Characterizing liquid phase partitioning in heterogeneous low/intermediate moisture food matrices using advanced imaging techniques.

使用先进的成像技术表征异质低/中水分食品基质中的液相分配。

• 批准号: 2310082
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2310082
• 关键词: Characterizing; liquid; phase; partitioning; heterogeneous

To develop new analytical tools, primarily CRM, to map the microstructure and particularly the spatial location of ingredients for low-intermediate moisture and fat continuous food systems. Apply the CRM technique to gain understanding of how liquid components such as water and lipids move or partition in complex heterogeneous food matrices.Approach:Confocal Raman Microscopy techniques will be developed and applied to food models with <15% moisture content and comprising sugars, lipids, complex carbohydrates (including starch and plant fibres) and proteins. The project will provide the baseline data and methodology from which to develop confocal Raman imaging as a new tool for mapping food ingredients and monitoring phase mobility within low and intermediate moisture food matrices. The studentship will be based at the University of Nottingham with a minimum of 3 months placement in the Reading Science Centre. Annual short visits to Reading will be scheduled to update the industry stakeholders and the Reading team on progress.1. Year 1 outputsa. Development of new Confocal Raman Microscopy (CRM) techniques acquiring spectra from primary ingredients and mixtures. b. Produce multiphase model microstructures (sugar, fat, protein water)c. Characterise Raman spectra of ingredients and map to model microstructures. 2. Year 2 outputsa. Application of CRM to systems with reduced moisture content and fat continuous model systems.b. Develop CRM techniques to characterise fat composition and polymorphic transitions.c. 3 month placement in Reading.3. Year 3 outputsa. Apply CRM technique to complex multiphase fat based food models.b. Characterise moisture mobility using CRM and relate to other phase analysis (NMR, MRI etc.).c. Establish key physico-chemical drivers of migration and phase partitioning as a precursor to predictive modelling.4. Year 4: a. Establish methodology for characterising and quantifying complex food matrices using CRM. b. Present key outputs to Mondelez stakeholders.c. Correlate imaging and spectral data with formulation & process variables to develop predictive models.

开发新的分析工具，主要是CRM，用于绘制中低水分和脂肪连续食品系统的微观结构，特别是成分的空间位置。应用CRM技术来了解液体成分如水和脂质如何在复杂的异质食物基质中移动或分裂。方法：将开发共聚焦拉曼显微镜技术，并将其应用于水分含量<15%、由糖、脂类、复合碳水化合物（包括淀粉和植物纤维）和蛋白质组成的食品模型。该项目将提供基线数据和方法，据此开发共聚焦拉曼成像，作为绘制食品成分和监测低水分和中水分食品基质中相流动性的新工具。该奖学金将在诺丁汉大学工作，并在雷丁科学中心工作至少3个月。每年将安排对Reading的短期访问，以更新行业利益相关者和Reading团队的进展。第一年的输出。新型共聚焦拉曼显微镜（CRM）技术的发展，从主要成分和混合物中获取光谱。b.生成多相模型微观结构（糖、脂肪、蛋白质、水）表征成分的拉曼光谱并绘制模型微观结构。2. 第二年的产出。CRM在低含水率系统和脂肪连续模型系统中的应用。发展CRM技术来表征脂肪组成和多态转变。3个月的阅读实习。第三年的产出。将CRM技术应用于复杂的多相脂肪食品模型。利用CRM和其他相分析（核磁共振、核磁共振等）表征水分迁移率。建立迁移和相分配的关键物理化学驱动因素，作为预测建模的先驱。第4年：a.使用CRM建立表征和量化复杂食品矩阵的方法。b.向亿滋利益相关者展示关键产出。将成像和光谱数据与配方和工艺变量联系起来，建立预测模型。