2. Academic Validation
  3. A Data-Driven Approach to Link GC-MS and LC-MS with Sensory Attributes of Chicken Bouillon with Added Yeast-Derived Flavor Products in a Combined Prediction Model

A Data-Driven Approach to Link GC-MS and LC-MS with Sensory Attributes of Chicken Bouillon with Added Yeast-Derived Flavor Products in a Combined Prediction Model

  • Metabolites. 2025 May 8;15(5):317. doi: 10.3390/metabo15050317.
Simon Leygeber 1,  Carmen Diez-Simon 2,  Justus L Großmann 3,  Anne-Charlotte Dubbelman 1,  Amy C Harms 1,  Johan A Westerhuis 3,  Doris M Jacobs 4,  Peter W Lindenburg 5,  Margriet M W B Hendriks 6,  Brenda C H Ammerlaan 6,  Marco A van den Berg 7,  Rudi van Doorn 7,  Roland Mumm 8,  Age K Smilde 3,  Robert D Hall 2 8,  Thomas Hankemeier 1
Affiliations

Affiliations

  • 1 Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands.
  • 2 Laboratory of Plant Physiology, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands.
  • 3 Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.
  • 4 Unilever Foods Innovation Centre, Bronland 14, 6708 WH Wageningen, The Netherlands.
  • 5 Leiden Center for Applied Bioscience, University of Applied Sciences Leiden, Zernikedreef 11, 2333 CK Leiden, The Netherlands.
  • 6 Science & Research, DSM-Firmenich, Alexander Fleminglaan 1, 2613 AX Delft, The Netherlands.
  • 7 Taste, Texture & Health, DSM-Firmenich, Alexander Fleminglaan 1, 2613 AX Delft, The Netherlands.
  • 8 Wageningen Plant Research (Bioscience), Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands.
PMID: 40422893 DOI: 10.3390/metabo15050317
Abstract

Background: There is a continuous demand to create new, superior sensory food experiences. In the food industry, yeast-derived flavor products (YPs) are often used as ingredients in foods to create new aromas and taste qualities that are appreciated by consumers.

Methods: Chicken bouillon samples containing diverse YPs were chemically and sensorially characterized using statistical multivariate analyses. The sensory evaluation was performed using quantitative descriptive analysis (QDA) by trained panelists. Thirty-four sensory attributes were scored, including odor, flavor, mouthfeel, aftertaste and afterfeel. Untargeted metabolomic profiles were obtained using stir bar sorptive extraction (SBSE) coupled to GC-MS, RPLC-MS and targeted HILIC-MS.

Results: In total, 261 volatiles were detected using GC-MS, from chemical groups of predominantly aldehydes, esters, pyrazines and ketones. Random Forest (RF) modeling revealed volatiles associated with roast odor (2-ethyl-5-methyl pyrazine, 2,3,5-trimethyl-6-isopentyl pyrazine) and chicken odor (2,4-nonadienal, 2,4-decadienal, 2-acetyl furan), which could be predicted by our combined model with R2 > 0.5. In total, 2305 non-volatiles were detected for RPLC-MS and 34 for targeted HILIC-MS, where fructose-isoleucine and cyclo-leucine-proline were found to correlate with roast flavor and odor. Furthermore, a list of metabolites (glutamate, monophosphates, methionyl-leucine) was linked to umami-related flavor. This study describes a straightforward data-driven approach for studying foods with added YPs to identify flavor-impacting correlations between molecular composition and sensory perception. It also highlights limitations and preconditions for good prediction models. Overall, this study emphasizes a matrix-based approach for the prediction of food taste, which can be used to analyze foods for targeted flavor design or quality control.

Keywords

HILIC; RPLC; chicken bouillon; flavor prediction modeling; savory foods; sensory analysis; stir bar sorptive extraction–gas chromatography–mass spectrometry (SBSE-GC-MS); yeast-derived flavor products.

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