Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument
Biosensors,
Год журнала:
2025,
Номер
15(3), С. 168 - 168
Опубликована: Март 5, 2025
Raman
spectroscopy
enables
fast,
label-free,
qualitative,
and
quantitative
observation
of
the
physical
chemical
properties
various
substances.
Here,
we
present
a
785
nm
custom-built
instrument
designed
for
sensing
applications
in
400–1700
cm−1
spectral
range.
We
demonstrate
performance
by
fingerprinting
14
pesticide
reference
samples
with
over
twenty
technical
repeats
per
sample.
molecular
fingerprints
pesticides
comprehensively
distinguish
similarities
differences
among
them
using
multivariate
analysis
machine
learning
techniques.
The
same
were
additionally
investigated
commercial
532
to
see
potential
variations
peak
shifts
intensities.
developed
unique
fingerprint
library
pesticides,
which
is
documented
this
study
first
time.
comparison
shows
importance
selecting
an
appropriate
excitation
wavelength
based
on
target
analyte.
While
may
be
advantageous
certain
compounds
due
resonance
enhancement,
generally
more
effective
reducing
fluorescence
achieving
clearer
spectra.
By
employing
techniques
like
Random
Forest
Classifier,
automates
classification
different
streamlining
data
interpretation
non-experts.
Applying
such
combined
wider
range
agricultural
chemicals,
clinical
biomarkers,
or
pollutants
could
provide
impetus
develop
monitoring
technologies
food
safety,
diagnostics,
cross-industry
quality
control
applications.
Язык: Английский
Highly reproducible surface-enhanced raman scattering for detecting S-containing pesticides in river water using layer-by-layer substrates
Journal of Industrial and Engineering Chemistry,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 1, 2025
Язык: Английский
Computational Tool for Curve Smoothing Methods Analysis and Surface Plasmon Resonance Biosensor Characterization
Inventions,
Год журнала:
2025,
Номер
10(2), С. 31 - 31
Опубликована: Апрель 18, 2025
Biosensors
based
on
the
surface
plasmon
resonance
(SPR)
technique
are
widely
used
for
analyte
detection
due
to
their
high
selectivity
and
real-time
capabilities.
However,
conventional
SPR
spectrum
analysis
can
be
affected
by
experimental
noise
environmental
variations,
reducing
accuracy
of
results.
To
address
these
limitations,
this
study
presents
development
an
open-source
computational
tool
optimize
biosensor
characterization,
implemented
using
MATLAB
App
Designer
(Version
R2024b).
The
enables
importation
data,
application
different
smoothing
methods,
integration
traditional
hybrid
approaches
enhance
in
determining
angle.
proposed
offers
several
innovations,
such
as
both
(angle
vs
wavelength)
modes,
implementation
four
advanced
curve
techniques,
including
Gaussian
filter,
Savitzky–Golay,
splines,
EWMA,
well
a
user-friendly
graphical
interface
supporting
data
visualization,
import,
result
export.
Unlike
approaches,
framework
multidimensional
optimization
parameters,
resulting
greater
robustness
detecting
conditions.
Experimental
validation
demonstrated
marked
reduction
spectral
improved
consistency
angle
across
results
confirm
effectiveness
practical
relevance
tool,
contributing
advancement
analysis.
Язык: Английский
Raman spectroscopy – a visit to the literature on plant, food, and agricultural studies
Journal of the Science of Food and Agriculture,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 12, 2024
Raman
spectroscopy,
a
fast,
non-invasive,
and
label-free
optical
technique,
has
significantly
advanced
plant
food
studies
precision
agriculture
by
providing
detailed
molecular
insights
into
biological
tissues.
Utilizing
the
scattering
effect
generates
unique
spectral
fingerprints
that
comprehensively
analyze
tissue
composition,
concentration,
structure.
These
are
obtained
without
chemical
additives
or
extensive
sample
preparation,
making
spectroscopy
particularly
suitable
for
in-field
applications.
Technological
enhancements
such
as
surface-enhanced
scattering,
Fourier-transform-Raman
chemometrics
have
increased
sensitivity
precision.
other
advancements
enable
real-time
monitoring
of
compound
translocation
within
plants
improve
detection
contaminants,
essential
safety
crop
optimization.
Integrating
agronomic
practices
is
transformative
marks
shift
toward
more
sustainable
farming
activities.
It
assesses
quality
-
well
originated
from
production
early
stress
supports
targeted
breeding
programs.
Advanced
data
processing
techniques
machine
learning
integration
efficiently
handle
complex
data,
dynamic
view
conditions
health
under
varying
environmental
stresses.
As
global
faces
dual
challenges
increasing
productivity
sustainability,
stands
out
an
indispensable
tool,
enhancing
practices'
precision,
safety,
compatibility.
This
review
intended
to
select
briefly
comment
on
outstanding
literature
give
researchers,
students,
consultants
reference
works
in
mainly
focused
plant,
food,
sciences.
©
2024
Society
Chemical
Industry.
Язык: Английский