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Toxicity fingerprinting of pollutants and environmental effluents using lux bacterial biosensors

A constitutive <I>lux</I>-marked biosensor, <I>E. coli</I> HB101 pUCD607, was developed as a novel, acute, 'fingerprinting' technique by which individual pollutants, and whole effluents, could be identified and quantified. In this way, the advantages of both chemical analysis and toxicology were encompassed in one technique. An assay technique for obtaining temporal response curves was developed by injecting the biosensor into toxic samples, and then measuring luminescence continuously for 5 minutes. An algorithm was formulated, and implemented as a simple computer program, by which 'unknown' temporal response-curves could be compared against a database of 'reference' pollutant response-curves. Two different techniques were derived for analysing the output data; a binomial (yes or no) 'best-fit' technique, and a continuous 'comparative-fit' technique. These techniques were validated by 94% identification success for inter-comparisons of 7 pollutants, each at 5 concentrations, and 100% identification success of four pollutant spikes in three environmental samples. The technique was tested on three complex environmental effluents. Cu was identified as the main pollutant in a distillery effluent, and Zn the main pollutant in a metal processing effluent. Identification was successful despite the presence of complexing substrates and co-pollutants. The technique could also differentiate between three complex effluents and four artificial complex effluents without the need for actual pollutant identification. In conclusion, this technology can be used to 'fingerprint' any form of pollution that is bioavailable to the biosensor, without the need for prior knowledge of sample chemistry. This represents a potentially valuable tool for toxicity assessment and screening.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:368897
Date January 2001
CreatorsTurner, Nigel Laurence
PublisherUniversity of Aberdeen
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttp://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU145382

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