三维荧光光谱结合IGOA-SVM分类鉴别油类污染物  

Classification of Oil Pollutants by Three-Dimensional Fluorescence Spectroscopy Combined With IGOA-SVM

作　　者：程朋飞 朱燕萍 潘金燕 崔传金 张怡 CHENG Peng-fei;ZHU Yan-ping;PAN Jin-yan;CUI Chuan-jin;ZHANG Yi(School of Electrical and Control Engineering,Xuzhou University of Technology,Xuzhou 221018,China;College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China)

机构地区：[1]徐州工程学院电气与控制工程学院,江苏徐州221018 [2]华北理工大学电气工程学院,河北唐山063210

出　　处：《光谱学与光谱分析》2024年第4期1031-1038,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61803154);河北省自然科学基金项目(F2019209323,F2019209443,F2019209599);河北省研究生示范课程建设项目(KCJSX2021061)资助。

摘　　要：溢油污染是一种典型的环境污染形式,通过多重渠道危害着生物多样性和人类自身安全。因此,针对油类污染物自身组成成分及其特性,采用多种方法相结合的方式,对其进行实时、精确、高效的检测对生态环境监测具有重要意义。三维荧光光谱分析法以其检测精度高、实时性好、操作简便、干扰性小等优势在荧光类物质检测领域应用十分广泛。三维荧光光谱结合支持向量机等算法在物质分类鉴别和浓度预测方面取得较好的成效,但仍存在收敛速度慢、易陷入局部最优等缺陷。将三维荧光光谱与改进蚱蜢优化支持向量机算法(IGOA-SVM)相结合,提出一种对油类污染物分类鉴别的新方法。首先,以0.1 mol·L-1十二烷基硫酸钠溶液作为溶剂,将0#柴油、95#汽油和煤油以不同浓度配比配制成0#柴油和95#汽油、0#柴油和煤油两组分混合样本20个和18个,三组分混合样本20个,各取一半为训练集和测试集。然后,采用F-7000荧光光谱仪采集混合溶液的荧光数据,分析三种油的标准溶液及配制的混合溶液,发现荧光光谱均在一定范围内存在不同程度的重叠现象,仅利用光谱检测无法准确鉴别。最后,结合混沌初始化、精英优化算法和差分进化算法对蚱蜢优化算法进行改进,提取激发波长270 nm,发射波长270~450 nm范围内的荧光峰数据作为训练的输入值,以三种分类标签作为输出,将数据分别输入到蚱蜢优化支持向量机算法(GOA-SVM)、粒子群优化支持向量机算法(PSO-SVM)和遗传优化支持向量机算法(GA-SVM)中进行训练,IGOA-SVM模型在收敛速度、稳定性和跳出局部最优能力上明显优于GOA-SVM、PSO-SVM和GA-SVM,为油类污染物的准确鉴别提供了一种新思路。Oil spill pollution is a typical form of environmental pollution in today s era of rapid development,which harms biodiversity and human safety through multiple channels.Therefore,given the composition and characteristics of oil pollutants,it is particularly critical to improve the ecological environment and ensure the steady development of the economy and society by using multi-method cross-fusion to detect them in real-time,accurately and efficiently.Three-dimensional fluorescence spectroscopy is widely used in the substance detection field with fluorescence characteristics with its advantages of high detection accuracy,good real-time performance,simple operation and small interference.Three-dimensional fluorescence spectroscopy combined with a support vector machine and other algorithms have achieved good results in material classification and identification and concentration prediction,but there are still defects,such as slow convergence speed and easy fall into local optimum.A new method for the classification and identification of oil pollutants was proposed by combining a three-dimensional fluorescence spectrum with a support vector machine algorithm(IGOA-SVM)optimized by an improved grasshopper algorithm.Firstly,with 0.1 mol·L-1 sodium dodecyl sulfate as a solvent,0#diesel oil,95#gasoline and kerosene were prepared into 20 and 18 mixed samples of 0#diesel oil and 95#gasoline,0#diesel oil and kerosene,and 20 mixed samples of three components.Half of each was taken as a training set and a test set.The fluorescence data of the mixed solution were collected by an F-7000 fluorescence spectrometer.Matlab analyzed the standard solution of the three oils and the mixed solution.It was found that the fluorescence spectra had different degrees of overlap within a certain range,and it could not be accurately identified by spectral detection alone.Finally,the grasshopper optimization algorithm is improved by combining chaotic initialization,elite optimization,and differential evolution algorithms.The fluorescence peak

关 键 词：三维荧光光谱 改进蚱蜢优化算法 支持向量机 石油类污染物 

分 类 号：O657.3[理学—分析化学]