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Neuroorganoleptics: Organoleptic Testing Based on Psychophysiological Sensing


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There is an increasing interest, in consumer behaviour research related to food and beverage, in taking a step further from the traditional self-report questionnaires and organoleptic properties assessment. With the growing availability of psychophysiological data acquisition devices, and advancements in the study of the underlying signal sources seeking affective state assessment, the use of psychophysiological data analysis is a natural evolution in organoleptic testing. In this paper we propose a protocol for what can be defined as neuroorganoleptic analysis, a method that combines traditional approaches with psychophysiological data acquired during sensory testing. Our protocol was applied to a case study project named MobFood, where four samples of food were tested by a total of 83 participants, using preference and acceptance tasks, across three different sessions. Best practices and lessons learned regarding the laboratory setting and the acquisition of psychophysiological data were derived from this case study, which are herein described. Preliminary results show that certain Heart Rate Variability (HRV) features have a strong correlation with the preferences self-reported by the participants.
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Neuroorganoleptics: Organoleptic Testing Based on
Psychophysiological Sensing
João Valente 1,2,*, Leonor Godinho 3, Cristina Pintado 4, Cátia Baptista 4, Veronika Kozlova 1,2, Luís Marques 2,
Ana Fred 3,5 and Hugo Plácido da Silva 3,5
Citation: Valente, J.; Godinho, L.;
Pintado, C.; Baptista, C.; Kozlova, V.;
Marques, L.; Fred, A.; Plácido da
Silva, H. Neuroorganoleptics:
Organoleptic Testing Based on
Psychophysiological Sensing. Foods
2021,10, 1974.
Academic Editor: Cristina
Received: 21 July 2021
Accepted: 19 August 2021
Published: 24 August 2021
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1Instituto Politécnico de Castelo Branco (IPCB), Av. Pedro Álvares Cabral 12,
6000-084 Castelo Branco, Portugal;
BrainAnswer, Lda., R. Eng. Pires Marques 61, 6000-406 Castelo Branco, Portugal;
3Department of Bioengineering (DBE), Instituto Superior Técnico (IST), Av. Rovisco Pais 1,
1049-001 Lisboa, Portugal; (L.G.); (A.F.); (H.P.d.S.)
4CATAA—Associação Centro de Apoio Tecnológico Agro-Alimentar, Zona Industrial de Castelo Branco,
Rua A, 6000-459 Castelo Branco, Portugal; (C.P.); (C.B.)
5Instituto de Telecomunicações (IT) , Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
*Correspondence:; Tel.: +351-272-241-400
There is an increasing interest, in consumer behaviour research related to food and beverage,
in taking a step further from the traditional self-report questionnaires and organoleptic properties
assessment. With the growing availability of psychophysiological data acquisition devices, and
advancements in the study of the underlying signal sources seeking affective state assessment,
the use of psychophysiological data analysis is a natural evolution in organoleptic testing. In this
paper we propose a protocol for what can be defined as neuroorganoleptic analysis, a method that
combines traditional approaches with psychophysiological data acquired during sensory testing.
Our protocol was applied to a case study project named MobFood, where four samples of food were
tested by a total of 83 participants, using preference and acceptance tasks, across three different
sessions. Best practices and lessons learned regarding the laboratory setting and the acquisition of
psychophysiological data were derived from this case study, which are herein described. Preliminary
results show that certain Heart Rate Variability (HRV) features have a strong correlation with the
preferences self-reported by the participants.
Keywords: consumer behavior research; sensory analysis; organoleptics; psychophysiological data;
1. Introduction
In recent years, increasing attention has been given to the challenge of understanding
the reasons behind consumers’ choices and selections, particularly in the food and beverage
industry [
]. One of the big questions researchers are trying to answer nowadays is whether
psychophysiological responses can provide new insights into the decision-making process
of consumers. Specifically concerning edible products, there is a great interest in obtaining
accurate and objective data about the sensory perception of food and beverages and, conse-
quently, about consumption intention [
]. This field of study can be greatly reinforced
through the development of new and more in-depth protocols of sensory analysis, capable
of complementing self-reporting questionnaires with the use of psychophysiological traits.
When it comes to tasting food, the authors of [
] defined three main emotional pro-
cessing levels: The low processing level, when subjects integrate information from the five
senses through psychophysiological, unconscious, processes; the intermediate processing
level, when subjects connect their perceptions to past experiences and information already
present in their memory, through both conscious and unconscious processes; and lastly,
the high processing level, when subjects consciously recognise what the samples they
Foods 2021,10, 1974.
Foods 2021,10, 1974 2 of 20
are tasting are able to express which emotions they evoke (e.g., if the taste matched their
expectations, if the visual appearance of the product is pleasant, among many others) [6].
In addition to organoleptic assessment, the traditional sensory testing methods rely on
self-report questionnaires, as they present unquestionable advantages, such as being easy to
deploy and being cost-effective [
]. However, this approach also presents some limitations.
The main one is that, by only being able to target the high processing level and obtaining
conscious responses, they are not depicting the low and intermediate processing levels,
potentially leaving out relevant unconscious processes present in these [
]. Moreover,
self-report questionnaires, by targeting the high processing level, are also subjected to
the inherent difficulty of subjects having to reconstruct and interpret their thoughts and
motivations, and/or to verbally express and assess them [
]. This, combined with
the fact that the expression and assessment of emotions (e.g., liking, pleasure, etc.) varies
across cultures and languages [
], can make the results of self-assessment questionnaires
only representative by enrolling a sufficiently high number of subjects. Finally, the last
constraint of self-assessment questionnaires is that they only obtain declared opinions, not
revealing responses that the consumers may not wish to disclose (e.g., because they are
socially undesirable) [1012].
The use of psychophysiological data has the potential to overcome these limitations,
with the advantage of being able to depict the low and intermediate processing levels,
and not depending on the verbal declared expression of subjects to obtain measurable
results [9,13]
. The physiological data and derived features used in this area can be divided
into two main groups, one related to recording the activity of the brain, by electroen-
cephalography (EEG), and another using peripheral measurements. The latter is generally
easier to deploy, and includes signals such as ElectroDermal Activity (EDA), Heart Rate
(HR), Skin Temperature (ST), Blood Pressure (BP), pupillometry, eye-tracking (ET), and
facial expressions [
]. These signals are common in studies related to neuropsychology
and affective state assessment, since they provide valuable insights into the Autonomic
Nervous System (ANS) [14].
The ANS integrates the Sympathetic Nervous System (SNS), which conducts the
body’s responses in case of a threat, and the Parasympathetic Nervous System (PNS),
which is in charge of homeostasis [
]. Both the SNS and PNS control the cardiac
dynamics (e.g., HR), the dilation and constriction of blood vessels’ diameters (BP), pupils
(pupillometry and ET), bronchial tubes (respiration), and the contraction and relaxation of
muscles, modulating these responses according to the environment and stimuli [
]. The
EDA response is, on the other hand, only influenced by the SNS, being more associated
with stressful or arousing events [
]. As a consequence of many of these responses (e.g.,
muscle constriction/relaxation, sweating and vasoconstriction/vasodilatation), the body
temperature varies, which makes it an indirect measure of ANS responses [
]. All in
all, the ANS is responsible for the unconscious responses of the human body, which are
associated with an emotional response, arousal, and stress [14].
That being said, using psychophysiological sensing in food and beverage sensory
testing is a natural complement to traditional consumer research methods. This approach
can be designated as neuroorganoleptics, and it allows reaching every processing level
involved in the tasting of food and beverages, enabling researchers and other stakeholders
to more accurately obtain information about the consumer’s food perceptions and prefer-
ences. It has only recently started to be investigated in the state-of-the-art, as shown by a
group of researchers from the University of Melbourne, who applied this novel sensory
analysis approach to assess consumers’ acceptability of different samples of beer [18] and
chocolate [19].
With this in mind, this paper describes a comprehensive protocol for neuroorganolep-
tic testing. It has been applied to a case study project (MobFood), further described in
Section 3. The case study had the goal of analysing the acceptability and consumption
intention of four different food samples that were part of a breakfast kit, combining the
typical self-reporting questionnaires with psychophysiological data analysis. The signals
Foods 2021,10, 1974 3 of 20
included in this protocol were EDA, Photoplethysmography (PPG), Respiration (PZT),
Electrocardiography (ECG), and Eye-Tracking (ET). A webcam and microphone were also
used to record the environmental context of the experiment.
The remainder of the work is organised as follows: Section 2describes the design
considerations of the proposed neuroorganoleptic testing protocol; Section 3details the
application of the devised protocol to the MobFood case study; Section 4summarises the
main findings and results from this case study; and, finally, Section 5provides a brief
discussion and future work guidelines.
2. Neuroorganoleptic Testing Protocol
2.1. Assessment Scales
This protocol expands standardised tests with sensory analysis tasks, more specifically,
affective tests designed to assess acceptance, liking, or product preference. These methods
do not require experienced assessors, and include two main types of quantitative tests:
preference and acceptance [
]. Both types are present in our protocol, and questions to
assess the consumers’ buying intentions are also included. At the beginning of the protocol,
the tasks consist of presenting digital visual stimuli (images of the samples in the computer)
for preference tests. After that, the physical samples are used for the remaining acceptance
and preference tests.
In preference tests, the assessors are given two or more samples and required to
indicate which one they prefer [
]. If more than two samples are presented, the assessors
rank them by their preference [
]. This is a simple test to implement and also easy to
analyse statistically, since the values of significance between rank sums are defined for the
number of samples compared and the number of assessors [
]. A disadvantage of this test
is that the degree of satisfaction is not determined.
On the other hand, in acceptance tests, assessors are presented with the samples
and are required to assess the degree of liking of the product, globally, or of a certain
attribute on a scale [22]. The most widely used scale is the 9-point hedonic scale, which is
a balanced bipolar scale with a neutral point at the center, having four negative and four
positive options on each side [
]. In this protocol, the scale is horizontal and has a label
on the left extreme, center, and right extreme (e.g., ‘Extremely unpleasant’, ‘Indifferent’
and ‘Extremely pleasant’). This scale has the advantage of being accessible and easy to
use for both participants and researchers, due to its categorical nature and limited set of
options [
]. Statistically, analysis of variance (if normality of data is proved) and mean
values comparison tests (e.g., Duncan’s multiple range test) are usually conducted to
analyse the results of food and beverage tests using scales [26].
Another type of input proposed in our protocol is the Net Promoter Score (NPS), a
10-point scale used for questions asking if the consumer would recommend the
product [27]
Depending on the answer, the assessor is grouped into one of the categories: promoter
(9–10 points); passively satisfied (7–8 points); and detractor (0–6 points).
Finally, at the end of the protocol there are questions presented with the purpose
of characterising the level of food neophobia of the subjects (Table A1). These questions
use the 7-point Likert scale, in order to assess the attitude of the subjects towards the
statements presented [
]. The extremes of this scale are labelled with ‘Strongly disagree’
and ‘Strongly agree’.
2.2. Laboratory Setting
Regarding the laboratory setting, the international standard of sensory analysis guide-
lines regarding staff [
], methodology and test room design [
] are taken into account as
the primary reference.
The experimental protocol is carried out in individual testing booths, in order to
avoid distractions and assessors influencing the answers of other peers through gestures
or expressions. These individual booths are placed on the opposite side of the wall of
the sample preparation area, and each booth possesses a horizontal sliding door (1 in
Foods 2021,10, 1974 4 of 20
Figure 1
) that is used to provide the samples to the assessors. The door is kept closed in
the remaining moments, to prevent the assessors from watching the sample preparation.
An important consideration taken into account in this protocol is to reduce the external
interference to the bare minimum. To minimise ambient noise, one of the precautions is
for the assessors to signal they are ready to receive the samples through a light signaling
system connecting the booth to the preparation area. The signal switch (2 in
Figure 1
) is
placed over the sliding door and, as the assessors flip the switch, a light is activated on
the sample preparation area. Moreover, as the protocol involves the assessors asking for
a particular sample at a particular time, a selection system using paper cards (three in
Figure 1
) is proposed. The assessors have a set of cards, each with a type of sample written
on it, and these are used to request the samples to the operators in the preparation area, as
a form of eliminating potential language barriers and avoiding talking (which would be an
interference to the remaining assessors).
Besides noise, the temperature is kept at a level considered by the assessors to be
comfortable. In our experimental setting, we seek to maintain a neutral environment,
ensuring that the space is free from odors, and that the walls and surfaces are white, as
well as the lighting option (four in Figure 1) used throughout the whole experiment.
Furthermore, each booth is numbered with a tag above the sliding door, and has a
small sink and a water faucet (5 in Figure 1). The assessors are seated in an adjustable
revolving stool with back support (six in Figure 1) and, on the table in front of them, there
is a glass covered by a napkin (seven in Figure 1), the aforementioned paper cards and
finally, the computer (eight in Figure 1) used to answer the questions present in the protocol.
Lastly, as this experiment involved physiological data acquisition, on top of the table there
is also a bottle of hydrogen peroxide (nine in Figure 1) and cotton, to be used during the
placement of the sensors’ electrodes (detailed in Section 2.3.3). A small trash can (10 in
Figure 1) can be used by the participant to dispose of electrodes and other used items.
As the experiment in our case study took place during the COVID-19 pandemic,
special care was taken, such as having an empty booth between two occupied booths to
ensure a 2 m distance between assessors (as seen on the right of Figure 1). The assessors
were, however, asked to remove their masks during the protocol.
Figure 1. Overall layout of the individual test booths and room for neuroorganoleptic assessment.
2.3. Collected Data
2.3.1. Self-Reporting Questionnaire
The questionnaire was presented and filled in using the BrainAnswer platform (http:
//, accessed on 23 August 2021). Each question was presented individually,
occupying the whole screen, in order to focus the attention of the assessor (example in
Figure A1). The assessors used the keyboard to answer written questions and the mouse to
select points in a scale, choose the preferred stimulus, or order stimuli. All of the questions
were written in clear, simple terms, in the native language of the assessors.
Foods 2021,10, 1974 5 of 20
2.3.2. Eye-Tracking, Webcam and Audio
Regarding eye-tracking, our protocol used a GP3 Eye Tracker from Gazepoint (http:
//, ac-
cessed on 23 August 2021), a portable camera for which the main specifications are pre-
sented in
Table 1
. Moreover, throughout the whole experiment, the assessors were recorded
both with the computer webcam and microphone. These sources are important, on one
hand, for the validation of the experiment, as it allows the researchers to verify in post-
processing if external influencing events have occurred when a signal is very abnormal
(e.g., someone walked to the booth or ambient noise). On the other hand, the webcam also
allows the analysis of facial expressions.
Table 1.
Specifications of GP3 Eye Tracker from Gazepoint, the eye-tracking software used in
the experiment.
Spatial resolution 0.1
System latency <50 ms
Sampling rate 60 Hz
Operating distance 50–80 cm
Calibration 5 or 9 point
Eyewear compatibility Works with most glasses and contact lenses
Data provided Timestamp, gaze (x/y coord.), eye position,
pupil diameter
2.3.3. Physiological Data
During the experiment, various psychophysiological signals were collected, which
is a significant difference from standard protocols. All of these acquired signals allow the
ANS response study, by analysing the changes over time on the Heart Rate Variability
(HRV) [31],
respiration levels, and skin conductance. In our case, data were acquired using
the BITalino (r)evolution Plugged BT system [
], a Bluetooth wireless acquisition unit.
This device is used in a 10-bit resolution and 1 kHz sampling frequency configuration;
Table 2describes the main technical specifications of the acquisition unit.
Table 2.
Specifications of BITalino (r)evolution Plugged BT, the acquisition unit used in the experiment
Connectivity Bluetooth
Range up to 10m
Resolution up to 10 bit
Sampling rate 1, 10, 100 or 1000 Hz
Size 50 ×65 ×6 mm
Battery 500 mA 3.7 V LiPo (rechargeable)
1, accessed on 23
August 2021.
This system integrates the sensors selected for assessing the neurosensory component,
characterised as follows. It is important to highlight that in the equations,
3.3 V (the
operating voltage of the circuit),
is the value sampled from the channel (digital code
assigned by the analog-to-digital converter to the input voltage), and
is the number of
bits of the channel.
The electrocardiography (
ecg-sensor-datasheet-revb-1.pdf, accessed on 23 August 2021) (ECG) sensor uses three
pre-gelled disposable Ag/AgCl Eurotrode electrodes (!
/ECG-Electrodes-51mm-100pcs/p/205640017/category=52356463, accessed on 23 August
2021). Each electrode has an associated color, where the white electrode (ground) is placed
on the right thoracic wall, the black electrode (IN-) is placed on the left thoracic wall (V5
precordial lead located in the left anterior axillary line), and the red electrode (IN+) is
placed below the neck, on the manubrium (see Figure 2). The ECG sensor characteristics
Foods 2021,10, 1974 6 of 20
are described in Table 3, and its transfer function is given by Equation (1), in which the
gain Gis 570 (customised for this application to maximise the signal-to-noise ratio).
The piezoelectric respiration (
sensor-datasheet-revb.pdf, accessed on 23 August 2021) (PZT) sensor used is placed inside
an elastic chest strap to secure it in place. This strap is placed around the chest, with the
sensor to the left side, following the ribcage curvature (represented in blue in Figure 2).
The PZT sensor characteristics are also described in Table 3, and its transfer function is
given by Equation (2).
PZT(%) = ( ADC
2)×100% (2)
The electrodermal activity (
datasheet-revb.pdf, accessed on 23 August 2021) (EDA) sensor used in the experiment
has two dry AgCl electrodes (
parameters-obtained-using-moisture-electrodes-with-artificial-sweat_tbl2_323794069 (first
row), accessed on 23 August 2021), where one is placed on the index finger and the other
on the ring finger of the left hand (represented in yellow in Figure 2). They are strapped to
the finger by adhesive tape. The EDA sensor characteristics are described in Table 3, and
the transfer function is given by Equation (3).
ED A(µS) =
0.132 (3)
Finally, a photoplethysmography (
photoplethysmography-ppg-sensor-datasheet.pdf, accessed on 23 August 2021) (PPG)
sensor is also used, with an integrated adjustable velcro fastening strap (placed on the
middle finger, represented in green in Figure 2). This sensor uses wavelengths of 520 nm
and has a voltage output of 0.3 V to VCC .
Table 3. Specifications of the ECG, EDA, and PZT sensors used in the experiment.
Bandwidth 0.5–40 Hz 0–2.8 Hz 0.59–0.9 Hz
Range ±1.5 mV 0–25 µS
Gain 570
Input impedance 7.5 GOhm
CMRR 86 dB
The electrodes of every one of the above-mentioned sensors are placed by the par-
ticipants themselves, through the instructions provided in the form of a video (https:
//, accessed on 23 August 2021) that partici-
pants can review autonomously. Figure 3depicts an example of the results obtained from
the acquisition of signals described in this section.
Foods 2021,10, 1974 7 of 20
Figure 2.
Schematic of the placement for the ECG (red) electrodes, and PZT (blue), PPG (green), and
EDA (yellow) sensors.
Figure 3.
Psychophysiological data obtained during the experiment: (
) Eye-tracking, (
) Movement
and facial expressions (Webcam), (
) Sound (Microphone), (
) PZT, (
) ECG, (
) EDA, (
) PPG, and
(8) Heart Rate (HR) derived from the ECG.
3. MobFood Case Study
3.1. Overview
For testing purposes, our protocol was applied to a case study project named MobFood
(, accessed on 23 August 2021). The focus of this study was to
create a breakfast kit composed of three food and beverage products, chosen according to
the perceived preferences of a panel of assessors tasting four samples, namely: a cereal
bar, cheese, chocolate milk, and an oatmeal cookie. The case study was conducted in
the certified sensory analysis laboratory of CATAA (Centro de Apoio Tecnológico Agro-
Alimentar) in Castelo Branco, which follows the requirements mentioned in Section 2.2.
3.2. Population
The participants in this experiment were recruited by an external company. This was a
professional recruitment firm that handled the participant selection, the institutional review
board approval, and informed consent. Exclusion conditions from the study included
the intake of certain medications (e.g., muscle relaxants and sedatives), as well as specific
clinical conditions, such as vision impairment and neurological, cardiac, and dermatological
diseases. The results analysed were collected from 83 participants (40 male and 43 female),
Foods 2021,10, 1974 8 of 20
with ages between 23 and 38 years old (average of 28.80
4.36 years). The participants
received monetary compensation for their participation in the study.
There were two training sessions before the actual experiment. These sessions allowed
the participants to become familiarised with the protocol, minimising the novelty effect.
A shorter protocol was followed in these sessions, involving only two food and beverage
samples, one solid (a cookie), and one liquid (a glass of water).
The main experiment was composed of three sessions on different days. Every par-
ticipant considered in the results participated in the three sessions. The three sessions
followed the same protocol and the same type of food samples were used. This approach
allows testing repeatability between sessions, in terms of self-reported data and even
physiological data.
3.3. Methodology
The experiment was performed according to predefined scheduling, for which each
participant had a total time slot of 1 h and 30 min allocated (Table 4). In each session,
as the participants arrived at their individual booth, they placed the sensors themselves
(using an instructional video as guidance), calibrated the eye-tracking software, opened
the BrainAnswer platform, and started our proposed protocol (Section 2). The completion
time of the protocol was between 15 and 62 min, with an average of 29.1
9.0 min. A total
of 290 data collection sessions were performed over the course of 3 months.
Table 4. Factors considered for the experiment scheduling.
Time (in Minutes) Purpose
10 Maximum lag time for participants to arrive
15 Participants occupy testing booths, place the sensors and verify
the signal quality
60 Maximum time for participants to follow the protocol
5 Cleaning and preparation of testing booth for next test
The main stages to the neuroorganoleptic protocol were: (a) relaxing; (b) preference-
ordering tasks with visual stimulus; (c) acceptance assessment questions regarding the
samples of each product using the hedonic scales; (d) preference and acceptance questions
about the breakfast kit (three of the four products tasted together); and, finally, (e) general
questions about food neophobia and socio-demographic characteristics.
The first step involved participants looking at an image meant to relax the volunteers,
containing several small figures working out, selected due to the food and beverage neutral
content. This first step was important to establish a baseline for the physiological signals
and test the eye-tracking calibration.
After this, the protocol proceeded to the preference-ordering tasks with visual stimulus.
The main focus of physiological data acquisition in this task was eye-tracking. The visual
stimuli in this step were presented to the participants in random order on the screen,
and they stayed on the screen for 10 s. Before presenting each set of stimuli images, the
participants were requested to stare at a calibration cross in the middle of the screen. The
first task of this step was to order the four elements of the kit by preference. The second task
was to order a set, including one of the products of the kit and two similar products from
competitors, by preference. For instance, the cereal bar of the kit was presented alongside a
chocolate cereal bar and an apple cereal bar. This task was repeated four times (one for
each product of the kit), and the order of this repetition was random.
Following this came the acceptance assessment questions regarding the samples of
each product, using hedonic scales. A product of the kit appeared on the screen, and
the assessor then used the switch of the light signal system (connecting the booth to the
preparation area), to indicate that he/she was ready to receive the product. The sliding
door opened, the assessor used non-verbal communication by giving the paper card with
the product, and received the sample from the preparation area. The participant was
Foods 2021,10, 1974 9 of 20
then asked to assess the visual look of the sample before tasting it, to taste it for 60 s and,
following that, to assess the various organoleptic characteristics present in Table 5using
the scales indicated in Section 2(illustrated in Figure A1). This task was repeated four
times, one for each product of the kit, and the order of this repetition was random.
Table 5.
Questions of the self-report questionnaire and corresponding scales used with the labels of
the extremes and middle point indicated.
Question Left Extreme Center Right Extreme
Assess the visual appearance
of the product before tasting
Is the product below or
above your expectations?
below Coincident Extremely
unpleasant Indifferent Extremely
Texture in the mouth
Evaluation of salt in the
The product is satiable Extremely
unsatisfying Indifferent Extremely
Global acceptance I dislike it
I do not like
or dislike it
I like it
Preferred consumption
temperature Cold Room
temperature Hot
From 0 to 10, how likely are
you to recommend this
product to your friends and
Never –
With total
Would you buy the product? Never I do not
With total
After this came the stage related to the breakfast kit composed of three of the four
products studied. In this stage, only visual stimuli were used, and once again they were
presented in random order on the screen to the participants, being shown for 10 s. Before
each set of stimuli appeared, the participants were requested to stare at a cross in the
middle of the screen (neutral content). This step included two tasks, the first one was a
preference test in which the assessor had to order two different kits by preference. The only
difference between the kits was that one had the oatmeal cookie and the other one had a
cereal bar (both included the chocolate milk and the cheese). The second task included
answering questions about the chosen kit regarding recommendation, purchase intention,
and suitability for breakfast.
Throughout the whole protocol, the assessors were able to leave comments and justify
their choices and assessments. Finally, the protocol ended with questions assessing the
level of food neophobia and socio-demographic characteristics, such as inquiries about
their financial situation and how that affects their expenses on food. These questions are
presented in Table A1.
Foods 2021,10, 1974 10 of 20
3.4. Data Format
All of the data were stored in the BrainAnswer platform for more efficient post-
processing. In the platform, the data is visually presented in dashboards (Figure 3) and
it is possible to select the data sources that the researcher intends to analyse at the same
time (e.g., webcam, ECG, etc.). It is also possible to export the BITalino and Gazepoint
data in a CSV format, the audio files in a MP3 format, and the webcam recordings in a
MP4 format, for further processing. Each recording is labelled with a date/timestamp and
the identification code of each participant. Furthermore, this CSV file also includes the
indication of the stimulus presentation order in each recording and, in the questions where
images with more than one element were presented, the file indicates what was the order
of the elements inside the image for that recording. Self-report questionnaire results can
also be exported as CSV files.
4. Results and Discussion
4.1. Qualitative Assessment
Multiple findings have been obtained from the practical application of our neu-
roorganoleptic protocol in the MobFood case study. Concerning the questionnaires used,
improvement points include adding a description of the parameters that are being assessed,
such as aroma and texture, rather than just the textual description. Moreover, during the
case study, the software presented the scales with the slider in the left end (as it is possible
to observe in Figure A1), rather than in the middle of the scale. This could result in biased
results and should be amended in future studies.
Furthermore, concerning the psychophysiological signals acquisition, there were some
difficulties found during the case study, in both the self-placement of the electrodes by the
participants and during the experiment. In what concerns the placement of the different
sensors, common problems encountered were (for each sensor):
ECG: swap of the electrode positions; electrodes placed distantly from the intended
position; electrode placement locations not thoroughly clean; high levels of perspiration;
PZT: strap positioned inside out; strap not properly adjusted or with the cables very
stretched; strap on top of the ECG electrodes generating noise due to friction between
the two;
EDA: poorly cleaned fingers; excess perspiration (hyperhidrosis); dry skin (hypohidrosis);
• PPG:sensor placed upside down; sensor too tight; saturated signal due to vasodilation;
Eye-Tracking:dry eyes (more difficult to calibrate); participants moving after calibration.
Having these caveats identified, it is possible to avoid them in subsequent studies
by being alert and preventive about them (as it happened from session to session in
the MobFood case study). The instructions for placement could, for instance, include
these alerts.
Still regarding the self-placement of the electrodes, and the fact that there were two
training sessions and three tasting sessions, some participants considered that they already
knew the placement procedure, because they had previously repeated it. This led to
skipping or swapping steps, and also being more carefree about aspects such as skin
cleansing and accurate electrode placement.
Problems also occurred during the experiment; the main complication found was
the temporary or permanent detachment of electrodes as a consequence of movements
performed during the experiment. It happened, for example, during the tasting of a drink
when the participants raised their arm and tilted their head, in order to drink the entire
contents of the glass, and also while eating if the participants lowered the head so that the
crumbs would reach the plate. Moreover, when the participant was trying to reach the plate
left near the sliding door, motion artifacts were detected in cases where the plate was far
from the participant, or when switching the light to indicate they are ready to receive the
sample. These movements cause the skin to stretch and shrink, facilitating the movement
of the electrodes against the clothing or respiratory belt, leading to the detachment of one
or more electrodes.
Foods 2021,10, 1974 11 of 20
Figure 4presents an example of the consequences of the problems described on the
physiological data. From this, we conclude that it is important to have the samples and
actionable items that the participant needs within easy reach as much as possible. Neverthe-
less, overall, it is essential that the participants do not get overly conscious about the signal
acquisition; otherwise, such concerns might influence the psychophysiological results.
Concerning the psychophysiological data, a final potential improvement point was
identified regarding the baseline data acquisition; in the current protocol a visually ’relaxing’
stimuli was used rather than a truly neutral stimuli, such as a blank image. This could be
something improved in the following experiments.
Figure 4.
Examples of outlier waveforms obtained for (
) PZT, (
) ECG, (
) EDA, and (
) PPG signals.
4.2. Psychophysiological Data Quality and Validity
During the experiment of the case study, a pre-validation algorithm was devised to
assess the signals quality. This algorithm, which was run for the data of each assessor,
extracted basic characteristics of the biosignals (mean and standard deviation) and calcu-
lated the probability of the biosignal belonging to one of three clusters created (centered
at 20%, 50% and 90% of full scale). The clusters were created on these points based on
the observation of the signals’ histogram (Figure A2). The algorithm flagged a problem,
if one of these probabilities were abnormal. For instance, if the ECG signal presented a
probability lower than 70% of belonging to the cluster 50% of full scale (corresponding to
0.065 mV), the signal segment was considered noisy and unusable.
This was then validated by checking the time series for such signal segments. The
assessor was asked to repeat the session on another day only if there was a problem
with the ECG signal; abnormal behaviour in the other biosignals were not considered an
exclusion parameter.
Overall, from the 290 sessions, 2 presented a complete loss of signals from one or
more sensors, 20 presented severe problems on the signals caused by the detachment of
electrodes and 11 presented high levels of noise, caused by excessive movement of the
participants. Finally, 15 participants of the initial sample did not gather three valid sessions
for the repeatability test, having been excluded from the database.
4.3. Self-Report Data
The repeatability of the self-report data between the three sessions was also analysed.
Even though there were changes within the same participant across sessions, the overall
Foods 2021,10, 1974 12 of 20
results of the samples ranking were identical across sessions. This suggests that one session
would be sufficient.
Significant differences in the global acceptance were only found for the chocolate milk
between the first and last sessions, and for the cookie between the first and the third session
(Table 6). A change in the ranking of the visual aspect of the samples at the beginning of
the questionnaire did happen between the first and the two last sessions, but that can be
explained by the fact that the participants had already tasted the samples previously.
Comparing the actual results obtained for the global acceptance of the four products
(Figure 5), it is possible to see that there was a consistent preference for the cheese, followed
by the cereal bar, oatmeal cookie and chocolate milk (in this order). This was further
confirmed by the results of the preference test between the cereal bar and oatmeal cookie
(Figure 6) since more assessors selected the cereal bar throughout the three sessions. That
being said, it was decided that the cereal bar would be part of the breakfast kit (it was
beforehand set that the kit would include the cheese and a drink, the chocolate milk; the
question was regarding which type of flour-based baked food product would be present).
Table 6.
Results of the comparison between the results of the global acceptance of the products
between sessions using the Kendal method.
Product Sessions Compared p-Value rho.tau
1st, 2nd 0.8288 0.0606 uncorrelated
1st, 3rd 0.112 0.4677 uncorrelated
2nd, 3rd 0.01 0.7454 correlated
1st, 2nd 0.0331 0.5882 correlated
1st, 3rd 0.0059 0.7537 correlated
2nd, 3rd 0.0261 0.6088 correlated
Cereal bar
1st, 2nd 0.0011 0.9241 correlated
1st, 3rd 0.0017 0.8792 correlated
2nd, 3rd 9e-04 0.9525 correlated
1st, 2nd 0.0204 0.6288 correlated
1st, 3rd 0.0747 0.4789 uncorrelated
2nd, 3rd 0.0148 0.6667 correlated
Figure 5. Histogram of the results of the global acceptance for the four products in the three sessions.
Foods 2021,10, 1974 13 of 20
Figure 6.
Results of the preference test between a breakfast kit with a cereal bar (
) and an oatmeal
cookie (right) throughout the three sessions.
4.4. Psychophysiological Data Analysis
In order to validate the potential of the psychophysiological data dimension of this
neuroorganoleptics protocol, the ECG signal was processed and analysed. We focus on this
modality, given that heart rate variability (HRV) analysis is frequently present in emotion
evaluation studies using psychophysiological data [16,3436].
Given that the ECG may be contaminated by artifacts, the first pre-processing step was
outlier detection. For this purpose we used unsupervised learning, more specifically by
performing clustering using the DBSCAN algorithm after segmenting the raw ECG samples
into heartbeat templates, as described in [
]. The toolbox BioSPPy [
] implements the
described method, hence being used in this step. From the results of the clustering, the
samples that presented a percentage of the signal belonging to the outlier cluster higher
than 5% were eliminated. This threshold value, which ensures the full quality of the
samples selected in the relevant time frames, was defined based on the visual assessment
of the plots of the signals. Thirty samples of ECG were discarded based on this criteria.
The selected samples were then submitted to a pre-processing step, consisting of a
Butterworth bandpass filter of order 3 between 0.5 and 40 Hz. Given that powerline noise
persisted, a notch filter in the 50 Hz was also used. After that, the data was normalised and
converted to the HRV domain.
After the selection and processing of the ECG samples, feature extraction was per-
formed for relevant sections of the tests (mainly the sections where subjects tasted the food
and answered the questions) to check the relation between the results of the self-report
questionnaire (more specifically, the classification obtained in the global acceptance question)
and the extracted features. The focus of this study was on the frequency features HF
(High Frequency power) and LF (Low Frequency power), since they have been shown in
previous studies to be valid in short-term recordings [
], and because of their relevance in
emotional studies [39].
Our first step was to investigate if there was a clear difference within each subject
between their physiological response to a high and a low acceptance score product. A
set of 97 experiments where the subject classified two products with opposed scores in
the global acceptance question were selected (the criteria was a minimal difference of five
points, e.g., one product got a global acceptance of nine and another got four). For each
experiment, the first 2.5 min during which the subject contacted with the product (1 min
of tasting +1.5 min of question answering), of the two products that got opposed scores,
were analysed to compute the HF and LF features. The most relevant result of this analysis
was that for 70% and 69% of the experiments the value of the LF and HF, respectively, was
higher for the low acceptance score product (see Figure 7).
Foods 2021,10, 1974 14 of 20
Figure 7.
Bar plots of the difference between the normalised values of LF (
) and HF (
) in
the low classification product and the high classification product. The difference in LF and HF was
positive for 70% and 69% of this set of 97 experiments, respectively.
After establishing there were noticeable differences between high/low acceptance
score tastings, we focused our attention on checking if there were significant differences
between the features obtained from each product, and if these matched the differences
between the global acceptance score of the four products. For this purpose, we analyzed
the distribution of the HRV power frequency features and global acceptance score for each
product through violin plots (LF in Figure 8; HF in the Appendix AFigure A3). The main
observation from the plots was that the dispersion of the classifications for the milk product
was matched by a dispersion of feature values (for both HF and LF).
Figure 8.
Violin plots representing the distributions of the HRV LF feature values and global
acceptance scores for each product across 162 experiments (experiments with outlier values of LF
were removed). On
: Distribution of LF values obtained in the first 2.5 min of contact with each
product. Bottom: Distribution of the scores obtained in the global acceptance question.
Foods 2021,10, 1974 15 of 20
Furthermore, for each feature type, a statistic Wilcoxon analysis was conducted to
check if there was a significant difference between the feature values obtained for each
product and if this matched the differences between the acceptance scores (see Table 7).
Significant differences were found for every product in terms of global acceptance
scores. In the features domain, even though most pairs obtained significant differences, it
was found that the cookie was harder to distinguish from the bar (neither LF or HF obtained
significant difference between the two products) and the cheese (LF obtained a significant
difference between the two products, but HF did not). To sum up, the main positive result
of this analysis was that through LF it was possible to distinguish the responses to almost
every product studied.
Table 7.
p-values obtained from the Wilcoxon analysis intended to check significant differences
between products concerning features values (HF and LF) and global acceptance scores. The LF
analysis was conducted from a set of 162 experiments (set without LF outliers) and the HF analysis
from a set of 156 experiments (set without HF outliers). The grey cells indicate pairs where no
significant difference was found (p-value > 0.01)
LF Analysis HF Analysis
LF Global
Acceptance HF Global
Bar vs. Cheese 1.611× 1062.978 × 1037.547 × 1041.735 × 104
Bar vs. Cookie 3.477 × 1011.518 × 1049.074 × 1023.013 × 103
Bar vs. Milk 9.944 × 1017 3.467× 1082.945 × 1019 2.190 × 106
Cheese vs. Cookie 3.270 × 1042.086 × 1053.602 × 1026.719 × 106
Cheese vs Milk 2.385 × 1072.007 × 1010 4.515 × 1012 1.953 × 1010
Cookie vs. Milk 2.697 × 1012 1.258 × 1039.273 × 1014 3.777 × 103
5. Conclusions and Future Work
In this paper, we described a protocol for organoleptic testing combining self-report
questionnaires with video, audio, eye-tracking, and physiological signals (in particular,
ECG, EDA, PZT and PPG). In order to prove its applicability and replicability, the protocol
was applied to the case study project, MobFood, that aimed to constitute a breakfast kit of
three products from the four assessed. From this study, some improvements to the protocol
were identified, mostly concerning the acquisition of the psychophysiological signals,
either related with the sensor placement or electrode detachment during the experiment.
Suggestions and recommendations regarding these factors were presented, allowing future
studies to integrate and address them.
Two factors regarding this proposed protocol can be highlighted. First, the methodol-
ogy for verifying the quality of the signals and the request for repetition when the signals
did not present a minimum quality allowed us, in the end, to have a balanced database of
physiological signals with 83 participants, 43 female and 40 male, each with three records
made on different days. This was a requirement of the study in order to have sufficient
data available to study the physiological signals collected from the participants on different
days, but subjected to the same stimuli.
Another big step that was achieved with this work was the strategy used to enable
the assessors to self-place the sensors. For this, the participants had two training sessions
with which they became autonomous regarding the signals acquisition setup. Through
the previous analysis of the dataset, we can conclude that it is possible to train assessors,
contributing to streamline the protocols.
Regarding the results of the case study, besides the self-report results, it was also
possible to obtain some preliminary results regarding the relation between the self-report
and psychophysiological data, most specifically the ECG-derived data. Most importantly,
it was possible, in most cases, to differentiate the responses between the four products
through both the scores of global acceptance and the HRV features considered in this work
Foods 2021,10, 1974 16 of 20
(LF and HF, although LF obtained better results). A possible connection between higher
values of HF and LF and low levels of satisfaction was also suggested by the results, and
should be further studied.
Ultimately, by presenting this protocol, recommendations and preliminary results, we
seek to raise awareness to the interest (and feasibility) in moving forward from traditional
questionnaires in sensory analysis and consumer behavior research. Neuroorganoleptics
can contribute to a more holistic approach, in order to accommodate the needs of the
increasing competitive food and beverage market.
Future work involves a deeper analysis of the psychophysiological data in order to
characterise in more detail possible correlations with the self-report data, across different
questions, samples and possibly across the three sessions, based on the initial statistical
analysis of the questionnaire results.
Author Contributions:
Conceptualization, J.V. and C.P.; methodology, J.V., C.P., C.B. and V.K.;
software, J.V. and L.M.; validation, J.V. and C.P.; formal analysis, J.V., L.G., A.F. and H.P.d.S.; resources,
C.P. and C.B.; data curation, J.V. and V.K.; writing—original draft preparation, J.V., L.G., A.F. and
H.P.d.S.; writing—review and editing, J.V., L.G., A.F. and H.P.d.S.; project administration, J.V., C.P.
and V.K.; funding acquisition, C.P. All authors have read and agreed to the published version of
the manuscript.
This research was partially funded by Compete 2020, Lisboa 2020, Portugal 2020, and by
the European Union through FEDER, under grant number POCI-01-0247-FEDER-024524_LISBOA-
01-0247-FEDER-02 “MobFood” project, and by FCT/MCTES through national funds and when
applicable co-funded EU funds under the project UIDB/50008/2020.
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Institutional Review Board of the external recruitment
company under the protocol POCI-01-0247-FEDER-024524_LISBOA-01-0247-FEDER-02 “MobFood”
started in 1 December 2017.
Informed Consent Statement:
Written informed consent was obtained from all subjects involved in
the study.
Data Availability Statement:
The data are not publicly available due to privacy restrictions, although
it may be made privately available upon reasonable request made to the corresponding author.
The authors would like to thank CATAA-Associação Centro de Apoio Tec-
nológico Agroalimentar de Castelo Branco and IT-Instituto de Telecomunicações for the administra-
tive, technical support, facilities, and materials involved in the experiment.
Conflicts of Interest: The authors declare no conflict of interest.
The following abbreviations are used in this manuscript:
ANS Autonomic Nervous System
ECG Electrocardiography
EDA ElectroDermal Activity
HF High Frequency power
HR Heart Rate
HRV Heart Rate Variability
LF Low Frequency power
PPG Photoplethysmography
PZT Piezoelectric Respiration
SNS Sympathetic Nervous System
Foods 2021,10, 1974 17 of 20
Appendix A
Figure A1.
Example of acceptance question of the protocol using an 9-point hedonic scale concerning
aroma. Translation from top to bottom: ‘Aroma’, ‘Extremely unpleasant’, ‘Indifferent’, ‘Extremely
pleasant’, ‘Comments (optional)’.
Figure A2.
Plot (
) and histogram (
) of the psychophysiological signals acquired in four different moments
) during one of the sessions of one assessor. It is possible to observe in the plots at the top that in the first moment (
the signals are valid, in the second moment (
) a failure appears and in the last moments (
) the signals are invalid.
From the histogram (bottom image), it is possible to observe that, in the first moment (
) when the signals are valid, these
are concentrated around a central point while in the last moments (
) when the signals are invalid, these are dispersed.
It was based on this that 3 different clusters (at 20%, 50% and 90% of full scale) were created in order to test the validity of
the data.
Foods 2021,10, 1974 18 of 20
Table A1. Food neofobia assessment statements and scale used.
Statement Left Extreme Center Right Extreme
I constantly try new and different foods
Neither agree
nor disagree
I don’t trust new foods
If I do not know the ingredients of a food, I do not try it
I like food from different countries
Ethnic food seems too weird to taste
At convivial dinners I try new foods
I am afraid to eat things I have never experienced
I am selective about the food I eat
I can eat just about everything
I like to try new ethnic restaurants
Figure A3.
Distribution of the HRV HF feature values and global acceptance score for each product
across 156 experiments (experiments with outlier values of HF were removed). On
: Distribution
of HF values obtained in the first 2.5 min of contact with each product.
: Distribution of the
classifications obtained in the global acceptance question.
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... This sensor is placed at the ankle, as this location allows a suitable integration of the sensor in the sock and has been reported as a suitable anatomical location for PPG acquisitions [10]. For the EDA acquisition, the BITalino EDA 081217 sensor (from PLUX, S.A., Lisbon, Portugal) was considered; this sensor works under the DC exosomatic measurement method, has been used in multiple research projects [37][38][39] and has a conductance measurement range of [0, 25] µS. ...
... As a reference device, the BITalino (r)evolution Plugged (PLUX, S.A., Lisbon, Portugal) was used, which was connected to the same type of sensors. It is worth mentioning that the BITalino has been widely used within the research community for EDA acquisitions using the BITalino EDA sensor (as used in this experiment) [37][38][39]41]. Furthermore, this device has also been used for PPG acquisitions in laboratory research contexts [42,43]. ...
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Pervasive exposure to a vast and varied food repertoire has contributed to the obesity epidemic. Within this issue, there is a need for a better understanding of the psychophysiological responses to food cues that precede food choice and food intake to establish how these responses contribute to the link between food availability and increasing obesity levels. Biometric measures such as eye tracking, electrodermal activity and facial expressions may separately or collectively provide deeper insight into psychophysiological processes underlying food reward and food intake. We examined how biometric responses differed in foods varying in fat and taste and explored how these biometric signatures to food cues were related to food preference behaviours, food choice, and food intake. We developed and tested a biometric food preference task designed to concurrently assess biometric responses (eye tracking, electrodermal activity and facial expressions) and food reward to visual food stimuli from different food categories in 100 normal weight adults. Food intake and selection was examined using a simultaneous choice ad libitum buffet. The results from this cross-sectional study showed significant differences in visual attention towards foods varying in fat content and taste prior to making rapid food choice decisions. Furthermore, the study found positive associations between maintained attention during a forced choice paradigm and subsequent food reward and food intake measures. Attention, arousal and facial expression during passive viewing were not associated with food reward or intake measures, except for an association between negative valence and explicit liking such that less liked foods elicited stronger negative facial expressions. The findings indicate that implicit, biometric responses to food cues predict both food reward and actual food intake.
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This review summarises the main sensory methods (traditional techniques and the most recent ones) together with consumer research as a key part in the development of new products, particularly meat products. Different types of sensory analyses (analytical and affective), from conventional methods (Quantitative Descriptive Analysis) to new rapid sensory techniques (Check All That Apply, Napping, Flash Profile, Temporal Dominance of Sensations, etc.) have been used as crucial techniques in new product development to assess the quality and marketable feasibility of the novel products. Moreover, an important part of these new developments is analysing consumer attitudes, behaviours, and emotions, in order to understand the complex consumer–product interaction. In addition to implicit and explicit methodologies to measure consumers’ emotions, the analysis of physiological responses can also provide information of the emotional state a food product can generate. Virtual reality is being used as an instrument to take sensory analysis out of traditional booths and configure conditions that are more realistic. This review will help to better understand these techniques and to facilitate the choice of the most appropriate at the time of its application at the different stages of the new product development, particularly on meat products.
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The appearance of food provides certain expectations regarding the harmonization of taste, delicacy, and overall quality, which subsequently affects not only the intake itself but also many other features of the behavior of customers of catering facilities. The main goal of this article is to find out what effect the visual design of food (waffles) prepared from the same ingredients and served in three different ways—a stone plate, street food style, and a white classic plate—has on the consumer’s preferences. In addition to the classic tablet assistance personal interview (TAPI) tools, biometric methods such as eye tracking and face reading were used in order to obtain unconscious feedback. During testing, air quality in the room by means of the Extech device and the influence of the visual design of food on the perception of its smell were checked. At the end of the paper, we point out the importance of using classical feedback collection techniques (TAPI) and their extension in measuring subconscious reactions based on monitoring the eye movements and facial expressions of the respondents, which provides a whole new perspective on the perception of visual design and serving food as well as more effective targeting and use of corporate resources.
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Despite impressive market growth, increasing demand and economic importance of convenience food in emerging economies such as India, comprehensive research regarding the role of psychological and social determinants on convenience food choice is lacking. Therefore, this research aims to investigate the influence of convenience orientation, social status, moral attitude, mood, spiritual concern, religious beliefs and ethical values on purchase intention and consumption of convenience food. The non-probability purposive sampling method was adopted for recruitment of participants. A pre-tested questionnaire was used to collect data from 501 consumers. The descriptive statistics, confirmatory factor analysis and structural equation modeling were carried out to analyse the data. The factor loading, Cronbach’s alpha, composite reliability, average variance extracted, and correlations demonstrated good internal consistency and reliability of scale items as well as convergent and discriminant validity of the constructs. The model fit indices revealed that measurement and structural models fitted well with data. The path analysis of the structural model demonstrated that convenience orientation (β = 0.789 ***, t = 32.462), moral attitude (β = 0.594 ***, t = 20.984), mood (β = 0.586 ***, t = 18.683), spiritual concern (β = 0.145 ***, t = 3.23), religious beliefs (β = 0.451 ***, t = 14.787) and ethical values (β = 0.497 ***, t = 16.678) were positively related with purchase intention and consumption of convenience food (*** Significant at p ≤ 0.01). The path analysis of structural model also indicated that social status was not linked with purchase intention and consumption of convenience food. The convenience orientation was the key determinant influencing purchase intention and consumption of convenience food.
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Acceptance and preference of the sensory properties of foods are among the most important criteria determining food choice. Sensory perception and our response to food products and finally food choice itself are affected by a myriad of intrinsic as well as extrinsic factors. The pressing question is, how do these factors specifically affect our acceptance and preference for foods, both in and of themselves, and in combination in various contexts, both fundamental and applied? In addition, which factors overall play the largest role in how we perceive and behave towards food in daily life? Finally, how can these factors be utilized to affect our preferences and final acceptance of real food and food products from industrial production and beyond for healthier eating? A closer look at trends in research showcasing the influence that these factors and our senses have on our perception and affective response to food products and our food choices is timely. Thus, in this Special Issue collection "Consumer Preferences and Acceptance of Food Products", we bring together articles which encompass the wide scope of multidisciplinary research in the space related to the determination of key factors involved linked to fundamental interactions, cross-modal effects in different contexts and eating scenarios, as well as studies that utilize unique study design approaches and methodologies.
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Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.
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Emotional responses are associated with distinct body alterations and are crucial to foster adaptive responses, well-being, and survival. Emotion identification may improve peoples’ emotion regulation strategies and interaction with multiple life contexts. Several studies have investigated emotion classification systems, but most of them are based on the analysis of only one, a few, or isolated physiological signals. Understanding how informative the individual signals are and how their combination works would allow to develop more cost-effective, informative, and objective systems for emotion detection, processing, and interpretation. In the present work, electrocardiogram, electromyogram, and electrodermal activity were processed in order to find a physiological model of emotions. Both a unimodal and a multimodal approach were used to analyze what signal, or combination of signals, may better describe an emotional response, using a sample of 55 healthy subjects. The method was divided in: (1) signal preprocessing; (2) feature extraction; (3) classification using random forest and neural networks. Results suggest that the electrocardiogram (ECG) signal is the most effective for emotion classification. Yet, the combination of all signals provides the best emotion identification performance, with all signals providing crucial information for the system. This physiological model of emotions has important research and clinical implications, by providing valuable information about the value and weight of physiological signals for emotional classification, which can critically drive effective evaluation, monitoring and intervention, regarding emotional processing and regulation, considering multiple contexts.
Acceptance (affective) testing, the measurement of liking or preference, is a valuable and necessary component of every sensory program. In the product evaluation process, acceptance testing usually follows discrimination testing or descriptive analysis. Acceptance testing within sensory science should have specific research objectives, methodology, participant qualification criteria, and key deliverables. Most often, acceptance testing within sensory science is unbranded. This is a logical and necessary step in the product development process, before substantial capital has been invested in equipment, packaging, distribution, and advertising. Branded versus unbranded testing is discussed in this chapter as is the role of sensory science versus marketing research/consumer insights. Sensory acceptance or guidance testing is neither a substitute for larger scale consumer tests nor is it a competitive alternative. Discussion focuses on the various types of sensory acceptance tests, locations such as laboratory, central location, casual bar, and home use, choice of methods, participant qualification, questionnaire, and scorecard development.
Purpose As the risks associated with sugar-sweetened beverages (SSBs) increase, various policies require sugar to be reduced in beverages. This paper segmented consumers according to food-related lifestyle (FRL), analyzed beverage selection attributes and preference for sugar-reduced beverages (SRBs) for each group and presented basic data for the strategies of SRBs for each consumer group. Design/methodology/approach In total, 1,000 Korean consumer panels responded to the online survey. The questionnaire consisted of FRL, beverage selection attributes and attitude toward SRBs. Findings Consumer groups were divided according to FRL: rational, value seeking and careless consumer. Rational consumers tended to be in their 30s or 50s and women, and they focused on product quality/hygiene when choosing beverages. Value seeking consumers were mainly in their 40s and 50s and were characterized by high education and income. They showed high scores in quality/hygiene, economy and sensory traits. Careless consumers were more likely to be in their 20s–30s, unmarried men and considered sensory traits as the most important factor. Research limitations/implications The main limitation of this study is the lack of generalization of consumer panels to represent the entire population because they were part of an online research firm. Originality/value This study implies that segmenting consumers according to FRL allows detailed analysis of consumer attitudes and behaviors. Using this analysis, the complex consumer pattern can be used as basic data for promoting sugar-reduced beverages.