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Cell viability is a key factor in biotechnological industries and researches in which cells of all kinds of organisms are dealing with. As final products are strongly influenced by the cell performance, monitoring the cell viability during the fermentations is a necessity. A real time in-situ microscopic sensor using dark-field technology is developed for this purpose. Cell images taken at different time points are processed for automatically identification of living and dead cells. This procedure is based on supervised machine learning technique. Support vector machine is applied and satisfying results show promising applications in fermentation industries.
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Author(s): Ning Wei Axel Saalbach Thorsten Twellmann Tim W NattkemperErwin Flashel
Viability, Time-lapse image, Dark field, In-situ, Machine learning, Support vector machine