Sensomics-Assisted Aroma Decoding of Pea Protein Isolates (<i>Pisum sativum</i> L.)

Abstract

The aroma of pea protein (Pisum sativum L.) was decrypted for knowledge-based flavor optimization of new food products containing pea protein. Sensomics helped to determine several volatiles via ultra-high performance liquid chromatography tandem mass spectrometry and 3-nitrophenylhydrazine derivatization. Among the investigated volatiles, representatives of aldehydes, ketones, and acids were reported in literature as especially important in pea and pea-related matrices. After validation of the method and quantitation of the corresponding analytes, sensory reconstitution as well as omission studies of a selected pea protein were performed and revealed nine odor-active compounds as key food odorants (3-methylbutanal, hexanal, acetaldehyde, (E,E)-2,4-nonadienal, (E)-2-octenal, benzaldehyde, heptanal, 2-methylbutanal, and nonanoic acid). Interestingly, eight out of nine compounds belonged to the chemical class of aldehydes. Statistical heatmap and cluster analysis of all odor activity values of different pea proteins confirmed the obtained sensory results and generalize these nine key food odorants in other pea proteins. The knowledge of key components gained shows potential for simplifying industrial flavor optimization of pea protein-based food.

Keywords

• pea protein aroma
• aldehydes
• high-throughput UHPLC-MS/MS
• big data analysis
• sustainable and innovative food