基于TSFS结合高阶张量特征提取方法的海水半潜油种类鉴别研究

半潜油是一种隐藏于海面之下并呈现悬浮状态的溢油，其长期毒害并侵蚀着海洋生态环境。然而，针对半潜油污染到目前还未形成有效地监测手段和处理方式，致使其污染的突发性和危害性更甚于海面溢油。因此，研究有效地半潜油鉴别方法对保护海洋生态环境具有重要意义。三维荧光光谱技术中的总同步荧光光谱(TSFS)在油类污染物检测与鉴别中具有不存在瑞利散射干扰以及冗余数据少的优势，但由于TSFS数据本身不具备三线性结构，使得多维校正分析方法在其应用上受到了一定的限制。基于此，开展基于TSFS结合高阶张量特征提取方法的海水半潜油种类鉴别研究。首先，利用有机分散剂和六种不同种类的油品配制了90个半潜油实验样本；然后，利用FS920荧光光谱仪采集实验样本的TSFS数据，并对该数据进行标准化预处理；最后，通过高阶张量特征提取方法二维线性判别分析(2D-LDA)以及二维主成分分析(2D-PCA)分别建立了半潜油样本的鉴别模型；并将所建模型与常规方法多元曲线分辨率交替最小二乘法(MCR-ALS)结合线性判别分析(LDA)以及多维偏最小二乘判别分析(NPLS-DA)进行了对比。分析结果表明，2D-LDA和2D-PCA所建立的半潜油样本鉴别模型具有可靠的性能，鉴别模型的精确率、灵敏度及特异性分别为100%，100%和100%。并且，2D-LDA和2D-PCA能够直接提取TSFS光谱图像矩阵在空间、统计学以及图形学上的精细光谱特征，为区分半潜油样本带来更为精准的鉴别依据。因此，相较于常规的基于展开或分解数据的方法，高阶张量特征提取方法所建立鉴别模型所得到的预测结果更加精确。该研究为半潜油种类鉴别提供了一种参考。

Study on Identification Seawater Submersible Oil Based on Total Synchronous Fluorescence Spectroscopy Combined With High-Order Tensor Feature Extraction Algorithm

Submersible oil is a kind of oil spill hidden under the sea surface in a suspended state. It has poisoned and eroded the marine ecological environment for a long time. However, effective monitoring means and treatment methods have not been formed for submersible oil pollution, which makes its pollution more sudden and harmful than a sea oil spill. Therefore, it is of great significance to studying effective submersible oil identification methods to protect the marine ecological environment. The TSFS in three-dimensional fluorescence spectroscopy has the advantages of no Rayleigh scattering interference and less redundant data in detecting and identifying oil pollutants. The application of the multidimensional correction analysis method to TSFS data is limited because it does not have a trilinear structure. Thus, a new identification method for seawater submersible oil samples was proposed by combining TSFS with a high-order tensor feature extraction algorithm. First, 90 submersible samples were prepared by using organic dispersants and six different kinds of oil products. Then, the TSFS data of samples were collected using an FS920 fluorescence spectrometer, and the data were preprocessed by standardized. Finally, the identification models of submersible oil samples were established by 2D-LDA and 2D-PCA in the high-order tensor feature extraction method. The established model was compared with the identification model established by conventional MCR-ALS-LDA and NPLS-DA. The results show that the submersible oil sample identification models established by 2D-LDA and 2D-PCA have robust and reliable performance, and the accuracy, sensitivity and specificity of the identification models were 100%, 100% and 100%, respectively. In addition, the fine spectral features of the TSFS spectral image matrix in space, statistics, and graphics can be directly extracted by 2D-LDA and 2D-PCA, which brings a more accurate identification basis for distinguishing submersible oil samples. Therefore, compared with the conventional methods based on expansion or decomposition of data, the more accurate prediction results were obtained by the discrimination model established by the high-order tensor feature extraction method. This study provides a reference for submersible oil identification.