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.

这项国际合作将开发人工智能方法来优化发酵过程，利用食品侧流作为底物来生产供人类消费的可持续蛋白质。该项目将由利兹大学 (UoL) 和联邦科学与工业研究组织 (CSIRO) 领导，并包括来自农业食品领域的四个工业合作伙伴。目前的替代蛋白质生产通常使用高糖底物。该项目旨在利用食品侧流作为发酵底物，以提高蛋白质生产的可持续性和经济可行性。考虑到不同食品副流之间的差异，人工智能将用于帮助优化发酵参数，如固体和水分含量、预处理方法、pH 水平、酵母菌株和营养补充。生产的食品中大约有三分之一被浪费。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不断增长的世界人口的粮食安全。该项目与竞赛的两个主题相一致，即可持续农业和食品中的人工智能以及推动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 带来扩大这些流程的专业知识，以确保它们具有更广泛的现实世界影响。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通用性的适应性模型。该项目活动分为五个工作包 (WP)： WP1 (UoL)：实验数据收集：使用食品侧流（保质期短的软果和菊芋）作为生产蛋白质的底物，从发酵过程中收集数据。WP2 （UoL 和 CSIRO）：单侧流模型：利用 WP1 的数据，开发 AI 模型，根据侧流成分预测发酵产品（例如微生物动态、产量和蛋白质浓度） WP3 (UoL)：自适应建模技术：使用 WP1 的数据，开发迁移学习和贝叶斯优化方法，以显着减少新侧流或设备的数据收集负担。WP4 (CSIRO)：纵向扩展和横向扩展：WP3 的方法将指导 CSIRO 使用新的食品副流从扩大发酵试验中收集数据，旨在最大限度地提高可持续性和经济性。 WP5（UoL 和 CSIRO）：合作伙伴关系和影响：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；伦敦大学学院和 CSIRO 之间的合作成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受益人使用的适应性强的人工智能模型。广泛有效地优化发酵过程并评估新食品副产品的潜力。