Determining the drivers for colloidal aggregation in plant protein systems for sustainable foods

Project description

Aggregation of colloidal particles is a critical process in food manufacture that influences food structure, stability and mouthfeel. This process is traditionally utilised in animal protein systems to form products such as yoghurt and plant protein systems to produce tofu. However, if aggregation is not carefully controlled it can lead to unstable food systems and poor mouthfeel due to in-mouth detection of large or hard aggregates.
The project aims to elucidate the key drivers for plant protein aggregation using Monte-Carlo simulation with Brownian cluster dynamics [1] and validate these models using physical measurements for the fractal dimension and particle size using techniques such as dynamic light scattering, atomic force microscopy and size exclusion chromatography. We aim to carry out physical characterisation of a range of plant protein solutions to relate their predicted structure to their rheological and tribological properties that are relevant to mouthfeel.
Physical characterisation will include measuring the bulk rheology of aqueous solutions of plant proteins from a range of sources, including yellow pea, soy and faba bean protein, and relating this to predicted and measured aggregate properties using the Maron Pierce Quemada model with random close packing fraction determined from the particle size distribution [2, 3]. Gap dependent rheological techniques will be used to evaluate suspension micromechanics under confinement to elucidate aggregate softness and size, which are relevant to sensory texture and mouthfeel attributes [4].
[1] V.A. Varma, Kritika, J. Singh, S.B. Babu, Advanced Theory and Simulations 6(3) (2023) 2200666.
[2] H.M. Shewan, J.R. Stokes, J Colloid Interface Sci 442(0) (2015) 75-81.
[3] H.M. Shewan, J.R. Stokes, Journal of Non-Newtonian Fluid Mechanics 222(0) (2015) 72-81.
[4] H.M. Shewan, J.R. Stokes, H.E. Smyth, Food Hydrocolloids 103 (2020) 105662.

Outcomes

Knowledge of the key drivers for this aggregation process in plant proteins will enable expedited product development and design of consumer acceptable, sustainable plant protein-based foods. To enable this outcome deliverable are:
A Monte-Carlo simulation that enables prediction of plant protein aggregation for a range of aqueous protein suspensions.
Experimentally quantify the fractal dimension and particle characteristics to validate the Monte-Carlo simulation.
Develop a relationship between particle volume fraction and bulk rheology for colloidal aggregate suspensions with particle-particle charge interactions.

Essential capabilities

A general understanding of modelling. Basic knowledge of colloid science. Team work skills

Desireable capabilities

Experience in managing a project and write a critical literature review. Knowledge of protein chemistry

Expected qualifications (Course/Degrees etc.)

Degree in physics or engineering