Research Square (Research Square),
Год журнала:
2022,
Номер
unknown
Опубликована: Ноя. 30, 2022
Abstract
In
this
study,
a
novel
analytical
approach
was
developed
for
detecting
and
predicting
adulteration
of
goat
milk
with
cow
using
combination
voltammetric
fingerprints
chemometrics
analysis.
The
fresh
samples
were
obtained
from
local
farmers
analyzed
cyclic
voltammetry
technique
glassy
carbon
electrode
as
the
working
KClO
4
supporting
electrolyte.
fingerprint
both
showed
an
anodic
peak
between
potential
range
0.40
to
0.75
V
vs.
Ag/AgCl.
This
is
mainly
attributed
several
electroactive
species
contained
in
samples.
current
intensities
at
0
+
1
vs
Ag/AgCl
further
selected
due
majority
components
having
their
oxidation
range.
pre-treated
maximum
normalization
submitted
chemometric
tools
multivariate
Orthogonal
partial
least
square-discriminant
analysis
provided
clear
discrimination
milk.
Meanwhile,
prediction
achieved
squares
regression
These
enabled
satisfactory
successful
model
predict
percentage
adulterants
demonstrated
results
revealed
that
might
offer
low-cost,
simple,
rapid
which
be
possible
promising
method
detection
adulterants.
Advanced Intelligent Systems,
Год журнала:
2023,
Номер
5(8)
Опубликована: Апрель 21, 2023
Technology
advancements
in
energy
storage,
photocatalysis,
and
sensors
have
generated
enormous
impedimetric
data.
Electrochemical
impedance
spectroscopy
(EIS)
results
play
an
essential
role
analyzing
the
interfacial
properties
of
materials.
Nonetheless,
many
situations,
data
is
misinterpreted
due
to
complexity
electrochemical
system
or
compromise
between
experimental
result
theoretical
model,
resulting
partiality
interpretation
process,
especially
for
results.
Typically,
experimenter
interprets
using
a
searching
approach
based
on
model
until
best‐fitting
obtained,
which
time‐consuming
errors
can
occur.
To
reduce
misinterpretation
by
experimenter,
herein,
machine‐learning
strategy
demonstrated
classification
EIS
circuit
parameter
prediction
deep
neural
network
(DNN).
The
DNN
shows
highly
accurate
classifier
commonly
used
with
average
area
under
receiver
operating
characteristic
curve
more
than
0.95.
Additionally,
demonstrates
high
accuracy
parameters
complex
system,
maximum
R
2
0.999.
These
reveal
that
may
open
new
room
studying
systems.
Botanical
sourcing
seriously
impacts
the
safety
and
potency
of
herbal
medicines,
restricting
development
traditional
Chinese
medicinal
industry.
Rapid
convenient
identification
plant
resources
is
important
to
address
this
problem.
Herein,
we
innovated
a
portable,
intelligent,
integrated
platform,
termed
Smart
Electronic
Tongue
(SET),
for
right
recognizing
Dendrobium
bonsai
different
subspecies
origins.
The
device
miniaturized
with
hollow
microneedle
array
leaf-face
extraction,
press-pumping
pipeline
on-demand
sample
sap
suction,
plus
five-electrode
chip
electrochemical
readout.
Differential
pulse
voltammograms
on
three
simplexes
self-assembled
monolayers
as
well
their
multiplexed
configurations
are
specialized
generate
high-dimensional
fingerprinting
subtypes.
Taking
advantage
machine
learning
algorithms,
platform
achieves
automatic
authentication
over
eight
varieties
discriminatory
accuracy
95%.
Given
this,
anticipate
that
such
bionic
sensing
setup
would
offer
versatile
solution
toward
classification
diverse
officinals
at
point
need.
PeerJ,
Год журнала:
2025,
Номер
13, С. e19058 - e19058
Опубликована: Апрель 4, 2025
To
strengthen
the
agriculture
sector,
it
is
crucial
to
combine
efforts
of
industrialization
(field
mechanization
and
fertilizer
production),
technology
(genome
editing
manipulation),
information
sector
(for
application
current
technologies
in
precision
agriculture).
The
challenge
modern
sustainable
increasing
agricultural
output
while
using
least
amount
resources
capital
expenditure
possible
considering
variables
contributing
environmental
damage.
Different
factors
adversely
affect
medicinal
plant
populations,
leading
extinction
these
valuable
species.
These
difficulties
drew
attention
international
scientific
community
farm
sustainability
energy
efficiency
studies
that
put
forth
idea
(site-specific
crop
management)
plants.
It
a
systems-based
method
monitors
responds
changes
intra-
inter-field
conditions
for
environmentally
friendly
optimum
output.
Farming
systems
have
significantly
benefited
from
visualization
morphological
analysis
areas
(both
open
fields
greenhouse
experiments)
remote
sensing
technology,
geographic
(GIS),
scouting,
variable
rate
(VRT),
Global
Positioning
System
(GPS).
form
backbone
fourth
technological
revolution,
Agriculture
4.0.
This
review
concisely
summarizes
innovative
technologies’
use
potential
future
advancements
intended
researchers,
professionals
cultivation,
herbal
medicine
research,
science,
related
fields.
Analytical Chemistry,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 10, 2025
Selectively
differential
identification
of
natural
components
with
similar
chemical
structures
in
complex
matrices
is
still
a
challenging
task
by
conventional
analytical
strategies.
Herein,
we
developed
landmark
(DaXing
airport)-inspired
laser
engraving
sensor
array
that
combined
multiplex
electrochemical
fingerprinting
technology
one-dimensional
convolutional
neural
network
(1D-CNN)
for
rapidly
precise
detection
three
tea
polyphenols
and
the
differentiation
24
distinct
types
Chinese
teas.
This
sensing
strategy
employs
diverse
different
working
electrode
configurations
as
multivariate
(bare
electrode,
nanoenzyme
bioenzyme
electrode),
generating
fingerprints
samples.
By
utilizing
self-designed
1D-CNN
algorithm
feature
extraction,
significantly
improved,
thereby
enhancing
predictive
accuracy
platform
successfully
achieves
polyphenols,
distinguishing
six
series
varieties
rates
98.84
97.68%,
respectively.
Notably,
deep
learning-assisted
multiplexed
technique
better
compared
other
representative
machine
learning
methods.
advancement
offers
rapid
reliable
approach
to
development
authentication
processes
agricultural
products.
