Improved multianalyte detection of organophosphates and carbamates with disposable multielectrode biosensors using recombinant mutants of Drosophila acetylcholinesterase and artificial neural networks

Engineered variants of Drosophila melanogaster acetylcholinesterase (AChE) were used as biological receptors of AChE-multisensors for the simultaneous detection and discrimination of binary mixtures of cholinesterase-inhibiting insecticides. The system was based on a combination of amperometric multielectrode biosensors with chemometric data analysis of sensor outputs using artificial neural networks (ANN). The multisensors were fully manufactured by screen-printing, including enzyme immobilisation. Two types of multisensors were produced that consisted of four AChE variants each. The AChE mutants were selected in order to obtain high resolution, enhanced sensitivity and minimal assay time. This task was successfully achieved using multisensor I equipped with wild-type Drosophila AChE and mutants Y408F, F368L, and F368H. Each of the AChE variants was selected on the basis of displaying an individual sensitivity pattern towards the target analytes. For multisensor II, the inclusion of F368W, which had an extremely diminished paraoxon sensitivity, increased the sensor's capacity even further. Multisensors I and II were both used for inhibition analysis of binary paraoxon and carbofuran mixtures in a concentration range 0-5 mu g/l, followed by data analysis using feed-forward ANN. The two analytes were determined with prediction errors of 0.4 mu g/l for paraoxon and 0.5 mu g/l for carbofuran. A complete biosensor assay and subsequent ANN evaluation was completed within 40 min. In addition, multisensor II was also investigated for analyte discrimination in real water samples. Finally, the properties of the multisensors were confirmed by simultaneous detection of binary organophosphate mixtures. Malaoxon and paraoxon in composite solutions of 0-5 mu g/l were discriminated with predication errors of 0.9 and 1.6 mu g/l, respectively. (C) 2000 Elsevier Science S.A. All rights reserved.