One of the major aims of bioprocess engineering is the real-time monitoring of important process variables. This is the basis of precise process control and is essential for high productivity as well as the exact documentation of the overall production process. Infrared spectroscopy is a powerful analytical technique to analyze a wide variety of organic compounds. Thus, infrared sensors are ideal instruments for bioprocess monitoring. The sensors are non-invasive, have no time delay due to sensor response times, and have no influence on the bioprocess itself. No sampling is necessary, and several components can be analyzed simultaneously. In general, the direct monitoring of substrates, products, metabolites, as well as the biomass itself is possible. In this review article, insights are provided into the different applications of infrared spectroscopy for bioprocess monitoring and the complex data interpretation. Different analytical techniques are presented as well as example applications in different areas.
On-line sensors for the detection of crucial process parameters are desirable for the monitoring, control and automation of processes in the biotechnology, food and pharma industry. Fluorescence spectroscopy as a highly developed and non-invasive technique that enables the on-line measurements of substrate and product concentrations or the identification of characteristic process states. During a cultivation process significant changes occur in the fluorescence spectra. By means of chemometric modeling, prediction models can be calculated and applied for process supervision and control to provide increased quality and the productivity of bioprocesses. A range of applications for different microorganisms and analytes has been proposed during the last years. This contribution provides an overview of different analysis methods for the measured fluorescence spectra and the model-building chemometric methods used for various microbial cultivations. Most of these processes are observed using the BioView® Sensor, thanks to its robustness and insensitivity to adverse process conditions. Beyond that, the PLS-method is the most frequently used chemometric method for the calculation of process models and prediction of process variables.
For most neurodegenerative diseases the precise duration of an individual cell's death is unknown, which is an obstacle when counteractive measures are being considered. To address this, we used the rd1 mouse model for retinal neurodegeneration, characterized by phosphodiesterase-6 (PDE6) dysfunction and photoreceptor death triggered by high cyclic guanosine-mono-phosphate (cGMP) levels. Using cellular data on cGMP accumulation, cell death, and survival, we created mathematical models to simulate the temporal development of the degeneration. We validated model predictions using organotypic retinal explant cultures derived from wild-type animals and exposed to the selective PDE6 inhibitor zaprinast. Together, photoreceptor data and modeling for the first time delineated three major cell death phases in a complex neuronal tissue: (1) initiation, taking up to 36 h, (2) execution, lasting another 40 h, and finally (3) clearance, lasting about 7 h. Surprisingly, photoreceptor neurodegeneration was noticeably slower than necrosis or apoptosis, suggesting a different mechanism of death for these neurons.
In industrial fed-batch cultivations it is often necessary to control substrate concentrations at a low level to prevent the production of overflow metabolites and thus optimize the biomass yield. A new method for on-line monitoring and fed-batch control based on fluorescence measurements has been developed. Via instantaneous in situ measurements and multivariate data analysis a chemometric model has been established, which enables the rapid detection of ethanol production at aerobic Saccharomyces cerevisiae fed-batch cultivations. The glucose feed rate is controlled by predicting the metabolic state directly from the fluorescence intensities. Thus, ethanol production could be avoided completely while increasing the biomass yield accordingly. The robust instrumentation is suitable for industrial applications.
Nature has the impressive ability to efficiently and precisely control biological processes by applying highly evolved principles and using minimal space and relatively simple building blocks. The challenge is to transfer these principles into technically applicable and precise analytical systems that can be used for many applications. This article summarizes some of the new approaches in sensor technology and control strategies for different bioprocesses such as fermentations, biotransformations, and downstream processes. It focuses on bio- and chemosensors, optical sensors, DNA and protein chip technology, software sensors, and modern aspects of data evaluation for improved process monitoring and control.
For better control of productivity and product quality, detailed monitoring of various parameters is required. Since disposable bioreactors become more and more important for biotechnological applications, adequate sensors for this type of reactor are necessary. The required properties of sensors used in disposable reactors differ from those of sensors for multiuse reactors. For example, sensors which are in direct contact with the medium must be inexpensive, but do not need a long life-time, since they can be used only once.This chapter gives an overview on the state of the art and future trends in the field of sensors suited for use in disposable bioreactors. The main focus here is on in situ sensors, which can be based on optical, semiconductor and ultrasonic technologies, but current concepts for disposable sampling units are also reviewed.
The best method for process control is the use of model‐based solutions, based on process analytical technology for online monitoring of critical process variables, product quality attributes, or a holistic process state estimation. Mechanistic models as well as data‐driven techniques are essential for real‐time process monitoring. Their main characteristics, advantages and disadvantages, and the link between both are discussed as well as the synergetic effects, benefits, and drawbacks resulting from their combination. Aspects and differences of the computational model life cycle management are highlighted.
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