This
paper
presents
a
cactus-like
NiO
nanostructure-based
chemiresistive
gas
sensor
for
monitoring
saltwater
fish
freshness.
The
sensing
material
was
synthesized
via
low-temperature,
facile
hydrothermal
method.
cubic
crystal
system
and
morphology
of
the
were
revealed
using
X-ray
diffraction
field
emission
scanning
electron
microscope.
integrated
with
gold
interdigitated
electrodes
measurements.
operating
temperature
optimized
to
be
250
°C.
fabricated
more
sensitive
toward
trimethylamine
(TMA).
A
response
56%
obtained
in
presence
10
ppm
recovery
times
being
30
205
s,
respectively.
experimental
limit
detection
100
ppb.
exposed
vapors
emitted
from
or
marine
fish.
Fish
samples
categorized
according
storage
conditions
room
deep
fridge.
measurements
performed
at
time
interval
24
h.
Initially,
fresh
sample
around
10%.
After
that,
increased
time.
study
suggests
simple
method
detecting
freshness
technology
efficacious
manner.
Nano-Micro Letters,
Journal Year:
2024,
Volume and Issue:
16(1)
Published: Aug. 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.
Foods,
Journal Year:
2025,
Volume and Issue:
14(3), P. 447 - 447
Published: Jan. 29, 2025
The
application
of
smart
packaging
technology
in
fruit
and
vegetable
preservation
has
shown
significant
potential
with
the
ongoing
advancement
science
technology.
Smart
leverages
advanced
sensors,
materials,
Internet
Things
(IoT)
technologies
to
monitor
regulate
storage
environment
fruits
vegetables
real
time.
This
approach
effectively
extends
shelf
life,
enhances
food
safety,
reduces
waste.
principle
behind
involves
real-time
monitoring
environmental
factors,
such
as
temperature,
humidity,
gas
concentrations,
precise
adjustments
based
on
data
analysis
ensure
optimal
conditions
for
vegetables.
encompass
various
functions,
including
antibacterial
action,
humidity
regulation,
control.
These
functions
enable
automatically
adjust
its
internal
according
specific
requirements
different
vegetables,
thereby
slowing
growth
bacteria
mold,
prolonging
freshness,
retaining
nutritional
content.
Despite
advantages,
widespread
adoption
faces
several
challenges,
high
costs,
limited
material
diversity
reliability,
lack
standardization,
consumer
acceptance.
However,
matures,
costs
decrease,
degradable
materials
are
developed,
is
expected
play
a
more
prominent
role
preservation.
Future
developments
likely
focus
innovation,
deeper
integration
IoT
big
data,
promotion
environmentally
sustainable
solutions,
all
which
will
drive
industry
toward
greater
efficiency,
intelligence,
sustainability.
Current Research in Food Science,
Journal Year:
2024,
Volume and Issue:
8, P. 100723 - 100723
Published: Jan. 1, 2024
Fruit
and
vegetable
freshness
testing
can
improve
the
efficiency
of
agricultural
product
management,
reduce
resource
waste
economic
losses,
plays
a
vital
role
in
increasing
added
value
fruit
products.
At
present,
detection
mainly
relies
on
manual
feature
extraction
combined
with
machine
learning.
However,
features
has
problem
poor
adaptability,
resulting
low
detection.
Although
exist
some
studies
that
have
introduced
deep
learning
methods
to
automatically
learn
characterize
fruits
vegetables
cope
diversity
variability
complex
scenes.
performance
these
needs
be
further
improved.
Based
this,
this
paper
proposes
novel
method
fusion
different
models
extract
images
correlation
between
various
areas
image,
so
as
detect
more
objectively
accurately.
First,
image
size
dataset
is
resized
meet
input
requirements
model.
Then,
characterizing
are
extracted
by
fused
Finally,
parameters
model
were
optimized
based
model,
was
evaluated.
Experimental
results
show
CNN_BiLSTM
which
convolutional
neural
network
(CNN)
bidirectional
long-short
term
memory
(BiLSTM),
parameter
optimization
processing
achieve
an
accuracy
97.76%
detecting
vegetables.
The
research
promising
