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 MULTI-DIMENSIONAL GAS CHROMATOGRAPHY IN FOOD-OMICS BY ARTIFICIAL INTELLIGENCE ALGORITHMS

Chiara Cordero
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
Challenges in food flavour and volatile compounds analysis
Poznan, Polonia
22-23 Settembre, 2022
Poznań University of Life Sciences
1
1
comprehensive two-dimensional gas chromatography, Artificial Intelligence (AI) algorithms, computer vision, smelling machine
Chiara Cordero
File in questo prodotto:
File Dimensione Formato  
Chiara Cordero Talk_Poznan.pdf

Accesso aperto

Descrizione: Slides presentazione
Tipo di file: MATERIALE NON BIBLIOGRAFICO
Dimensione 8.85 MB
Formato Adobe PDF
8.85 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1876766
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact