Characterizing liquid phase partitioning in heterogeneous low/intermediate moisture food matrices using advanced imaging techniques.

Lead Research Organisation: University of Nottingham
Department Name: Sch of Chemistry

Abstract

To develop new analytical tools, primarily CRM, to map the microstructure and particularly the spatial location of ingredients for low-intermediate moisture and fat continuous food systems. Apply the CRM technique to gain understanding of how liquid components such as water and lipids move or partition in complex heterogeneous food matrices.

Approach:
Confocal Raman Microscopy techniques will be developed and applied to food models with <15% moisture content and comprising sugars, lipids, complex carbohydrates (including starch and plant fibres) and proteins.
The project will provide the baseline data and methodology from which to develop confocal Raman imaging as a new tool for mapping food ingredients and monitoring phase mobility within low and intermediate moisture food matrices. The studentship will be based at the University of Nottingham with a minimum of 3 months placement in the Reading Science Centre. Annual short visits to Reading will be scheduled to update the industry stakeholders and the Reading team on progress.
1. Year 1 outputs
a. Development of new Confocal Raman Microscopy (CRM) techniques acquiring spectra from primary ingredients and mixtures.
b. Produce multiphase model microstructures (sugar, fat, protein water)
c. Characterise Raman spectra of ingredients and map to model microstructures.

2. Year 2 outputs
a. Application of CRM to systems with reduced moisture content and fat continuous model systems.
b. Develop CRM techniques to characterise fat composition and polymorphic transitions.
c. 3 month placement in Reading.

3. Year 3 outputs
a. Apply CRM technique to complex multiphase fat based food models.
b. Characterise moisture mobility using CRM and relate to other phase analysis (NMR, MRI etc.).
c. Establish key physico-chemical drivers of migration and phase partitioning as a precursor to predictive modelling.
4. Year 4:
a. Establish methodology for characterising and quantifying complex food matrices using CRM.
b. Present key outputs to Mondelez stakeholders.
c. Correlate imaging and spectral data with formulation & process variables to develop predictive models.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/T508500/1 01/10/2019 30/09/2023
2310082 Studentship BB/T508500/1 01/10/2019 30/09/2023 Taranvir Bedi Singh