.08 55 ns
Coarse 23 ns 5 - 23 ns 5 - 46 -0.15 18 -
Soft 22 ns 18 ns 23 ns 18 - 60 ns 30 -0.43 043
Odourless 30 ns 20 ns 30 ns 18 - 31 -0.09 20 ns
Tasteless 22 ns 10 - 27 -0.29 13 - 31 -0.12 5 -
Mealy 24 ns 10 - 24 ns 8 - 71 -0.15 48 ns
Apple flavour 48 ns 55 ns 51 -0.11 50 ns 58 -0.11 43 ns
Astringent 26 ns 13 - 30 ns 13 - 29 ns 3 -
Intercept 90 9.0 82 8.2 76 7.6 82 8.2 78 7.8 88 8.8
Mean drop 1.3 1.9 0.2 2.1 2.6 0.6
11th Sensometrics, 10-13 July 2012, Rennes,
France 26

Discussion and Conclusions
Th The methodology th d l was able bl t to id identify tif th the sensory
characteristics of the ideal product product, which were similar to
those of the most liked products products.
Simple and flexible add add-on on to usual CATA ballots. ballots
Provides information for the identification of drivers of liking, liking
even for consumers with different preference patterns patterns, and
recommendations for product reformulation
reformulation.
Does not provide a measure of the degree of difference
between the product and the ideal ideal.
11th Sensometrics, 10-13 July 2012, Rennes,
France 27

References
Ares, G., Barreiro, C., Deliza, Deliza R., Giménez, GiménezA.,
&Gámbaro Gámbaro, A. (2010 2010). Application of a check check-all all-that that-
apply question to the development of chocolate milk desserts desserts. Journal of Sensory Studies, 25 25, 67– 67
86 86.
Ares, G., Varela, P., Rado, Rado G., & Giménez Giménez, A. (2011 2011). Identifying ideal products using three different
consumer profiling methodologies. methodologies Comparison with external preference mapping mapping. Food Quality
and Preference, 22, 22 581-591 581 591.
Bayarri Bayarri, S., Carbonell, Carbonell I., Barrios, E.X., &Costell Costell, E. (2011 2011). Impact of sensory differences on consumer
acceptability of yoghurt and yoghurt yoghurt-like like products. products International Dairy Journal, 21 21, 111-118 111 118.
Bayarri Bayarri, S., Carbonell, Carbonell I., Barrios, E.X., &Costell Costell, E. (2011 2011). Impact of sensory differences on consumer
acceptability of yoghurt and yoghurt yoghurt-like like products. products International Dairy Journal, 21 21, 111-118 111 118.
Costa, A.I.A., & Jongen Jongen, W.M.F.(2006 2006). New insights into consumer-led
consumer led food product development
development.
Trends in Food Science & Technology, 17 17, 457-465 457 465.
Cowden, J., Moore, K., & Vanluer Vanluer, K. (2009 2009). Application of check check-all all-that that-apply apply response to identify and
optimize attributes important to consumer's ideal product. product In 8th th Pangborn Sensory Science
Symposium, 26 26-30 30 July 2009 2009, Florence, Italy Italy.
Dooley, L., Lee, Y.S., & Meullenet Meullenet, J.F.(2010 2010). The application of check check-all all-that that-apply apply (CATA) consumer
profiling to preference mapping of vanilla ice cream and its comparison to classical external
preference mapping. mapping Food Quality and Preference, 21, 21 394–401 394401.
Epler Epler, S., Chambers, E., IV., IV & Kemp, K.E. (1998 1998). Hedonic scales are better predictors than just-about just about-
right scales of optimal sweetness in lemonade. lemonade Journal of Sensory Studies, 13 13, 191–197 191197.
Lawless, H. T., &Heymann Heymann, H. (2010 2010). Sensory Evaluation of Food. Food Principles and practices practices. Second
Edition Edition. (pp. (pp227-253
227 253). New York York:Springer Springer.
11th Sensometrics, 10-13 July 2012, Rennes,
France 28

References
Moskowitz Moskowitz, H.R., & Hartmann, J. (2008 2008). Consumer research: research creating a solid base for innovative
strategies strategies. Trends in Food Science & Technology, 19, 19 581-589 581589.
Plaehn Plaehn, D. (2012 2012). CATA penalty/reward
penalty/reward. Food Quality and Preference, 24 24, 141-152 141152.
Pohjanheimo
Pohjanheimo, T., & Sandell Sandell, M. (2009 2009). Explaining the liking for drinking yoghurt yoghurt: the role of sensory
quality, food choice motives, health concern and product information information. International Dairy Journal,
19 19, 459-466 459 466.
Popper, R., Rosentock, Rosentock W., Schraidt, SchraidtM.,
& Kroll, B.J. (2004 2004). The effect of attribute questions on overall
liking ratings. ratings Food Quality and Preference, 15, 15 853–858 853858
van Kleef Kleef, E., van Trijp Trijp, H.C.M., &Luning Luning, P.(2006 2006). Internal versus external preference <strong>analysis</strong> <strong>analysis</strong>: An
exploratory p y study studyy on end end-user user evaluation evaluation. Food Quality Qualityy and Preference, , 17 17, , 387 387-399 399.
Varela, P., &Ares, Ares, G. (2012 2012). Sensory profiling, the blurred line between sensory and consumer science science.
Areview review of novel methods for product characterization
characterization. Food Research International, In press. press
Worch Worch, T., Dooley, L., Meullenet, Meullenet J.F., Punter, P.H. (2010 2010). Comparison of PLS dummy variables and
Fishborne method to determine optimal product characteristics from ideal profiles profiles. profiles profiles. Food Quality and
Preference, 21 21, 1077-1087 1077 1087.
Worch Worch, T., Lê, LêS.,
Punter, P., & Pagès Pagès, J. (2012 2012a) a).. Extension of the consistency of the data obtained by
the Ideal Profile Method: Method Would the ideal products be more liked than the tested products? Food
Quality Qualityy and Preference, , 26 26, , 74 74-80 80.
Worch Worch, T., Lê, Lê S., Punter, P., & Pagès Pagès, J. (2012 2012b) b). Assessment of the consistency of ideal profiles
according to non-ideal non ideal data for IPM. IPM Food Quality and Preference, 24 24, 99-110 99110.
Xiong Xiong, R., & Meullenet, Meullenet J. F.(2006 2006). APLS PLS dummy variable approach to assess the impact of JAR
attributes on liking liking. g Food Quality Q QQuality y and Preference, , 17 17, ,
188 188–198 198.
11th Sensometrics, 10-13 July 2012, Rennes,
France 29

Thank you very much
for your kind attention! attention
Gastón Ares
Facultad de Química Química. Universidad de la República República. Montevideo Montevideo, Uruguay
Email: gares@fq.edu.uy
11th Sensometrics, 10-13 July 2012, Rennes,
France
30