Naam
Naam C Diez Simon BSc
RoepnaamCarmen
Emailcarmen.diezsimon@wur.nl

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OmschrijvingPromovendus
OrganisatieDepartement Plantenwetenschappen
OrganisatieeenheidLaboratorium voor Plantenfysiologie
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Telefoon 2
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BezoekadresDroevendaalsesteeg 1
6708PB, WAGENINGEN
Gebouw/Kamer107/W1.Be.065
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Bodenummer
OmschrijvingPromovendus
OrganisatieDepartement Plantenwetenschappen
OrganisatieeenheidLaboratorium voor Plantenfysiologie

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Using different un-targeted GC-MS trapping techniques reveals the comprehensiveness of volatile compounds in flavouring ingredients

 

Metabolomics has become a valuable data-driven, holistic approach that is able to generate novel hypotheses, identify novel biomarkers and elucidate underlying modes of action. Whereas metabolomics has been applied to diverse plant materials, applications in processed foods is still in its infancy, especially in relation to flavour aspects. Nevertheless, recent studies have shown that metabolomics has great potential to identify sensory-relevant compounds (1-3). From this, it became evident that existing technologies have huge potential but need further development to obtain robust analytical data for complex food products.

We optimize methods for the untargeted analysis of volatiles from process flavours. The challenge lies in developing optimal techniques that give the most comprehensive picture together with an efficient and robust detection of volatiles. Several trapping techniques including solid-phase microextraction (SPME), stir-bar sorptive extraction (SBSE), and dynamic headspace (DHS) are compared for their trapping efficiency and robustness for the analysis of the volatiles present in different types of process flavours using GCMS. Our results show that the techniques differ in their efficiency in trapping different classes of volatile compounds suggesting a need to tailor the volatile extraction technique depending on the specific composition of the process flavour. Methodologies are being developed which: deliver robust data on sensory-relevant compounds; allow for identifying unknown (Maillard-related) compounds that contribute to the prediction of sensory attributes and; give us deeper insights into the most relevant chemical reactions.

 

References:

[1] M. Dresel; A. Dunkel; T. Hofmann J Agric Food Chem. 2015, 63(13), 3402-18.

[2] J. Liu; H. Song; Y. Liu; P. Li; J. Yao; J. Xiong J Sci Food Agric. 2015, 95, 3183-3194.

[3] T. Hofmann; P. Schieberle In: Flavour Science - Proceedings of the XIV Weurman Flavour Research Symposium (Taylor AJ, Mottram DS). 2014, Context Products; ISBN 9781899043705:3-12.

 

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