Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
11,
No.
6,
December
2021,
pp.
4767
4773
ISSN:
2088-8708,
DOI:
10.11591/ijece.v11i6.pp4767-4773
r
4767
On
data
collection
time
by
an
electr
onic
nose
Piotr
Bor
o
wik
1
,
Leszek
Adamo
wicz
2
,
Rafał
T
arak
o
wski
3
,
Krzysztof
Siwek
4
,
T
omasz
Grzywacz
5
1,2,3
F
aculty
of
Ph
ysics,
W
arsa
w
Uni
v
ersity
of
T
echnology
,
W
arsza
w
a,
Poland
4,5
F
aculty
of
Electrical
Engineering,
Institute
of
Theory
of
Electrical
Engineering,
Measurement
and
Information
Systems,
W
arsa
w
Uni
v
ersity
of
T
echnology
,
W
arsza
w
a,
Poland
Article
Inf
o
Article
history:
Recei
v
ed
Oct
17,
2020
Re
vised
May
12,
2021
Accepted
Jun
12,
2021
K
eyw
ords:
Electronic
nose
Measurement
time
Multisensor
measurement
Odor
classification
ABSTRA
CT
W
e
use
electronic
nose
data
of
odor
measurements
to
b
uild
machine
learning
clas-
sification
models.
The
presented
analysis
focused
on
determining
the
optimal
time
of
measurement,
leading
to
the
best
model
performance.
W
e
observ
e
that
the
most
v
aluable
information
for
classification
is
a
v
ailable
in
data
collected
at
the
be
ginning
of
adsor
ption
and
the
be
ginning
of
the
desorption
phase
of
measurement.
W
e
demon-
strated
that
the
usage
of
comple
x
features
e
xtracted
from
the
sensors’
response
gi
v
es
better
classification
performance
than
use
as
features
only
ra
w
v
alues
of
sensors’
re-
sponse,
normalized
by
baseline.
W
e
use
a
group
shuf
fling
cross-v
alidation
approach
for
determining
the
reported
models’
a
v
erage
accurac
y
and
standard
de
viation.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Leszek
Adamo
wicz
F
aculty
of
Ph
ysics
W
arsa
w
Uni
v
ersity
of
T
echnology
ul.
K
oszyk
o
w
a
75,
00-662
W
arsza
w
a,
Poland
Email:
Leszek.Adamo
wicz@pw
.edu.pl
1.
INTR
ODUCTION
Electronic
noses
(e-noses)
[1]-[3]
are
artificial
de
vices
that
consist
of
an
array
of
g
as
sensors
supported
by
machine
learning
pattern
recognition
techniques.
One
of
the
critical
fields
of
application
of
e-nose
is
the
food
industry
[4]-[10]
for
which
odor
characteristics
o
f
the
products
are
one
of
the
essential
indi
cations
of
product
quality
.
De
v
elopment
and
v
erification
of
the
machine
learning
methods
in
application
to
the
odors
classification
by
e-nose
measurements
data
ha
v
e
a
similar
le
v
el
of
importance
as
the
de
v
elopment
of
the
sensors
and
sensors
arrays
consisting
of
the
e-nose
hardw
are.
Similar
techniques
can
be
emplo
yed
re
g
ardless
of
the
domain
of
application
of
the
e-nose.
One
of
the
main
obstacles
in
such
de
v
elopment
is
the
relati
v
ely
long
time
needed
to
collect
suf
ficient
measurement
data.
T
o
o
v
ercome
this,
one
can
use
publicly
a
v
ailable
datasets,
which
became
f
amous
as
testbeds
for
machine
learning
modeling
reported
by
multiple
authors.
In
the
present
studies,
we
use
publicly
a
v
ail
able
datasets
[11]
of
odor
measurements
by
electronic
no
s
e.
The
same
dataset
w
as
used
in
our
pre
vious
research
[12].
W
e
focused
on
features
e
xtraction
and
selection,
optimization
of
the
number
of
used
sensors,
and
the
possibility
to
use
for
classification
only
single-sensor
electronic
nose.
The
original
studies
of
the
same
dataset
[13]
were
focused
on
the
possibility
of
spoilage
odor
detection
after
a
v
ery
short
e
xposure
of
the
electronic
nose
to
the
odor
sample,
lasting
a
fe
w
seconds.
Zhang
and
co
w
ork
ers
[14]
used
this
dataset
to
demonstrate
proposed
analytical
algorithms’
performance.
In
this
report,
we
deal
with
the
dif
ferent
subjects
of
optimization
concerning
the
time
of
odor
m
ea-
surement.
W
e
are
interested
in
the
analysis
of
the
dependence
of
the
classification
accurac
y
on
the
odor
mea-
surement
time.
Recently
Rodriguez
Gamboa
and
co
w
ork
ers
[15]
e
xamined
se
v
eral
datasets
and
used
deep
J
ournal
homepage:
http://ijece
.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
4768
r
ISSN:
2088-8708
learning
and
support
v
ector
machine
models
to
demonstrate
the
potential
of
using
only
a
part
of
electronic
nose
measurement
data
for
correct
odor
classification.
The
used
dataset
is
collected
by
custom-made
e-nose
consisting
of
T
aguchi
type
MQ-series
g
as
sen-
sors.
In
recent
years
one
can
find
man
y
suggestions
for
constructing
lo
w-cost
electronic
noses,
and
se
v
eral
groups
propose
de
vices
based
on
similar
sensors
[13],
[16]-[22].
The
findings
presented
in
this
report
can
be
rele
v
ant
to
other
applications
of
similar
de
vices.
Considerable
research
concerning
e-nose
data
is
focuse
d
on
the
e
xtraction
of
the
comple
x
features
describing
curv
es
of
sensors’
response
to
the
g
as
e
xposure.
Ho
we
v
er
,
there
are
also
other
reports,
especially
applying
deep
learning
neural
netw
orks,
in
which
ra
w
measurement
data
are
used.
It
is
interesting
to
compare
both
approaches
and
demonstrate
the
influence
of
the
dimensionality
reduction
by
the
principal
components
method.
2.
METHODS
AND
PR
OCEDURES
2.1.
Odor
measur
ement
Rodriguez
Gamboa
and
co
w
ork
ers
[13]
presented
the
measurement
of
odor
at
v
arious
spoilage
stages.
T
wenty-tw
o
samples
of
bottles
of
commercially
a
v
ailable
wines
of
dif
ferent
v
arieties
and
vintages
from
four
producers
from
the
S
˜
ao
Francisco
v
alle
y
(Pernamb
uco-Brazil)
were
used.
Thirteen
randomly
selected
bottles
were
left
open
for
six
months,
which
g
a
v
e
the
population
of
lo
w-quality
wines.
F
our
randomly
selected
bottles
were
left
open
for
tw
o
weeks
before
measurement,
and
the
y
are
considered
as
a
v
erage
quality
wines.
The
remaining
fi
v
e
bottles
are
labeled
as
high-quality
wines.
Except
for
these
samples,
samples
of
ethanol
diluted
in
distilled
w
ater
in
six
dif
ferent
concentrations
were
used,
which
may
be
considered
additional
six
measured
bottles.
That
gi
v
es
four
cate
gories
of
odor
that
are
classified.
In
total,
the
dataset
consists
of
measurements
of
300
samples
as
collections
of
sensors’
response
of
3
300
points
for
each
sensor
.
The
e-nose
de
v
eloped
at
Uni
v
ersidade
Federal
Rural
de
Pernamb
uco
[13]
consists
of
six
com
mercially
a
v
ailable
metal-oxide
g
as
sensors
produced
by
Hanwei
Sensors
(www
.hwsensor
.com).
T
w
o
sensors
of
each
type
(MQ-3,
MQ-4,
MQ-6)
ha
v
e
been
used
in
the
presented
construction.
During
the
measurement,
the
first
10
seconds
were
used
to
collect
baselines
of
sensors’
response
when
e-nose
w
as
e
xposed
to
pure
air
.
Then,
the
odor’
s
prepared
sample
w
as
pumped
into
the
sensor
chamber
,
and
80
seconds
of
sensors’
response
during
the
adsorption
phase
w
as
collected.
After
that,
the
sensors’
response
during
90
seconds
of
the
desorption
phase
w
as
collected
when
pure
air
w
as
pumped
to
the
sensors’
chamber
.
After
the
measurement,
the
e-nose
w
as
e
xposed
to
pure
air
for
10
minutes
to
pur
ge
the
e
xperimental
setup.
2.2.
Classification
modeling
The
measurement
data
are
a
series
of
sensors’
responses
e
xpressed
as
their
resistance
R
o
v
er
time.
As
the
first
step
of
data
processing,
the
measurement
data
are
di
vided
by
the
sensor
resistance’
s
baseline
v
alue
R
0
collected
just
before
electronic
nose
e
xposure
to
the
measured
odor
sample.
In
Figure
1,
we
present
an
e
xample
of
sensor
signals
collected
during
the
measurement
of
the
odor
sample,
with
a
schematic
representation
of
the
time
span
used
for
the
e
xtracti
on
of
modeling
features.
The
signal
collection
is
performed
with
a
frequenc
y
of
18.5
Hz.
As
a
first
step
of
the
data
processing
to
reduce
the
measurement
noise,
the
a
v
erage
response
with
20
observ
ations
is
calculated.
Then
tw
o
approaches
of
e
xtraction
of
features
used
for
classification
within
machine
learning
model
training
ha
v
e
been
emplo
yed.
First,
we
decided
to
use
modeling
features,
just
the
magnitudes
of
sensors’
re-
sponse
relati
v
e
to
the
baselines:
R
=R
0
and
in
v
ersion
of
these
v
alues
representing
sensors’
conductanc
e
G=G
0
.
W
e
emplo
yed
the
second
approach
to
e
xtract
the
comple
x
features
describing
sensors’
response
curv
es,
e.g.,
a
v
erage
v
alue,
maximum
v
alue,
and
maximum
slope.
The
complete
list
of
the
features
that
we
ha
v
e
used
for
training
classification
models
is
presented
in
our
pre
vious
report
[12].
Since
our
studies
are
focused
on
the
de-
pendence
of
the
classification
performance
on
the
measurement
time
by
the
e-nose,
the
model
ing
features
are
calculated
using
only
part
of
a
v
ailable
data,
in
the
range
from
the
be
ginning
of
g
as
e
xposure
until
the
considered
time.
W
e
represent
this
by
the
dashed
re
gion
in
Figure
1.
The
odor
samples
were
prepared
from
28
bottles,
and
each
of
them
w
as
used
for
about
ten
measure-
ments.
It
should
be
noted
that
such
an
e
xperimental
procedure
leads
to
a
correlation
between
training
obser
-
v
ations.
Hence,
to
obtain
a
reliable
estimation
of
the
classification
models’
performance,
we
applied
a
group
shuf
fle
cros
s-v
alidation
procedure,
assuring
that
all
observ
ations
from
a
gi
v
en
bottle’
s
odor
measurements
are
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
6,
December
2021
:
4767
–
4773
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
4769
attrib
uted
either
to
training
or
testing
dataset.
The
cross-v
alidation
procedure
has
been
performed
200
times
in
a
loop,
and
model
performance
results
a
v
eraged.
Figure
1.
Example
of
sensors
signals
of
odor
measurement,
(a)
normalized
resistance
R
=R
0
and
(b)
normalized
conductance
G=G
0
;
the
dashed
rectangle
schematically
represents
an
e
xample
of
the
time
span
of
data
used
to
e
xtract
the
modeling
features
T
w
o
types
of
modeling
techniques
ha
v
e
been
applied:
logistic
re
gression
with
multinomial
classi-
fication
(
LogReg
)
and
support
v
ector
machine
classification
(
SVC
)
with
radial
basis
functions
k
ernel
and
one-vs-one
multi-class
scheme.
F
or
both
algorithms,
we
performed
tw
o
types
of
tests.
In
the
first
case,
the
modeling
features,
as
described
abo
v
e,
were
used.
In
the
second
ca
se,
these
input
v
ariables
were
transformed
using
the
principal
component
analysis
method.
Only
the
six
most
important
components
were
used
as
the
modeling
features
(
PCAReg,
PCASVC
).
The
prepared
features
dataset
w
as
transformed
using
the
standard
scaller
method.
W
e
decided
to
use
only
these
classical
modeling
techniques
[23]
Moreo
v
er
,
we
disre
g
arded
more
comple
x
algorithms
such
as
multilayer
neural
netw
orks
s
ince
the
number
of
observ
ations
a
v
ailable
for
modeling
is
quite
limited.
In
total,
the
used
datasets
[11]
contain
measurements
of
300
odor
samples.
Ev
en
though
more
fle
xible
modeling
techniques
can
pro
vide
more
e
xpressi
v
e
classification
models,
the
number
of
fitted
parameters
is
much
higher
than
in
the
applied
methods.
Agg
arw
al
[24]
(page
25)
indicate
that
the
total
number
of
training
data
points
should
be
at
least
2
to
3
times
lar
ger
than
the
number
of
parameters
in
the
neural
netw
ork.
Ho
we
v
er
,
the
precise
number
of
data
instances
depends
on
the
specific
model
at
hand.
Hence,
the
simpler
models
that
we
applied
in
principle
should
be
less
prone
to
o
v
er
-fitting.
The
modeling
has
been
performed
using
computer
codes
in
Python
3.7
language
with
a
scikit-learn
module
[25].
3.
RESUL
TS
AND
DISCUSSION
In
Figure
2,
we
present
a
comparison
of
the
a
v
erage
cross-v
alidation
accurac
y
of
v
arious
types
of
models
as
a
function
of
time
from
the
be
ginning
of
sensors’
e
xposure
to
e
xamined
odor
,
from
which
data
ha
v
e
been
used
for
model
b
uilding.
Besides,
in
tw
o
subfigures,
we
w
ould
lik
e
to
distinguish
between
v
arious
approaches
to
the
e
xtraction
of
modeling
features.
Figure
2(a)
sho
ws
the
ra
w
data
of
sensors’
response
relati
v
e
to
the
baseline.
While
in
Figure
2(b),
models
are
b
uilt
using
an
e
xtensi
v
e
set
of
comple
x
features
[12].
The
first
observ
ation
from
these
results
is
that
the
logistic
re
gression
model
e
xhibits
the
best
model
accurac
y
performance.
It
is
also
interesting
to
notice
that
this
is
confirmed
in
tw
o
considered
modeling
feature
sets.
When
we
compare
Figures
2(a)
and
(b),
we
can
also
observ
e
that
models
trained
on
comple
x
features
On
data
collection
time
by
an
electr
onic
nose
(Piotr
Bor
owik)
Evaluation Warning : The document was created with Spire.PDF for Python.
4770
r
ISSN:
2088-8708
e
xhibit
better
cl
assification
accurac
y
than
the
m
odels
with
the
sensors’
r
esponse’
s
ra
w
v
alues.
The
significance
of
the
feature
e
xtraction
procedures
de
v
eloped
by
the
e-nose
research
community
is
thus
visible.
Figure
2.
The
a
v
erage
accurac
y
of
v
arious
classification
techniques,
as
a
function
of
time
of
measurement,
from
which
data
are
used
for
model
training
and
testing:
(a)
normalized
sensors’
responses
used
as
modeling
features
(both
R
=R
0
and
G=G
0
)
and
(b)
comple
x
modeling
features
e
xtracted
from
sensors’
response
curv
es
(the
first
10
seconds
of
measurement
is
not
presented
as
it
w
as
the
phase
of
baseline
le
v
el
collection)
Another
important
observ
ation
can
be
deduced
from
Figure
2(b).
There
is
an
abrupt
increase
in
the
model
performance
just
at
the
be
ginning
of
the
sensors’
e
xposur
e
to
the
studied
odor
.
Similar
beha
vior
can
be
noticed
at
the
starting
moment
of
desorption
when
the
sensors
are
ag
ain
e
xposed
to
the
clean
air
.
W
e
deduce
that
precise
measurement
can
gi
v
e
the
most
rele
v
ant
information
that
can
be
used
for
odor
classification
during
these
moments.
Special
care
[26]
in
the
design
of
an
electronic
nose
is
required
to
pro
vide
a
rapid
change
of
sensors’
e
xposure
to
dif
ferent
g
ases,
remembering
to
ensure
repeatability
of
measurement
conditions.
Szczurek
et
al.
[27]
and
Staymates
et
al.
[28]
reported
measurements
in
”s
nif
fing”
mode
when
frequent
changes
between
studied
odor
and
pure
air
occur
or
in
the
initial
time
of
the
sensors’
action
[29].
In
Figure
3,
we
present
another
comparison
of
models’
performance
as
a
function
of
time
of
measurement
from
which
data
are
a
v
ailable
for
model
b
uilding.
W
e
focus
on
logistic
re
gression
models
and
compare
six
types
of
modeling
feature
sets,
which
are
a
combination
of
tw
o
cases,
as
we
summarize
in
the
T
able
1.
As
we
already
noticed,
the
results
presented
in
Figure
3(a)
confirm
that
better
classification
perfor
-
mance
can
be
achie
v
ed
when
comple
x
features
e
xtracted
from
the
sensors’
response
curv
es
are
used
compared
to
models
b
uilt
on
just
ra
w
v
alues
of
normalized
s
ensors’
response.
Another
interesting
observ
ation
in
this
figure
is
that
the
models
in
which
features
are
based
on
sensors’
conductance
G
also
e
xhibit
better
performance,
especially
when
the
time
of
odor
measurement
by
the
electronic
nose
is
reduced.
Suppose
the
model
is
b
uilt
on
the
sensors’
resistance
R
data.
In
that
case,
this
requires
performing
odor
measurement
for
a
longer
time
and
mainly
includes
measuring
the
desorption
phase
of
the
sensors’
response.
The
same
observ
ation
concerning
models
b
uilt
on
the
resistance
data
is
v
alid
for
both
types
of
sets
of
features
considered
in
the
present
studies.
In
Figure
3(a),
one
can
notice
that
for
both
”
R
”
curv
es,
the
y
e
xhibit
a
kind
of
saturation
re
gion.
After
the
be
ginning
of
the
desorption
phase,
at
100
seconds,
the
models’
accurac
y
is
ag
ain
impro
v
ed.
Figure
3(a)
suggests
that
it
may
be
enough
to
reduce
the
odor
measurement
time
for
about
30
seconds
when
comple
x
features
are
e
xtracted
from
resistance
or
resistance
and
conductance
response
curv
es.
In
that
case,
the
increase
of
measurement
time
and
measurement
in
the
desorption
re
gime
does
not
lead
to
better
classification
performance.
More
insights
gi
v
e
e
xamination
of
Figure
3(b),
in
which
the
standard
de
viation
of
the
accurac
y
of
200
Int
J
Elec
&
Comp
Eng,
V
ol.
11,
No.
6,
December
2021
:
4767
–
4773
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
4771
models
trained
during
group
shuf
fle
cross-v
alidation
procedure
is
presented.
W
e
can
conclude
that
the
odor
measurement
time
should
be
in
the
ra
n
ge
of
70–90
seconds
(including
10
seconds
of
baseline
conditions
mea-
surements),
allo
wing
us
to
obtain
more
stable
classification
results.
When
the
data
e
xtracted
from
the
sensors’
resistance
v
alues
are
included
in
the
modeling,
it
introduces
some
additional
noise,
which
only
slightly
reduces
the
cla
ssification
accurac
y
and
leads
to
less
stable
models.
The
reduction
of
the
classification
performance
on
ne
w
data
may
appear
in
this
w
ay
.
Figure
3.
Cross-v
alidation
estimation
of
logistic
re
gression
models
classification
performance:
(a)
a
v
erage
accurac
y
and
(b)
standard
de
viation
of
accurac
y
(the
first
10
seconds
of
measurement
is
not
presented
as
it
w
as
the
phase
of
baseline
le
v
el
collection)
As
one
can
notice,
e
xamining
Figure
1
and
the
description
of
the
measurement
procedure
in
section
2.1,
such
optimal
time
of
data
collection
is
shorter
than
half
of
the
measurement
time
gi
v
en
in
[11],
[13].
In
other
research,
[15]
similar
results
ha
v
e
been
found
for
measurements
of
other
types
of
odors.
An
adv
antage
of
shortening
the
time
of
odor
detection
by
an
electronic
nose
is
noticeable.
Ho
we
v
er
,
one
can
k
eep
in
mi
nd
that
this
time
is
not
directly
related
to
the
number
of
odor
samples
measured
by
the
electronic
nose
de
vice
in
a
gi
v
en
time.
After
the
measurement,
there
is
still
a
need
for
de
vice
pur
ging
and
sensors’
base
state
reco
v
ery
in
clear
air
,
much
longer
than
the
odor
measurement
time.
T
able
1.
T
ypes
of
modeling
feature
sets
sensors
response
(
R
=R
0
;
G=G
0
)
comple
x
features
resistance
values
R
feat
R
conductance
values
G
feat
G
both
values
RG
feat
RG
4.
CONCLUSION
In
the
paper
,
we
presented
machine
learning
classification
models
b
uilt
on
publicly
a
v
ailable
datasets
of
e-nose
measurement
o
f
spoilage
odor
.
The
researc
h
focused
on
v
erifying
the
optimal
choice
of
odor
mea-
surement
time
by
e-nose
to
collect
data
for
training
a
machine
learning
classification
model
with
superior
performance.
W
e
presented
a
comparison
of
v
arious
modeling
features
based
on
sensors’
response
resistance
and
conductance.
A
group
shuf
fling
cross-v
alidation
approach
w
as
used
for
determining
the
reported
models’
a
v
erage
accurac
y
and
standard
de
viation.
W
e
demonstrate
that
most
of
the
information
used
by
the
models
for
classification
is
a
v
ailable
firstly
in
the
data
from
the
be
ginning
of
the
adsorption
phase,
which
means
sensors
On
data
collection
time
by
an
electr
onic
nose
(Piotr
Bor
owik)
Evaluation Warning : The document was created with Spire.PDF for Python.
4772
r
ISSN:
2088-8708
e
xposure
to
the
studied
odor
,
and
secondarily
in
the
data
from
the
be
ginning
of
the
desorption
phase,
which
means
sensors
e
xposure
to
the
clear
air
after
e
xposure
to
the
studied
g
as.
The
performed
analysis
leads
us
to
the
conclusions:
i)
that
for
the
considered
case,
only
comple
x
features
e
xtracted
from
the
sensors’
conductance
curv
es
G
should
be
used
for
a
classification
model,
ii)
it
is
suf
ficient
to
use
data
of
measurement
performed
during
g
as
adsorption
phase
only
,
iii)
and
that
the
logistic
re
gression
algorithm
should
be
used.
There
is
a
conclusion
concerning
the
recommended
machine
learning
classification
method.
In
man
y
reports,
the
support
v
ector
machine
is
used
as
a
gold
standard
for
such
applications.
As
we
demonstrated,
it
may
depend
on
the
considered
application,
and
there
are
cases
when
the
logistic
re
gression
algorithms
pro
v
e
superior
performance.
A
CKNO
WLEDGEMENT
This
w
ork
w
as
supported
by
the
National
Centre
for
Research
and
De
v
elopment
by
the
grant
agree-
ment
BIOSTRA
TEG3/347105/9/NCBR/2017.
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