Development of models for predicting toxicity from sediment chemistry by partial least squares-discriminant analysis and counter-propagation artificial neural networks |
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Authors: | Manuel Alvarez-Guerra José Manuel Amigo Javier R Viguri |
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Institution: | a Department of Chemical Engineering and Inorganic Chemistry, ETSIIT, University of Cantabria, Avda. de los Castros s/n, 39005 Santander, Spain b Milano Chemometrics and QSAR Research Group, Department of Environmental Sciences, University of Milano-Bicocca, P.za della Scienza 1, 20126 Milano, Italy c Department of Food Science, Quality and Technology, Faculty of Life Sciences, University of Copenhagen, Rolighedsvej 30, 1958 Frederiksberg C, Denmark |
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Abstract: | There is strong interest in developing tools to link chemical concentrations of contaminants to the potential for observing sediment toxicity that can be used in initial screening-level sediment quality assessments. This paper presents new approaches for predicting toxicity in sediments, based on 10-day survival tests with marine amphipods, from sediment chemistry, by means of the application of Partial Least Squares-Discriminant Analysis (PLS-DA) and Counter-propagation Artificial Neural Networks (CP-ANNs) to large historical databases of chemical and toxicity data. The exploration of the internal structure of the developed models revealed inherent limitations of predicting toxicity from common chemical analyses of bulk contaminant concentrations. However, the results obtained in the validation of these models combined relevant values of non-error classification rate, sensitivity and specificity of, respectively, 76, 87 and 73% with PLS-DA and 92, 75 and 97% with CP-ANNs, outperforming the results reported for previous approaches. |
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Keywords: | Sediment Quality assessment Toxicity Prediction Mathematical models |
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