Literature DB >> 12701901

On-line monitoring of biofilm formation in a brewery water pipeline system with a fibre optical device.

L Tamachkiarow1, H C Flemming.   

Abstract

Any advanced anti-fouling strategy must be based on early warning systems which allow for timely, precisely directed and optimized countermeasures. Such systems must be able to detect biofilm growth on representative surfaces. In order to meet this requirement, a fibre optical device (FOS) has been developed. It is based on light which is scattered by objects deposited on the tip of an optical fibre. A receiving fibre collects the signal and forwards it to a detection and quantification unit. Both the sending and the receiving fibre are mounted in a measuring head which is integrated evenly on the inner surface of a water pipeline at representative sites. This device was applied to a water system of a brewery in order toput its reliability to test under practical conditions. The FOS detected the build-up of a deposit which was identified independently as consisting of microorganisms, i.e., a biofilm. A stable, well detectable and reproducible signal could be obtained above a colonization of 10(5) cells cm-2. Adjustment of the sensitivity of the amplifier allowed for detection of biofilms up to 10(10) cells cm-2. Cleaning countermeasures could be detected clearly by a decrease of backscattered light intensity. The system proved to be suitable for on-line, non-destructive, real-time and automatic monitoring for a period of almost two years, and thus, provides an important constituent for an advanced anti-fouling strategy.

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Year:  2003        PMID: 12701901

Source DB:  PubMed          Journal:  Water Sci Technol        ISSN: 0273-1223            Impact factor:   1.915


  4 in total

1.  Biofilm thickness measurement using an ultrasound method in a liquid phase.

Authors:  R Maurício; C J Dias; N Jubilado; F Santana
Journal:  Environ Monit Assess       Date:  2013-03-15       Impact factor: 2.513

2.  CO2 production as an indicator of biofilm metabolism.

Authors:  Otini Kroukamp; Gideon M Wolfaardt
Journal:  Appl Environ Microbiol       Date:  2009-04-03       Impact factor: 4.792

3.  Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression.

Authors:  Alessandro Simeone; Elliot Woolley; Josep Escrig; Nicholas James Watson
Journal:  Sensors (Basel)       Date:  2020-06-29       Impact factor: 3.576

Review 4.  Optical Sensing of Microbial Life on Surfaces.

Authors:  M Fischer; G J Triggs; T F Krauss
Journal:  Appl Environ Microbiol       Date:  2015-12-04       Impact factor: 5.005

  4 in total

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