随着海洋中石油资源的不断开发, 泄漏到海洋环境中的石油也日益增多, 它不仅威胁着海洋生态环境, 同时也严重影响着人们的身体健康。 因此, 快速、 有效地检测出海洋环境中的石油类污染物对于保护海洋生态环境和人类健康具有重要意义。 石油产品中含有大量的多环芳烃, 其具有较强的荧光特性。 因此, 荧光光谱技术成为检测石油类污染物的重要手段之一。 利用三维荧光光谱技术结合平行因子分析算法和模式识别方法, 对石油类污染物进行表征和分类。 首先, 以海水和十二烷基硫酸钠(SDS)配制的胶束溶液作为溶剂, 分别配制不同浓度的柴油、 航空煤油、 汽油和润滑油溶液, 最终得到80个实验样本; 然后, 利用FLS920型荧光光谱仪采集实验样本的三维荧光光谱数据, 并通过Delaunay三角形内插值法对所获得的三维荧光光谱数据进行去散射处理; 其次, 利用平行因子分析（PARAFAC）算法分解去散射后的三维荧光光谱数据, 通过运用核一致诊断法和残差分析法对组分数进行估计; 最后, 为了建立稳健的分类模型, 利用Kennard-Stone算法将80个实验样本分为60个训练集样本和20个测试集样本, 运用K最近邻(KNN)算法、 主成分判别分析(PCA-LDA)算法以及偏最小二乘判别分析(PLS-DA)算法分别建立分类模型, 并利用灵敏度、 特异性和准确率对分类效果进行评估。 研究结果表明: 三种分类模型对测试集中样本的识别准确率分别为85%, 90%和94%, 其中, PLS-DA分类模型对测试集样本的识别准确率最高, 具有最佳的分类效果。 因此, 在利用平行因子分析算法提取石油类污染物荧光光谱数据的基础上, 结合模式识别方法可以很好的对不同种类油品进行分类研究。 利用三维荧光光谱技术结合平行因子分析算法和模式识别方法快速、 有效地检测油类污染物, 为石油类污染物的快速检测提供了一种新的研究思路和重要参考。

With the continuous development of petroleum resources in the ocean, more and more petroleum is leaking into the marine environment. It not only threatens the marine ecological environment but also seriously affects people’s health.Therefore, the rapid and effective detection of petroleum pollutants in the marine environment is of great significance for the protection of the marine ecological environment and human health.Petroleum products contain a large number of polycyclic aromatic hydrocarbons, which have strong fluorescence characteristics.Therefore, fluorescence spectroscopy technology has become one of the important means to detect petroleum pollutants. In this paper, three-dimensional fluorescence spectroscopy combined with parallel factor analysis algorithm and pattern recognition method is used to characterize and classify petroleum pollutants. Firstly, the micelle solution prepared by seawater and sodium dodecyl sulfate (SDS) was used as a solvent to prepare different concentrations of diesel,jet fuel, gasolineand lube solutions, and 80 experimental samples were finally obtained. Then, three-dimensional fluorescence spectra of experimental samples were collected by FLS920 fluorescence spectrometer, and the effect of scattering was removed by using the Delaunay triangle interpolation method. Secondly, the paralleled factor analysis (PARAFAC) algorithm is used to decompose the three-dimensional fluorescence spectrum data after scattering, and the component number is estimated by using the nuclear consistency diagnosis method and residual analysis method. Finally, in order to establish a robust classification model, 80 experimental samples were divided into 60 training set samples, and 20 test set samples by Kennard-Stone algorithm.The K-nearest neighbor (KNN) algorithm, principal component discriminant analysis (PCA-LDA) algorithm and partial least squares discriminant analysis (PLS-DA) algorithm are used to establish the classification model respectively, and sensitivity, specificity and accuracy are used to evaluate the classification effect.The results show that the recognition accuracy of the three classification models is 85%, 90% and 94% respectively. The PLS-DA classification model has the highest recognition accuracy and the best classification effect.Therefore, based on extracting the fluorescence spectrum data of petroleum pollutants by using parallel factor analysis algorithm and combining with the pattern recognition method, the classification of different kinds of oil products can be well studied.In this paper, three-dimensional fluorescence spectroscopy combined with parallel factor analysis algorithm and pattern recognition method is used to detect petroleum pollutants quickly and effectively, which provides a new research idea and an important reference for the rapid detection of petroleum pollutants.