Bioreactors are closed systems in which microorganisms can be cultivated under defined, controllable conditions that can be optimized with regard to viability, reproducibility, and product-oriented productivity. To drive the biochemical reaction network of the biological system through the desired reaction optimally, the complex interactions of the overall system must be understood and controlled. Optical sensors which encompass all analytical methods based on interactions of light with matter are efficient tools to obtain this information. Optical sensors generally offer the advantages of noninvasive, nondestructive, continuous, and simultaneous multianalyte monitoring. However, at this time, no general optical detection system has been developed. Since modern bioprocesses are extremely complex and differ from process to process (e.g., fungal antibiotic production versus mammalian cell cultivation), appropriate analytical systems must be set up from different basic modules, designed to meet the special demands of each particular process. In this minireview, some new applications in bioprocess monitoring of the following optical sensing principles will be discussed: UV spectroscopy, IR spectroscopy, Raman spectroscopy, fluorescence spectroscopy, pulsed terahertz spectroscopy (PTS), optical biosensors, in situ microscope, surface plasmon resonance (SPR), and reflectometric interference spectroscopy (RIF).

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The in-situ microscope is a system developed to acquire images of mammalian cells directly inside a bioreactor (in-situ) duringa fermentation process. It requires only minimal operator intervention and it is well suited for either batch or long-termperfusion fermentation runs. The system fits into a 25 mm standard port and has a retractable housing, similar to the industry standard InTrac. Therefore, it can be cleaned and serviced without interruption of the process or risking contamination. A sampling zone inside the bioreactor encloses adefined volume of culture and an image sequence is taken. The height of the sampling zone is set by the control program and canbe adjusted during the cultivation to accommodate a wide range of change in cell density. The system has an infinity correctedoptical train and uses a progressive scan CCD camera to acquirehigh quality images. Process relevant information like cell density is extracted fromthe images by digital image processing software, currently in development for mammalian cells (CHO, BHK). The first version ofthe software will be able to estimate the cell density, cellsize distribution and to give information of the degree of aggregation (single and double cells, cell clusters).

This paper describes a new cell cluster segmentation algorithm based on global and local thresholding for in-situ microscopy. The global threshold is estimated by applying a known Maximum Likelihood Thresholding technique. Assuming that the background pixels around a cluster have similar intensity values, the local threshold used to improve the segmented region after global thresholding is estimated as the average of the intensity values of a set of selected surrounding background pixels of that region. First, all pixels on the border of the segmented region are defined as possible candidates of surrounding background pixels. Then, an algorithm based on RANSAC (RANdom SAmple Consensus) is applied to detect outliers within the candidates. Only the inliers are used for estimation of the local threshold value. The algorithm was applied to real intensity images captured by an in-situ microscope. The experimental results show that the segmentation accuracy improved by 82%.