Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark.
Grupo Química Analítica Aplicada (QANAP), Departamento de Química Analítica, Universidade da Coruña, Campus da Zapateira, 15071 A Coruña, Spain.
Mar Pollut Bull. 2015 Jul 15;96(1-2):313-20. doi: 10.1016/j.marpolbul.2015.04.053. Epub 2015 Apr 29.
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.
溢油识别通常依赖于大量的色谱数据，这需要先进的数据处理（化学计量学）。本文提出了一种基于 Kohonen 神经网络处理三维数组的简单方法。考虑了一组 28 种诊断比值来监测四个月内的六种油。结果发现，一些传统的诊断比值不够稳定。特别是烷基化多环芳烃（如 1-甲基二苯并噻吩、4-甲基芘、27bbSTER 和 TA21 和 TA26 三芳甾醇）似乎比生物标志物更容易受到中等风化的影响。发现一个（或两个）比值可以区分每种产品：30O、28ab（和 25nor30ab）、C3-dbt/C3-phe、27Ts、TA26 和 29Ts 分别代表 Ashtart、Brent、Maya、Sahara、IFO 和 Prestige 油。
