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
  • 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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