Automated Morphological Characterisation of Hyphae (AMCH)

Thyme PoC project to improve production of meat-alternatives. This project uses a combination of advanced microscopy techniques and artificial intelligence & machine-learning algorithms to develop an automated platform that will allow early detection and real-time quantitation of mycoprotein hyphal morphology. The goal is to extend the fermentation cycle time and therefore optimise production and quality of the product. This Thyme PoC project is carried out by Teesside University (being M. Angeles Juanes, PI - leader), University of York and Quorn Foods.

The rise in the world population at an accelerated pace creates demands for many vital resources, most importantly: availability of water, oil, living space and food.
In relation to food requirements, protein is an essential macronutrient for humans, as well as other animals, with the need ideally for high-quality protein in the diet; determined by the abundance and the variety of the amino acids it contains, with up to 9 of the 20 standard amino acids being deemed essential.

As a result of the explosion in population growth in the twentieth century, the global community was forced to consider other alternatives to the cultivation of livestock. A promising alternative is the cultivation of microorganisms with the goal of producing edible biomass, commonly referred to as the formation of single-cell protein like filamentous fungi (Fusarium venenatum A3/5 - myco-protein), e.g. Quorn process.
Quorn has been a successful “meat-alternative” provider since 1980s with massive production capacity plants to fulfil its global demand, yet there are challenges. The Quorn production process of myco-protein takes place in 150,000L continuous flow reactors. However, colonial mutants (known as C-variants) appear after about 30~35 days. The C-variant mutant strains develop altered branching patterns and increase growth rates, which eventually end up displacing the parental strain. This leads to changes in the texture of myco-protein e.g. more easily crumbled (friable), so the product standard cannot be maintained, and production needs to be prematurely terminated.

This academia-industry consortium uses a combination of advanced microscopy techniques and artificial intelligence & machine-learning algorithms to develop an automated spectrophotometric platform that will allow early detection and real-time quantitation of hyphal morphology to extend the fermentation cycle time and therefore optimise production and quality of the product.

Optimisation of the production process that might increase productivity by even 1% (~multi-million pounds) is of immense importance to the bioeconomy, food security, and to reduce the associated carbon footprint.