The BSF is delighted to welcome prof. Chiara Cordero for her tal
k: "Artificial Intelligence Smelling: can multidimensional chromatography p
lay a (key) role?"
Abstract:
Multidimensional ch
romatography has been successfully applied for many years as core technolog
y for odorant patterns characterisation in the flavour and fragrance field.
Very recently a sensomics-based expert system (SEBES) [1] capable to predi
ct key-aroma signatures of food without using human olfaction has been impl
emented in a comprehensive two-dimensional gas chromatography (GC×GC) platf
orm. The strategy, also referred to as Artificial Intelligence Smelling, co
nceptually opens many opportunities for odorants pattern recognition, accur
ate quantification avoiding time-consuming sample preparation/extraction st
eps, and samples sensory qualification/discrimination based on computer vis
ion strategies. Moreover, due to the information density of each analysis,
fingerprinting can be extended to different samples features (e.g., origin
traceability, shelf-life evolution, processing impact etc.).
The cont
ribution illustrates the potentials of MDGC platforms in the context of Art
ificial Intelligence Smelling and extended untargeted/targeted fingerprinti
ng through a key-food product for confectionery industry: high-quality haze
lnuts (Corylus avellana L.).
Sensory quality of raw and roasted hazel
nuts depends to the presence of non-volatile precursors patterns and to (ke
y)-aroma compounds in well-balanced proportions. Moreover, odorants derivin
g by lipid oxidation processes and/or enzyme-catalysed reactions carried ou
t by bacteria and moulds [2,3] might evoke unpleasant notes with detrimenta
l impact on kernels quality.
Odorant patterns, strongly correlated to
sensorial qualities, can be effectively detected by computer vision with c
omposite-class images generated combining 2D chromatographic signals from s
amples showing specific features (e.g., good reference kernels, spoiled ker
nels with mould, rancid, stale, solvent-like odors). On the other hand, the
comprehensive exploration of the detectable volatilome (including all vola
tiles) by untargeted/targeted fingerprinting drives machine learning toward
samples identification highlighting botanical/geographical signatures [4],
shelf-life trajectories, and climate impact on hazelnut metabolome.
The virtuous synergy between Academic research and industry defines new qua
lity concepts, accelerates innovation, and shapes the future of our food. T
he key is the implementation of modern omics concepts and strategies that b
ring food analysis closer to flavour characterisation.
The speaker:<
/h3>
Chiara Cordero is Full Professor of Food Chemistry at the University
of Turin (Italy).
Research interests focus on: (a)
development of innovative and advanced instrumental configurations for det
ailed chemical characterisation of food (profiling and fingerprinting strat
egies based on multidimensional platforms); (b) development and application
of miniaturized, fully automated and solvent-free sample preparation appro
aches (Solid Phase Microextraction SPME, Stir Bar Sorptive Extraction SBSE)
for sensomic characterisation of food; (c) discovery of food intake marker
s (low molecular weight metabolites, Advanced Glycation End-products and vo
latiles in bio-fluids) and metabolic fingerprint after diet induced derange
ments. In 2008, she was awarded with the "Leslie S. Ettre” Award for “origi
nal research in capillary gas chromatography with an emphasis on environmen
tal and food safety" and in 2014 received the “John B. Phillips Award” for
her research activity in the field of comprehensive two-dimensional gas chr
omatography (GC×GC). In 2016 Chiara was included by The Analytical Scientis
t in the “Women Power List” as one of the 50 most influencing women in the
analytical sciences.
References
Nicolotti, L.;
Mall, V.; Schieberle, P. J. Agric. Food Chem., 67 (2019) 4011–4022
Kiefl, J.; Pollner, G.; Schieberle, P. J. Agric. Food Chem. 61 (2013) 5226–
5235.
CONTACT:This email address is being protected from spambots. You need JavaScript enabled to view it.
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