浓度-发射矩阵（CEM）光谱结合 GA-SVM：一种识别海洋中油种的分析方法。
Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine.
机构信息
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China.
Hefei Institutes of Physical Science, University of Science and Technology of China, Hefei 230026, China.
出版信息
Molecules. 2020 Nov 4;25(21):5124. doi: 10.3390/molecules25215124.
The establishment and development of a set of methods of oil accurate recognition in a different environment are of great significance to the effective management of oil spill pollution. In this work, the concentration-emission matrix (CEM) is formed by introducing the concentration dimension. The principal component analysis (PCA) is applied to extract the spectral feature. The classification methods, such as Probabilistic Neural Networks (PNNs) and Genic Algorithm optimization Support Vector Machine (SVM) parameters (GA-SVM), are used for oil identification and the recognition accuracies of the two classification methods are compared. The results show that the GA-SVM combined with PCA has the highest recognition accuracy for different oils. The proposed approach has great potential in rapid and accurate oil source identification.
建立和发展一套在不同环境下进行油类准确识别的方法，对于有效管理溢油污染具有重要意义。在这项工作中，通过引入浓度维形成浓度发射矩阵（CEM）。应用主成分分析（PCA）提取光谱特征。采用概率神经网络（PNNs）和遗传算法优化支持向量机（SVM）参数（GA-SVM）等分类方法进行油类识别，并比较了两种分类方法的识别准确率。结果表明，GA-SVM 与 PCA 相结合对不同油类具有最高的识别准确率。该方法在快速准确的油源识别方面具有很大的潜力。
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