International audienceClassical physicochemical or sensory methodologies are often inadequate to describe the perception of water and their correlation is complicated. Indeed, the taste of water is difficult to describe due to low concentrations of sapid molecules. The aim of this work was to combine sensory and physiological measurements in order to correlate data with chemical properties of sapid solutions

de Oliveira, Fabrice

Haese, Gwenaelle

Humeau, Philippe

Le Callet, P.

Le Cloirec, Pierre

HAL-Rennes 1

Development of Partial Least Square models for the prediction of the concentration of a gustative stimulus in water from physiological data

De Oliveira, Fabrice

Archive Ouverte en Sciences de l'Information et de la Communication

HAL Id: hal-02378253
https://hal-cstb.archives-ouvertes.fr/hal-02378253
Submitted on 3 Dec 2019
HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
Development of Partial Least Square models for the
prediction of the concentration of a gustative stimulus in
water from physiological data
Gwenaelle Haese, Philippe Humeau, Fabrice de Oliveira, P. Le Callet, Pierre
Le Cloirec
To cite this version:
Gwenaelle Haese, Philippe Humeau, Fabrice de Oliveira, P. Le Callet, Pierre Le Cloirec. Development
of Partial Least Square models for the prediction of the concentration of a gustative stimulus in water
from physiological data. Eurosense 2016, Sep 2016, Dijon, France. ￿hal-02378253￿
EUROSENSE 2016 > 11 - 14th September, Dijon 
 
Development of Partial Least Square models for the prediction of the concentration of a gustative 
stimulus in water from physiological data 
 
 
GWENAELLE HAESE1, PHILIPPE HUMEAU1, FABRICE DE OLIVEIRA1, PATRICK LE CALLET2, 
PIERRE LE CLOIREC3 
1CSTB, AQUASIM, 11 rue Henri Picherit, BP 82341, 44323 Nantes Cedex 3, France 
2IRCCyN lab. Polytech’Nantes, Rue Ch. Pauc, BP 50609, 44306 Nantes, France 
3Ecole Nationale Supérieure de Chimie de Rennes, CNRS, UMR 6226, 11 allée de Beaulieu, CS 50837, 
35708 Rennes Cedex 7, France 
 
Introduction: 
Classical physicochemical or sensory methodologies are often inadequate to describe the perception of 
water and their correlation is complicated. Indeed, the taste of water is difficult to describe due to low 
concentrations of sapid molecules. The aim of this work was to combine sensory and physiological 
measurements in order to correlate data with chemical properties of sapid solutions. 
 
Methods: 
Eighteen subjects out of 43 were selected from their performances in taste recognition tasks. The four 
basic tastes (sweet, salty, acid and bitter) were used at four different concentrations (Table 1) in Evian 
water used as a standard solution. 
 
Table 1: Threshold values and concentration ranges of the taste stimuli (g.L-1) 
Taste Chemical Detection 
threshold 
Solutions 
1 2 3 4 
Acid  Citric acid  0.384 0.31 0.38 0.48 0.60 
Bitter  Caffeine  0.05-0.35 0.14 0.17 0.22 0.27 
Salty  NaCl  0.584 0.69 0.98 1.40 2.00 
Sweet  Sucrose  6.846 2.59 4.32 7.20 12.00 
 
Non-invasive physiological methodologies were applied such as electrodermal, heart rate variations and 
skin blood flow measurements, recorded in real-time during tasting tests. A questionnaire about the 
intensity and pleasantness of the stimuli was filled by subjects. Six repetitions were made. 
Physiological parameters were extracted from the signals (Figure 1). 
a) 
 
b) 
 
Figure 1: Acquisition screen of the physiological signals(a) and example of parameters extraction 
from a peak analysis (b) 
 
Analyses were individual because each subject has his own “preferential channel”. 
ANOVA measuring the product effect on the physiological parameters enabled the determination of 
discriminant parameters, used in the PCA for visualizing the product space. 
 
Results: 
The individual product space was used as a reference map for basic taste stimuli in water at known 
concentrations. The Partial Least Square models were built on this reference data, for each taste. 
Then, new physiological measurements were performed on taste stimuli (Exp2 – 2 repetitions). They have 
been projected on the PCA reference map (Figure 1) and their concentrations in sapid molecules was 
predicted from the PLS models developed previously (Table 2). 
 
ECG
EDR
BF
HR
Time (s)
B
F 
(P
U
)
Width (s)
H
ei
gh
t(
P
U
)
Peak Area (PU.s)
Width50 (s)
EUROSENSE 2016 > 11 - 14th September, Dijon 
 
a) 
 
b) 
 
 
Figure 2: Representation of the variables (continuous black line for active variables, dotted blue line 
for illustrative ones) (a) and of the gustative stimuli (black dots for active individuals, blue dots for 
illustrative ones) (b) from the PCA (F1-F2) on blood flow data obtained with one subject. 
 
Table 2: Comparison between the real concentrations and the predicted ones by the PLS models 
developed for each taste 
 Concentration (g.L-1) 
 real predicted 
Acid 4_Exp2  0.60 0.65 ± 0.01 
Caffeine 3_Exp2  0.22 0.25 ± 0.03 
NaCl 1_Exp2  0.69 0.87 ± 0.02 
Sucrose 2_Exp 2  4.32 14.31 ± 4.21 
 
Discussion: 
A reliable model for predicting the concentrations of flavouring substances in water was proposed 
directly from the physiological responses of a subject. This prediction is more realistic and stable with 
negative hedonic valence stimuli, due to strongest and more easily identifiable physiological responses. 
Furthermore, models are applicable with low concentrations close to detection thresholds. 
 
 
TMinSlope
SlopeMin
TMaxSlope
Height
PeakArea
TFall
TimeToPeak
TRise
Width30
Width50
Width90
Width
Hedonic
Intensity
F1 (71,92%)
F2 (11,38%)
-5 0 5
-4
-2
0
2
4
F1 (71,92%)
F
2
 (
1
1
,3
8
%
)
Acid 1
Acid 2
Acid 3
Acid 4
Caffeine 1
Caffeine 2
Caffeine 3
Caffeine 4
Evian
NaCl 1
NaCl 2
NaCl 3
NaCl 4
Sucrose 1
Sucrose 2
Sucrose 3
Sucrose 4
Acid 4_Exp2
Caffeine 3_Exp2
Evian_Exp2
NaCl 1_Exp2
Sucrose 2_Exp2
Classical physicochemical analysis or sensory methodologies are often inadequate to describe the perception of tastes and odors 
of water, the taste of water is difficult to describe due to low concentrations of sapid molecules. 
 
An alternative way would be to reach the involuntary and unconscious responses via the study of the nervous system 
responses to assess the emotional reactions to the tastes and odors of water. 
 
The first aim of this study was to combine sensory and physiological measurements in order to correlate data with 
chemical properties of sapid solutions. 
 
The second objective was to obtain a reliable prediction of the chemical characteristics of a stimulus based on 
physiological responses. 
Development of Partial Least Square models for the prediction
of the concentration of a gustative stimulus in water from
physiological data 
EUROSENSE 2016 > 11 - 14th September, Dijon 
G. HAESE | P. HUMEAU | F. DE OLIVEIRA | P. LE CALLET | P. LE CLOIREC 
CONTACT 
Gwénaëlle HAESE 
Research and Consulting Engineer in Sensory Analysis and Physiology  
Tel.: +33 (0)2 40 37 20 65 / gwenaelle.haese@cstb.fr 
CONTEXT AND OBJECTIVES 
MAIN RESULTS 
CONCLUSION AND PERSPECTIVES
CENTRE SCIENTIFIQUE ET TECHNIQUE DU BATIMENT 
Tel.: +33 (0)2 40 37 20 00 – Fax: + 33 (0)2 40 37 20 60 – www.cstb.fr 
11 Rue Henri Picherit, 44323 Nantes France 
MARNE-LA-VALLÉE / PARIS / GRENOBLE / NANTES / SOPHIA ANTIPOLIS 
MATERIALS AND METHODS 
18 naïve subjects were selected from 43 volunteers 
because of their high performances in three taste recognition tasks: 
> Ranking 
> Detection 
> Matching 
Attribution of a score. 
1 | Panel and products 
4 basic tastes: 
> Acid 
> Bitter 
> Salty 
> Sweet 
X 4 concentrations (1: low to 4: high) 
In Evian water also used as a reference and as a blank. 
Table 1: Concentrations for each basic taste (g.L-1) 
Taste Chemical Detection Solutions 
  threshold 1 2 3 4 
Acid Citric acid 0.384 0.31 0.38 0.48 0.60 
Bitter Caffeine 0.05-0.35 0.14 0.17 0.22 0.27 
Salty NaCl 0.584 0.69 0.98 1.40 2.00 
Sweet Sucrose 6.846 2.59 4.32 7.20 12.00 
2 | Protocol 
3 | Physiological measurements and parameters extractions 
Figure 1: Physiological signals and peak analysis 
4 | Statistical analysis 
The individual product space was used as 
a reference map for basic taste stimuli in 
water with known concentrations. 
The Partial Least Square models were 
built on this reference data, for each one 
of the 4 basic tastes (Figure 2) 
Specific analysis 
was carried out for 
each subject as 
he/she has his/her 
own “preferential 
channel”. 
The ANOVA, which 
measures the product 
effect on the physiological 
parameters, enabled the 
determination of 
discriminant parameters 
used in the PCA for 
visualizing the product 
space. 
Figure 2: Regression line between the real concentration 
and the predicted one for NaCl 
Then, new physical measurements were performed on taste 
stimuli (Exp2-2 repetitions). They have been projected on 
the PCA reference map (Figure 3). 
Figure 3: Representation of the variables (Continuous white line for active variables, dotted red line for illustra-
tive ones) (a) and of the gustative stimuli (White dots for active individuals, red dots for illustrative ones) (b) 
from the PCA (F1-F2) built from blood flow data obtained with one subject.  
The concentrations in sapid molecules of these new stimuli were predicted from the PLS models pre-
viously developed (Table 2). 
Table 2: Comparison between the real 
concentrations and the predicted ones 
by the PLS models developed for each 
taste. 
 Concentration (g.L-1) 
 Real Predicted 
Acid 4_Exp2 0.60 0.65 ± 0.01 
Caffeine 3_Exp2 0.22 0.25 ± 0.03 
NaCl 1_Exp2 0.69 0.87 ± 0.02 
Sucrose 2_Exp2 4.32 14.31 ± 4.21 
1. A reliable model for predicting the concentrations 
of flavoring substances in water directly from 
the physiological responses of a subject is proposed. 
2. This prediction is more realistic and stable with negative 
hedonic valence stimuli, due to stronger and more easily 
identifiable physiological responses. 
3. Furthermore, models are applicable with low 
concentrations, close to detection thresholds. 
Au
gu
st
 2
01
6 
- D
es
ig
n:
 G
. H
ae
se
 | 
A.
 B
ru
llo
n 
- ©
 C
ST
B 
| F
re
ep
ik
 
 
Co
nc
en
tra
tio
n 
Predicted concentration (g.L-1) 
Physiological responses: 
Electrodermal responses (EDR), skin blood flow 
(BF) and heart rate (HR) 
16 stimuli in randomized order but balanced for the intensity levels and with an 
Evian water sample as a reference 
Sensory questionnaires: 
Intensity and hedonic scales 
Correlations (Pearson)? Extraction of parameters (statistics, spectral and shape parameters) and normalization 


English

HAL-Univ-Nantes

https://hal-cstb.archives-ouvertes.fr/hal-02378253/file/Eurosense2016_abstract%2Bposter.pdf

Development of Partial Least Square models for the prediction of the concentration of a gustative stimulus in water from physiological data

Abstract

Similar works

Full text

Available Versions

HAL-Rennes 1

Archive Ouverte en Sciences de l'Information et de la Communication

HAL-Univ-Nantes