The volatile metabolome — gas chromatography–mass spectrometry approaches in the context of food fraud

This work discusses food authentication tools based on the volatile metabolome assessed by gas chromatography-mass spectrometry (GC-MS). Volatile organic compounds (VOCs) serve as markers for food flavour and aroma and, in some cases, they hold potential for food authentication. On top of this, advanced data analysis approaches enhance analytical data extraction and understanding. Untargeted methods provide comprehensive authenticity insights, surpassing traditional targeted approaches, and automated processing improves robustness and reduces user dependency. Chemometric tools, particularly classification methods, are extensively used for authentication based on both GC-MS fingerprinting and untargeted profiling of VOCs. Despite challenges in transferability, untargeted methods perform well in authentication models, making them valuable for internal quality control and official control guidance for fraud detection. This work highlights the need for further research to establish untargeted authentication tools as official methods in the near future. • Besides economic impact, food fraud loss of traceability jeopardizes food safety. • Food volatile metabolome is a reliable marker for quality authentication. • Untargeted GC-MS analysis provides analytical key-features for food authentication • Cumbersome identification of certain semi-VOCs is avoided in untargeted methods • Chemometrics tools allow building authentication models based on GC-MS data

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Publication Details

Journal
Current Opinion in Food Science
Published
2024-11-08
DOI
https://doi.org/10.1016/j.cofs.2024.101235
Citations
15
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
3.51

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article

The volatile metabolome — gas chromatography–mass spectrometry approaches in the context of food fraud

Stefania Vichi, Beatriz Quintanilla‐Casas, Rasmus Bro, Berta Torres-Cobos et al.
15 citations
Current Opinion in Food Science
Identification and Quantification in Food
3.51
article

The volatile metabolome — gas chromatography–mass spectrometry approaches in the context of food fraud

Stefania Vichi, Beatriz Quintanilla‐Casas, Rasmus Bro, Berta Torres-Cobos, Francesc Guardiola, Alba Tres
article en
15 citations

Abstract

This work discusses food authentication tools based on the volatile metabolome assessed by gas chromatography-mass spectrometry (GC-MS). Volatile organic compounds (VOCs) serve as markers for food flavour and aroma and, in some cases, they hold potential for food authentication. On top of this, advanced data analysis approaches enhance analytical data extraction and understanding. Untargeted methods provide comprehensive authenticity insights, surpassing traditional targeted approaches, and automated processing improves robustness and reduces user dependency. Chemometric tools, particularly classification methods, are extensively used for authentication based on both GC-MS fingerprinting and untargeted profiling of VOCs. Despite challenges in transferability, untargeted methods perform well in authentication models, making them valuable for internal quality control and official control guidance for fraud detection. This work highlights the need for further research to establish untargeted authentication tools as official methods in the near future. • Besides economic impact, food fraud loss of traceability jeopardizes food safety. • Food volatile metabolome is a reliable marker for quality authentication. • Untargeted GC-MS analysis provides analytical key-features for food authentication • Cumbersome identification of certain semi-VOCs is avoided in untargeted methods • Chemometrics tools allow building authentication models based on GC-MS data

Current Opinion in Food ScienceVol. 61
University of Copenhagen (DK), Departament de Salut (ES), Universitat de Barcelona (ES)
Indian National Science Academy, Novo Nordisk, Novo Nordisk Fonden, Agencia Estatal de Investigación
Zero hunger
Openalex Percentile: Top 6%
Identification and Quantification in Food
3.51
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