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  Measuring and Modelling Confectionery Fat behaviours during chocolate processing


   Faculty of Environment

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  Prof Nicholas Watson, Prof Megan Povey  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

One full scholarship is available in the School of Food Science and Nutrition in 2024/25. This scholarship is open to UK and international applicants and covers UK tuition fees plus £19,237 maintenance per year (plus enhanced top-up stipend of £1,700 per year). Please note that international applicants would have to pay the difference between UK and International student tuition fees.

Moulded chocolate has been commercially available for over 150 years. The moulding process, and the quality of the product created, rely heavily on the phase change of the chocolate fats from liquid to solid. However, the ability to monitor confectionery fat crystallisation in-situ on industrial manufacturing plants remains an unachieved target for manufacturers seeking techniques to deliver process optimisation, fault finding and quality control for both existing and new chocolate recipes.

Traditional methods of establishing the status of the fat in chocolate must either be carried out off-line or after the event, and an extrapolation therefore made back to the process. While many studies have hypothesised the ideal conditions for crystallising and cooling chocolate, the direct application of these hypotheses to industrial lines becomes limited by the large scale and variable nature of the lines, and dependence on recipe of the product. This gap in knowledge most simply manifests as an inability to truly measure, and therefore understand, the behaviour of the fat on an industrial moulding line. 

The opportunity therefore exists both to assess novel approaches to the direct measurement of the fat which benefit from advances in measurement techniques (e.g. multi-sensor fusion), and to couple with modern data analysis/modelling methods (e.g. AI and machine learning). The goal is to open up a greater understanding of this topic and highlight where the confectionery industry can use a combination of sensors, data and modelling to optimise how chocolate is processed in a moulding line.

Key facts

Type of research degree

4 year PhD

Application deadline

Sunday 30 June 2024

Project start date

Tuesday 1 October 2024

Country eligibility

UK only

Funding

Funded

Source of funding

Doctoral training partnership

Supervisors

Professor Megan Povey and Professor Nicholas Watson

Summary

One full scholarship is available in the School of Food Science and Nutrition in 2023/24. This scholarship covers UK tuition fees plus £19,237 maintenance per year (plus enhanced top-up stipend of £1,700 per year). Please note that international applicants would have to pay the difference between UK and International student tuition fees.

Full description

Moulded chocolate has been commercially available for over 150 years. The moulding process, and the quality of the product created, rely heavily on the phase change of the chocolate fats from liquid to solid. However, the ability to monitor confectionery fat crystallisation in-situ on industrial manufacturing plants remains an unachieved target for manufacturers seeking techniques to deliver process optimisation, fault finding and quality control for both existing and new chocolate recipes.

Traditional methods of establishing the status of the fat in chocolate must either be carried out off-line or after the event, and an extrapolation therefore made back to the process. While many studies have hypothesised the ideal conditions for crystallising and cooling chocolate, the direct application of these hypotheses to industrial lines becomes limited by the large scale and variable nature of the lines, and dependence on recipe of the product. This gap in knowledge most simply manifests as an inability to truly measure, and therefore understand, the behaviour of the fat on an industrial moulding line. 

The opportunity therefore exists both to assess novel approaches to the direct measurement of the fat which benefit from advances in measurement techniques (e.g. multi-sensor fusion), and to couple with modern data analysis/modelling methods (e.g. AI and machine learning). The goal is to open up a greater understanding of this topic and highlight where the confectionery industry can use a combination of sensors, data and modelling to optimise how chocolate is processed in a moulding line.

This project aims to:

  • Define what can be measured during the process, in terms of fat crystallisation and/or associated properties connected to fat crystallisation.
  • Model on-line measurements against attributes of the chocolate bar which are impacted by the fat crystallisation promoted during manufacturing.
  • Explore which additional measurements will improve any modelling and/or enable machine learning of the process to deliver the optimal level of fat crystallisation in the finished product.

This project will enable future generations of chocolate manufacturers to both minimise losses associated with incorrect cooling on industrial lines, as well as enabling the use of the wide range of ingredients to meet the needs and demands of a sustainably sourced confectionery product.

Food Sciences (15)

Funding Notes

This fully funded PhD place provides an exciting opportunity to pursue postgraduate research in a range of fields relating to Food Science, Food Engineering and Data Analytics with the industry partner Nestlé.

The award is open to full-time candidates who have been offered a place on a PhD degree at the School of Food Science and Nutrition and in collaboration with Nestlé as part of the Food Consortium Collaborative Training Partnership (providing additional cohort training activities). 


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