Quantification and Bitter Taste Contribution of Lipids and Their Oxidation Products in Pea-Protein Isolates (Pisum sativum L.).
Peter GläserVerena Karolin Mittermeier-KleßingerAndrea SpaccasassiThomas Frank HofmannCorinna DawidPublished in: Journal of agricultural and food chemistry (2021)
An ultra-high-performance liquid chromatography-differential ion mobility (DMS)-tandem mass spectrometry method was developed to quantify 14 bitter-tasting lipids in 17 commercial pea-protein isolates (Pisum sativum L.). The DMS technology enabled the simultaneous quantification of four hydroxyoctadecadienoic acid isomers, namely, (10E,12Z)-9-hydroxyoctadeca-10,12-dienoic acid (5), (10E,12E)-9-hydroxyoctadeca-10,12-dienoic acid (6), (9Z,11E)-13-hydroxyoctadeca-9,11-dienoic acid (7), and (9E,11E)-13-hydroxyoctadeca-9,11-dienoic acid (8). Based on quantitative data and human bitter taste recognition thresholds, dose-over-threshold factors were determined to evaluate the individual lipids' bitter impact and compound classes. The free fatty acids α-linolenic acid (10) and linoleic acid (13), as well as the trihydroxyoctadecenoic acids, especially 9,10,11-trihydroxyoctadec-12-enoic (3), and 11,12,13-trihydroxyoctadec-9-enoic acids (4), were shown to be key inducers to bitterness in the isolates. Additionally, the impact of 1-linoleoyl glycerol (9) on the bitter taste could be shown for 14 of the 17 tested pea-protein isolates.
Keyphrases
- tandem mass spectrometry
- ultra high performance liquid chromatography
- fatty acid
- simultaneous determination
- liquid chromatography
- endothelial cells
- high performance liquid chromatography
- mass spectrometry
- gas chromatography
- high resolution mass spectrometry
- small molecule
- machine learning
- hydrogen peroxide
- deep learning
- protein protein
- artificial intelligence
- solid phase extraction