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Beyond the nutrition label: A foodomics approach to characterize plant-based meat alternatives

Year: 2024
Type: dissertation
Abstract: The food matrix is complex, housing numerous molecular components that influence nutritive value and sensory attributes. The environmental impact of food production, namely animal agriculture, has been a hot topic amongst regulatory agencies and consumers. Moreover, consumers are more conscious about what they put in their body. Both topics have stimulated the development of new generation plant-based meat alternatives that mimic the beef hamburger eating experience as well as meeting the macronutrient profile of ground beef. Therefore, the objective of this dissertation was to characterize plant-based meat alternatives using traditional nutritional profiling, high resolution mass spectrometry-based metabolomics and lipidomics, descriptive sensory analysis, and volatilomics. Three soy (IMP and GEN) or pea (BEY and GEN) protein-based brands of plant-based meat alternatives and 2 lean levels of ground beef (Regular [80 to 85% lean, RGB] and Lean [>93% lean, LGB]) were purchased from retail stores across the United States. Nutritional profiling showed ground beef to greater in moisture, fat, and protein content, primarily monounsaturated (oleic acid), and have greater essential amino acids whereas PBMA had increased ash and subsequent mineral concentration, B-vitamin content, and primarily unsaturated fatty acids (GEN and BEY). Metabolomic profiling revealed divergent metabolomes with metabolites that have implications on health and food quality. Bioactive lipids were identified in both GB and PBMA products. Moreover, cooking induced various changes in the lipidomes of both GB and PBMA. The results of this research demonstrate divergent matrices between GB and PBMA. These differences were driven by the formulations of PBMA. These studies demonstrate the complexity of the food matrix and its diverse molecular components have implications of human health and product taste and flavor.
Language: English
Subfield: Food Science
Open Access status: green
OpenAlex ID: W7045610573
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