The volatile fraction of food, also referred to as food volatilome, encrypts information on quality and authenticity of a specific product. In this contribution, the industrial quality of hazelnuts (Corylus avellana L.), is exploited to show the potentials of Artificial Intelligence (AI) smelling based on sensomics and realized on fully automated GC×GC platforms. Hazelnut volatilome encrypts information on cultivar/geographical origin, post-harvest treatments, bacteria/molds contamination, oxidative stability, and overall sensory perception. By comprehensive two-dimensional gas chromatography (GC×GC), untargeted/targeted features patterns can be effectively mapped (i.e., fingerprinting) across the volatile fingerprint of many samples to answer different questions. However, chromatographic fingerprinting consistency, is hardly compromised in long-term studies by random errors generated by chromatographic issues (e.g., retention times shifts) or detector response fluctuations, generally identified as analytical batch effects. Simpler, yet more robust, approaches are therefore preferred in long-term monitoring, usually adopting 1D-GC and targeted quantifications. In this study, the AI smelling machine approach is implemented on a flow modulated GC×GC-MS/FID platform, improvements related to extended accurate quantification, via multiple headspace solid phase microextraction (MHS-SPME), of over 40 analytes are discussed. Within targeted compounds, key-aromas, potent odorants, and spoilage markers are included. Quantitative “AI” perceptive maps, based on odor activity values (OAVs), enable consistent samples clustering based on the overall smell, while guaranteeing results transferability over time. Of particular interest is the accurate detection of spoilage smells, which makes this technique suitable for industrial quality control purposes.

Artificial Intelligence (AI) smelling based on GC×GC: a key-tool to make a step forward in food quality measurements

Simone Squara;Andrea Caratti;Erica Liberto;Carlo Bicchi;Chiara Emilia Irma Cordero
2022

Abstract

The volatile fraction of food, also referred to as food volatilome, encrypts information on quality and authenticity of a specific product. In this contribution, the industrial quality of hazelnuts (Corylus avellana L.), is exploited to show the potentials of Artificial Intelligence (AI) smelling based on sensomics and realized on fully automated GC×GC platforms. Hazelnut volatilome encrypts information on cultivar/geographical origin, post-harvest treatments, bacteria/molds contamination, oxidative stability, and overall sensory perception. By comprehensive two-dimensional gas chromatography (GC×GC), untargeted/targeted features patterns can be effectively mapped (i.e., fingerprinting) across the volatile fingerprint of many samples to answer different questions. However, chromatographic fingerprinting consistency, is hardly compromised in long-term studies by random errors generated by chromatographic issues (e.g., retention times shifts) or detector response fluctuations, generally identified as analytical batch effects. Simpler, yet more robust, approaches are therefore preferred in long-term monitoring, usually adopting 1D-GC and targeted quantifications. In this study, the AI smelling machine approach is implemented on a flow modulated GC×GC-MS/FID platform, improvements related to extended accurate quantification, via multiple headspace solid phase microextraction (MHS-SPME), of over 40 analytes are discussed. Within targeted compounds, key-aromas, potent odorants, and spoilage markers are included. Quantitative “AI” perceptive maps, based on odor activity values (OAVs), enable consistent samples clustering based on the overall smell, while guaranteeing results transferability over time. Of particular interest is the accurate detection of spoilage smells, which makes this technique suitable for industrial quality control purposes.
Multidimensional Chromatography Workshop MDCW
Web
January 31 - February 2, 2022
Multidimensional Chromatography Workshop MDCW- Book of abstracts
Not Provided
12
12
Simone Squara, Andrea Caratti, Erica Liberto, Carlo Bicchi, Stephen E. Reichenbach, Chiara Emilia Irma Cordero
File in questo prodotto:
File Dimensione Formato  
MDCW presentazione_Squara.pdf

Accesso aperto

Descrizione: Presentation Slides
Tipo di file: POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione 2.13 MB
Formato Adobe PDF
2.13 MB Adobe PDF Visualizza/Apri
13_MDCW_abstract_book.pdf

Accesso aperto

Descrizione: Book of Abstracts
Tipo di file: PDF EDITORIALE
Dimensione 707.24 kB
Formato Adobe PDF
707.24 kB 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: http://hdl.handle.net/2318/1847475
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact