title: An in situ probe for on-line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning.
abstract: Fermentation industries would benefit from on-line monitoring of important parameters describing cell growth such as cell density and viability during fermentation processes. For this purpose, an in situ probe has been developed, which utilizes a dark field illumination unit to obtain high contrast images with an integrated CCD camera. To test the probe, brewer's yeast Saccharomyces cerevisiae is chosen as the target microorganism. Images of the yeast cells in the bioreactors are captured, processed, and analyzed automatically by means of mechatronics, image processing, and machine learning. Two support vector machine based classifiers are used for separating cells from background, and for distinguishing live from dead cells afterwards. The evaluation of the in situ experiments showed strong correlation between results obtained by the probe and those by widely accepted standard methods. Thus, the in situ probe has been proved to be a feasible device for on-line monitoring of both cell density and viability with high accuracy and stability.
authors.affiliation: Bielefeld University, Faculty of Technology, Fermentation Engineering, 33615 Bielefeld, Germany.
authors.forename: Ning
authors.initials: N
authors.lastname: Wei
authors.forename: Jia
authors.initials: J
authors.lastname: You
authors.forename: Karl
authors.initials: K
authors.lastname: Friehs
authors.forename: Erwin
authors.initials: E
authors.lastname: Flaschel
authors.forename: Tim Wilhelm
authors.initials: TW
authors.lastname: Nattkemper
mesh_terms: D001185:Artificial Intelligence; D002452:Cell Count / Q000295:instrumentation* / Q000379:methods; D018929:Cell Culture Techniques / Q000295:instrumentation / Q000379:methods; D002470:Cell Survival / Q000502:physiology*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D008029:Lighting / Q000295:instrumentation* / Q000379:methods; D008853:Microscopy / Q000295:instrumentation* / Q000379:methods; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods; D012441:Saccharomyces cerevisiae / Q000166:cytology* / Q000502:physiology*; D014159:Transducers