基于稀疏非负矩阵分解和支持向量机的海洋溢油近红外光谱鉴别分析  

Identification of Spilled Oil by NIR Spectroscopy Technology Based on Sparse Nonnegative Matrix Factorization and Support Vector Machine

作　　者：谈爱玲[1] 毕卫红[1] 赵勇[2] 

机构地区：[1]燕山大学信息科学与工程学院光电子工程系,河北秦皇岛066004 [2]燕山大学电气工程学院生物医学工程系,河北秦皇岛066004

出　　处：《光谱学与光谱分析》2011年第5期1250-1253,共4页Spectroscopy and Spectral Analysis

基　　金：教育部高等学校科技创新工程重大项目培育基金项目(708025);河北省自然科学基金项目(F2010001268);秦皇岛市科学技术研究与发展计划项目(200901A032)资助

摘　　要：提出一种海洋溢油近红外光谱特征提取与种类鉴别新方法。海面溢油种类鉴别对现场应急处置方案的制定和可疑溢油源的追踪具有重要意义。采用傅里叶变换近红外光谱仪测定汽油、柴油、煤油三类模拟海洋溢油样本的近红外光谱,基于稀疏非负矩阵分解算法对光谱进行特征提取,采用五重交叉检验,对210个样本进行训练,建立基于支持向量机的溢油光谱定性分析模型,同时讨论非负特征基数目以及稀疏因子对分类正确率的影响;利用训练好的分类器对90个未知样本进行鉴别,识别正确率达97.78%。所提出的稀疏非负矩阵分解结合支持向量机的近红外光谱定性分析方法,识别正确率高,模型泛化能力强,具有很好的分类效果,为海洋溢油的快速鉴别提供了新途径。A novel method was proposed to discriminate different kinds of spilled oil.The identification of the spilled oils has great significance to developing the treatment program and tracking the source.The present method adapts to Fourier transform NIR spectrophotometer to collect the spectral data of simulation gasoline,diesel fuel and kerosene oil spills.The Sparse Nonnegative Matrix Factorization algorithm was used to extract features.Through training with 210 samples and 5-fold cross-validation,the authors constructed the qualitatvie analysis model based on support vector machine.The authors also researched the effect of the number of features and sparseness factor.The proposed method has the identification capabilities with the accuracy of 97.78% for 90 samples for validation.The present method of SNMF-SVM has a good identification effect and strong generalization ability,and can work as a new method for rapid identification of spilled oil.

关 键 词：近红外光谱 海洋溢油 稀疏非负矩阵分解 支持向量机 

分 类 号：O657.3[理学—分析化学]