10 research outputs found
Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning
This study examines the volatilome of good and oxidised coffee samples from two commercial coffee species (i.e., Coffea arabica (arabica) and Coffea canephora (robusta)) in different packagings (i.e., standard with aluminium barrier and Eco-caps) to define a fingerprint potentially describing their oxidised note, independently of origin and packaging. The study was carried out using HS-SPME-GC-MS/FPD in conjunction with a machine learning data processing. PCA and PLS-DA were used to extrapolate 25 volatiles (out of 147) indicative of oxidised coffees, and their behaviour was compared with literature data and critically discussed. An increase in four volatiles was observed in all oxidised samples tested, albeit to varying degrees depending on the blend and packaging: acetic and propionic acids (pungent, acidic, rancid), 1-H-pyrrole-2-carboxaldehyde (musty), and 5-(hydroxymethyl)-dihydro-2(3H)-furanone
A Further Tool To Monitor the Coffee Roasting Process: Aroma Composition and Chemical Indices
Coffee quality is strictly related to its flavor and
aroma developed
during the roasting process, that, in their turn, depend on variety
and origin, harvest and postharvest practices, and the time, temperature,
and degree of roasting. This study investigates the possibility of
combining chemical (aroma components) and physical (color) parameters
through chemometric approaches to monitor the roasting process, degree
of roasting, and aroma formation by analyzing a suitable number of
coffee samples from different varieties and blends. In particular,
a correlation between the aroma composition of roasted coffee obtained
by HS-SPME-GC-MS and degree of roasting, defined by the color, has
been researched. The results showed that aroma components are linearly
correlated to coffee color with a correlation factor of 0.9387. The
study continued looking for chemical indices: 11 indices were found
to be linearly correlated to the color resulting from the roasting
process, the most effective of them being the 5-methylfurfural/2-acetylfuran
ratio (index)
Chemometric Modeling of Coffee Sensory Notes through Their Chemical Signatures: Potential and Limits in Defining an Analytical Tool for Quality Control
Aroma
is a primary hedonic aspect of a good coffee. Coffee aroma quality
is generally defined by cup tasting, which however is time-consuming
in terms of panel training and alignment and too subjective. It is
challenging to define a relationship between chemical profile and
aroma sensory impact, but it might provide an objective evaluation
of industrial products. This study aimed to define the chemical signature
of coffee sensory notes, to develop prediction models based on analytical
measurements for use at the control level. In particular, the sensory
profile was linked with the chemical composition defined by HS-SPME-GC-MS,
using a chemometric-driven approach. The strategy was found to be
discriminative and informative, identifying aroma compounds characteristic
of the selected sensory notes. The predictive ability in defining
the sensory scores of each aroma note was used as a validation tool
for the chemical signatures characterized. The most reliable models
were those obtained for woody, bitter, and acidic properties, whose
selected volatiles reliably represented the sensory note fingerprints.
Prediction models could be exploited in quality control, but compromises
must be determined if they are to become complementary to panel tasting