Faba Bean ( Vicia faba L. minor ) Bitterness: An Untargeted Metabolomic Approach to Highlight the Impact of the Non-Volatile Fraction.
Adeline KarolkowskiEmmanuelle MeudecAntoine BruguièreAnne-Claire Mitaine-OfferEmilie BouzidiLoïc LevavasseurNicolas SommererLoïc BriandChristian SallesPublished in: Metabolites (2023)
In the context of climate change, faba beans are an interesting alternative to animal proteins but are characterised by off-notes and bitterness that decrease consumer acceptability. However, research on pulse bitterness is often limited to soybeans and peas. This study aimed to highlight potential bitter non-volatile compounds in faba beans. First, the bitterness of flours and air-classified fractions (starch and protein) of three faba bean cultivars was evaluated by a trained panel. The fractions from the high-alkaloid cultivars and the protein fractions exhibited higher bitter intensity. Second, an untargeted metabolomic approach using ultra-high-performance liquid chromatography-diode array detector-tandem-high resolution mass spectrometry (UHPLC-DAD-HRMS) was correlated with the bitter perception of the fractions. Third, 42 tentatively identified non-volatile compounds were associated with faba bean bitterness by correlated sensory and metabolomic data. These compounds mainly belonged to different chemical classes such as alkaloids, amino acids, phenolic compounds, organic acids, and terpenoids. This research provided a better understanding of the molecules responsible for bitterness in faba beans and the impact of cultivar and air-classification on the bitter content. The bitter character of these highlighted compounds needs to be confirmed by sensory and/or cellular analyses to identify removal or masking strategies.
Keyphrases
- high resolution mass spectrometry
- ultra high performance liquid chromatography
- gas chromatography
- liquid chromatography
- tandem mass spectrometry
- mass spectrometry
- climate change
- simultaneous determination
- amino acid
- gas chromatography mass spectrometry
- healthcare
- machine learning
- blood pressure
- deep learning
- computed tomography
- high intensity
- risk assessment
- image quality
- water soluble
- resistance training