Literature DB >> 17274069

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.

Ning Wei1, Jia You, Karl Friehs, Erwin Flaschel, Tim Wilhelm Nattkemper.   

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. (c) 2007 Wiley Periodicals, Inc.

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Year:  2007        PMID: 17274069     DOI: 10.1002/bit.21368

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  5 in total

1.  Real-time monitoring of cell viability and cell density on the basis of a three dimensional optical reflectance method (3D-ORM): investigation of the effect of sub-lethal and lethal injuries.

Authors:  Alison Brognaux; Jörg Bugge; Friedel H Schwartz; Philippe Thonart; Samuel Telek; Frank Delvigne
Journal:  J Ind Microbiol Biotechnol       Date:  2013-04-21       Impact factor: 3.346

2.  Mycelium differentiation and antibiotic production in submerged cultures of Streptomyces coelicolor.

Authors:  Angel Manteca; Ruben Alvarez; Nuria Salazar; Paula Yagüe; Jesus Sanchez
Journal:  Appl Environ Microbiol       Date:  2008-04-25       Impact factor: 4.792

3.  Nominated texture based cervical cancer classification.

Authors:  Edwin Jayasingh Mariarputham; Allwin Stephen
Journal:  Comput Math Methods Med       Date:  2015-01-14       Impact factor: 2.238

4.  A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification.

Authors:  Ning Wei; Erwin Flaschel; Karl Friehs; Tim Wilhelm Nattkemper
Journal:  BMC Bioinformatics       Date:  2008-10-21       Impact factor: 3.169

5.  Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering.

Authors:  Mengmeng Wang; Lee-Ling Sharon Ong; Justin Dauwels; H Harry Asada
Journal:  J Med Imaging (Bellingham)       Date:  2018-06-13
  5 in total

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