Since its introduction, comprehensive two-dimensional gas chromatography (GC×GC), has unrevealed its potentials in many fields helping scientists to better understand the Nature’s complexity, facilitating highly-informative screenings, supporting markers discovery in omics applications and offering many opportunities to implement system biology-like strategies for investigation, the so-called integrationist approach [1]. In food “omics” the analytical platform design and configuration plays a key role to achieve the suitable information capacity, resolution and sensitivity to answer the many questions posed by application needs. The contribution deals with the challenging task of designing a multidimensional platform for food metabolomics [2] implemented by an effective data processing workflow. A strategy capable to answer many questions about product qualities (e.g., sensory quality, freshness, authenticity, presence of sensory defects etc.) with a single measure realized by combining many analytical dimensions (e.g., sample preparation, separation, multiple detection, olfactometry, etc.). Within this context, the key-role of Artificial Intelligence (AI) algorithms for computer vision (i.e., “…a field of AI that enables computers and systems to derive meaningful information from digital images…” [3]) and smelling (e.g., AI smelling machine [4]) is discussed and proof-of-evidence on the feasibility and effectiveness of such “comprehensive” approaches presented through the authors research experience on high-quality extra-virgin olive oil.

UNLOCKING THE FUTURE OF COMPREHENSIVE TWO-DIMENSIONAL CHROMATOGRAPHY IN FOOD-OMICS BY ARTIFICIAL INTELLIGENCE ALGORITHMS

C. Cordero;S. Squara;A. Caratti;E. Alladio;M. Vincenti;Humberto Bizzo;C. Bicchi
2022-01-01

Abstract

Since its introduction, comprehensive two-dimensional gas chromatography (GC×GC), has unrevealed its potentials in many fields helping scientists to better understand the Nature’s complexity, facilitating highly-informative screenings, supporting markers discovery in omics applications and offering many opportunities to implement system biology-like strategies for investigation, the so-called integrationist approach [1]. In food “omics” the analytical platform design and configuration plays a key role to achieve the suitable information capacity, resolution and sensitivity to answer the many questions posed by application needs. The contribution deals with the challenging task of designing a multidimensional platform for food metabolomics [2] implemented by an effective data processing workflow. A strategy capable to answer many questions about product qualities (e.g., sensory quality, freshness, authenticity, presence of sensory defects etc.) with a single measure realized by combining many analytical dimensions (e.g., sample preparation, separation, multiple detection, olfactometry, etc.). Within this context, the key-role of Artificial Intelligence (AI) algorithms for computer vision (i.e., “…a field of AI that enables computers and systems to derive meaningful information from digital images…” [3]) and smelling (e.g., AI smelling machine [4]) is discussed and proof-of-evidence on the feasibility and effectiveness of such “comprehensive” approaches presented through the authors research experience on high-quality extra-virgin olive oil.
2022
XXIX Congresso della Divisione di Chimica Analitica della Società Chimica Italiana - Analitica 2022
Milazzo (ME)
11-15 Settembre 2022
Atti del XXIX Congresso della Divisione di Chimica Analitica della Società Chimica Italiana
Società Chimica Italiana 2022
60
60
978-88-94952-30-8
comprehensive two-dimensional gas chromatography, Artificial Intelligence (AI), computer vision, food sensory quality
C. Cordero, S. Squara, A. Caratti, E. Alladio, M. Vincenti, Humberto Bizzo, Stephen E. Reichenbach, C. Bicchi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1876765
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