Learning to Predict: Exploring Fully the Compositional Diversity of Natural Ingredients and Modulating their Structures to Improve their Antimicrobial/Antioxidant Properties
Department of Food Science and Agricultural Chemistry, Macdonald Campus, McGill University, firstname.lastname@example.org
Because of growing consumers’ demand for natural, safe and preservative‐free products, plant-derived ingredients have gained much attention. In particular, essential oils (EOs) and plant extracts (PEs) are becoming popular natural substitutes for synthetic antimicrobial and antioxidant ingredients as they address consumer concerns regarding the side effects of synthetic ones. The chemical diversity in EOs and PEs composition has been reported, but the comparison across studies, to characterize this diversity and to identify similarity and complementarity between chemical profiles, is difficult, as various techniques and approaches have been used. Understanding the chemical diversity of natural ingredients and their relationships with their antimicrobial and antioxidant properties regardless of their sources are needed in order to maximize their functionalities and optimize their uses as natural ingredients. Herein, in this presentation, I will provide new insights regarding the chemical diversity of EOs and PEs and shed some light on how chemical profiling can be used to identify the inherent synergistic, additive, or antagonistic interactions within or between the natural ingredients through multivariate analyses. Our work evidences that the determination of functional properties based on the chemical profiles can be achieved using predictive models. Our ability to modulate enzymatically the structures of some natural ingredients to limit the effect of their strong flavor profiles or to enhance their solubility will also be discussed.
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