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Consumer preference mapping of dry fermented lamb sausages
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s0950.3293(96)00037-7
Food Qmlig and Prcfermcc Vol. 8, No. 2, pp. 97-109, 1997 0 1997 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0950-3293/97 s17.00+ .oo
CONSUMER PREFERENCEMAPPINGOFDRYFERMENTED LAMBSAUSAGES _ _ Hilde Helgesen,
Ragnhild Solheim & Tormod Na;s*
Norwegian Food Research Institute, Osloveien 1, N-1430 As, Norway
MA1’FORSK,
(Received 18 May 1996; revised uersion received 8 JuQ
Shepherd et al. (1988), Nute et al. (1989), Jones et al. ( 1989)) M&wan and Thomson ( 1989)) MacFie and Thomson ( 1988)) Beilken et al. ( 1990)) Gains and Gutteridge ( 199 1) and Greenhofl and MacFie ( 1994). It can also be used to compare preferences and to relate them to sensory characteristics. A properly designed study can also illuminate the segmentation of the market into a number of more or less well defined groups of consumers (i.e. the nature, size and strength of differing likes/dislikes). In new product development preference mapping can be helpful in two ways. It can be used to check that a prototype product is acceptable and, in terms of preference, falls into the correct segment of the market. In this respect, preference mapping can be a useful (and rnst effective) bridge between in-house assessment and full scale quantitative market research (Gains and Gutteridge, 1991). Preference mapping can in addition be a powerful method for optimising products using a twostage procedure. In the first stage, data from trained panels and consumer preference data of a prototype product along with its near market competitors, can determine segmentation and the key sensory characteristics which drive preference. In stage two, product variants are prepared with recipe or processing variations to increase or decrease the degree of these sensory characteristics. Subsequent preference mapping of these new products can identify the most preferred. Thus recipe and process can be optimised prior to a final product launch (Gains and Gutteridge, 1991). Preference mapping may be divided into two categories, external analysis (PREFMAP) and internal analysis (MDPREF). Internal analysis is the only choice for data sets that consist exclusively of consumer preference data. The information obtained from the internal ‘map’ of consumers and products can be related to other types of data as in the present study where consumer preferences are related to QDA. External analyses are similar, but relate the two types of information in opposite order (Carroll, 1980) - first a PCA is conducted for the external data (for instance sensory data) and the consumers are related one by one to the information in the external PCA space. Preference mapping techniques can also enable the investigator to identify groups of consumers with different preference and consumption patterns.
ABSTRACT This
paper focuses
mapping squares
(both (PLS)
on using
internal regression
data of six di$rent
homogenous
least
descriptive analysis-
the consumers into smaller,
consumer groups, nameb
preference
and partial
lamb sausages to conven-
(quantitative
For segmenting
compared,
comparing
in relating consumer preference
dry fermented
tional sensory profiling QDA).
and
and external)
two d@erent
more
methods were
cluster-analysis, and uisual inspection of
plots. The consumer groups were related to demographic and sociological variables. The vided
alternative approaches the same general
approaches, prejerence
to preference
conclusions.
For
mapping proall
the three
was positively intuenced by juiciness,
acidic flavour and odour, greasiness and lamb j2avour. cluster
analysis
preference
identzjied four
patterns
and indicated
subgroups
The
dzyerent
that there were market
segments for each of the six dry fermented 0
with
lamb sausages.
1997 EIsevier Science Ltd. AR rights reserved
Keywords:
Fermented
MDPREF;
PLS;
QDA;
lamb
sausages;
segmentation;
1996)
PREFMAP; cluster-analysis.
INTRODUCTION The long term success of a food product depends a great deal on its ‘performance’ once it is placed in the mouth. Good marketing will at best result in the first sale. As a consequence, it is very important to analyse the extent to which products are liked or disliked in a manner that is not compromised by marketing imagery (branding, packaging, advertising, etc.). These results should be related directly to the ingredients and the manufacturing process, which together determine the sensory characteristics of the products. Preference mapping is one technique which has been developed to fulfil these objectives, see *To whom correspondence should be addressed. 97
98
H. Hdgesen et al. The
present
Helgesen
paper,
which
and Nas (1995),
ing preference
mapping
partial least squares preference
is based on an article
(both internal
(PLS)
and external)
regression
data to data from QDA.
in the food industry these methods. trate through determine
to research regarding
to relate consumer
preference formulation.
of the pre-screened
lished consumer product
as target
consumers.
database,
Agricultural
consumers,
indicating
From
about
an estab-
demographic
and
usage profiles for a large group of consumers,
staff
participate.
of
Out
100 were defined
consumers
(in
are important whether
through
A third objective of the market preference
in
there
a change
in
of the paper is to
for dried fermented
the identification
analysis and visual inspection
sumers,
who met the requirements From
109 participated
38
were invited
the whole group of 138 invited
to
con-
in the test.
of consumer
patterns,
both cluster-
of plots were compared.
A
Consumer testing The
109 consumers,
sausage,
all weekly eaters of dry fermented
comprised
aged between
48 females
and 61 males,
The consumers
were separated
19 participants
who attended
an ordinary
classroom.
into eight groups with 9the test at different
Prior
were given the same information study and other
practical
on the purpose
matters
the segments
(sub-
session. The six sausage samples,
and sociological
vari-
by a trained
insight
into
whether
these
might
numbers
panel,
times in
to the test all consumers
to demographic
to gain
with 40
18 and 39, and 69 aged 40 years or more.
is to characterise
groups) according predict consumer
can
performance
groups with different objective
developer
of a food product
lamb sausage)
lamb sausages. To conduct
and
at the campus at the Norwegian
University.
and
applications
and then to establish
derive a segmentation
ables
practical
characteristics
is scope for improved
fourth
and development
a case study how a product
this case: dry fermented
product
differed
employees
The second aim of the paper is to illus-
which
product
and
The aim is to study
how the results from these three methods also to give guidelines
by
focuses on using and compar-
related
all previously
were coded
with
of the
to the taste assessed
3-digit
random
and served at the same time. No guidance
was
given on how to use the scale.
preference.
The consumers ised order
tasted the samples once in a random-
given
by
the
questionnaire.
Ten
different
orders of tasting the six samples were distributed
EXPERIMENTAL
a way that about 10 consumers,
in such
about one to two in each
of the eight groups, tasted the samples in the same order.
Materials
Each participant
was provided
with biscuits suitable
sensory tests, paper cups to expectorate According authors
to a prior
(Helgesen
study
undertaken
and Naes, 1995),
(6) was selected for preference based on information
testing. The selection
from QDA and chemical
a larger set of 14 commercially lamb sausages produced sausage
identification
Helgesen
by the same
a subset of samples
available
in Norway.
codes,
and Naes (1995)
was
profiles of
dry fermented
For simplicity,
6, 7, 8, 10, 11 and
the 14 in
were in this paper replaced
by
the following new codes, 1, 2, 3, 4, 5 and 6. All necessary details can be found in Helgesen It should be mentioned of view, should
six samples
is the absolutely
be used. From
however, to ensure
a consumer
using a small number reliability
and Nies ( 1995).
that from a data analysis point minimum
test point
especially
tapwater
sumers
to
rinse
between
session lasted
marked
their degree
tinuous line anchored marks were linearly like very much’
recorded
recorded
‘Degree
as 9.0.
taste con-
of liking on a 15 cm con-
transformed
into numbers
their
with ‘dis-
as 1.0 and ‘like very much’ of liking’
is for simplification
called preference.
Questionnaire design
of view,
for samples
Ideas
for statements
tionnaire
or answer
were collected
and Schutz
et al. (1988),
preference
of dry fermented
contained
a total
six samples was con-
trade-off.
with
two
categories
in the ques-
from the work of Solheim
quite strong in taste, and therefore
along
consumers
with individual on
food
habits
(1993) depth and
sausages. The questionnaire
of five pages,
where
the first page
recorded information of gender and age along with information on preference scores for six dry fermented
Selection of consumers consumers
The
on the left by ‘dislike very much’
interviews
Prior to recruiting
The
10 min.
and on the right with ‘like very much’. Afterwards,
with strong flavour. The samples in the present study are sidered to be an acceptable
samples.
for about
for
sample
that
of samples is important
of the data,
and
evaluation
unwanted
for the combined
preference
test and questionnaire survey, the target consumer was defined. This was according to product usage, that is a person who consumed dry fermented sausage once a week or more often. A pre-screening questionnaire with demographic and product usage tributed through the mail-system
questions to more
were than
dis200
lamb sausages. The rest of the questionnaire consisted of 46 statements, with 19 statements concerning general food habits, 13 statements related to usage of dry fermented sausage and 14 lifestyle and attitude questions. Except for frequency of use and food habits which were answered utilising an 8 point scale, the rest of the questions were answered by a multiple choice, 5 point category scale. The last page of the questionnaire recorded
99
Consumer Preference Mapping of Dry Fermented Lamb Sausages information grew
on geographical
up and
permanent
number
Partial
district where the consumers
of residential
years
in previous
Least Squares
datasets,
address.
-
preference
Data analysis
matrix
preference
mapping
or multidimensional
erence analysis (MDPREF) (1972))
is a principal
(as first described
component
analysis
pref-
by Carroll (PCA)
on a
PLS
PREFMAP information
solely
on the preference
principal
component
of variation identifies The
data.
and extracts
result of internal
that
MDPREF
within the preference
map,
of the PCA.
in the score
preferences
way
this as components, preference
map and a consumer samples
the same
analysis identifies the major sources
the major variation
and loadings
In
based
plot
mapping
is a sample
in
plot of samples
ing in the direction and
become
of preference,
moving
samples become
in the opposite
direction,
‘less liked’.
Often
is used for situations
available
with only con-
and thus no information
about why the samples are liked or disliked. is possible, as described McEwan
link sensory information analysis
space.
ponents which
In
sensory
the
following
it
( 1994)
to
to the internal
the
term
sensory
This can be done by regressing
variables
from the PCA. attributes
and MacFie
(e.g. QDA-data)
refers to QDA.
of the
However
in this article and by for instance
( 1995) and Greenhoff
preference
is given
on
the
principal
com-
In this way, information
are related
to which
samples
about will be
revealed. External described
preference by Schiffman
mapping
et al. ( 1981))
first using PCA on the QDA each of the consumers analysis. usually
The
consumers
represented
(PREFMAP)
(as first
is conducted
by
data and then by relating
to this PCA space by regression are as for the internal
as points
or arrows
analysis
variation
difference
in
between
here is the way the
PLS
uses
the
preference
the components. PLS
components
pre-
the covar-
of X-variables
and a
In the present
paper,
of providing
more con-
than the PREFMAP
(Martens
sensory to consumer standardization
More
maximizes
of the r-variables.
In this way, it has a potential
which
and Naes, 1989).
all three
methods
for relating
data were used on data without any
or
any
other
type
of
pretreatment
(except centering). For the purpose of understanding the preferences plete linkage cluster
distances.
(Mardia
analysis
consumer
were also analysed
using Euclidean
MDPREF
sumer data
each
‘more
samples
The
a linear combination
linear combination
is based on a PCR
the differences
data.
was
combina-
of the sensory
the systematic
component
between
In the joint
a few linear
as described
extracting
The
and consumers, the consumers are represented as directions of increasing preference on the sample map. Movliked’,
when for each
sumer relevant
the
extracts
extracted.
to the scores
distances
was a 6 x 109
(1T-data) as well as the sensory information
corresponding represent
for the samples.
(X-data) cisely,
are
the two
and consumer
In other words, a PLS2
preference
iance between
data.
(X-matrix)
or latent variables)
and PLS
components
of the stimuli
method
was used as a
between
The r-matrix
data.
tions (PLS-components
achieve
configuration
profiles
data that are used to predict the consumer
regression
the relationship
scores (Y-matrix).
consisting of samples or products matrix of data, (objects) and consumers (variables). The objective is to a consensus
sensory
of preference
used. The Internal
(PLS)
third way of studying
responses,
by cluster
analysis
In this study we used com-
et al., 1979) which is a hierarchical
methods.
The
that are close in multivariate
method
assigns
samples
space to the same subgroup
or cluster. In this way it can identify groups of consumers which
are homogeneous
samples. The consumer graphic
in their preferences
for the six
groups were compared
to demo-
data for the consumers.
tabulation
This was done by simple
and by the so-called
is an approximate
chi-square
homogeneity
test, which
test for similarity
among
the groups. Principal
component
squares
(PLS)
external
preference
analysis
regression, mapping
(PCA),
internal
(PREFMAP)
out using the UNSCRAMBLER
partial
(MDPREF)
extended
least and
were carried version
5.03
(Camo a/s, Trondheim, Norway). The cluster analysis and the chi-square tests were performed in SAS Version 6.10 (SAS Institute
Inc., Cary, NC, USA).
on the sample
map. Internal ference ponent datasets
(MDPREF)
and external
(PREFMAP)
pre-
mapping can both be viewed as principal comregression (PCR) methods applied on the two (preference
data and sensory data),
two sets used in a opposite lysis, the sensory data (explanatory
variables)
order.
but with the
For the external
are used as a so-called and the preference
Summary statistics
ana-
X-matrix
data
RESULTS
as the
In Fig. 1 is presented consumers.
a box-plot
on the basis of the 109
For this plot 50% of the preference
scores are
Y-matrix (response data), while the internal analysis uses the two data sets in the opposite way. Note that in both situations PCR is used for several Y-variables.
found inside the box. The remaining scores are distributed outside the box (25% at the bottom and 25% at the top). The median value is marked with a horizontal
This multivariate PCR regresses each single Y-variable onto the principal components obtained from the Xmatrix.
line within the box. The vertical lines end up in the lowest and highest preference scores given. The mean preference
scores
plus
standard
deviations
of the
six
100
H. Helgesen et al.
products are presented in Table 1. The median value is less vulnerable than the mean to extreme values in the distribution. Sample 3 has the lowest median. At the same time no other sausage had a median closer to the bottom value of the box. This illustrates that this sausage was the least preferred and that there existed a higher degree of agreement for the lower preference values for this particular sausage compared to the others. This is also reflected in the difference between the median and the mean which is also larger for this sample than for the rest of the samples. For the rest of the samples there are no great differences between mean and median values. It is also clear from the plot (Fig. l), that for all samples, almost all the scale is used. In other words, for each sample there are consumers who like it and consumers who dislike it. TABLE 1. Mean preference
scores and total standard deviation for the six sausage samples presented according to increasing preference
Sample Sample Sample Sample Sample Sample
3 2 1 4 6 5
Mean
SD
4.10 4.52 5.01 5.16 5.38 5.55
2.30 2.44 2.32 1.95 2.09 2.18
Internal preference mapping -
MDPREF
The PCA of the preference scores showed that about 53% of the variation in the preferences were explained by the two first principal components. Adding an extra component to this proportion increased the explained variance to about 74%. The regression of the sensory data onto the three principal components from preference analysis explained about 89% of their variance. Figure 2 represents a quite common way of presenting the internal preference loading plot. This plot is derived by scaling each consumer’s PCA vector to unit length. The direction of each preference vector indicates the direction of highest preference for this particular consumer. Using this way of plotting the results makes it clear in which direction a consumer has his preferences, but masks the strength of his preference (see also the section on external preference mapping). Examining the MDPREF consumer plots (Figs 2A and B) and the relationship of the sensory data to the principal
.8
6.9 8.8
.
I -1
-
0
Component
L 1
1 (30%)
5.8
i.8
I
0 0
1
2
3
4
5
6
Sample number FIG. 1. Box-plot showing the median values of the six sausage samples on the basis of 109 preference scores.
Component
1 (30%)
FIG. 2. Internal preference mapping of sausage data derived by scaling each consumers’s PCA vector to unit length. Numbers 1-6 represent sausage sample codes. (A) Component 1 vs component 2; (B) Component 1 vs component 3.
101
Consumer Preference Mapping of Dry Fermented Lamb Sausages components
of the preferences
get a clear impression samples
are
preferences
of how preferences
distributed are related
reflected
(Figs 3A and B) one can
over
can
for the different
the space
to the different
and
how
samples
for the second
the
odour).
and the
From
Fig.
flavour
3A,
preference
to be most strongly and acidic
flavour,
flavour
odour
the difference
number
to juiciness,
component,
1
(Table
hardness
1 is primarily
and colour
a component
with samples
preferred
between
is essentially
sample
a component
2 and sample
two samples
have
dis-
profiles
quite similar, but they differ in red-bluish of smoke, (Helgesen
sour
flavour
and
and NZS, 1995).
even
These
which
The
colour, flavour
ponent
size of fat particles differences
(with sample
sample
2
This
indicates
second
clearly
that
to note that sample
(Fig.
between
The
third
sweetness
preference
From
0.10
(such
whiteness,
attributes
I
Figs
properties
a com-
code 1 and the other
component
seems to be
of spiciness, spicy odour
as code
1 in Fig.
colour intensity
3B)
vs lamb
and juiciness.
3A and
B,
liking,
one
can
see that
is dominated
as dry outer rim, flavour
and red-blue
ofsmoke
2 has the lowest
sausages investigated
Other
are also important.
with lowest mean
l Uniform size of fat particles
for differences
3B) is essentially
related to a unique combination and
lowest some of
and Naes (1995).
for the difference
samples.
are clearly
scores 6 were
6 the second
the
are more important
third component
flavour,
.flavwr
mean preference 2 and
6
high
than others.
It is interesting
are
2 vs sample
samples
pH value of all the dry fermented
6 with the
and acidic
by the fact that
a sample
that
different
sample).
in Helgesen
sensory
whiteness
and
the sensory attributes
1, 2 and 4 in the
other samples lying in between. These
as quite preferred
in preference
The second component
is not solely
1) also illustrate
highest
between sample 3 on one side, and samples
5 and 6 on the other,
(for instance
but one
are important
but it can also be due to the rather
perceived
on the
describing
middle. criminating
axis
attributes
noise in the data. The consumers’
acidic
on one side vs dry rim, sour
of smoke,
other. Component
component related
plot (Fig. 3A),
some other
This can possibly be explained
the component
sensory attributes. appeared
in the sensory loading
also see that
sample
3
by such sensory
of smoke,
hardness
colour. Sample 5, with highest mean liking,
was dominated
by juiciness,
acidic
flavour
and
acidic
odour. The
largest
right quadrant
group
of consumers
fall into
the upper
in Fig. 2A and in the lower right quad-
rant in Fig. 2B. Since
the most preferred
samples from
the analysis of averages above, namely samples 4, 5 and 6 are positioned
in these quadrants,
is in clear correspondence I
I
I
0
0.05
0.10
I -0.05
Component 1
I
preferred
by some of the subjects.
This important
aspect
will be discussed in more detail below in connection
B
0.10 l Dry outer rim
d1 0 uniform size of fat partides
Hardness. Colwr .
analysis
results. It is,
however, also clear that the rest of the samples seem to be
segmentation
0.05
the MDPREF
with the average
of the consumers’
External preference mapping The first step for this method
I
the sensory data. vector
model.
The
PREFMAP
was to perform
PREFMAP
only).
a PCA on
model used was the
An ideal point model is usually better
use, but in this case such a model because
with
loading plot.
the number
of samples
The
two first components
information
in X (sensory data).
is difficult
is so small described
to
to apply (6 samples
84%
of the
For 3 components,
the
percentage was equal to 93%. The regression model for the preference data vs the three principal components -0.10
-0.05
0
0.05
0.10
Component 1 FIG. 3. Correlation between internal preference components in Fig. 2 and sensory attributes as evaluated by trained assessors. Numbers l-6 represent sausage sample codes. (A) Component 1 vs component 2; (B) Component 1 vs component 3.
from sensory analysis explained about 72% of variance. The loading plots for the three components presented in Figs 4A and B.
the are
The number and density of the consumer preference vectors, indicate a higher preference in the direction of the upper right quadrant of the plot in Fig. 4A, indicating
sample
5 as the
most
preferred
sample.
The
H. Helgesen et al.
102
density of consumers decreased towards the lower left quadrant indicating 3 as the least preferred sample. The results presented in Fig. 4 are based on all consumers tested, with no reference to how well they fit to a liner model in the scores from the sensory analysis. An additional PREFMAP analysis was therefore performed for those consumers only who can be significantly described by the two first principal components. Because of large noise in consumer data and only six samples in the data set, a significance level as high as 0.20 was used. Thirty-six percent of the consumers were selected. The consumer plot for the two first dimensions is presented in Fig. 5. As can be seen, the plot shows exactly the same tendency as the plot for all consumers. By relating the positions of the consumer vectors to the sensory components within the external data (Figs 4A and B and Figs 6A and B), one can, as for MDPREF, infer important relationships between sensory attributes and the consumer preferences. For instance, consumers positioned to the left in the plots preferred products with dry outer rim, red-blue colour, uniform size of fat
1
particles, hardness and sour flavour (Fig. 4A and 6). The consumers to the right preferred sausages with juiciness, whiteness, lamb flavour, greasiness, acidic flavour, acidic odour and to a certain extent also stickiness and colour intensity. Similarly, consumers positioned at the bottom of component 2 preferred samples with flavour of smoke and rejected spicy sausages. As can be seen, the PREFMAP scoreplots (Fig. 4A and B) are somewhat different from the corresponding scoreplots for MDPREF. Looking more closely at the two sets of plots, however, reveals that the first component is about the same for the two cases and that component 2 for the MDPREF analysis corresponds approximately to component 3 in the PREFMAP study and vice versa. The same can be said about the interpretation of the two loading plots. Therefore, we can conclude that, although the importance of the two components 2 and 3 is interchanged in the two analyses, the two methods gave about the same qualitative conclusions. It is important, however, to stress that the interchange of axes may indicate a different weighting of the differences among the samples. Knowing that the sensory profile is possibly much larger than the set of attributes given emphasis by the consumers, this difference in weighting is quite natural.
Relationships between the preference data and the sensory data investigated by PLS2
N
E
2
ix0 E 8
The percentage of sensory data explained by the PLS components and percentage of consumer preferences explained by the sensory data, were very similar to the percentages for external preference mapping. The sample scores and the loadings for PLS2 were so similar to the corresponding PREFMAP results, that for reasons of
-1 -1
CompOonent 1
1
”
CompEnent 1
Component 1 FIG. 4. External preference mapping of sausage data derived by scaling each consumers’s PCA vector to unit length. Numbers l-6 represent sausage sample codes. (A) Component 1 vs component 2; (B) Component 1 vs component 3.
FIG. 5. External preference mapping of sausage data for consumers who can be significantly described by model in the two first principal components from the data (significance level 0.20). Numbers 1-6 represent sample codes.
derived a linear sensory sausage
Consumer Preference Mapping of Dry Fermented Lamb Sausages
space, only the PREFMAP plots are presented here. The same as above can therefore be concluded for the relationship between PLS2 and the MDPREF. In other words, the PCR and the PLS2 of the preference data vs the sensory data gave very similar results in this case. One explanation may be that the differences in preference are actually related to the primary components extracted by the PCA of the sensory data.
Segmentation
by similarity
of preference
The basis for the segmentation considered in this study is the MDPREF analysis. Similar studies could, however, have been conducted using one of the other two methods. The main focus is on the relationship between segmentation of consumers performed by a visual inspection of the loading-plots from the internal preference mapping and a segmentation done by cluster analysis (complete linkage). The latter is a technique that assigns samples that are close in multivariate space to the same subgroup or cluster.
0.6 A . Spiciness l Spicy odour 0 N
0.2 -
.
Dry outer rim
, Sweetness
E I E
0
E 8
-0.2
-
Uniform fat size diihfbution 0
-0.6 -
103
The cluster analysis was run on the 6~ 109 matrix of consumer data. Inspection of the dendrogram indicated that four clusters could be a reasonable number of clusters to study further. This indication was, however, not very clear. In Fig. 7 the scoreplot from internal preference mapping is shown with positions of consumer preference vectors within each of the four subgroups labelled with different symbols. It is clear that the four subgroups define different regions in the loading plot, showing that cluster analysis in this case splits the data into subgroups which correspond approximately to subgroups based on the loading plot of the two most dominating components only. In other words, the two methods gave partly the ‘same’ subgroups. However, when the number of consumers is large, a visual inspection is not straightforward and there is a need for an automatic tool for identifying homogeneous clusters of consumers. As a conclusion, in this case with a fairly small number of consumers, both cluster analysis and visual inspection of the plots, identified more or less the same segmentation pattern. The scoreplot of the two first dimensions was compared to the 10 different orders of tasting the six samples. If consumers with the same tasting order were grouped in the scoreplot, this could indicate a serious problem in the setup of the experiment. The results would have been very difficult to interpret. No such relation was, however, found in this experiment. The mean scores of the six products by the total group and by clusters of consumers are presented in Table 2. The size of the clusters are not very balanced, with clusters 1 and 2 as close to half the size of clusters 3 and 4. For each cluster it is possible to find a winner (in bold in Table 2) and a looser (italic in Table 2) product, and for most of the groups these products are different among clusters.
0 Flavour of smoke
I
I
I
-0.6
I
-0.2
0
I
I _
0.2
0.6
Component 1
0.2t I-
I
0.6
l Uniform siz: of fat particles
t
6
N 0.4 -
E
CY
t
E
g
E
E s
g E
0
-@---
o-
8 -0.4
-
0 SOUR
-0.20
fh
\-unifcfm fat
-J
size distribution
-0.20
Compinent
0.20
1 -0.6
0.4
-0.4
Campkent
0.86
1
and loadings for the sensory data. Numbers l-6 represent sausage sample codes. (A) Component 1 vs component 2; (B) Component 1 vs component 3.
FIG. 6. Scores
0 = subgroup1,$
= subgroup
2. 0 = subgroup
3, 0 =
subgroup 4
FIG. 7. Score plot from internal preference mapping showing the positions of consumer preference vectors for each subgroup labelled with different symbols.
104
H. Helgesen et al.
TABLE 2. Mean scores of the six products by the whole group and by consumer subgroups. products are presented in bold, whereas the ‘looser’ products are presented in italics
For each cluster
(line), the ‘winner’
Product mean scores Subgroup (cluster)
Freq.
5
6
4
2
1
3
11 18 43 37 109
7.75 4.28 7.01 3.83 5.55
3.14 4.22 6.54 5.17 5.38
5.74 3.74 5.36 5.26 5.16
7.00 6.62 4.45 2.98 4.52
6.10 5.74 4.20 5.11 5.01
2.2 4.12 3.57 5.43 4.10
1 2 3 4 All
Histograms
of the
four subgroups ferent Fig.
opinions 8 the
intervals,
where
scores (black
3.7 to 6.3 (grey shading (white
from
preference
scores
in Fig. 8, emphasising
of the products.
preference
like of the sample and scores
product
are shown
For
interval from
divided
into
1 to 3.6 were defined of the bars),
of the bars)
6.4 to 9 were
By this classification, rant of Fig. 7 (referred
the presentation
was
shading
for the the dif-
as dis-
scores from
were defined
defined
in sub-
as like
as like very much
“r
Group 1
Group 2
8
had
no clear
consumers
liked
samples
5 and
1 and
disliked
leaving
sample
low mean
of subgroup
1 and
on sample
liking
of sample
4 gave
sample
2 very
2 as the
2. to
much,
they
3 and
liking
‘winner’,
6.
5 out of 6 but
to the ‘winner’
3. Subgroup
with
a
products
2 and 4 had very differ-
2. Subgroup
5 and disliked
1 and
sample
3 the highest
sample (referred
samples
preferences,
score compared
ent opinions
disliked
quadrant
4 and
2 had no extreme
samples,
and
right
left quad-
4) liked most of the
1) liked
samples
Subgroup
in the upper
favourite
in the lower
as subgroup
fairly
bars).
gi-
sausages, The
consumers
to as subgroup
mean
3 agreed
3, while
liking
on
subgroup
score
of all the
groups.
Relating
6
the segments
If there
are several
a preference assume
e t!! .s P a
that
521463
1463
association
with which
Group 4
Group 3
related
8 c
8
to the
major
F
the
to be less affected associated
fermented smoke, appear
5
2
1
4
6
3
Sample number m
Like, score 3.7 0
- 6.3
Dislike, score 1 .O 3.6 Like very much, score 6.4 - 9.0
FIG. 8. Histogram of the mean product preference scores within each of the four sub-groups. The samples are presented according to declining preference order defined by subgroup 1.
with critical sages.
sensory sausages
hardness,
red-blue
to be more and
and
to hardness,
parameters.
degree
a
they
Consumers
prefer
of flavour rancid
of also
flavour,
in subgroup
seem to prefer the exact namely fermented lamb These
the and
and dry rim. They
to saltiness,
flavour.
not
4)
flavour
Instead
high
into
7 (subgroup lamb
tolerant
lamb
two.
are to play
clustered
and
bitterness.
right quadrant) parameters, -
stickiness
unlikely
by whiteness,
colour
of the
preference.
3A
with
in the
it is not possible which
vectors
of Figs
lamb
flavour
(upper sensory ”
quadrant
are
sensory
other
important
consumer
preference
appear other
521463
with
left
sour
space
role in determining
Consumers
to each
characteristics
preference
upper
in explain-
they will have some
model),
or least
that
with
automatically
importance
close
a linear
most
it is true
not
but for two or more
are placed (using
associated
should
Obviously,
preference,
space
However,
9
9
preference.
parameters
data
characteristics one
they are all of equal
to determine
”
52
sensory
component,
ing consumer
preference
to the sensory
respondents
dry rim and sour flavour
3
opposite sausages are
in the sau-
In the lower, right quadrant of Figs 3 and 7, consumers in subgroups 1 and 2 preferred juicy and sticky sausages with three classes of flavour and odour characteristics; namely spiciness, acidity and sweetness. Sausages with such flavours,
combined
with more
red colour
Consumer Preference Mapping of Dry Fermented Lamb Sausages
than the other four samples (sample 2 and 5), seemed to be very appealing to these two groups of consumers.
Segmentation related to demographic variables and food-related lifestyle So far the consumers have been segmented by similarity of preference. The next step would be to ascertain whether it is possible to characterise the consumers within the subgroups, and perhaps gain an insight into whether gender, age or lifestyle might predict product preference. This was done by simple tabulation and by the so-called
105
homogeneity test, which is an approximate chi-square test for similarity among the groups. As the precise identity of all consumers of dry fermented lamb sausages is known, the four preference groups can be explored to determine which products appeal to consumers of different age, gender and with different food habits. Such information is of special interest as it might enable marketing and product development to ‘tailor-make’ products to a specific market segment. Tables 3 and 4 summarises selected parts of the sociodemographic information recorded for the consumers. In this table, the 8 point frequency scale for frequency of use
TABLE 3. Socio-demographic details of consumers within preference subgroups. Information about the total group of consumers is listed to the left. The proportion (in %) of the consumers in the different subgroups and the significance level f$) of differences between subgroups (homogeneity test) for each of the variables are also presented
Total Demographic variables
Gender Male Female Age 18-29 yr 30-49 yr 50-69 yr Health A B c D E Quiet A B C D E Friends A B C D E Appearance A B C D E Lunch 1 2 3 4 5 Bread
1 2 3 4 5 Codes and variable
%
sample
P
sub!group (16.1%)
1
Preference subgroups (%) subgroup 2 subgroup 3 (16.5%) (39.4%)
sub!group (33.9%)
number
0.317 56 44
61 48
37 49 14
40 54 15
56 20 18 6 0
61 22 20 6 0
16 28 41 15 0
18 30 45 16 0
24 40 22 10 4
26 44 24 11 4
23 34 30 10 3
25 37 33 11 3
69 4 9 17 1
75 4 10 19 1
36 10 51 3 0
39 11 56 3 0
36 64
67 33
51 49
62 38
45 45 10
22 56 22
37 47 16
41 51 8
28 36 36 0 0
83 6 5 6 0
53 23 17 7 0
56 20 18 6 0
19 18 36 27 0
28 28 33
11 0
9 23 54 14 0
19 35 32 14 0
36 18 37 9 0
28 17 33 11 11
9 54 23 12 2
35 43 11 8 3
9 36 45 9 0
33 33 225 6
21 35 35 7 2
24 32 32 8 4
45 0 0 55 0
56 0 11 28 5
72 9 14 5 0
79 0 5 16 0
36 9 55 0 0
56 5 39 0 0
35 9 53 5 0
27 14 54 5 0
0.697
0.197
0.506
0.062
0.623
0.003
0.737
explanation
are listed in Table 4.
4
106
H. Helgesen et al.
and food habits was simplified to a 5 point scale. The five statements presented (out of a total of 46) were selected due to large or at least moderate variation between the subgroups. In some cases, however, statements which are not presented in this table, are mentioned in the description of the subgroups. It is quite clear that one can find differences in distribution, associated with certain subgroups. However, these high or low frequency biases are merely indications of interesting relationships and as we see, only a few of the variables are actually significant (or close to) if all subgroups are considered simultaneously. In Table 3 below, the highest frequency biases in distribution of the presented statements in the questionnaire, are in bold. In subgroup 1 (preferred sausages high in spiciness, acidity, sweetness and juiciness) nearly all consumers are less than 50 years, the majority are females and many of them tend to seek pleasure. They are confident of own judgements and enjoy unforeseen and challenging situations. In this group far more emphasis is put on the importance of the link between appearance, personality and clothing, compared to the other groups. The sensory aspects of foods are more interesting than the wholesomeness, and crisps are eaten once a week or more often. Close to 50% never prepare or eat ‘low-calorie’ meals. These consumers have a strong bias towards never eating packed lunch at work. This group may be called the ‘modern food consumers’, oriented towards a life style characterised by convenience, impulsiveness, spontaneity and materialistic values. Subgroup 2 (preferred spicy and sweet sausages) has a bias towards males, and age from 30 to 50 years. They are very concerned about the health risks involved with smoking and drinking alcohol and share a confidence in alternative medicine. High-calorie foods like snacks (crisps, peanuts) are consumed no more than three times a month. These consumers eat home-made bread on more occasions than the other consumers and are far more concerned about additives in foods than the other conTABLE 4. Codes used in Table
A: B: C: D: E:
For 1: 2: 3: 4: 5:
disagree partly disagree partly agree agree no answer
3
variables ‘lunch’ and ‘bread’ 3-5 times a week l-2 times a week 2-3 times a month or less never no answer
Variablexplanation: Health:
I think the health dangers
beverages Quiet:
and smoking
linked to drinking
alcoholic
are exaggerated.
I prefer to live a quite and calm life.
Friends: rather
cigarettes
I prefer
to have a lot of friends
than a small number
Appearance: emphasise
I think
it is important
my personality.
Lunch:
I eat a packed
Bread:
I eat home-made
and acquaintances,
of very close friends.
lunch at work. bread.
to wear
clothes
which
sumers. These consumers may be called the ‘rational food consumers’ with an interest in quality aspects such as wholesomeness and partly also ecological growing methods. Subgroup 3 (preferred sausages high in lamb flavour, stickiness and juiciness, - traditional lamb sausages) has an equal distribution of males and females, but more people over 50 years than in subgroups 1 and 4. These consumers tend to be rational human beings who are not ruled by their feelings and they tend to prefer a quiet and calm lifestyle. This group may be called the ‘conservative food consumers’ who put great value on stability and security. The consumers in subgroup 4 (low ratings for spicy sausage) have a bias towards males younger than 50 years who like to have a few close friends rather than a lot of acquaintances. A large share of these consumers never eat or prepare ‘low-calorie’ meals, and close to 80% eat a packed lunch almost daily at work. This group may be called the ‘moderate food consumer’ characterised by frugality and general sobriety.
DISCUSSION Methodological
aspects
Three methods were used for combining the sensory attributes and preference data. The first method was based on generating a preference space using PCA on the preference data (MDPREF). The sensory profile data were linked to the internal preference mapping space. The other two methods used the two datasets in opposite order. One of the techniques was based on relating the individual preference scores to a PCA analysis of the sensory data (PREFMAP) and the other was based on PLS2. The PLS2 and PREFMAP results were almost identical and quite similar to the results from the MDPREF study. The importance of the second and third axis was interchanged, but apart from this relatively small difference, the information obtained from the three methods was the same for most practical purposes. The consumer data were also analysed by cluster analysis. A complete linkage analysis was performed on the raw preference scores and stopped at four clusters. The four subgroups were identified on the MDPREF plot of the two first components. In this plot the four subgroups were quite well separated, indicating that the two methods can be used to give similar segments. However, how to set the limits between the groups is more difficult for a visual inspection than for the mathematical clustering technique. We recommend that both methods are used, cluster analysis for determining natural segments and visual inspection to understand more about the subgroups.
107
Consumer Preference Mapping of Dry Fermented Lamb Sausages In
the present
without
paper
we decided
any prior weighting
in contrast
or standardization.
This is
to the first paper in the series where weighting
of the sensory data was done. paring
to use raw data
the two analyses,
from standardized non-significant
As can be seen by com-
however,
sensory analysis
variables
results obtained
towards the sensory properties
product
in cluster
ness (Fig. 2). Another
scope for improvement,
the results obtained of the
into cluster
(espe-
5, would
comparable
1 in Table imply
2. This direction,
increase
of sensory
analysis
For
all the
methods
employed,
seemed
to be positively
flavour
and odour,
flavour
by juiciness, and partly
acidic
greasiness
erence
and flavour
and sour flavour. as a whole
were
smoke
of smoke,
group,
that
in combination
with
data were related
this type of information
with the above flavour
‘negative’
attri-
the sensory
data for the sausages,
problem
with
using
data
preference
is that
and
mapping
the data
requires a large or at least moderate statistical
Providing
consumer
of samples is difficult
blocking
of
can be very useful in such an
for consumer
consumers.
number
developer
or decrease
the other
samples to give an adequate the
colour
process.
A general normally
a product
and Nes (1995),
to chemical
pref-
the consumers
in combination
attributes
butes, or both. In Helgesen
techniques
between
dry rim, red-blue
suggest
‘positive’
optimization
indicated
The results, considering
should increase juiciness mentioned
Today
various
developed
is possible,
analysis
results indicate
opinions
stability
and traditions
and puritanism, On
the
towards
for
a
reasonable
response model).
regression
that
analysis.
that in this study only a linear vector model
is used. If a larger number complex,
of samples
of samples is available,
but also sometimes surface
models
In Helgesen
tifying important
more realistic
can
be
applied
and Nass (1995)
models, (ideal
a method
samples for consumer
more like point
for iden-
testing based on
sensory analysis of a larger set of samples was discussed. One of the important
results from the clustering
there seems to be a market dry based
fermented
lamb
on averages
(‘All’ line in Table important
point.
pany producing
is that
for most of the six different
sausages.
A
for the whole
traditional group
analysis
of consumers
2) would most likely have missed this From
such an analysis,
samples
scores) would probably
the food com-
1 and 3 (with low preference
have been advised to reformulate
their products towards one of the ‘winner’ sausages. In fact sample 3 was defined as a ‘winner’ product by clus-
For
a Nor-
1991,
1993),
lifestyle groups.
The
groups with very
People oriented
towards
some values.
extreme,
consumption
people
of goods,
who
who
are
oriented
are in favour
of
spontaneity and positive to use of new technologies, are classified as the lifestyle group with so-called ‘modern’ values. In addition materialistic
one will often find a dimension
(oriented
people.
number
have
are in favour of law-abidingness
to mention
other
oriented
but this is not always a good solu-
analysis
(Hellevik,
different
and interests.
model for each of In some cases
companies
lifestyle groups and
two main value/lifestyle
different
In this paper, decide
towards (healthiness
and sociological
which
status,
variables
that describe
The clue is to of variables
the ‘identity’
Even after such a description,
up’ of each
cluster,
‘tailor-making’ channels
advertising
adapted
Information
is of particular
By comparing
the classifications described
there seems to be indications lifestyle.
Subgroup The
subgroup
factor could be used
any of the four subgroups.
1993) with the subgroups grouping.
when
communication
to that specific segment in the market.
In this case no single demographic to describe
as the
on the ‘make-
importance
and selecting
of
the conclu-
sion might still be in favour of sensory properties decisive factor for preference.
and
ecological)
clusters were related
variables.
or combinations
age, food habits)
with
appearance
and partly
the four identified
by demographic
each cluster.
used
ideal
and consumption.
institute
has been able to identify
study is the absolute be
some.
research
on food purchase
vs idealistic
for a large
and
for identifying
wegian market-research
(gender,
Remember
to mention to determine
by the use of correspondence
egoism)
data
number
instance,
tion. The six samples tested by the consumers in the present can
market
of
in practice.
minimum
as
of the constituents.
instruments
their influence
associations
such
in general
and stickiness. Negative
toward sample
Demographics and lifestyle patterns
preference
influenced
lamb
might be
properties
would be necessary
levels and combinations
Sausage samples and segmentation
to
score by moving horizontally
acidic flavour, greasiness and juiciness, Further
without weighting.
of sample 2
2) could be one direction
go. Such a move would imply more spiciness and sweet-
after removal
to the corresponding
using all variables
(winner
to gain a higher preference
were quite
cially for the two first components)
reformulation
3 and partly
( 199 1,
of a similar value/lifestyle
1 represents
‘stability’
in Hellevik
in this study (Fig. 7)
oriented
the so-called people
‘modern’
were found
in
also in group 4. As in Hellevik
( 1991, 1993) this dimension
is represented
by the vertical
axis. Both group 3 and 4 may be seen as representatives of the
more
conservative
and
traditional
lifestyle.
In
did not like this
Hellevik (1991, 1993) the horizontal axis is called the materialistic vs idealistic dimension. When looking for this second dimension in Fig. 7, this pattern is not so easy
sausage. If a company wanted to improve the preference for sample 1, there seems to be at least two possibilities: a
to spot. However, there are elements in subgroup 1 which indicate materialistic values such as status, appearance and convenience. The opposite lifestyle
ter 4, even though
two other segments
108
H. Helgesen
et al.
values (idealistic) are far more easy to catch sight of, by looking at subgroup 2 with their interest in healthiness and ecological/naturalness. In other words, the segmentation obtained above based on preferences only for the different sausage samples indicated a similar pattern as the more general map described by Hellvik ( 199 1, 1993). This example does not include the importance of price, brand or packaging aspect for product purchase, but this will be considered in a later study with the same food items. Success with product innovation and reformulation, requires knowledge of the relationships between factors influencing food choice and the relative impact they have on consumer purchase decisions. In a future study some factors that influence purchase and repurchase of dry fermented lamb sausages will be investigated. Conjoint analysis may be a useful tool for integrating liking and sensory description (product testing) and other types of attributes of a product such as e.g. price level and brand (concept testing).
also a quite strong indication that the differences in preference were actually related to the primary components extracted by the PCA of the sensory data. In the MDPREF, PREFMAP and PLS analysis, which gave very similar results, preference seemed to be positively influenced by juiciness, acidic flavour and odour, greasiness and lamb flavour.
ACKNOWLEDGEMENTS Financial support from the Agricultural Food Research Society is gratefully acknowledged. Laura Bltimlein, Inger Johanne Fjosne and Sigrid Hurv are thanked for technical assistance. Ulla Dyrnes is thanked for graphical assistance and Dr Marit Risberg Ellekjipr, Professor Tore Hoyem and Marit Rodbotten are thanked for valuable discussions. We also wish to thank the referees for useful comments.
CONCLUSIONS The study revealed that sample 5 had the highest average preference. Ths sample was dominated by the attributes like for instance juiciness, acidic flavour and acidic odour. Sample 3 had the lowest mean liking. This sausage is dominated by attributes such as dry outer rim, flavour of smoke, hardness and red-blue colour. Cluster analysis identified four subgroups with different taste preferences and ascertained that there seemed to be room on the market for most of the six dry fermented lamb sausages. A traditional analysis based on the mean scores and visual inspection of the preference plots, would most likely have missed that important point. The ‘identity’ of each subgroup, described by demographic, sociological and attitude variables, indicated four quite distinct lifestyle groups. When comparing the ‘make-up’ of the subgroups in this study, with a sociological based study by Hellevik (1991, 1993), more or less the same lifestyle pattern was found. Subgroup 1 was made up of consumers with a tendency to ‘modern’ and materialistic lifestyle, with subgroup 2 representing a more idealistic lifestyle and subgroup 3 and 4 in favour of stability and tradition. It is apparent from the result and discussion of this case studies of six dry fermented lamb sausages, that the two alternative approaches to preference mapping, internal and external, in spite of a interchange between component 2 and 3, gave about the same qualitative conclusions. However, the interchange does indicate a different weighting of the differences among the samples. In this study, the PCR (preference mapping) and the PLS2 of the preference data vs the sensory data, gave very similar results. One of the reasons for this was probably the relatively low prediction ability. There was
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i=I SFVIFR _-_.-..
s0950.3293(96)00037-7
Food Qmlig and Prcfermcc Vol. 8, No. 2, pp. 97-109, 1997 0 1997 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0950-3293/97 s17.00+ .oo
CONSUMER PREFERENCEMAPPINGOFDRYFERMENTED LAMBSAUSAGES _ _ Hilde Helgesen,
Ragnhild Solheim & Tormod Na;s*
Norwegian Food Research Institute, Osloveien 1, N-1430 As, Norway
MA1’FORSK,
(Received 18 May 1996; revised uersion received 8 JuQ
Shepherd et al. (1988), Nute et al. (1989), Jones et al. ( 1989)) M&wan and Thomson ( 1989)) MacFie and Thomson ( 1988)) Beilken et al. ( 1990)) Gains and Gutteridge ( 199 1) and Greenhofl and MacFie ( 1994). It can also be used to compare preferences and to relate them to sensory characteristics. A properly designed study can also illuminate the segmentation of the market into a number of more or less well defined groups of consumers (i.e. the nature, size and strength of differing likes/dislikes). In new product development preference mapping can be helpful in two ways. It can be used to check that a prototype product is acceptable and, in terms of preference, falls into the correct segment of the market. In this respect, preference mapping can be a useful (and rnst effective) bridge between in-house assessment and full scale quantitative market research (Gains and Gutteridge, 1991). Preference mapping can in addition be a powerful method for optimising products using a twostage procedure. In the first stage, data from trained panels and consumer preference data of a prototype product along with its near market competitors, can determine segmentation and the key sensory characteristics which drive preference. In stage two, product variants are prepared with recipe or processing variations to increase or decrease the degree of these sensory characteristics. Subsequent preference mapping of these new products can identify the most preferred. Thus recipe and process can be optimised prior to a final product launch (Gains and Gutteridge, 1991). Preference mapping may be divided into two categories, external analysis (PREFMAP) and internal analysis (MDPREF). Internal analysis is the only choice for data sets that consist exclusively of consumer preference data. The information obtained from the internal ‘map’ of consumers and products can be related to other types of data as in the present study where consumer preferences are related to QDA. External analyses are similar, but relate the two types of information in opposite order (Carroll, 1980) - first a PCA is conducted for the external data (for instance sensory data) and the consumers are related one by one to the information in the external PCA space. Preference mapping techniques can also enable the investigator to identify groups of consumers with different preference and consumption patterns.
ABSTRACT This
paper focuses
mapping squares
(both (PLS)
on using
internal regression
data of six di$rent
homogenous
least
descriptive analysis-
the consumers into smaller,
consumer groups, nameb
preference
and partial
lamb sausages to conven-
(quantitative
For segmenting
compared,
comparing
in relating consumer preference
dry fermented
tional sensory profiling QDA).
and
and external)
two d@erent
more
methods were
cluster-analysis, and uisual inspection of
plots. The consumer groups were related to demographic and sociological variables. The vided
alternative approaches the same general
approaches, prejerence
to preference
conclusions.
For
mapping proall
the three
was positively intuenced by juiciness,
acidic flavour and odour, greasiness and lamb j2avour. cluster
analysis
preference
identzjied four
patterns
and indicated
subgroups
The
dzyerent
that there were market
segments for each of the six dry fermented 0
with
lamb sausages.
1997 EIsevier Science Ltd. AR rights reserved
Keywords:
Fermented
MDPREF;
PLS;
QDA;
lamb
sausages;
segmentation;
1996)
PREFMAP; cluster-analysis.
INTRODUCTION The long term success of a food product depends a great deal on its ‘performance’ once it is placed in the mouth. Good marketing will at best result in the first sale. As a consequence, it is very important to analyse the extent to which products are liked or disliked in a manner that is not compromised by marketing imagery (branding, packaging, advertising, etc.). These results should be related directly to the ingredients and the manufacturing process, which together determine the sensory characteristics of the products. Preference mapping is one technique which has been developed to fulfil these objectives, see *To whom correspondence should be addressed. 97
98
H. Hdgesen et al. The
present
Helgesen
paper,
which
and Nas (1995),
ing preference
mapping
partial least squares preference
is based on an article
(both internal
(PLS)
and external)
regression
data to data from QDA.
in the food industry these methods. trate through determine
to research regarding
to relate consumer
preference formulation.
of the pre-screened
lished consumer product
as target
consumers.
database,
Agricultural
consumers,
indicating
From
about
an estab-
demographic
and
usage profiles for a large group of consumers,
staff
participate.
of
Out
100 were defined
consumers
(in
are important whether
through
A third objective of the market preference
in
there
a change
in
of the paper is to
for dried fermented
the identification
analysis and visual inspection
sumers,
who met the requirements From
109 participated
38
were invited
the whole group of 138 invited
to
con-
in the test.
of consumer
patterns,
both cluster-
of plots were compared.
A
Consumer testing The
109 consumers,
sausage,
all weekly eaters of dry fermented
comprised
aged between
48 females
and 61 males,
The consumers
were separated
19 participants
who attended
an ordinary
classroom.
into eight groups with 9the test at different
Prior
were given the same information study and other
practical
on the purpose
matters
the segments
(sub-
session. The six sausage samples,
and sociological
vari-
by a trained
insight
into
whether
these
might
numbers
panel,
times in
to the test all consumers
to demographic
to gain
with 40
18 and 39, and 69 aged 40 years or more.
is to characterise
groups) according predict consumer
can
performance
groups with different objective
developer
of a food product
lamb sausage)
lamb sausages. To conduct
and
at the campus at the Norwegian
University.
and
applications
and then to establish
derive a segmentation
ables
practical
characteristics
is scope for improved
fourth
and development
a case study how a product
this case: dry fermented
product
differed
employees
The second aim of the paper is to illus-
which
product
and
The aim is to study
how the results from these three methods also to give guidelines
by
focuses on using and compar-
related
all previously
were coded
with
of the
to the taste assessed
3-digit
random
and served at the same time. No guidance
was
given on how to use the scale.
preference.
The consumers ised order
tasted the samples once in a random-
given
by
the
questionnaire.
Ten
different
orders of tasting the six samples were distributed
EXPERIMENTAL
a way that about 10 consumers,
in such
about one to two in each
of the eight groups, tasted the samples in the same order.
Materials
Each participant
was provided
with biscuits suitable
sensory tests, paper cups to expectorate According authors
to a prior
(Helgesen
study
undertaken
and Naes, 1995),
(6) was selected for preference based on information
testing. The selection
from QDA and chemical
a larger set of 14 commercially lamb sausages produced sausage
identification
Helgesen
by the same
a subset of samples
available
in Norway.
codes,
and Naes (1995)
was
profiles of
dry fermented
For simplicity,
6, 7, 8, 10, 11 and
the 14 in
were in this paper replaced
by
the following new codes, 1, 2, 3, 4, 5 and 6. All necessary details can be found in Helgesen It should be mentioned of view, should
six samples
is the absolutely
be used. From
however, to ensure
a consumer
using a small number reliability
and Nies ( 1995).
that from a data analysis point minimum
test point
especially
tapwater
sumers
to
rinse
between
session lasted
marked
their degree
tinuous line anchored marks were linearly like very much’
recorded
recorded
‘Degree
as 9.0.
taste con-
of liking on a 15 cm con-
transformed
into numbers
their
with ‘dis-
as 1.0 and ‘like very much’ of liking’
is for simplification
called preference.
Questionnaire design
of view,
for samples
Ideas
for statements
tionnaire
or answer
were collected
and Schutz
et al. (1988),
preference
of dry fermented
contained
a total
six samples was con-
trade-off.
with
two
categories
in the ques-
from the work of Solheim
quite strong in taste, and therefore
along
consumers
with individual on
food
habits
(1993) depth and
sausages. The questionnaire
of five pages,
where
the first page
recorded information of gender and age along with information on preference scores for six dry fermented
Selection of consumers consumers
The
on the left by ‘dislike very much’
interviews
Prior to recruiting
The
10 min.
and on the right with ‘like very much’. Afterwards,
with strong flavour. The samples in the present study are sidered to be an acceptable
samples.
for about
for
sample
that
of samples is important
of the data,
and
evaluation
unwanted
for the combined
preference
test and questionnaire survey, the target consumer was defined. This was according to product usage, that is a person who consumed dry fermented sausage once a week or more often. A pre-screening questionnaire with demographic and product usage tributed through the mail-system
questions to more
were than
dis200
lamb sausages. The rest of the questionnaire consisted of 46 statements, with 19 statements concerning general food habits, 13 statements related to usage of dry fermented sausage and 14 lifestyle and attitude questions. Except for frequency of use and food habits which were answered utilising an 8 point scale, the rest of the questions were answered by a multiple choice, 5 point category scale. The last page of the questionnaire recorded
99
Consumer Preference Mapping of Dry Fermented Lamb Sausages information grew
on geographical
up and
permanent
number
Partial
district where the consumers
of residential
years
in previous
Least Squares
datasets,
address.
-
preference
Data analysis
matrix
preference
mapping
or multidimensional
erence analysis (MDPREF) (1972))
is a principal
(as first described
component
analysis
pref-
by Carroll (PCA)
on a
PLS
PREFMAP information
solely
on the preference
principal
component
of variation identifies The
data.
and extracts
result of internal
that
MDPREF
within the preference
map,
of the PCA.
in the score
preferences
way
this as components, preference
map and a consumer samples
the same
analysis identifies the major sources
the major variation
and loadings
In
based
plot
mapping
is a sample
in
plot of samples
ing in the direction and
become
of preference,
moving
samples become
in the opposite
direction,
‘less liked’.
Often
is used for situations
available
with only con-
and thus no information
about why the samples are liked or disliked. is possible, as described McEwan
link sensory information analysis
space.
ponents which
In
sensory
the
following
it
( 1994)
to
to the internal
the
term
sensory
This can be done by regressing
variables
from the PCA. attributes
and MacFie
(e.g. QDA-data)
refers to QDA.
of the
However
in this article and by for instance
( 1995) and Greenhoff
preference
is given
on
the
principal
com-
In this way, information
are related
to which
samples
about will be
revealed. External described
preference by Schiffman
mapping
et al. ( 1981))
first using PCA on the QDA each of the consumers analysis. usually
The
consumers
represented
(PREFMAP)
(as first
is conducted
by
data and then by relating
to this PCA space by regression are as for the internal
as points
or arrows
analysis
variation
difference
in
between
here is the way the
PLS
uses
the
preference
the components. PLS
components
pre-
the covar-
of X-variables
and a
In the present
paper,
of providing
more con-
than the PREFMAP
(Martens
sensory to consumer standardization
More
maximizes
of the r-variables.
In this way, it has a potential
which
and Naes, 1989).
all three
methods
for relating
data were used on data without any
or
any
other
type
of
pretreatment
(except centering). For the purpose of understanding the preferences plete linkage cluster
distances.
(Mardia
analysis
consumer
were also analysed
using Euclidean
MDPREF
sumer data
each
‘more
samples
The
a linear combination
linear combination
is based on a PCR
the differences
data.
was
combina-
of the sensory
the systematic
component
between
In the joint
a few linear
as described
extracting
The
and consumers, the consumers are represented as directions of increasing preference on the sample map. Movliked’,
when for each
sumer relevant
the
extracts
extracted.
to the scores
distances
was a 6 x 109
(1T-data) as well as the sensory information
corresponding represent
for the samples.
(X-data) cisely,
are
the two
and consumer
In other words, a PLS2
preference
iance between
data.
(X-matrix)
or latent variables)
and PLS
components
of the stimuli
method
was used as a
between
The r-matrix
data.
tions (PLS-components
achieve
configuration
profiles
data that are used to predict the consumer
regression
the relationship
scores (Y-matrix).
consisting of samples or products matrix of data, (objects) and consumers (variables). The objective is to a consensus
sensory
of preference
used. The Internal
(PLS)
third way of studying
responses,
by cluster
analysis
In this study we used com-
et al., 1979) which is a hierarchical
methods.
The
that are close in multivariate
method
assigns
samples
space to the same subgroup
or cluster. In this way it can identify groups of consumers which
are homogeneous
samples. The consumer graphic
in their preferences
for the six
groups were compared
to demo-
data for the consumers.
tabulation
This was done by simple
and by the so-called
is an approximate
chi-square
homogeneity
test, which
test for similarity
among
the groups. Principal
component
squares
(PLS)
external
preference
analysis
regression, mapping
(PCA),
internal
(PREFMAP)
out using the UNSCRAMBLER
partial
(MDPREF)
extended
least and
were carried version
5.03
(Camo a/s, Trondheim, Norway). The cluster analysis and the chi-square tests were performed in SAS Version 6.10 (SAS Institute
Inc., Cary, NC, USA).
on the sample
map. Internal ference ponent datasets
(MDPREF)
and external
(PREFMAP)
pre-
mapping can both be viewed as principal comregression (PCR) methods applied on the two (preference
data and sensory data),
two sets used in a opposite lysis, the sensory data (explanatory
variables)
order.
but with the
For the external
are used as a so-called and the preference
Summary statistics
ana-
X-matrix
data
RESULTS
as the
In Fig. 1 is presented consumers.
a box-plot
on the basis of the 109
For this plot 50% of the preference
scores are
Y-matrix (response data), while the internal analysis uses the two data sets in the opposite way. Note that in both situations PCR is used for several Y-variables.
found inside the box. The remaining scores are distributed outside the box (25% at the bottom and 25% at the top). The median value is marked with a horizontal
This multivariate PCR regresses each single Y-variable onto the principal components obtained from the Xmatrix.
line within the box. The vertical lines end up in the lowest and highest preference scores given. The mean preference
scores
plus
standard
deviations
of the
six
100
H. Helgesen et al.
products are presented in Table 1. The median value is less vulnerable than the mean to extreme values in the distribution. Sample 3 has the lowest median. At the same time no other sausage had a median closer to the bottom value of the box. This illustrates that this sausage was the least preferred and that there existed a higher degree of agreement for the lower preference values for this particular sausage compared to the others. This is also reflected in the difference between the median and the mean which is also larger for this sample than for the rest of the samples. For the rest of the samples there are no great differences between mean and median values. It is also clear from the plot (Fig. l), that for all samples, almost all the scale is used. In other words, for each sample there are consumers who like it and consumers who dislike it. TABLE 1. Mean preference
scores and total standard deviation for the six sausage samples presented according to increasing preference
Sample Sample Sample Sample Sample Sample
3 2 1 4 6 5
Mean
SD
4.10 4.52 5.01 5.16 5.38 5.55
2.30 2.44 2.32 1.95 2.09 2.18
Internal preference mapping -
MDPREF
The PCA of the preference scores showed that about 53% of the variation in the preferences were explained by the two first principal components. Adding an extra component to this proportion increased the explained variance to about 74%. The regression of the sensory data onto the three principal components from preference analysis explained about 89% of their variance. Figure 2 represents a quite common way of presenting the internal preference loading plot. This plot is derived by scaling each consumer’s PCA vector to unit length. The direction of each preference vector indicates the direction of highest preference for this particular consumer. Using this way of plotting the results makes it clear in which direction a consumer has his preferences, but masks the strength of his preference (see also the section on external preference mapping). Examining the MDPREF consumer plots (Figs 2A and B) and the relationship of the sensory data to the principal
.8
6.9 8.8
.
I -1
-
0
Component
L 1
1 (30%)
5.8
i.8
I
0 0
1
2
3
4
5
6
Sample number FIG. 1. Box-plot showing the median values of the six sausage samples on the basis of 109 preference scores.
Component
1 (30%)
FIG. 2. Internal preference mapping of sausage data derived by scaling each consumers’s PCA vector to unit length. Numbers 1-6 represent sausage sample codes. (A) Component 1 vs component 2; (B) Component 1 vs component 3.
101
Consumer Preference Mapping of Dry Fermented Lamb Sausages components
of the preferences
get a clear impression samples
are
preferences
of how preferences
distributed are related
reflected
(Figs 3A and B) one can
over
can
for the different
the space
to the different
and
how
samples
for the second
the
odour).
and the
From
Fig.
flavour
3A,
preference
to be most strongly and acidic
flavour,
flavour
odour
the difference
number
to juiciness,
component,
1
(Table
hardness
1 is primarily
and colour
a component
with samples
preferred
between
is essentially
sample
a component
2 and sample
two samples
have
dis-
profiles
quite similar, but they differ in red-bluish of smoke, (Helgesen
sour
flavour
and
and NZS, 1995).
even
These
which
The
colour, flavour
ponent
size of fat particles differences
(with sample
sample
2
This
indicates
second
clearly
that
to note that sample
(Fig.
between
The
third
sweetness
preference
From
0.10
(such
whiteness,
attributes
I
Figs
properties
a com-
code 1 and the other
component
seems to be
of spiciness, spicy odour
as code
1 in Fig.
colour intensity
3B)
vs lamb
and juiciness.
3A and
B,
liking,
one
can
see that
is dominated
as dry outer rim, flavour
and red-blue
ofsmoke
2 has the lowest
sausages investigated
Other
are also important.
with lowest mean
l Uniform size of fat particles
for differences
3B) is essentially
related to a unique combination and
lowest some of
and Naes (1995).
for the difference
samples.
are clearly
scores 6 were
6 the second
the
are more important
third component
flavour,
.flavwr
mean preference 2 and
6
high
than others.
It is interesting
are
2 vs sample
samples
pH value of all the dry fermented
6 with the
and acidic
by the fact that
a sample
that
different
sample).
in Helgesen
sensory
whiteness
and
the sensory attributes
1, 2 and 4 in the
other samples lying in between. These
as quite preferred
in preference
The second component
is not solely
1) also illustrate
highest
between sample 3 on one side, and samples
5 and 6 on the other,
(for instance
but one
are important
but it can also be due to the rather
perceived
on the
describing
middle. criminating
axis
attributes
noise in the data. The consumers’
acidic
on one side vs dry rim, sour
of smoke,
other. Component
component related
plot (Fig. 3A),
some other
This can possibly be explained
the component
sensory attributes. appeared
in the sensory loading
also see that
sample
3
by such sensory
of smoke,
hardness
colour. Sample 5, with highest mean liking,
was dominated
by juiciness,
acidic
flavour
and
acidic
odour. The
largest
right quadrant
group
of consumers
fall into
the upper
in Fig. 2A and in the lower right quad-
rant in Fig. 2B. Since
the most preferred
samples from
the analysis of averages above, namely samples 4, 5 and 6 are positioned
in these quadrants,
is in clear correspondence I
I
I
0
0.05
0.10
I -0.05
Component 1
I
preferred
by some of the subjects.
This important
aspect
will be discussed in more detail below in connection
B
0.10 l Dry outer rim
d1 0 uniform size of fat partides
Hardness. Colwr .
analysis
results. It is,
however, also clear that the rest of the samples seem to be
segmentation
0.05
the MDPREF
with the average
of the consumers’
External preference mapping The first step for this method
I
the sensory data. vector
model.
The
PREFMAP
was to perform
PREFMAP
only).
a PCA on
model used was the
An ideal point model is usually better
use, but in this case such a model because
with
loading plot.
the number
of samples
The
two first components
information
in X (sensory data).
is difficult
is so small described
to
to apply (6 samples
84%
of the
For 3 components,
the
percentage was equal to 93%. The regression model for the preference data vs the three principal components -0.10
-0.05
0
0.05
0.10
Component 1 FIG. 3. Correlation between internal preference components in Fig. 2 and sensory attributes as evaluated by trained assessors. Numbers l-6 represent sausage sample codes. (A) Component 1 vs component 2; (B) Component 1 vs component 3.
from sensory analysis explained about 72% of variance. The loading plots for the three components presented in Figs 4A and B.
the are
The number and density of the consumer preference vectors, indicate a higher preference in the direction of the upper right quadrant of the plot in Fig. 4A, indicating
sample
5 as the
most
preferred
sample.
The
H. Helgesen et al.
102
density of consumers decreased towards the lower left quadrant indicating 3 as the least preferred sample. The results presented in Fig. 4 are based on all consumers tested, with no reference to how well they fit to a liner model in the scores from the sensory analysis. An additional PREFMAP analysis was therefore performed for those consumers only who can be significantly described by the two first principal components. Because of large noise in consumer data and only six samples in the data set, a significance level as high as 0.20 was used. Thirty-six percent of the consumers were selected. The consumer plot for the two first dimensions is presented in Fig. 5. As can be seen, the plot shows exactly the same tendency as the plot for all consumers. By relating the positions of the consumer vectors to the sensory components within the external data (Figs 4A and B and Figs 6A and B), one can, as for MDPREF, infer important relationships between sensory attributes and the consumer preferences. For instance, consumers positioned to the left in the plots preferred products with dry outer rim, red-blue colour, uniform size of fat
1
particles, hardness and sour flavour (Fig. 4A and 6). The consumers to the right preferred sausages with juiciness, whiteness, lamb flavour, greasiness, acidic flavour, acidic odour and to a certain extent also stickiness and colour intensity. Similarly, consumers positioned at the bottom of component 2 preferred samples with flavour of smoke and rejected spicy sausages. As can be seen, the PREFMAP scoreplots (Fig. 4A and B) are somewhat different from the corresponding scoreplots for MDPREF. Looking more closely at the two sets of plots, however, reveals that the first component is about the same for the two cases and that component 2 for the MDPREF analysis corresponds approximately to component 3 in the PREFMAP study and vice versa. The same can be said about the interpretation of the two loading plots. Therefore, we can conclude that, although the importance of the two components 2 and 3 is interchanged in the two analyses, the two methods gave about the same qualitative conclusions. It is important, however, to stress that the interchange of axes may indicate a different weighting of the differences among the samples. Knowing that the sensory profile is possibly much larger than the set of attributes given emphasis by the consumers, this difference in weighting is quite natural.
Relationships between the preference data and the sensory data investigated by PLS2
N
E
2
ix0 E 8
The percentage of sensory data explained by the PLS components and percentage of consumer preferences explained by the sensory data, were very similar to the percentages for external preference mapping. The sample scores and the loadings for PLS2 were so similar to the corresponding PREFMAP results, that for reasons of
-1 -1
CompOonent 1
1
”
CompEnent 1
Component 1 FIG. 4. External preference mapping of sausage data derived by scaling each consumers’s PCA vector to unit length. Numbers l-6 represent sausage sample codes. (A) Component 1 vs component 2; (B) Component 1 vs component 3.
FIG. 5. External preference mapping of sausage data for consumers who can be significantly described by model in the two first principal components from the data (significance level 0.20). Numbers 1-6 represent sample codes.
derived a linear sensory sausage
Consumer Preference Mapping of Dry Fermented Lamb Sausages
space, only the PREFMAP plots are presented here. The same as above can therefore be concluded for the relationship between PLS2 and the MDPREF. In other words, the PCR and the PLS2 of the preference data vs the sensory data gave very similar results in this case. One explanation may be that the differences in preference are actually related to the primary components extracted by the PCA of the sensory data.
Segmentation
by similarity
of preference
The basis for the segmentation considered in this study is the MDPREF analysis. Similar studies could, however, have been conducted using one of the other two methods. The main focus is on the relationship between segmentation of consumers performed by a visual inspection of the loading-plots from the internal preference mapping and a segmentation done by cluster analysis (complete linkage). The latter is a technique that assigns samples that are close in multivariate space to the same subgroup or cluster.
0.6 A . Spiciness l Spicy odour 0 N
0.2 -
.
Dry outer rim
, Sweetness
E I E
0
E 8
-0.2
-
Uniform fat size diihfbution 0
-0.6 -
103
The cluster analysis was run on the 6~ 109 matrix of consumer data. Inspection of the dendrogram indicated that four clusters could be a reasonable number of clusters to study further. This indication was, however, not very clear. In Fig. 7 the scoreplot from internal preference mapping is shown with positions of consumer preference vectors within each of the four subgroups labelled with different symbols. It is clear that the four subgroups define different regions in the loading plot, showing that cluster analysis in this case splits the data into subgroups which correspond approximately to subgroups based on the loading plot of the two most dominating components only. In other words, the two methods gave partly the ‘same’ subgroups. However, when the number of consumers is large, a visual inspection is not straightforward and there is a need for an automatic tool for identifying homogeneous clusters of consumers. As a conclusion, in this case with a fairly small number of consumers, both cluster analysis and visual inspection of the plots, identified more or less the same segmentation pattern. The scoreplot of the two first dimensions was compared to the 10 different orders of tasting the six samples. If consumers with the same tasting order were grouped in the scoreplot, this could indicate a serious problem in the setup of the experiment. The results would have been very difficult to interpret. No such relation was, however, found in this experiment. The mean scores of the six products by the total group and by clusters of consumers are presented in Table 2. The size of the clusters are not very balanced, with clusters 1 and 2 as close to half the size of clusters 3 and 4. For each cluster it is possible to find a winner (in bold in Table 2) and a looser (italic in Table 2) product, and for most of the groups these products are different among clusters.
0 Flavour of smoke
I
I
I
-0.6
I
-0.2
0
I
I _
0.2
0.6
Component 1
0.2t I-
I
0.6
l Uniform siz: of fat particles
t
6
N 0.4 -
E
CY
t
E
g
E
E s
g E
0
-@---
o-
8 -0.4
-
0 SOUR
-0.20
fh
\-unifcfm fat
-J
size distribution
-0.20
Compinent
0.20
1 -0.6
0.4
-0.4
Campkent
0.86
1
and loadings for the sensory data. Numbers l-6 represent sausage sample codes. (A) Component 1 vs component 2; (B) Component 1 vs component 3.
FIG. 6. Scores
0 = subgroup1,$
= subgroup
2. 0 = subgroup
3, 0 =
subgroup 4
FIG. 7. Score plot from internal preference mapping showing the positions of consumer preference vectors for each subgroup labelled with different symbols.
104
H. Helgesen et al.
TABLE 2. Mean scores of the six products by the whole group and by consumer subgroups. products are presented in bold, whereas the ‘looser’ products are presented in italics
For each cluster
(line), the ‘winner’
Product mean scores Subgroup (cluster)
Freq.
5
6
4
2
1
3
11 18 43 37 109
7.75 4.28 7.01 3.83 5.55
3.14 4.22 6.54 5.17 5.38
5.74 3.74 5.36 5.26 5.16
7.00 6.62 4.45 2.98 4.52
6.10 5.74 4.20 5.11 5.01
2.2 4.12 3.57 5.43 4.10
1 2 3 4 All
Histograms
of the
four subgroups ferent Fig.
opinions 8 the
intervals,
where
scores (black
3.7 to 6.3 (grey shading (white
from
preference
scores
in Fig. 8, emphasising
of the products.
preference
like of the sample and scores
product
are shown
For
interval from
divided
into
1 to 3.6 were defined of the bars),
of the bars)
6.4 to 9 were
By this classification, rant of Fig. 7 (referred
the presentation
was
shading
for the the dif-
as dis-
scores from
were defined
defined
in sub-
as like
as like very much
“r
Group 1
Group 2
8
had
no clear
consumers
liked
samples
5 and
1 and
disliked
leaving
sample
low mean
of subgroup
1 and
on sample
liking
of sample
4 gave
sample
2 very
2 as the
2. to
much,
they
3 and
liking
‘winner’,
6.
5 out of 6 but
to the ‘winner’
3. Subgroup
with
a
products
2 and 4 had very differ-
2. Subgroup
5 and disliked
1 and
sample
3 the highest
sample (referred
samples
preferences,
score compared
ent opinions
disliked
quadrant
4 and
2 had no extreme
samples,
and
right
left quad-
4) liked most of the
1) liked
samples
Subgroup
in the upper
favourite
in the lower
as subgroup
fairly
bars).
gi-
sausages, The
consumers
to as subgroup
mean
3 agreed
3, while
liking
on
subgroup
score
of all the
groups.
Relating
6
the segments
If there
are several
a preference assume
e t!! .s P a
that
521463
1463
association
with which
Group 4
Group 3
related
8 c
8
to the
major
F
the
to be less affected associated
fermented smoke, appear
5
2
1
4
6
3
Sample number m
Like, score 3.7 0
- 6.3
Dislike, score 1 .O 3.6 Like very much, score 6.4 - 9.0
FIG. 8. Histogram of the mean product preference scores within each of the four sub-groups. The samples are presented according to declining preference order defined by subgroup 1.
with critical sages.
sensory sausages
hardness,
red-blue
to be more and
and
to hardness,
parameters.
degree
a
they
Consumers
prefer
of flavour rancid
of also
flavour,
in subgroup
seem to prefer the exact namely fermented lamb These
the and
and dry rim. They
to saltiness,
flavour.
not
4)
flavour
Instead
high
into
7 (subgroup lamb
tolerant
lamb
two.
are to play
clustered
and
bitterness.
right quadrant) parameters, -
stickiness
unlikely
by whiteness,
colour
of the
preference.
3A
with
in the
it is not possible which
vectors
of Figs
lamb
flavour
(upper sensory ”
quadrant
are
sensory
other
important
consumer
preference
appear other
521463
with
left
sour
space
role in determining
Consumers
to each
characteristics
preference
upper
in explain-
they will have some
model),
or least
that
with
automatically
importance
close
a linear
most
it is true
not
but for two or more
are placed (using
associated
should
Obviously,
preference,
space
However,
9
9
preference.
parameters
data
characteristics one
they are all of equal
to determine
”
52
sensory
component,
ing consumer
preference
to the sensory
respondents
dry rim and sour flavour
3
opposite sausages are
in the sau-
In the lower, right quadrant of Figs 3 and 7, consumers in subgroups 1 and 2 preferred juicy and sticky sausages with three classes of flavour and odour characteristics; namely spiciness, acidity and sweetness. Sausages with such flavours,
combined
with more
red colour
Consumer Preference Mapping of Dry Fermented Lamb Sausages
than the other four samples (sample 2 and 5), seemed to be very appealing to these two groups of consumers.
Segmentation related to demographic variables and food-related lifestyle So far the consumers have been segmented by similarity of preference. The next step would be to ascertain whether it is possible to characterise the consumers within the subgroups, and perhaps gain an insight into whether gender, age or lifestyle might predict product preference. This was done by simple tabulation and by the so-called
105
homogeneity test, which is an approximate chi-square test for similarity among the groups. As the precise identity of all consumers of dry fermented lamb sausages is known, the four preference groups can be explored to determine which products appeal to consumers of different age, gender and with different food habits. Such information is of special interest as it might enable marketing and product development to ‘tailor-make’ products to a specific market segment. Tables 3 and 4 summarises selected parts of the sociodemographic information recorded for the consumers. In this table, the 8 point frequency scale for frequency of use
TABLE 3. Socio-demographic details of consumers within preference subgroups. Information about the total group of consumers is listed to the left. The proportion (in %) of the consumers in the different subgroups and the significance level f$) of differences between subgroups (homogeneity test) for each of the variables are also presented
Total Demographic variables
Gender Male Female Age 18-29 yr 30-49 yr 50-69 yr Health A B c D E Quiet A B C D E Friends A B C D E Appearance A B C D E Lunch 1 2 3 4 5 Bread
1 2 3 4 5 Codes and variable
%
sample
P
sub!group (16.1%)
1
Preference subgroups (%) subgroup 2 subgroup 3 (16.5%) (39.4%)
sub!group (33.9%)
number
0.317 56 44
61 48
37 49 14
40 54 15
56 20 18 6 0
61 22 20 6 0
16 28 41 15 0
18 30 45 16 0
24 40 22 10 4
26 44 24 11 4
23 34 30 10 3
25 37 33 11 3
69 4 9 17 1
75 4 10 19 1
36 10 51 3 0
39 11 56 3 0
36 64
67 33
51 49
62 38
45 45 10
22 56 22
37 47 16
41 51 8
28 36 36 0 0
83 6 5 6 0
53 23 17 7 0
56 20 18 6 0
19 18 36 27 0
28 28 33
11 0
9 23 54 14 0
19 35 32 14 0
36 18 37 9 0
28 17 33 11 11
9 54 23 12 2
35 43 11 8 3
9 36 45 9 0
33 33 225 6
21 35 35 7 2
24 32 32 8 4
45 0 0 55 0
56 0 11 28 5
72 9 14 5 0
79 0 5 16 0
36 9 55 0 0
56 5 39 0 0
35 9 53 5 0
27 14 54 5 0
0.697
0.197
0.506
0.062
0.623
0.003
0.737
explanation
are listed in Table 4.
4
106
H. Helgesen et al.
and food habits was simplified to a 5 point scale. The five statements presented (out of a total of 46) were selected due to large or at least moderate variation between the subgroups. In some cases, however, statements which are not presented in this table, are mentioned in the description of the subgroups. It is quite clear that one can find differences in distribution, associated with certain subgroups. However, these high or low frequency biases are merely indications of interesting relationships and as we see, only a few of the variables are actually significant (or close to) if all subgroups are considered simultaneously. In Table 3 below, the highest frequency biases in distribution of the presented statements in the questionnaire, are in bold. In subgroup 1 (preferred sausages high in spiciness, acidity, sweetness and juiciness) nearly all consumers are less than 50 years, the majority are females and many of them tend to seek pleasure. They are confident of own judgements and enjoy unforeseen and challenging situations. In this group far more emphasis is put on the importance of the link between appearance, personality and clothing, compared to the other groups. The sensory aspects of foods are more interesting than the wholesomeness, and crisps are eaten once a week or more often. Close to 50% never prepare or eat ‘low-calorie’ meals. These consumers have a strong bias towards never eating packed lunch at work. This group may be called the ‘modern food consumers’, oriented towards a life style characterised by convenience, impulsiveness, spontaneity and materialistic values. Subgroup 2 (preferred spicy and sweet sausages) has a bias towards males, and age from 30 to 50 years. They are very concerned about the health risks involved with smoking and drinking alcohol and share a confidence in alternative medicine. High-calorie foods like snacks (crisps, peanuts) are consumed no more than three times a month. These consumers eat home-made bread on more occasions than the other consumers and are far more concerned about additives in foods than the other conTABLE 4. Codes used in Table
A: B: C: D: E:
For 1: 2: 3: 4: 5:
disagree partly disagree partly agree agree no answer
3
variables ‘lunch’ and ‘bread’ 3-5 times a week l-2 times a week 2-3 times a month or less never no answer
Variablexplanation: Health:
I think the health dangers
beverages Quiet:
and smoking
linked to drinking
alcoholic
are exaggerated.
I prefer to live a quite and calm life.
Friends: rather
cigarettes
I prefer
to have a lot of friends
than a small number
Appearance: emphasise
I think
it is important
my personality.
Lunch:
I eat a packed
Bread:
I eat home-made
and acquaintances,
of very close friends.
lunch at work. bread.
to wear
clothes
which
sumers. These consumers may be called the ‘rational food consumers’ with an interest in quality aspects such as wholesomeness and partly also ecological growing methods. Subgroup 3 (preferred sausages high in lamb flavour, stickiness and juiciness, - traditional lamb sausages) has an equal distribution of males and females, but more people over 50 years than in subgroups 1 and 4. These consumers tend to be rational human beings who are not ruled by their feelings and they tend to prefer a quiet and calm lifestyle. This group may be called the ‘conservative food consumers’ who put great value on stability and security. The consumers in subgroup 4 (low ratings for spicy sausage) have a bias towards males younger than 50 years who like to have a few close friends rather than a lot of acquaintances. A large share of these consumers never eat or prepare ‘low-calorie’ meals, and close to 80% eat a packed lunch almost daily at work. This group may be called the ‘moderate food consumer’ characterised by frugality and general sobriety.
DISCUSSION Methodological
aspects
Three methods were used for combining the sensory attributes and preference data. The first method was based on generating a preference space using PCA on the preference data (MDPREF). The sensory profile data were linked to the internal preference mapping space. The other two methods used the two datasets in opposite order. One of the techniques was based on relating the individual preference scores to a PCA analysis of the sensory data (PREFMAP) and the other was based on PLS2. The PLS2 and PREFMAP results were almost identical and quite similar to the results from the MDPREF study. The importance of the second and third axis was interchanged, but apart from this relatively small difference, the information obtained from the three methods was the same for most practical purposes. The consumer data were also analysed by cluster analysis. A complete linkage analysis was performed on the raw preference scores and stopped at four clusters. The four subgroups were identified on the MDPREF plot of the two first components. In this plot the four subgroups were quite well separated, indicating that the two methods can be used to give similar segments. However, how to set the limits between the groups is more difficult for a visual inspection than for the mathematical clustering technique. We recommend that both methods are used, cluster analysis for determining natural segments and visual inspection to understand more about the subgroups.
107
Consumer Preference Mapping of Dry Fermented Lamb Sausages In
the present
without
paper
we decided
any prior weighting
in contrast
or standardization.
This is
to the first paper in the series where weighting
of the sensory data was done. paring
to use raw data
the two analyses,
from standardized non-significant
As can be seen by com-
however,
sensory analysis
variables
results obtained
towards the sensory properties
product
in cluster
ness (Fig. 2). Another
scope for improvement,
the results obtained of the
into cluster
(espe-
5, would
comparable
1 in Table imply
2. This direction,
increase
of sensory
analysis
For
all the
methods
employed,
seemed
to be positively
flavour
and odour,
flavour
by juiciness, and partly
acidic
greasiness
erence
and flavour
and sour flavour. as a whole
were
smoke
of smoke,
group,
that
in combination
with
data were related
this type of information
with the above flavour
‘negative’
attri-
the sensory
data for the sausages,
problem
with
using
data
preference
is that
and
mapping
the data
requires a large or at least moderate statistical
Providing
consumer
of samples is difficult
blocking
of
can be very useful in such an
for consumer
consumers.
number
developer
or decrease
the other
samples to give an adequate the
colour
process.
A general normally
a product
and Nes (1995),
to chemical
pref-
the consumers
in combination
attributes
butes, or both. In Helgesen
techniques
between
dry rim, red-blue
suggest
‘positive’
optimization
indicated
The results, considering
should increase juiciness mentioned
Today
various
developed
is possible,
analysis
results indicate
opinions
stability
and traditions
and puritanism, On
the
towards
for
a
reasonable
response model).
regression
that
analysis.
that in this study only a linear vector model
is used. If a larger number complex,
of samples
of samples is available,
but also sometimes surface
models
In Helgesen
tifying important
more realistic
can
be
applied
and Nass (1995)
models, (ideal
a method
samples for consumer
more like point
for iden-
testing based on
sensory analysis of a larger set of samples was discussed. One of the important
results from the clustering
there seems to be a market dry based
fermented
lamb
on averages
(‘All’ line in Table important
point.
pany producing
is that
for most of the six different
sausages.
A
for the whole
traditional group
analysis
of consumers
2) would most likely have missed this From
such an analysis,
samples
scores) would probably
the food com-
1 and 3 (with low preference
have been advised to reformulate
their products towards one of the ‘winner’ sausages. In fact sample 3 was defined as a ‘winner’ product by clus-
For
a Nor-
1991,
1993),
lifestyle groups.
The
groups with very
People oriented
towards
some values.
extreme,
consumption
people
of goods,
who
who
are
oriented
are in favour
of
spontaneity and positive to use of new technologies, are classified as the lifestyle group with so-called ‘modern’ values. In addition materialistic
one will often find a dimension
(oriented
people.
number
have
are in favour of law-abidingness
to mention
other
oriented
but this is not always a good solu-
analysis
(Hellevik,
different
and interests.
model for each of In some cases
companies
lifestyle groups and
two main value/lifestyle
different
In this paper, decide
towards (healthiness
and sociological
which
status,
variables
that describe
The clue is to of variables
the ‘identity’
Even after such a description,
up’ of each
cluster,
‘tailor-making’ channels
advertising
adapted
Information
is of particular
By comparing
the classifications described
there seems to be indications lifestyle.
Subgroup The
subgroup
factor could be used
any of the four subgroups.
1993) with the subgroups grouping.
when
communication
to that specific segment in the market.
In this case no single demographic to describe
as the
on the ‘make-
importance
and selecting
of
the conclu-
sion might still be in favour of sensory properties decisive factor for preference.
and
ecological)
clusters were related
variables.
or combinations
age, food habits)
with
appearance
and partly
the four identified
by demographic
each cluster.
used
ideal
and consumption.
institute
has been able to identify
study is the absolute be
some.
research
on food purchase
vs idealistic
for a large
and
for identifying
wegian market-research
(gender,
Remember
to mention to determine
by the use of correspondence
egoism)
data
number
instance,
tion. The six samples tested by the consumers in the present can
market
of
in practice.
minimum
as
of the constituents.
instruments
their influence
associations
such
in general
and stickiness. Negative
toward sample
Demographics and lifestyle patterns
preference
influenced
lamb
might be
properties
would be necessary
levels and combinations
Sausage samples and segmentation
to
score by moving horizontally
acidic flavour, greasiness and juiciness, Further
without weighting.
of sample 2
2) could be one direction
go. Such a move would imply more spiciness and sweet-
after removal
to the corresponding
using all variables
(winner
to gain a higher preference
were quite
cially for the two first components)
reformulation
3 and partly
( 199 1,
of a similar value/lifestyle
1 represents
‘stability’
in Hellevik
in this study (Fig. 7)
oriented
the so-called people
‘modern’
were found
in
also in group 4. As in Hellevik
( 1991, 1993) this dimension
is represented
by the vertical
axis. Both group 3 and 4 may be seen as representatives of the
more
conservative
and
traditional
lifestyle.
In
did not like this
Hellevik (1991, 1993) the horizontal axis is called the materialistic vs idealistic dimension. When looking for this second dimension in Fig. 7, this pattern is not so easy
sausage. If a company wanted to improve the preference for sample 1, there seems to be at least two possibilities: a
to spot. However, there are elements in subgroup 1 which indicate materialistic values such as status, appearance and convenience. The opposite lifestyle
ter 4, even though
two other segments
108
H. Helgesen
et al.
values (idealistic) are far more easy to catch sight of, by looking at subgroup 2 with their interest in healthiness and ecological/naturalness. In other words, the segmentation obtained above based on preferences only for the different sausage samples indicated a similar pattern as the more general map described by Hellvik ( 199 1, 1993). This example does not include the importance of price, brand or packaging aspect for product purchase, but this will be considered in a later study with the same food items. Success with product innovation and reformulation, requires knowledge of the relationships between factors influencing food choice and the relative impact they have on consumer purchase decisions. In a future study some factors that influence purchase and repurchase of dry fermented lamb sausages will be investigated. Conjoint analysis may be a useful tool for integrating liking and sensory description (product testing) and other types of attributes of a product such as e.g. price level and brand (concept testing).
also a quite strong indication that the differences in preference were actually related to the primary components extracted by the PCA of the sensory data. In the MDPREF, PREFMAP and PLS analysis, which gave very similar results, preference seemed to be positively influenced by juiciness, acidic flavour and odour, greasiness and lamb flavour.
ACKNOWLEDGEMENTS Financial support from the Agricultural Food Research Society is gratefully acknowledged. Laura Bltimlein, Inger Johanne Fjosne and Sigrid Hurv are thanked for technical assistance. Ulla Dyrnes is thanked for graphical assistance and Dr Marit Risberg Ellekjipr, Professor Tore Hoyem and Marit Rodbotten are thanked for valuable discussions. We also wish to thank the referees for useful comments.
CONCLUSIONS The study revealed that sample 5 had the highest average preference. Ths sample was dominated by the attributes like for instance juiciness, acidic flavour and acidic odour. Sample 3 had the lowest mean liking. This sausage is dominated by attributes such as dry outer rim, flavour of smoke, hardness and red-blue colour. Cluster analysis identified four subgroups with different taste preferences and ascertained that there seemed to be room on the market for most of the six dry fermented lamb sausages. A traditional analysis based on the mean scores and visual inspection of the preference plots, would most likely have missed that important point. The ‘identity’ of each subgroup, described by demographic, sociological and attitude variables, indicated four quite distinct lifestyle groups. When comparing the ‘make-up’ of the subgroups in this study, with a sociological based study by Hellevik (1991, 1993), more or less the same lifestyle pattern was found. Subgroup 1 was made up of consumers with a tendency to ‘modern’ and materialistic lifestyle, with subgroup 2 representing a more idealistic lifestyle and subgroup 3 and 4 in favour of stability and tradition. It is apparent from the result and discussion of this case studies of six dry fermented lamb sausages, that the two alternative approaches to preference mapping, internal and external, in spite of a interchange between component 2 and 3, gave about the same qualitative conclusions. However, the interchange does indicate a different weighting of the differences among the samples. In this study, the PCR (preference mapping) and the PLS2 of the preference data vs the sensory data, gave very similar results. One of the reasons for this was probably the relatively low prediction ability. There was
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