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Volatile fingerprint of food products with untargeted SIFT-MS data coupled with mixomics methods for profile discrimination: application case on cheese

Abstract : Volatile organic compounds (VOCs) emitted by food products are decisive for the perception of aroma and taste. The analysis of gaseous matrices is traditionally done by detection and quantification of few dozens of characteristic markers. Emerging direct injection mass spectrometry technologies offer rapid analysis based on a soft ionisation of VOCs without previous separation. The recent increase of selectivity offered by the use of several precursor ions coupled with untargeted analysis increases the potential power of these instruments. However, the analysis of complex gaseous matrix results in a large number of ion conflicts, making the quantification of markers difficult, and in a large volume of data. In this work, we present the exploitation of untargeted SIFT-MS volatile fingerprints of ewe PDO cheeses in a real farm model, using mixOmics methods allowing us to illustrate the typicality, the manufacturing processes reproducibility and the impact of the animals' diet on the final product.
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https://hal.mines-ales.fr/hal-03318841
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Soumis le : lundi 30 août 2021 - 17:09:19
Dernière modification le : mardi 31 août 2021 - 03:27:31

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Marine Reyrolle, Mylène Ghislain, Noëlle Bru, Germain Vallverdu, Thierry Pigot, et al.. Volatile fingerprint of food products with untargeted SIFT-MS data coupled with mixomics methods for profile discrimination: application case on cheese. Food Chemistry, Elsevier, 2022, 369, pp.130801. ⟨10.1016/j.foodchem.2021.130801⟩. ⟨hal-03318841⟩

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