Raw data HILIC underlying the research of a data-driven approach to link GCMS and LCMS with sensory attributes of chicken bouillon with added yeast-derived flavor products in a combined prediction mode

DOI:10.4121/f1fca011-141c-4994-989f-b98f44ceae5f.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/f1fca011-141c-4994-989f-b98f44ceae5f

Datacite citation style

Leygeber, Simon (2025): Raw data HILIC underlying the research of a data-driven approach to link GCMS and LCMS with sensory attributes of chicken bouillon with added yeast-derived flavor products in a combined prediction mode. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/f1fca011-141c-4994-989f-b98f44ceae5f.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Chicken bouillon samples containing diverse YP were chemically and sensorially characterized by using statistical multivariate analyses. Untargeted profiles were obtained using targeted HILIC-MS. This study was used for a straight-forward data-driven approach for studying foods with added YP to identify flavor-impacting correlations between molecular composition and sensory perception. It also highlights the limitations and preconditions for good prediction models. Overall, this study emphasises a matrix-based approach for the prediction of food taste, which can be used to analyse foods for targeted flavor design or quality control.

For more information use the DOI for the linked publication or the textfile uploaded here.

History

  • 2025-05-09 first online, published, posted

Publisher

4TU.ResearchData

Format

Sciex OS wiff.scan, wiff2, timeseries.data (one of each for every measured sample)

Organizations

Leiden University, LACDR, Metabolomics and Analytics Centre

DATA

Files (548)