Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality.

The application of mathematical tools in initial steps of sediment quality assessment frameworks can be useful to provide an integrated interpretation of multiple measured variables. This study reveals that the Self-Organizing Map (SOM) artificial neural network can be an effective tool for the integration of multiple physical, chemical and ecotoxicological variables in order to classify different sites under study according to their similar sediment quality. Sediment samples from 40 sites of 3 estuaries of Cantabria (Spain) were classified with respect to 13 physical, chemical and toxicological variables using the SOM.

Validated analytical strategy for the determination of polycyclic aromatic compounds in marine sediments by liquid chromatography coupled with diode-array detection and mass spectrometry.

The aim of this work was to optimise and validate the experimental conditions for the analysis of 20 polycyclic aromatic compounds (PACs) [19 polycyclic aromatic hydrocarbons (PAHs) and dibenzothiophene as polycyclic aromatic sulphur heterocycle (PASH)] in marine sediments by reversed-phase high-performance liquid chromatography (LC) coupled to photodiode array detection (DAD) and to mass spectrometry (MS). The LC-MS interface used was atmospheric pressure chemical ionization (APCI) in the positive ion mode. The operational parameters of the APCI interface and MS detection, such as organic modifier, fragmentation voltage, gain, vaporizer temperature, corona current, capillary voltage, drying gas (N2) and nebulizer pressure, were studied.