Oil spill identification relies usually on a wealth of chromatographic data which requires advanced data treatment (chemometrics). A simple approach based on Kohonen neural networks to handle three-dimensional arrays is presented. A suite of 28 diagnostic ratios was considered to monitor six oils along four months. It was found that some traditional diagnostic ratios were not stable enough. In particular, alkylated PAHs (e.g. 1-methyldibenzothiophene, 4-methylpyrene, 27bbSTER and the TA21 and TA26 triaromatic steroids) seemed less resistant to medium-weathering than biomarkers. One (or two) ratios were found to differentiate each product: 30O, 28ab (and 25nor30ab), C3-dbt/C3-phe, 27Ts, TA26 and 29Ts characterized Ashtart, Brent, Maya, Sahara, IFO and Prestige oils, respectively.
Fernández Varela, R., Gómez Carracedo, M., Ballabio, D., Andrade, J. (2015). The use of diagnostic ratios, biomarkers and 3-way Kohonen neural networks to monitor the temporal evolution of oil spills. MARINE POLLUTION BULLETIN, 96(1-2), 313-320 [10.1016/j.marpolbul.2015.04.053].
The use of diagnostic ratios, biomarkers and 3-way Kohonen neural networks to monitor the temporal evolution of oil spills
BALLABIO, DAVIDEPenultimo
;
2015
Abstract
Oil spill identification relies usually on a wealth of chromatographic data which requires advanced data treatment (chemometrics). A simple approach based on Kohonen neural networks to handle three-dimensional arrays is presented. A suite of 28 diagnostic ratios was considered to monitor six oils along four months. It was found that some traditional diagnostic ratios were not stable enough. In particular, alkylated PAHs (e.g. 1-methyldibenzothiophene, 4-methylpyrene, 27bbSTER and the TA21 and TA26 triaromatic steroids) seemed less resistant to medium-weathering than biomarkers. One (or two) ratios were found to differentiate each product: 30O, 28ab (and 25nor30ab), C3-dbt/C3-phe, 27Ts, TA26 and 29Ts characterized Ashtart, Brent, Maya, Sahara, IFO and Prestige oils, respectively.
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