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

人工智能优化发酵，从食品副产品中可持续生产蛋白质

• 批准号: BB/Y513933/1
• 负责人: Nicholas Watson
• 金额: $ 32.88万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY513933%2F1
• 关键词: AI; Optimised; Fermentation; Sustainable; Protein

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.

这种国际合作将开发AI方法，以优化使用食物侧流作为底物的发酵过程，以生产可持续的蛋白质供人类消费。它将由利兹大学（UOL）和英联邦科学和工业研究组织（CSIRO）领导，其中包括来自农业食品领域的四个工业伙伴。促成的替代蛋白质生产通常使用高糖基质材料。该项目旨在利用食物侧流作为发酵底物来提高蛋白质生产的可持续性和经济可行性。鉴于不同的食物侧流之间的差异，AI将用于帮助优化发酵参数，例如固体和水分含量，预处理方法，pH值，酵母菌菌株和养分补充剂。浪费了三分之一的食物。该项目解决了这种废物，有助于创造循环经济，同时生产蛋白质，以增强不断增长的世界人口的粮食安全。该项目与竞赛在可持续农业和食品和AI中的AI主题相符，以推动制造和清洁的增长。east机构具有互补的能力，利兹大学团队提供了实验性数据收集和初步模型开发的经验，并在这些过程中提供了这些过程，以确保他们扩大这些过程，以确保他们具有广泛的现实影响。该合作伙伴关系还解决了食品侧流的区域变化，从而可以开发具有更大可推广性的适应性模型。项目活动分为五个工作包（WPS）：WP1（UOL）：实验数据收集：使用食物侧流收集发酵过程的数据收集（短货架寿命柔软的水果和耶路撒冷的朝鲜蓟）作为生产蛋白质的底物来生产蛋白质。基于侧流组成和发酵参数的动态，产量和蛋白质浓度）.WP3（UOL）：自适应建模技术：使用WP1的数据，转移学习的发展和贝叶斯优化方法的发展，以显着减少来自新侧流的数据收集负担。 CSIRO使用旨在最大化可持续性和经济学的新食物侧流。WP5（UOL＆CSIRO）：伙伴关系和影响：专注于向更广泛的科学界传播发现，共享数据，代码，模型，方法论和学术报纸；并进行合作伙伴活动包括访问，研讨会和培训会议。UOL和CSIRO之间的合作伙伴关系的结果将在每个组织中提高AI功能，项目团队成员的职业发展和可适应性的AI模型，这些模型可以由全球范围内的受益人使用，以有效地优化发酵过程和评估新食物层流的潜力。