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

监控工业过程中的各种参数的能力能够提高产品质量、过程效率、可持续性，并最终降低成本。在生物过程中，产品的复杂性质给过程监测带来了许多挑战。光学传感器可以捕获数据，其中包含化学信息和与产品功能相关的信息。收集这个复杂的数据集后面临的部分挑战是“破译”数据以提供有关感兴趣的变量(S)的信息。破译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，荧光和近红外光谱。我们将把我们在液体样品分析和荧光方面的成功扩展到粉末样品，重点是开发一种新的、无溶剂的分离工艺，用于从磨制的农产品(如豆粉)中生产富含蛋白质和淀粉的馏分。近年来，人们对豆类或豆类蛋白的兴趣显著增加，因为这些来源的脂肪含量低，营养丰富，因此有许多动机来探索这一新方法。传统工艺是水和能源密集型工艺，因此无溶剂工艺在可持续性方面提供了改进，并有助于保存提取蛋白质的天然功能；从而扩大了潜在替代工业用途的花名册。加拿大农业部门以出口豆类和油籽而闻名，但很少对这些原材料进行二次加工以生产更高价值的馏分，导致失去重大经济机会和就业发展。这项拟议的研究旨在检验一种将来自多个传感器的数据与这一过程融合的新方法，作为案例研究。其结果将是一种优化的工艺和一套技术，使我们能够确定产品可能的替代工业应用。 这项研究将扩大到开发一种在线数据收集和处理方法，并将其应用于天然有机物(NOM)，因为它与饮用水处理有关。气候变化和风暴事件的影响引发了人们对更好的工具的兴趣，这些工具用于监测对原水的影响，并研究基于信息而不是完全基于专家的可能控制策略。这两个传感器的组合将增加可用的化学信息量，并允许与重大健康问题的重要参数(例如消毒副产品、基因和细胞毒性以及污染事件)相关联。这项研究将影响我们遵守或遵守及时监管和监测饮用水中这些产品的新要求的能力。

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. The research will be extended to develop an on-line approach of data collection and processing, with application to natural organic matter (NOM) as it relates to drinking water treatment. The effects of climate change and storm events are driving an interest in better tools for monitoring the impact on raw water and for looking at possible control strategies that are information based, rather than exclusively expert based. Combining the two sensors will enhance the amount of chemical information available and allow correlation with important parameters of significant health concern, (e.g. disinfection by-products, geno- and cytotoxicity and contamination events). This research will impact our ability to conform or comply with emerging requirements for regulating and monitoring these products in drinking water in a timely fashion.