Journal of Food Protection,
Journal Year:
2024,
Volume and Issue:
87(12), P. 100396 - 100396
Published: Nov. 8, 2024
Conventional
detection
methods
require
the
isolation
and
enrichment
of
bacteria,
followed
by
molecular,
biochemical,
or
culture-based
analysis.
To
address
some
limitations
conventional
methods,
this
study
develops
a
machine
learning
(ML)
approach
to
analyze
excitation-emission
matrix
(EEM)
fluorescence
data
generated
based
on
bacteriophage
T7
Escherichia
coli
interactions
for
in-situ
live
bacteria
in
presence
fresh
produce
homogenate.
We
trained
classification
models
using
various
ML
algorithms
3-D
EEM
with
their
phage.
These
algorithms,
including
linear
Support
Vector
Classifier
(SVC)
Random
Forest
(RF),
demonstrate
high
accuracy
(>0.85)
detecting
E.
at
10
Water Research,
Journal Year:
2024,
Volume and Issue:
268, P. 122616 - 122616
Published: Oct. 12, 2024
Contaminants
of
emerging
concern
(CEC)
pose
significant
challenges
to
environmental
and
human
health.
The
development
the
wastewater
reuse
sector,
coupled
with
progressively
stringent
regulations,
needs
innovative
systems
that
integrate
advanced
treatment
processes
in-situ
real-time
monitoring
CEC.
This
study
investigates
use
a
tryptophan-like
fluorescence
sensor
for
online
CEC
within
pilot
plant
employing
O
The
degradation
of
aromatic
organic
compounds
in
aquatic
environments
is
critical
due
to
their
persistence
and
toxicity.
This
study
establishes
a
machine
learning
(ML)-driven
quantitative
structure–activity
relationship
model
predict
the
pseudo-first-order
reaction
rate
constants
(K)
for
UV–H2O2
organics.
A
data
set
comprising
134
experimental
observations
30
was
constructed,
integrating
conditions,
quantum
chemical
parameters,
physicochemical
properties.
Among
six
ML
algorithms
evaluated,
gradient
boosting
decision
tree
emerged
as
optimal
model,
with
feature
importance
analysis
identifying
H2O2
concentration,
topological
polar
surface
area,
q(C)min
dominant
factors.
Theoretical
calculations
supported
by
linking
higher
reactivity
o,p'-dicofol
lower
energy
gaps
elevated
electrophilic
susceptibility.
Additionally,
establishment
interpretable
expressions
not
only
provides
transparency
clarity
predictions
but
also
aids
economic
analysis,
which
highlighted
that
mildly
acidic
pH
low
UV
light
intensity,
along
suitable
concentrations,
are
cost-effective
conditions
process.
work
bridges
chemistry
elucidate
mechanisms,
offering
rapid
resource-efficient
tool
optimizing
advanced
oxidation
processes.
