Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array
Nano-Micro Letters,
Год журнала:
2024,
Номер
16(1)
Опубликована: Авг. 14, 2024
Abstract
As
information
acquisition
terminals
for
artificial
olfaction,
chemiresistive
gas
sensors
are
often
troubled
by
their
cross-sensitivity,
and
reducing
cross-response
to
ambient
gases
has
always
been
a
difficult
important
point
in
the
sensing
area.
Pattern
recognition
based
on
sensor
array
is
most
conspicuous
way
overcome
cross-sensitivity
of
sensors.
It
crucial
choose
an
appropriate
pattern
method
enhancing
data
analysis,
errors
improving
system
reliability,
obtaining
better
classification
or
concentration
prediction
results.
In
this
review,
we
analyze
mechanism
We
further
examine
types,
working
principles,
characteristics,
applicable
detection
range
algorithms
utilized
gas-sensing
arrays.
Additionally,
report,
summarize,
evaluate
outstanding
novel
advancements
methods
identification.
At
same
time,
work
showcases
recent
utilizing
these
identification,
particularly
within
three
domains:
ensuring
food
safety,
monitoring
environment,
aiding
medical
diagnosis.
conclusion,
study
anticipates
future
research
prospects
considering
existing
landscape
challenges.
hoped
that
will
make
positive
contribution
towards
mitigating
gas-sensitive
devices
offer
valuable
insights
algorithm
selection
applications.
Язык: Английский
Electronic Tongues and Noses: A General Overview
Biosensors,
Год журнала:
2024,
Номер
14(4), С. 190 - 190
Опубликована: Апрель 13, 2024
As
technology
advances,
electronic
tongues
and
noses
are
becoming
increasingly
important
in
various
industries.
These
devices
can
accurately
detect
identify
different
substances
gases
based
on
their
chemical
composition.
This
be
incredibly
useful
fields
such
as
environmental
monitoring
industrial
food
applications,
where
the
quality
safety
of
products
or
ecosystems
should
ensured
through
a
precise
analysis.
Traditionally,
this
task
is
performed
by
an
expert
panel
using
laboratory
tests
but
sometimes
becomes
bottleneck
because
time
other
human
factors
that
solved
with
technologies
provided
tongue
nose
devices.
Additionally,
these
used
medical
diagnosis,
monitoring,
even
automotive
industry
to
gas
leaks.
The
possibilities
endless,
continue
improve,
they
will
undoubtedly
play
role
improving
our
lives
ensuring
safety.
Because
multiple
applications
developments
field
last
years,
work
present
overview
from
point
view
approaches
developed
methodologies
data
analysis
steps
aim.
In
same
manner,
shows
some
found
use
ends
conclusions
about
current
state
technologies.
Язык: Английский
Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics
ACS Sensors,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 10, 2025
Breath
sensors
represent
a
frontier
in
noninvasive
diagnostics,
leveraging
the
detection
of
volatile
organic
compounds
(VOCs)
exhaled
breath
for
real-time
health
monitoring.
This
review
highlights
recent
advancements
breath-sensing
technologies,
with
focus
on
innovative
materials
driving
their
enhanced
sensitivity
and
selectivity.
Polymers,
carbon-based
like
graphene
carbon
nanotubes,
metal
oxides
such
as
ZnO
SnO2
have
demonstrated
significant
potential
detecting
biomarkers
related
to
diseases
including
diabetes,
liver/kidney
dysfunction,
asthma,
gut
health.
The
structural
operational
principles
these
are
examined,
revealing
how
unique
properties
contribute
key
respiratory
gases
acetone,
ammonia
(NH3),
hydrogen
sulfide,
nitric
oxide.
complexity
samples
is
addressed
through
integration
machine
learning
(ML)
algorithms,
convolutional
neural
networks
(CNNs)
support
vector
machines
(SVMs),
which
optimize
data
interpretation
diagnostic
accuracy.
In
addition
sensing
VOCs,
devices
capable
monitoring
parameters
airflow,
temperature,
humidity,
essential
comprehensive
analysis.
also
explores
expanding
role
artificial
intelligence
(AI)
transforming
wearable
into
sophisticated
tools
personalized
enabling
disease
Together,
advances
sensor
ML-based
analytics
present
promising
platform
future
individualized,
healthcare.
Язык: Английский
Small Acetone Sensor with a Porous Colorimetric Chip for Breath Acetone Detection Using the Flow–Stop Method
Chemosensors,
Год журнала:
2025,
Номер
13(4), С. 136 - 136
Опубликована: Апрель 8, 2025
Acetone
is
a
well-known
biogas
involved
in
lipid
metabolism
and
considered
potential
biomarker
for
diabetes.
However,
the
conventional
detection
methods
acetone
face
limitations
of
large
size,
complex
usage,
cross-sensitivity.
In
this
study,
we
developed
portable
device
comprising
porous
colorimetric
analytical
chip
composed
2-nitrophenyl
hydrazine
glass.
The
was
highly
sensitive
selective
because
it
based
on
chemical
reaction
between
nanoporous
material,
which
provides
surface
area.
consisted
450
nm
laser
light
source
photodiode
detector
with
volume
less
than
40
mL.
gas
measured
atmosphere
10
min
using
flow–stop
method.
measurable
concentration
ranged
from
0
to
6.0
ppm
limit
0.22
ppm.
We
successfully
conducted
feasibility
study
human
exhaled
breath
analyzed
relationship
exercise
breath.
An
upward
trend
levels
seen
post-exercise
each
individual.
Язык: Английский
Assessing Data Fusion in Sensory Devices for Enhanced Prostate Cancer Detection Accuracy
Chemosensors,
Год журнала:
2024,
Номер
12(11), С. 228 - 228
Опубликована: Ноя. 1, 2024
The
combination
of
an
electronic
nose
and
tongue
represents
a
significant
advance
in
the
pursuit
effective
detection
methods
for
prostate
cancer,
widespread
form
cancer
affecting
men
across
globe.
These
cutting-edge
devices,
collectively
called
“E-Senses”,
use
data
fusion
to
identify
distinct
chemical
compounds
exhaled
breath
urine
samples,
potentially
improving
existing
diagnostic
techniques.
This
study
combined
information
from
two
sensory
perception
devices
detect
biological
samples
(breath
urine).
To
achieve
this,
patients
diagnosed
with
disease
control
individuals
were
collected
using
gas
sensor
array
electrodes.
signals
subjected
preprocessing
algorithms
prepare
them
analysis.
Following
datasets
each
device
individually
analyzed
subsequently
merged
enhance
classification
results.
was
assessed
it
successfully
improved
accuracy
detecting
prostate-related
conditions
distinguishing
healthy
patients,
achieving
highest
success
rate
possible
(100%)
through
machine
learning
methods,
outperforming
results
obtained
individual
devices.
Язык: Английский