Journal of Materials Chemistry B,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 31, 2024
We
have
demonstrated
a
straightforward
and
scalable
manufacturing
process
for
the
development
of
3D-printed
conducting
microneedle
array-based
electrochemical
point-of-care
device
minimally
invasive
sensing
chlorpromazine.
Foods,
Год журнала:
2025,
Номер
14(6), С. 922 - 922
Опубликована: Март 8, 2025
Integrating
advanced
computing
techniques
into
food
safety
management
has
attracted
significant
attention
recently.
Machine
learning
(ML)
algorithms
offer
innovative
solutions
for
Hazard
Analysis
Critical
Control
Point
(HACCP)
monitoring
by
providing
data
analysis
capabilities
and
have
proven
to
be
powerful
tools
assessing
the
of
Animal-Source
Foods
(ASFs).
Studies
that
link
ML
with
HACCP
in
ASFs
are
limited.
The
present
review
provides
an
overview
ML,
feature
extraction,
selection
employed
safety.
Several
non-destructive
presented,
including
spectroscopic
methods,
smartphone-based
sensors,
paper
chromogenic
arrays,
machine
vision,
hyperspectral
imaging
combined
algorithms.
Prospects
include
enhancing
predictive
models
development
hybrid
Artificial
Intelligence
(AI)
automation
quality
control
processes
using
AI-driven
computer
which
could
revolutionize
inspections.
However,
handling
conceivable
inclinations
AI
is
vital
guaranteeing
reasonable
exact
hazard
assessments
assortment
nourishment
generation
settings.
Moreover,
moving
forward,
interpretability
will
make
them
more
straightforward
dependable.
Conclusively,
applying
allows
real-time
analytics
can
significantly
reduce
risks
associated
ASF
consumption.
Biomimetics,
Год журнала:
2024,
Номер
9(8), С. 469 - 469
Опубликована: Авг. 2, 2024
Microneedles
(MNs),
characterized
by
their
micron-sized
sharp
tips,
can
painlessly
penetrate
the
skin
and
have
shown
significant
potential
in
disease
treatment
biosensing.
With
development
of
artificial
intelligence
(AI),
design
application
MNs
experienced
substantial
innovation
aided
machine
learning
(ML).
This
review
begins
with
a
brief
introduction
to
concept
ML
its
current
stage
development.
Subsequently,
principles
fabrication
methods
are
explored,
demonstrating
critical
role
optimizing
preparation.
Integration
between
applications
therapy
sensing
were
further
discussed.
Finally,
we
outline
challenges
prospects
learning-assisted
MN
technology,
aiming
advance
practical
field
smart
diagnosis
treatment.