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
Wei N, You J, Friehs K, Flaschel E, Nattkemper TW (2007)
Biotechnology and Bioengineering 97(6): 1489-1500.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Einrichtung
Centrum für Biotechnologie > Arbeitsgruppe E. Flaschel
Technische Fakultät > AG Biodata Mining
Centrum für Biotechnologie > Graduate Center
Centrum für Biotechnologie > Arbeitsgruppe T. Nattkemper
Centrum für Biotechnologie > Institut für Biochemie und Biotechnik
Centrum für Biotechnologie > Institut für Bioinformatik
Technische Fakultät > AG Fermentationstechnik
Technische Fakultät > AG Biodata Mining
Centrum für Biotechnologie > Graduate Center
Centrum für Biotechnologie > Arbeitsgruppe T. Nattkemper
Centrum für Biotechnologie > Institut für Biochemie und Biotechnik
Centrum für Biotechnologie > Institut für Bioinformatik
Technische Fakultät > AG Fermentationstechnik
Abstract / Bemerkung
Fermentation industries would benefit from on-line monitoring of important parameters describing cell growth such as cell density and variability 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 accuracy and stability.
Stichworte
image processing;
cell density;
support vector machine;
dark field microscopy;
viability;
on-line measurement
Erscheinungsjahr
2007
Zeitschriftentitel
Biotechnology and Bioengineering
Band
97
Ausgabe
6
Seite(n)
1489-1500
ISSN
0006-3592
eISSN
1097-0290
Page URI
https://pub.uni-bielefeld.de/record/1593120
Zitieren
Wei N, You J, Friehs K, Flaschel E, Nattkemper TW. 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. Biotechnology and Bioengineering. 2007;97(6):1489-1500.
Wei, N., You, J., Friehs, K., Flaschel, E., & Nattkemper, T. W. (2007). 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. Biotechnology and Bioengineering, 97(6), 1489-1500. https://doi.org/10.1002/bit.21368
Wei, Ning, You, Jia, Friehs, Karl, Flaschel, Erwin, and Nattkemper, Tim Wilhelm. 2007. “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”. Biotechnology and Bioengineering 97 (6): 1489-1500.
Wei, N., You, J., Friehs, K., Flaschel, E., and Nattkemper, T. W. (2007). 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. Biotechnology and Bioengineering 97, 1489-1500.
Wei, N., et al., 2007. 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. Biotechnology and Bioengineering, 97(6), p 1489-1500.
N. Wei, et al., “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”, Biotechnology and Bioengineering, vol. 97, 2007, pp. 1489-1500.
Wei, N., You, J., Friehs, K., Flaschel, E., Nattkemper, T.W.: 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. Biotechnology and Bioengineering. 97, 1489-1500 (2007).
Wei, Ning, You, Jia, Friehs, Karl, Flaschel, Erwin, and Nattkemper, Tim Wilhelm. “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”. Biotechnology and Bioengineering 97.6 (2007): 1489-1500.
Daten bereitgestellt von European Bioinformatics Institute (EBI)
11 Zitationen in Europe PMC
Daten bereitgestellt von Europe PubMed Central.
Nominated texture based cervical cancer classification.
Mariarputham EJ, Stephen A., Comput Math Methods Med 2015(), 2015
PMID: 25649913
Mariarputham EJ, Stephen A., Comput Math Methods Med 2015(), 2015
PMID: 25649913
A semiautomatic cell counting tool for quantitative imaging of tissue engineering scaffolds.
De Boodt S, Poursaberi A, Schrooten J, Berckmans D, Aerts JM., Tissue Eng Part C Methods 19(9), 2013
PMID: 23327105
De Boodt S, Poursaberi A, Schrooten J, Berckmans D, Aerts JM., Tissue Eng Part C Methods 19(9), 2013
PMID: 23327105
In situ microscopy: a perspective for industrial bioethanol production monitoring.
Belini VL, Wiedemann P, Suhr H., J Microbiol Methods 93(3), 2013
PMID: 23524154
Belini VL, Wiedemann P, Suhr H., J Microbiol Methods 93(3), 2013
PMID: 23524154
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.
Brognaux A, Bugge J, Schwartz FH, Thonart P, Telek S, Delvigne F., J Ind Microbiol Biotechnol 40(7), 2013
PMID: 23604555
Brognaux A, Bugge J, Schwartz FH, Thonart P, Telek S, Delvigne F., J Ind Microbiol Biotechnol 40(7), 2013
PMID: 23604555
Determination of yeast viability during a stress-model alcoholic fermentation using reagent-free microscopy image analysis.
Tibayrenc P, Ghommidh C, Preziosi-Belloy L., Biotechnol Prog 27(2), 2011
PMID: 21290616
Tibayrenc P, Ghommidh C, Preziosi-Belloy L., Biotechnol Prog 27(2), 2011
PMID: 21290616
Microscopic characterisation of filamentous microbes: towards fully automated morphological quantification through image analysis.
Barry DJ, Williams GA., J Microsc 244(1), 2011
PMID: 21812778
Barry DJ, Williams GA., J Microsc 244(1), 2011
PMID: 21812778
A review of non-invasive optical-based image analysis systems for continuous bioprocess monitoring.
Höpfner T, Bluma A, Rudolph G, Lindner P, Scheper T., Bioprocess Biosyst Eng 33(2), 2010
PMID: 19396466
Höpfner T, Bluma A, Rudolph G, Lindner P, Scheper T., Bioprocess Biosyst Eng 33(2), 2010
PMID: 19396466
In-situ imaging sensors for bioprocess monitoring: state of the art.
Bluma A, Höpfner T, Lindner P, Rehbock C, Beutel S, Riechers D, Hitzmann B, Scheper T., Anal Bioanal Chem 398(6), 2010
PMID: 20835863
Bluma A, Höpfner T, Lindner P, Rehbock C, Beutel S, Riechers D, Hitzmann B, Scheper T., Anal Bioanal Chem 398(6), 2010
PMID: 20835863
A non-destructive digital imaging method to predict immobilized yeast-biomass
Acevedo CristianA, Skurtys Olivier, Young ManuelE, Enrione Javier, Pedreschi Franco, Osorio Fernando., Lebenson Wiss Technol 42(8), 2009
PMID: IND44238360
Acevedo CristianA, Skurtys Olivier, Young ManuelE, Enrione Javier, Pedreschi Franco, Osorio Fernando., Lebenson Wiss Technol 42(8), 2009
PMID: IND44238360
Mycelium differentiation and antibiotic production in submerged cultures of Streptomyces coelicolor.
Manteca A, Alvarez R, Salazar N, Yagüe P, Sanchez J., Appl Environ Microbiol 74(12), 2008
PMID: 18441105
Manteca A, Alvarez R, Salazar N, Yagüe P, Sanchez J., Appl Environ Microbiol 74(12), 2008
PMID: 18441105
A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selection and classification.
Wei N, Flaschel E, Friehs K, Nattkemper TW., BMC Bioinformatics 9(), 2008
PMID: 18939996
Wei N, Flaschel E, Friehs K, Nattkemper TW., BMC Bioinformatics 9(), 2008
PMID: 18939996
25 References
Daten bereitgestellt von Europe PubMed Central.
In situ microscopy for on-line determination of biomass.
Bittner C, Wehnert G, Scheper T., Biotechnol. Bioeng. 60(1), 1998
PMID: 10099402
Bittner C, Wehnert G, Scheper T., Biotechnol. Bioeng. 60(1), 1998
PMID: 10099402
Castro-Concha, 2006
Claes, Bioproc Biosyst Eng 21(), 1999
Viability measurements in mammalian cell systems.
Cook JA, Mitchell JB., Anal. Biochem. 179(1), 1989
PMID: 2667390
Cook JA, Mitchell JB., Anal. Biochem. 179(1), 1989
PMID: 2667390
Cortes, Mach Learn 20(), 1995
Haykin, 1999
Heath, 2005
Heggart, Tech q. - Master Brew Assoc Am 37(), 2000
A computer-aided measuring system for the characterization of yeast populations combining 2D-image analysis, electronic particle counter, and flow cytometry.
Huls PG, Nanninga N, van Spronsen EA, Valkenburg JA, Vishcer NO, Woldringh CL., Biotechnol. Bioeng. 39(3), 1992
PMID: 18600951
Huls PG, Nanninga N, van Spronsen EA, Valkenburg JA, Vishcer NO, Woldringh CL., Biotechnol. Bioeng. 39(3), 1992
PMID: 18600951
In-situ microscopy: Online process monitoring of mammalian cell cultures.
Joeris K, Frerichs JG, Konstantinov K, Scheper T., Cytotechnology 38(1-3), 2002
PMID: 19003094
Joeris K, Frerichs JG, Konstantinov K, Scheper T., Cytotechnology 38(1-3), 2002
PMID: 19003094
Kronlof, 1991
Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedure.
Long X, Cleveland WL, Yao YL., Comput. Biol. Med. 36(4), 2006
PMID: 16488772
Long X, Cleveland WL, Yao YL., Comput. Biol. Med. 36(4), 2006
PMID: 16488772
Nattkemper, IEEE T Inf Technol B 5(), 2001
Human vs machine: evaluation of fluorescence micrographs.
Nattkemper TW, Twellmann T, Ritter H, Schubert W., Comput. Biol. Med. 33(1), 2003
PMID: 12485628
Nattkemper TW, Twellmann T, Ritter H, Schubert W., Comput. Biol. Med. 33(1), 2003
PMID: 12485628
Morphological characterization of yeast by image analysis.
Pons MN, Vivier H, Remy JF, Dodds JA., Biotechnol. Bioeng. 42(11), 1993
PMID: 18612963
Pons MN, Vivier H, Remy JF, Dodds JA., Biotechnol. Bioeng. 42(11), 1993
PMID: 18612963
Rätsch, Lect Notes Comput Sci 3175(), 2004
Scheper, 1991
Biomass determination.
Sonnleitner B, Locher G, Fiechter A., J. Biotechnol. 25(1-2), 1992
PMID: 1368462
Sonnleitner B, Locher G, Fiechter A., J. Biotechnol. 25(1-2), 1992
PMID: 1368462
In situ microscopy for on-line characterization of cell-populations in bioreactors, including cell-concentration measurements by depth from focus.
Suhr H, Wehnert G, Schneider K, Bittner C, Scholz T, Geissler P, Jahne B, Scheper T., Biotechnol. Bioeng. 47(1), 1995
PMID: 18623372
Suhr H, Wehnert G, Schneider K, Bittner C, Scholz T, Geissler P, Jahne B, Scheper T., Biotechnol. Bioeng. 47(1), 1995
PMID: 18623372
Applications of image analysis in cell technology.
Thomas CR, Paul GC., Curr. Opin. Biotechnol. 7(1), 1996
PMID: 8791309
Thomas CR, Paul GC., Curr. Opin. Biotechnol. 7(1), 1996
PMID: 8791309
Van, 1998
Biotechnological applications of image analysis: present and future prospects.
Vecht-Lifshitz SE, Ison AP., J. Biotechnol. 23(1), 1992
PMID: 1367947
Vecht-Lifshitz SE, Ison AP., J. Biotechnol. 23(1), 1992
PMID: 1367947
Studies of on-line viable yeast biomass with a capacitance biomass monitor.
Austin GD, Watson RW, D'Amore T., Biotechnol. Bioeng. 43(4), 1994
PMID: 18615698
Austin GD, Watson RW, D'Amore T., Biotechnol. Bioeng. 43(4), 1994
PMID: 18615698
Wei, 2005
Export
Markieren/ Markierung löschen
Markierte Publikationen
Web of Science
Dieser Datensatz im Web of Science®Quellen
PMID: 17274069
PubMed | Europe PMC
Suchen in