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- W1724871304 abstract "Bioprocess monitoring and control is a complex task that needs rapid and reliable methods which are adaptable to continuous analysis. Process monitoring during fermentation is widely applicable in the field of pharmaceutical, food and beverages and wastewater treatment. The ability to monitor has direct relevance in improving performance, quality, productivity, and yield of the process. In fact, the complexity of the bioprocesses requires almost real time insight into the dynamic process for efficient and effective control. Recently, much attention has been focused on improving process monitoring tools, especially since the launch of the Process Analytical Technology (PAT) initiative by the US Food and Drug Administration (FDA) in 2003. PAT was introduced as a concept in GMP-based bioproduction in order to improve process knowledge via continuous monitoring without a compromised product quality. In this context, many tools based on advanced sensing techniques, novel biosensors and model based software sensors are being pursued to provide advanced insight into bioprocesses. Efforts are being pursued to integrate different approaches for enhanced real time view of the bioprocess. To achieve this objective, modular systems are necessary to improve the robustness of biosensors and integrate them with other complementary techniques. This thesis work focuses on the development and automation of biosensors for process monitoring and control, employing flow-systems. Automation of biosensors in flow configurations like FIA, SIA or multi-commutation have been studied and interfaced with bioprocesses, choosing model analytes based on various biorecognition elements like enzymes, antibodies and microbial cells. Design and automation of biosensors adapting to online continuous monitoring and control of the fermentation of low molecular weight compounds like glucose, lactate, acetonitrile and higher molecular weight substances like proteins have been reported based on electrochemical and optical detections. In this work, a general and versatile automation system is described having high stability for continuous monitoring of bioprocesses. A system for flow based immunoassay useful for monitoring of protein production in a bioprocess is described. The design of such sensing systems, and their successful integration with bioprocesses provide an important step in adapting such systems for process monitoring and industrial production. An important issue is the stability of the biorecognition element. This has specificially been studied for enzymes and a method for improving the stability by a protein-based stabilizing agent is reported. In addition, a study aiming at improving the thermal stability of enzyme-based biosensors using the model glucose oxidase is described. Biosensors are becoming increasingly attractive for environment monitoring and food safety and quality. Integration of a microbial biosensor for monitoring and control of a degradation process of the organic pollutant acetonitrile is discussed. In fact, environmental applications certainly need rapid and sensitive detection techniques and here a novel immunosensor based on IgY antibody for detection of methyl parathion using chemiluminescence is reported. The sensitivity obtained in this application was close to 10 ppt of the target pesticide methyl parathion. Furthermore, an automated analyzer for precise control and stable analysis of methyl parathion is described. Finally, sensing systems utilizing both enzymes and microbial cells for the quality evaluation of food stuff and pharmaceutical applications have been developed. A highly specific microbial sensor for the detection of caffeine with rapid response time as well as a prototype for detection of polyphenols in tea has been developed and its applications illustrated. (Less)" @default.
- W1724871304 created "2016-06-24" @default.
- W1724871304 creator A5037204562 @default.
- W1724871304 date "2011-01-01" @default.
- W1724871304 modified "2023-09-25" @default.
- W1724871304 title "Biosensors and Automation for Bioprocess Monitoring and Control" @default.
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