The aim
of
this
work
was
to explore
the
possibility
to use
the
e-
nose
based
approach
to assess
the
quality
a food
commodity
rarely
assayed
with
gas
sensors
as
chocolate,
through
the
detection
of
off-
flavours.
For
this
purpose
two
different
types
of
gas
sensor
arrays,
carrying
different
ligands
have
been
used
in order
to compare
their
performances.
Porphyrins
exhibit
unique
binding
properties
that
are
widely
exploited
in nature
to accomplish
functions
essential
for
life;
the
potential
mimic
of
these
functions
with
synthetic
counter-
parts
has
provided
the
basis
of
many
kinds
of
chemical
sensors.
The
porphyrin
molecular
framework
offers
a wide
range
of
interaction
mechanisms
for
analyte
binding,
spanning
from
the
weak
Van
der
Waals
forces
to hydrogen
bond,
to ?–? interactions
and
finally
to
the
coordination
to the
central
metal
ion[14]. On
the
other
hand
the
ability
of
gold
nanoparticles
modified
with
oligopeptides
has
been
recently
proposed
[15–17]. Encouraging
results
have
been
obtained
due
to the
ease
of
derivatisation,
high
number
of
possible
configu-
ration
and
possibility
to design
via
molecular
modelling
the
ligands.
The
analysis
of
the
headspace
of
different
chocolate
samples
was
optimised
and
carried
out
by
using
the
two
different
arrays
while
GC-MS
was
carried
out
on
the
same
samples
in order
to obtain
a
conventional
characterisation
of
the
aroma
pattern
and
compare
the
data.
Sensors
data
were
used
to define
a Partial
Least
Squares-
Discriminant
Analysis
(PLS-DA)
classifier
aimed
at recognizing
the
artificially
prepared
off-flavoured
with
respect
to standard
sam-
ples.