Environmental Sciences Europe,
Journal Year:
2023,
Volume and Issue:
35(1)
Published: Sept. 4, 2023
Abstract
Increasing
production
and
use
of
chemicals
awareness
their
impact
on
ecosystems
humans
has
led
to
large
interest
for
broadening
the
knowledge
chemical
status
environment
human
health
by
suspect
non-target
screening
(NTS).
To
facilitate
effective
implementation
NTS
in
scientific,
commercial
governmental
laboratories,
as
well
acceptance
managers,
regulators
risk
assessors,
more
harmonisation
is
required.
address
this,
NORMAN
Association
members
involved
activities
have
prepared
this
guidance
document,
based
current
state
knowledge.
The
document
intended
provide
performing
high
quality
studies
data
interpretation
while
increasing
promise
but
also
pitfalls
challenges
associated
with
these
techniques.
Guidance
provided
all
steps;
from
sampling
sample
preparation
analysis
chromatography
(liquid
gas—LC
GC)
coupled
via
various
ionisation
techniques
high-resolution
tandem
mass
spectrometry
(HRMS/MS),
through
evaluation
reporting
context
NTS.
Although
most
experience
within
network
still
involves
water
polar
compounds
using
LC–HRMS/MS,
other
matrices
(sediment,
soil,
biota,
dust,
air)
instrumentation
(GC,
ion
mobility)
are
covered,
reflecting
rapid
development
extension
field.
Due
ongoing
developments,
different
questions
addressed
manifold
use,
feel
that
no
standard
operation
process
can
be
at
stage.
However,
appropriate
analytical
methods,
processing
databases
commonly
compiled
workflows
introduced,
limitations
discussed
recommendations
cases
provided.
Proper
assurance,
quantification
without
reference
standards
results
clear
confidence
identification
assignment
complete
together
a
glossary
definitions.
community
greatly
supports
sharing
experiences
open
science
hopes
guideline
effort.
Science,
Journal Year:
2020,
Volume and Issue:
367(6476), P. 392 - 396
Published: Jan. 24, 2020
Despite
extensive
evidence
showing
that
exposure
to
specific
chemicals
can
lead
disease,
current
research
approaches
and
regulatory
policies
fail
address
the
chemical
complexity
of
our
world.
To
safeguard
future
generations
from
increasing
number
polluting
environment,
a
systematic
agnostic
approach
is
needed.
The
“exposome”
concept
strives
capture
diversity
range
exposures
synthetic
chemicals,
dietary
constituents,
psychosocial
stressors,
physical
factors,
as
well
their
corresponding
biological
responses.
Technological
advances
such
high-resolution
mass
spectrometry
network
science
have
allowed
us
take
first
steps
toward
comprehensive
assessment
exposome.
Given
increased
recognition
dominant
role
nongenetic
factors
play
in
an
effort
characterize
exposome
at
scale
comparable
human
genome
warranted.
Critical Reviews in Food Science and Nutrition,
Journal Year:
2020,
Volume and Issue:
61(9), P. 1448 - 1469
Published: May 22, 2020
As
one
of
the
omics
fields,
metabolomics
has
unique
advantages
in
facilitating
understanding
physiological
and
pathological
activities
biology,
physiology,
pathology,
food
science.
In
this
review,
based
on
developments
analytical
chemistry
tools,
cheminformatics,
bioinformatics
methods,
we
highlight
current
applications
safety,
authenticity
quality,
traceability.
Additionally,
combined
use
with
other
techniques
for
"foodomics"
is
comprehensively
described.
Finally,
latest
advances,
practical
challenges
limitations,
requirements
related
to
application
are
critically
discussed,
providing
new
insight
into
analysis.
Metabolites,
Journal Year:
2020,
Volume and Issue:
10(6), P. 243 - 243
Published: June 13, 2020
The
metabolome
of
an
organism
depends
on
environmental
factors
and
intracellular
regulation
provides
information
about
the
physiological
conditions.
Metabolomics
helps
to
understand
disease
progression
in
clinical
settings
or
estimate
metabolite
overproduction
for
metabolic
engineering.
most
popular
analytical
metabolomics
platform
is
mass
spectrometry
(MS).
However,
MS
data
analysis
complicated,
since
metabolites
interact
nonlinearly,
structures
themselves
are
complex.
Machine
learning
methods
have
become
immensely
statistical
due
inherent
nonlinear
representation
ability
process
large
heterogeneous
rapidly.
In
this
review,
we
address
recent
developments
using
machine
processing
spectra
show
how
generates
new
biological
insights.
particular,
supervised
has
great
potential
research
because
supply
quantitative
predictions.
We
review
here
commonly
used
tools,
such
as
random
forest,
support
vector
machines,
artificial
neural
networks,
genetic
algorithms.
During
steps,
help
peak
picking,
normalization,
missing
imputation.
For
knowledge-driven
analysis,
contributes
biomarker
detection,
classification
regression,
biochemical
pathway
identification,
carbon
flux
determination.
Of
important
relevance
combination
different
omics
identify
contributions
various
regulatory
levels.
Our
overview
publications
also
highlights
that
quality
determines
quality,
but
adds
challenge
choosing
right
model
data.
applied
MS-based
ease
can
decisions,
guide
engineering,
stimulate
fundamental
discoveries.
