Journal of Agricultural and Food Chemistry,
Journal Year:
2024,
Volume and Issue:
72(43), P. 24109 - 24129
Published: Oct. 16, 2024
The
brown
marmorated
stink
bug
(Halyomorpha
halys)
poses
a
significant
threat
to
hazelnut
crops
by
affecting
kernel
development
and
causing
quality
defects,
reducing
the
market
value.
While
previous
studies
have
identified
bitter-tasting
compounds
in
affected
kernels,
impact
of
feeding
on
metabolome,
particularly
concerning
aroma
precursors,
remains
underexplored.
This
study
aims
map
nonvolatile
metabolome
volatilome
samples
obtained
caging
H.
halys
different
cultivars
two
locations
identify
markers
for
diagnosing
damage.
Using
multiomic
approach
involving
headspace
solid-phase
microextraction
(HS-SPME),
comprehensive
two-dimensional
gas
chromatography-time-of-flight
mass
spectrometry
(GC
×
GC-TOF
MS),
liquid
chromatography-high-resolution
(LC-HRMS),
both
raw
roasted
hazelnuts
are
analyzed,
with
artificial
intelligence
(AI)
machine
learning
tools
employed
explore
data
correlations.
finds
that
exhibit
high
chemical
complexity
classes
such
as
aldehydes,
ketones,
alcohols,
terpenes
hazelnuts.
Multivariate
analysis
indicates
orchard
location
significantly
impacts
followed
damage
type,
cultivar
differences
being
less
pronounced.
Partial
least-squares
discriminant
(PLS-DA)
models
achieve
predictive
accuracy
(99%)
type
(≈80%),
showing
highest
accuracy.
Correlation
matrices
reveal
relationships
between
metabolites
samples,
suggesting
potential
could
guide
assessment
mitigation
strategies.
Data
fusion
techniques
further
enhance
classification
performance,
predicting
underscoring
integrating
multiple
sets
assessment.
Food Research International,
Journal Year:
2024,
Volume and Issue:
194, P. 114873 - 114873
Published: Aug. 14, 2024
This
study
investigates
the
metabolome
of
high-quality
hazelnuts
(Corylus
avellana
L.)
by
applying
untargeted
and
targeted
profiling
techniques
to
predict
industrial
quality.
Utilizing
comprehensive
two-dimensional
gas
chromatography
liquid
coupled
with
high-resolution
mass
spectrometry,
research
characterizes
non-volatile
(primary
specialized
metabolites)
volatile
metabolomes.
Data
fusion
techniques,
including
low-level
(LLDF)
mid-level
(MLDF),
are
applied
enhance
classification
performance.
Principal
component
analysis
(PCA)
partial
least
squares
discriminant
(PLS-DA)
reveal
that
geographical
origin
postharvest
practices
significantly
impact
metabolome,
while
storage
conditions
duration
influence
volatilome.
The
demonstrates
MLDF
approaches,
particularly
supervised
MLDF,
outperform
single-fraction
analyses
in
predictive
accuracy.
Key
findings
include
identification
metabolites
patterns
causally
correlated
hazelnut's
quality
attributes,
them
aldehydes,
alcohols,
terpenes,
phenolic
compounds
as
most
informative.
integration
multiple
analytical
platforms
data
methods
shows
promise
refining
assessments
optimizing
processing
for
food
industry.
Food Chemistry X,
Journal Year:
2024,
Volume and Issue:
24, P. 101872 - 101872
Published: Oct. 2, 2024
Foodomics
is
an
interdisciplinary
field
that
integrates
various
omics
technologies
to
explore
the
complex
relationship
between
food
and
human
health
in
depth.
This
approach
offers
valuable
insights
into
biochemical,
molecular,
cellular
composition
of
by
employing
advanced
techniques.
Its
applications
span
industry
health,
including
efforts
combat
malnutrition,
provide
dietary
recommendations,
ensure
safety.
paper
critically
examines
successful
foodomics
across
areas
such
as
safety,
quality,
traceability,
processing,
bioactivity.
It
highlights
crucial
role
metabolomics,
proteomics,
transcriptomics
achieving
a
comprehensive
understanding
components,
their
functions,
interactions
with
biology.
