Dataset Information

Untargeted food contaminant detection using UHPLC-HRMS combined with multivariate analysis: Feasibility study on tea (Dataset 2 - Validation)

ABSTRACT: Powerful data pretreatment strategies inspired from the field of metabolomics were adapted to chemical food safety context to enable samples discrimination by multivariate methods based on low abundance ions. A highly automated workflow was produced. The open-source XCMS package was used and efficient data filtration strategies were set up. Data were treated using Independent Components Analysis, and data mining strategies developed to automatically detect and annotate ions of low abundance by coupling blind data exploration strategies with a broad scale database approach. Our method was efficient in discriminating tea samples based on their contamination levels (even at 10µg/kg) and detecting unexpected impurities in the spiking mix. Several “tracer” contaminants were considered, covering a broad range of physicochemical properties and structural diversity with overall 66% detected and annotated blindly. The methodology was successfully applied to a data set exhibiting only 3 “tracer” contaminants (at 50µg/kg) and more product diversity.

Dataset 2 with 2 teas spiked with 3 contaminants and referred to as the Validation datase is reported in the current study MTBLS754.
Dataset 1 with 1 tea spiked with 32 contaminants and referred to as the Development dataset is reported in MTBLS752.

Linked Studies: MTBLS752

OTHER RELATED OMICS DATASETS IN: PXD018322PXD006154

INSTRUMENT(S): Liquid Chromatography MS - Negative (LC-MS (Negative)), Liquid Chromatography MS - Positive (LC-MS (Positive))

SUBMITTER: Mathieu Cladiere

PROVIDER: MTBLS754 | MetaboLights | 2018-10-26

REPOSITORIES: MetaboLights

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Publications

Untargeted food contaminant detection using UHPLC-HRMS combined with multivariate analysis: Feasibility study on tea.

Delaporte Grégoire G Cladière Mathieu M Jouan-Rimbaud Bouveresse Delphine D Camel Valérie V

Food chemistry 20181019

Powerful data pretreatment strategies inspired from the field of metabolomics were adapted to chemical food safety context to enable samples discrimination by multivariate methods based on low abundance ions. A highly automated workflow was produced. The open-source XCMS package was used and efficient data filtration strategies were set up. Data were treated using Independent Components Analysis, and data mining strategies developed to automatically detect and annotate ions of low abundance by c ...[more]

PMID: 30502182

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Project description:This study aims at assessing the capability of comparing and combining different instrumental platforms in an untargeted approach with a view of detecting chemical contaminants in food matrices at low levels. A strategy based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) and chemometrics has been applied on two different complex food contamination scenarios, with tea as study product. The first scenario aimed at mimic the presence of a dozen of contaminants at levels just above regulatory limits (i.e. 10 and 30 μg/kg); the second scenario, more complex, aimed at simulate the presence of several different contaminations at levels close to regulatory limits (10 μg/kg) in different samples. This work was carried on two LC-HRMS platforms (with respectively ToF and Orbitrap mass analyzer technologies), and a highly automated data treatment workflow was implemented to deal with data acquired on both platforms. The untargeted approach performed well on all scenarios (even the most complex) and analytical platforms. Performance comparison between LC-HRMS technologies was made possible thanks to a vendor-neutral data treatment process. Sub-samples of black tea (Keemun type, gross, China) were spiked at 10 µg/kg levels with two different spiking mixes: Three sub-samples were spiked with a pool of 11 contaminants (spiking mix n°1); and Three sub-samples were spiked with 3 others contaminants (malathion, OTA, BPS, spiking mix n°2). Samples were then extracted with a generic method and analyzed by LC-HRMS (in both positive and negative ionization modes) on two platforms (respectively Orbitrap and ToF to generate a total of four data sets. Black tea spiked with contaminants is reported in the current study MTBLS772. Green tea spiked with contaminants is reported in MTBLS771. Linked Studies: <a href='https://www.ebi.ac.uk/metabolights/MTBLS771' target='_blank'>MTBLS771</a>

Dataset Information

Untargeted food contaminant detection using UHPLC-HRMS combined with multivariate analysis: Feasibility study on tea (Dataset 2 - Validation)

Publications

Untargeted food contaminant detection using UHPLC-HRMS combined with multivariate analysis: Feasibility study on tea.

Similar Datasets

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