Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research
Nanomaterials,
Год журнала:
2024,
Номер
14(21), С. 1688 - 1688
Опубликована: Окт. 22, 2024
The
synthesis,
characterization,
and
application
of
carbon
nanotubes
(CNTs)
have
long
posed
significant
challenges
due
to
the
inherent
multiple
complexity
nature
involved
in
their
production,
processing,
analysis.
Recent
advancements
machine
learning
(ML)
provided
researchers
with
novel
powerful
tools
address
these
challenges.
This
review
explores
role
ML
field
CNT
research,
focusing
on
how
has
enhanced
research
by
(1)
revolutionizing
synthesis
through
optimization
complex
multivariable
systems,
enabling
autonomous
reducing
reliance
conventional
trial-and-error
approaches;
(2)
improving
accuracy
efficiency
characterizations;
(3)
accelerating
development
applications
across
several
fields
such
as
electronics,
composites,
biomedical
fields.
concludes
offering
perspectives
future
potential
integrating
further
into
highlighting
its
driving
forward.
Язык: Английский
Recent advances on artificial intelligence-based approaches for food adulteration and fraud detection in the food industry: Challenges and opportunities
Food Chemistry,
Год журнала:
2024,
Номер
468, С. 142439 - 142439
Опубликована: Дек. 10, 2024
Язык: Английский
Fuzzy Logic-based Barcode Scanning System for Food Products Halal Identification
Food Control,
Год журнала:
2024,
Номер
unknown, С. 110926 - 110926
Опубликована: Сен. 1, 2024
Язык: Английский
Nondestructive Discrimination of Plant-Based Patty Containing Traditional Medicinal Roots Using Visible–Near-Infrared Hyperspectral Imaging and Machine Learning Techniques
Chemosensors,
Год журнала:
2025,
Номер
13(5), С. 158 - 158
Опубликована: Апрель 25, 2025
The
interest
in
traditional
meat
being
replaced
by
plant-based
food
has
increased
throughout
the
years.
Some
agricultural
products,
such
as
root
crops,
could
be
incorporated
into
alternative
products
due
to
health
benefits.
However,
relevant
studies
have
discovered
that
some
roots
are
considered
allergen
materials,
necessitating
further
identification
maintain
consumer
safety.
Aside
from
high
accuracy,
limitations
offered
methods
a
reason
employ
nondestructive
methods.
This
study
aimed
develop
hyperspectral
imaging
system
measuring
400
nm
1000
spectral
range
for
of
soybean-based
patty.
Four
thin-sliced
medicinal
(tianma
(Gastrodia
elata),
balloon
flower
(Platycodon
grandiflorum),
deodeok
(Codonopsis
lanceolata),
and
ginseng
(Panax
ginseng))
were
patty
with
concentration
5%
w/w.
Moreover,
support
vector
machine
(SVM)
learning
one-dimensional
convolutional
neural
networks
(1D-CNN)
realized
discrimination
model
tandem
data
extracted
image.
Our
demonstrated
SVM
effectively
discriminates
between
original
addition,
an
F1-score,
precision,
recall
beyond
96.77%.
optimum
was
achieved
using
standard
normal
variate
(SNV)
spectra.
Язык: Английский
Optimization of DNA Extraction from Fish Oil Residuals Based on Magnetic Bead Method
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Май 2, 2025
Abstract
As
a
high-value
functional
food,
fish
oil
has
substantial
market
demand,
yet
adulteration
issues
are
frequent.
PCR-based
detection
of
species-specific
genes
is
currently
the
most
direct
and
reliable
molecular
approach
for
identifying
source
species
their
relative
abundance
in
analysis.
The
concentration
purity
DNA
critical
ensuring
efficient
amplification
accurate
detection.
Therefore,
this
study
optimized
extraction
protocol
from
using
magnetic
bead-based
methods
by
refining
key
parameters,
including
bead
type,
binding
buffer
composition,
particle
size,
volume,
concentration,
washing
conditions,
elution
conditions.
optimal
conditions
were
determined
as
follows:
silica-coated
OH-500
beads,
containing
3
mol/L
guanidine
isothiocyanate,
50
µL
70%
ethanol
washing,
water
at
56°C
15
minutes.
Compared
to
commercial
kits,
method
improved
efficiency
nearly
10%
while
demonstrating
high
reproducibility
(CV
<
5%).
This
refined
provides
an
technical
solution
oil.
Язык: Английский
Comparison of Fuel Properties of Alternative Fuels from Insect Lipids and Their Blending with Diesel Fuel
Sustainability,
Год журнала:
2025,
Номер
17(10), С. 4295 - 4295
Опубликована: Май 9, 2025
Drop-in
fuels
are
renewable
alternatives
that
can
be
integrated
into
an
existing
fuel
infrastructure
without
modification.
Among
these,
synthesized
from
hydroprocessed
lipids
have
garnered
significant
attention
owing
to
their
compatibility
with
petroleum-based
diesel.
In
this
study,
we
investigated
the
feasibility
of
hydrodeoxygenated
insect
oil
(HIO),
derived
black
soldier
fly
larvae
(Hermetia
illucens;
BSFL),
as
a
drop-in
for
diesel
blend.
The
optimal
growth
conditions
BSFL
were
studied
maximize
lipid
production,
and
extracted
was
subjected
hydrodeoxygenation
(HDO)
via
catalytic
reaction.
HIO
blended
commercial
at
ratios
5–30%,
its
properties
compared
A
detailed
property
analysis
conducted
5%
blend
evaluate
suitability
fuel.
Characterization
fuels’
physicochemical
carried
out
assess
potential
insect-derived
applications.
Язык: Английский
Jackfruit-like ZnO gas sensor for monitoring ethyl formate emissions from fish meal
Sensor Review,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 7, 2025
Purpose
This
paper
aims
to
develop
a
highly
sensitive
resistive
gas
sensor
for
accurately
detecting
ethyl
formate
achieve
reliable
and
real-time
monitoring
of
fish
meal
spoilage.
Design/methodology/approach
Based
on
the
one-step
solvothermal
reduction
method,
in
specific
triethylene
glycol
solution
environment
high
temperature,
3D
ZnO
sensing
material
with
jackfruit-like
structure
was
prepared
meal.
Findings
The
based
displays
response
(69.68–100
ppm)
at
280°C
43%
RH
good
(12.18–100
80%
RH,
ultra-low
detection
limit
10
ppb
excellent
selectivity,
repeatability
long-term
stability.
mechanism
is
due
gain
or
loss
electrons
caused
by
surface
reaction.
unique
structure,
abundant
oxygen
vacancies
large
area
may
be
another
factor
contributing
its
performance.
Originality/value
authors
first
developed
an
sensor,
results
were
compared
previously
published
data.
analysis
showed
demonstrated
work
highlights
potential
sensors
evaluate
quality.
Язык: Английский
Rapid and noncontact identification of soybean flour in edible insect using NIR spectral imager: A case study in Protaetia brevitarsis seulensis powder
Food Control,
Год журнала:
2024,
Номер
169, С. 111019 - 111019
Опубликована: Ноя. 8, 2024
Язык: Английский
Comparison of machine learning models for classifying edible oils using Fourier‐transform infrared spectroscopy
Bulletin of the Korean Chemical Society,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 23, 2024
Abstract
Accurate
classification
and
authentication
of
edible
oils
are
essential
for
maintaining
product
quality,
ensuring
consumer
safety,
preserving
market
integrity.
Therefore,
this
study
aims
to
propose
Fourier‐transform
infrared
(FT‐IR)
spectroscopy,
combined
with
advanced
machine
learning
models,
as
a
rapid
non‐destructive
technique
classifying
oils.
The
FT‐IR
spectra
seven
oil
types
were
analyzed
across
three
spectral
regions:
the
full
range,
C‐H
stretching
fingerprint
region.
Both
absorbance
second
derivative
used
evaluate
influence
preprocessing
on
accuracy.
Six
models—principal
component
analysis
followed
by
linear
discriminant
(PCA‐LDA),
k‐nearest
neighbors,
decision
tree,
random
forest,
eXtreme
Gradient
Boosting,
support
vector
machines
(SVM)—were
employed
classify
oils,
achieving
training
accuracies
96.4%–100%
testing
88.1%–100%.
enhanced
model
performance
improving
resolution
overlapping
peaks,
particularly
in
CH
CO
regions.
Additionally,
SHapley
Additive
exPlanations
further
revealed
most
critical
features
influencing
predictions,
offering
valuable
insights
into
decision‐making
processes.
This
demonstrates
effectiveness
combining
preprocessing,
techniques
findings
highlight
benefits
enhancing
superior
PCA‐LDA
SVM
models.
These
results
offer
robust
framework
advancing
emphasize
potential
artificial
intelligence
food
quality
control.
Язык: Английский