Development of Spectroscopically-based Approaches for Bioprocess Monitoring and Bioproduct Characterization

开发基于光谱的生物过程监测和生物产品表征方法

• 批准号: RGPIN-2016-03786
• 负责人: Legge, Raymond
• 金额: $ 2.4万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637959
• 关键词: Development; Spectroscopically; based; Approaches; Bioprocess

The ability to monitor various parameters in an industrial process enables improvements in product quality, process efficiency, sustainability and, ultimately, to reduce costs. In bioprocesses, the complex nature of the products presents numerous challenges for process monitoring. Optical sensors can capture data, which contains chemical information and, information related to product functionality. Part of the challenge after collection of this complex data set is to “decipher” the data to provide the information on the variable(s) of interest. Deciphering is accomplished through the application of mathematical and statistical tools. We have chosen two optical sensor techniques, fluorescence and near infrared (NIR) spectroscopy. We will extend our success with liquid sample analysis and fluorescence to powder samples, with a focus on a novel, solvent-free separation process developed for the production of protein- and starch-enriched fractions from milled agricultural material, e.g. bean flour. There are numerous incentives to exploring this novel approach, as interest in bean or pulse proteins has increased substantially in recent years, as these sources are low in fat and excellent nutritionally. Conventional processes are water and energy intensive, so a solvent-free process offers improvements in sustainability, with the additional benefit of helping to preserve the native functionality of the extracted proteins; thus expanding the roster of potential alternate industrial uses. Canada’s agriculture sector is known for its export of pulses and oilseeds, but little secondary processing of these raw materials to produce higher value fractions is conducted resulting in loss of significant economic opportunities and job development. The proposed research aims to examine a novel approach of fusing the data from multiple sensors with this process, as a case study. The outcome will be an optimized process and a suite of techniques, allowing us to identify possible, alternate industrial applications of the products.

监控工业过程中各种参数的能力可以提高产品质量、工艺效率、可持续性，并最终降低成本。在生物过程中，产品的复杂性为过程监测提出了许多挑战。光学传感器可以捕获包含化学信息和与产品功能相关的信息的数据。收集这一复杂数据集后的部分挑战是“破译”数据，以提供有关变量的信息。解密是通过应用数学和统计工具来完成的。我们选择了两种光学传感器技术，荧光和近红外（NIR）光谱。我们将把液体样品分析和荧光分析的成功扩展到粉末样品，重点是开发一种新型的无溶剂分离工艺，用于从碾磨的农业材料（如豆粉）中生产富含蛋白质和淀粉的馏分。探索这种新方法有许多动机，因为近年来对豆类或豆类蛋白质的兴趣大幅增加，因为这些来源脂肪含量低，营养丰富。传统工艺是水和能源密集型的，因此无溶剂工艺可以提高可持续性，并有助于保留提取蛋白质的天然功能，从而扩大潜在的替代工业用途。加拿大的农业部门以其豆类和油籽出口而闻名，但很少对这些原材料进行二次加工以生产更高价值的部分，导致失去了重要的经济机会和就业机会。拟议的研究旨在研究一种新的方法，融合来自多个传感器的数据与此过程中，作为一个案例研究。结果将是一个优化的过程和一套技术，使我们能够确定产品的可能的替代工业应用。