AI-Optimised Fermentation for Sustainable Protein Production from Food Side Streams

这一国际合作将开发用于优化发酵过程的人工智能方法，这些发酵过程使用食物侧流作为底物来生产可持续的蛋白质供人类消费。它将由利兹大学(UOL)和英联邦科学与工业研究组织(CSIRO)领导，包括来自农业食品部门的四个工业合作伙伴。目前的替代蛋白生产通常使用高糖底物。该项目旨在利用食物侧流作为发酵底物，以提高蛋白质生产的可持续性和经济可行性。鉴于不同食物侧流之间的差异，人工智能将被用于帮助优化发酵参数，如固体和水分含量、前处理方法、pH水平、酵母菌株和营养补充。大约三分之一的食物被浪费。该项目解决了这种浪费问题，有助于创建循环经济，同时生产蛋白质，以增强日益增长的世界人口的粮食安全。该项目与比赛的两个主题保持一致，即可持续农业和食品领域的人工智能以及促进制造业和清洁增长的人工智能。每个机构都有互补的能力，利兹大学团队提供实验数据收集和初步模型开发方面的经验，CSIRO带来扩大这些过程的专业知识，以确保它们具有更广泛的现实影响。该伙伴关系还解决了食品侧流的区域差异，从而能够开发具有更大普适性的适应性模式。项目活动被组织成五个工作包(WPS)：WP1(UOL)：实验数据收集：收集使用食品侧流(保质期短的软果和菊芋)作为底物生产蛋白质的发酵过程的数据。WP2(UOL和CSIRO)：单侧流模型：利用WP1的数据，开发人工智能模型，根据侧流组成和发酵参数预测发酵产品(例如微生物动力学、产量和蛋白质浓度)。WP3(UOL)：自适应建模技术：使用WP1的数据，开发转移学习和贝叶斯优化方法，以显著减少新的侧流或设备的数据收集负担。WP4(CSIRO)：纵向和横向：WP3的方法将指导CSIRO使用旨在最大化可持续性和经济性的新食品侧流的扩大发酵试验的数据收集。WP5(UOL和CSIRO)：伙伴关系和影响：专注于向更广泛的科学界传播研究结果，共享数据、代码、模型、方法和学术论文；UOL和CSIRO之间的伙伴关系的结果将是增强每个组织的人工智能能力，项目团队成员的职业发展，以及可供世界各地的受益者使用的适应性人工智能模型，以有效地优化发酵过程和评估新的食品侧流的潜力。

This international collaboration will develop AI methods for optimising fermentation processes that use food side streams as substrates to produce sustainable proteins for human consumption. It will be led by the University of Leeds (UoL) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and includes four industrial partners from the agri-food sector.Current alternative protein production often uses high-sugar substrates. This project aims to utilise food side streams as the fermentation substrate to increase sustainability and economic viability of protein production. Given the variability between different food side streams, AI will be used to aid optimisation of fermentation parameters like solids and moisture content, pre-treatment methods, pH levels, yeast strains, and nutrient supplementation.Approximately one-third of food produced gets wasted. The project addresses this waste, contributes to creating a circular economy, while simultaneously producing protein to enhance food security for a growing world population. The project is aligned with two of the competition's themes on AI in sustainable agriculture and food and AI to advance manufacturing and clean growth.Each institution has complementary capabilities where the University of Leeds team offers experience in experimental data collection and preliminary model development and CSIRO brings expertise in scaling up these processes to ensure they have a broader, real-world impact. The partnership also addresses regional variations in food side streams enabling the development of adaptable models with greater generalisability. The projects activities are organised into five Work Packages (WPs):WP1 (UoL): Experimental data collection: Collection of data from fermentation processes using food side streams (short shelf-life soft fruit and Jerusalem artichokes) as substrates to produce protein.WP2 (UoL & CSIRO): Single side stream models: Leveraging the data from WP1, development of AI models to predict fermentation products (e.g., microbial dynamics, yield, and protein concentration) based on the side stream composition and fermentation parameters.WP3 (UoL): Adaptive modelling techniques: Using the data from WP1, development of transfer learning and Bayesian optimisation methodologies to significantly reduce the data collection burden for new side streams or equipment.WP4 (CSIRO): Scale up and out: The methodologies from WP3 will guide data collection from scaled-up fermentation trials at CSIRO using new food side streams aiming to maximise sustainability and economics.WP5 (UoL & CSIRO): Partnership and impact: Focusing on disseminating findings to the broader scientific community, sharing data, code, models, methodologies, and academic papers; and conducting partnership activities including visits, workshops, and training sessions.The outcome of the partnership between UoL and CSIRO will be enhanced AI capabilities at each organisation, the career development of project team members and adaptable AI models that can be used by beneficiaries world-wide to efficiently optimise fermentation processes and assess the potential of new food side streams.