40. Progress in preference mapping

3 18

Abstracts of Oral Presentations

descriptors

having

retained;

significant

for interaction

retention.

Reducing

retention

treatment

effects

the panel

of only 34%,

there

reduction

beforehand,

in degrees

only

to one-quarter

resp. 9%,

significant

for these effects. The loss of information, be predicted

effects

were

This external

of the individual

allowed descriptors

which can never

is discussed

of freedom

were 34%

in relation

inherent

to the

for smaller

the products

Garmt B. Dijksterhuis ID-DLO,

Institute

for Animal

Science

drawn from quantitative

in describing

Selected

recording

research

perceived

by human resulting, recorded

of

model

for each

so-called, intensity

in

tasting

and Health,

consistencies stance.

the more

sensible,

which

perhaps

of the difficulty

is available,

A method

individual

substance.

Curves

subjects,

prototype

is investigated.

conclusions

is compared

of

curve

factors for the Time and Intensity

about

of projected

the perceived

with other methods

that over

The method

that were recently

vis. Principal

and the analysis of TI-curve

curves.

curves

intensity

to the tasted substances.

posed to model TI-curves,

is often

to interpret

However,

the possible saddle

if a clustering of consumers

thanks to one of the two methods proposed,

contour

Last but not least, external

analysis assumes that there is a valid multivariate consensus

among

trained

ences. This assumption metric

permutation

assessors about

can be checked

tests. When

consensus

can

be defined,

procedure

is proposed

pro-

Mario Bertuccioli,

Erminio Monteleone and Ella Pagliarini,

Dip. Scienze e Tecnologie (DISTAM),

Universita

50 144 Firenze,

Alimentari di Firenze

e Microbiologiche

Via Donizetti,

The analysis of hedonic

MAPPING

overall

differences

data to determine in

samples and the relationships and the sensory

attribute

acceptability between

The traditional

stimulus space to preference

France

results by the calculation ideal point models.

improvements

Mapping

techniques,

and illustrates

associated proposes

their effectiveness

with some

on real

and simulated data. The interpretation of Internal Preference Mapping, which is basically a biplot of the product consumer preference data set, can be rather tricky when the number of consumers or the number of dimensions required is large. Preference Clustering techniques are preferred for deriving homogeneous clusters of consumers. Each cluster can be summarized by its vector of product mean scores and the smaller set of these new vectors may be used for External Preference Mapping.

an ideal space.

point

For

This

that

each

if there are among

consumers’

to map individual’s

duct acceptability.

17 rue Sully, BV 1540, 2 1034 Dijon Cedex,

Preference

6 -

Italy

INRA,

some problems

to each of

41. NEW METHOD FOR PREFERENCE MAPPING

data on the sample space are relevant

identifies

weighted

preference

Pascal Schlich and Philippe Callier,

paper

multivariate

the sensory attributes.

any

The

using non para-

no valid

a new univariate

for relating

sensory

sample differ-

Curve Analysis

shapes.

40. PROGRESS IN PREFERENCE

of their shape by means of iso-

plots for instance.

curves,

To this end,

onto the prototype

using the method

time in connection

consist

but also show clear

aim of the study is to find prototype

enable

The

and for a given tasted sub-

to find underlying substances,

by means of projection The

of

These curves display large dif-

curves are projected

axes separately,

consists

as they are indicated

Time-Intensity

within a subject

for the particular

Although

enough in the sensory field. Moreover,

model,

point in preference.

AA

sensometrics

a particular

ferences between individual

calculated.

seems to be avoided in the sensory applications, because

levels over a short range of time, typi-

cally one or two minutes.

cluster

can then be

models and a weigh-

the number of response surfaces becomes small enough to

taste-intensities

subjects

consumers

on the basis of individual

to be recognized

The Netherlands

type

models can be fitted,

this selected model is significant

preference.

allow visual investigation One

analy-

to test which one is the best for each

and whether

the elliptical

P.O. Box 15, NL-7360

descriptive

but it is possible consumer,

Different

these tests are classical tools in statistics, they do not seem

and Paul H.C. Eilers,

Sensory Laboratory, Beekbergen,

DATA

regression

scores onto a sensory map of

panel.

ted mean

39. MODELLING TIME-INTENSITY USING PROTOTYPE CURVES

hedonic

sis with a trained

clustered

panels.

analysis is basically a polynomial

pro-

method that relates a

data permits

to obtain of vector

the and

finds for each subject

is positioned

of these

hedonic

to maximize

of a hierarchy method

food liking

ideals

within

the stimulus

points,

the

squared

Euclidean distances from each stimulus to the ideal point are linearly (for metric model) or monotonically (for non-metric model) expressed by the subjects. map individual’s hedonic

related to the preferences Alternatively, it is possible to data on the sample space by

using

developed

another

procedure

to obtain

only

a

model (response surface) by partial least squares modelling, called CARS0 (computer-aided response surface optimization). Using CARSO, the coefficients of the polynomial describing the surface are obtained by PLS