Application of Smart Packaging in Fruit and Vegetable Preservation: A Review
Foods,
Год журнала:
2025,
Номер
14(3), С. 447 - 447
Опубликована: Янв. 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.
Язык: Английский
Nanotechnology Solutions in Food Packaging
Опубликована: Март 14, 2025
Язык: Английский
Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information Fusion
Foods,
Год журнала:
2025,
Номер
14(9), С. 1468 - 1468
Опубликована: Апрель 23, 2025
Microorganisms
play
a
key
role
in
fish
spoilage
and
quality
deterioration,
making
the
development
of
rapid,
accurate,
efficient
technique
for
detecting
surface
microbes
essential
enhancing
freshness
ensuring
safety
mandarin
consumption.
This
study
focused
on
total
viable
count
(TVC)
Escherichia
coli
levels
dorsal
ventral
parts
fish,
we
constructed
detection
model
using
hyperspectral
imaging
data
fusion.
The
results
showed
that
comprehensive
simplified
models
were
successfully
developed
quantitative
across
all
wavelengths.
performed
best
at
predicting
microbial
growth
side,
with
RAW-CARS-PLSR
proving
most
effective
TVC
E.
counts
region.
RAW-PLSR
was
identified
as
optimal
predictor
concentration
side.
A
fusion
decision
layer
Dempster–Shafer
theory
evidence
outperformed
relying
solely
spectral
or
textural
information,
it
an
approach
fish.
prediction
accuracy
achieved
Rp
value
0.9337,
whereas
reached
0.8443.
For
concentration,
values
0.8180
section
0.8512
separate
analysis.
Язык: Английский