AI and Ensemble approaches to model transient lipid deterioration in particulate food systems

人工智能和集成方法模拟颗粒食品系统中的瞬时脂质恶化

• 批准号: 2885044
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2885044
• 关键词: AI; Ensemble; approaches; model; transient

Lipids are a major constituent of foods which are worth around USD 50 Billion. Unsaturated lipid components [e.g. linoleic, arachidonic, eicosapentaenoic and docosahexaenoic acids] undergo oxidation which severely impacts on food quality and shelf-stability through flavour and taste deterioration and decreases in nutritive value. This project hypothesises that Artificial Intelligence and Machine Learning allow prediction of quality changes occurring in a product, or to formulate new food products which are stable. Such models result in a substantial reduction in food waste, time and resources.The student will gain considerable training in experimental and mathematical modelling methods used in shelf-life and keeping quality assessment at the University of Reading. In addition, the student will be trained in the use of latest Machine Learning and Artificial Intelligence methods and apply these to food systems - which is still an area in its infancy.

脂质是价值约 500 亿美元的食品的主要成分。不饱和脂质成分[例如亚油酸、花生四烯酸、二十碳五烯酸和二十二碳六烯酸]会发生氧化，通过风味和味道恶化以及营养价值降低，严重影响食品质量和货架稳定性。该项目假设人工智能和机器学习可以预测产品中发生的质量变化，或配制稳定的新食品。这些模型可以大幅减少食物浪费、时间和资源。学生将在雷丁大学获得关于保质期和保持质量评估的实验和数学建模方法的大量培训。此外，学生还将接受最新机器学习和人工智能方法使用的培训，并将这些方法应用于食品系统——这仍然是一个处于起步阶段的领域。