41. New method for 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

Abstracts of Oral Presentations instead

of MRA

followed

the PLS loadings. procedure,

by the subsequent

Accordingly,

to map

the strategy

the preference

space, can be briefly

collapse

outlined

data

nent model)

on the sample

as follows: a) the coordi-

ings

and cross-product

transformed

are

compo-

of sample space are coded and expanded

squared

into

terms;

b) PLS

polynomial

to

load-

coefficients

either

method

information

of CARS0

nate (sample scores of first and second principal include

of

for

nificance

about

similarities

information

a new software

aided

development,

maximum

the

software

the

experimental

are

necessary

experimental

value

domain;

preference

value

determined. to analyze

than

within

within

which

a certain

value,

to demonstrate

the results

new experiments.

of 20 families.

to

tem, specially

extra vir-

food industry,

In order

by a consumer

The flavor profile of the by a laboratory

trained

panel. The results obtained

by using the traditional

and

CARS0

very

and

procedures

demonstrates

show

CARS0

good

can facilitate

agreement

the understanding

of the results.

42.COMPUTEbAIDED DEVELOPMENTINTHE

Camo AS, Olav Tryuggvasonsgt Trondheim,

-

research

forsk (The

Norwegian

tematize

knowledge

methodology development

about

results through

techniques

concept

use of experimental

was applied

matter

of processed

variables

were maturity

design for ana-

has been an to processed

and processing

effects on its sensory quality.

processing

for efficient

PCA and ANOVA

tool. The approach

nificant

development

data. Sensory evaluation

cheese to identify ingredients the greatest

to sys-

values with

The most sigof cheese, dry

cheese and pH. Samples

that both

the

forward

proideas

analysts in the part of

for Food Research,

influence

the sensation

the acceptance

is correlated

to the characterized

a model. The preparation

Examples

conditions

parameters

design. The balanced

different

The

gel used as

of whey protein

gels were

experimental

design made it possible to

in pH, salt concentration

microstructure

microscopic

will be given

known to have an

in a factorial

factorial

identify effects of changes rate.

of tex-

and quantified

whey protein

on the microstructure

heating

with instruments

has been tested, where the sensory

of the product.

used as design

of a food into struc-

on the perception

from results with a particulate impact

of texture

that can be measured

vital information

microstructure

P.O.

Sweden

and texture

tural properties

work has given very promising

the extensive

lysis of multivariate

Institute)

product

and form a strategy work. Their

and the statistics important

Food Research

and

The

and challenging

Translating

can provide

has been done by Mat-

statistics

the user through bring

is

will be a

to (inexperienced)

29 Goteborg,

ture. A new concept

applied

knowledge

of a help sys-

The Swedish Institute

quality Interesting

for computer-

Maud Langton, Annika AstrOm and Anne-Marie Hermansson,

Flavor

Norway

A.S.,

43.lNFLUENCEOF MICROSTRUCTUREON THESENSORYQUALITY

product.

24, N-701 1

Nor-

Camo

Implementation

is an important

Box 5401, S-402

Suzanne Schbnkopf, Tormod Naes, Pernille Baardseth, Marit Risberg-Ellekjaer and Arne Midjo,

sensoric

(The

result

on multivariate

sig-

the project.

SIK

PRODUCT FOOD INDUSTRY

this

use. The

to continuously

tailored

and

the right conclusions.

is designed

produced

same oil samples were obtained

steps toward

about

way.

program

that guides

gram

we investigated

data

based

design,

that the latter permit

in an easier

gin olive oil preference

package

detailed important

Tine

company

where

for industrial

these

aim of this study is to compare

these methodologies,

group consisting

data)

d) the ranges,

is higher

The

two methodologies compare

(preference

of samples

are developing

each of the terms; c) the surface is studied to find out the

gives

Matforsk,

and the software

product

more

PCA/PLS

due to changes.

Dairies)

gives

effects and statistical

while

implemented

response

ANOVA

significant

of results,

differences wegian

alone.

about

319

was characterized

techniques

and

quantified

and by by

image analysis and by using a group of experienced microscopists evaluating micrographs from scanning electron done

microscopy

(SEM).

with a trained

panel

The sensory evaluation which

developed

was

a partial

have improved properties compared to today’s product and low intensity of undesirable properties were identi-

sensory profile using Quantitative Descriptive Analysis. The sensory characterization of the whey protein gels

fied. The same strategy

was focused

of dairy ingredients

was also used to study the effects

on sausage sensory properties,

the aim to assess the potential

of dairy ingredients.

addition to sensory analysis, textural was performed. They all indicated

with In

and color analysis the same results;

ingredient concentration was more important than the type of ingredient and starch. Both applications show that the strategy to combine PCA and ANOVA complementary information that is not available

gives from

though

on the

the perception

also included

properties

related

to consistency,

of an acid and salty

in the assessment

of the products.

taste was The pH

had a pronounced effect on all measured parameters, both as a main effect and as an interaction effect in combination with heating rate or salt addition. All sensory descriptors of the texture showed main effects of heating rate and interaction effects between pH and salt addition. A logarithmic correlation was found between