激光与光电子学进展, 2019, 56 (22): 222601, 网络出版: 2019-11-02   

基于三维荧光光谱的土壤中石油类有机物分类识别 下载: 1024次

Identification of Petroleum Organic Matter in Soil Based on Three-Dimensional Fluorescence Spectroscopy
左兆陆 1,2,3赵南京 1,3,*孟德硕 1,3黄尧 1,2,3殷高方 1,3刘建国 1,3谷艳红 4
作者单位
1 中国科学院安徽光学精密机械研究所环境光学与技术重点实验室, 安徽 合肥 230031
2 中国科学技术大学, 安徽 合肥 230026
3 中国科学院合肥物质科学研究院安徽省环境光学监测技术重点实验室, 安徽 合肥 230031
4 合肥学院先进制造工程学院, 安徽 合肥 230601
摘要
基于三维荧光光谱法,以含有不同类型润滑油、机油、柴油、汽油的土壤样品为研究对象,分别提取不同土壤样品的三维荧光光谱,然后计算不同样品的荧光强度均值、标准差、重心横纵坐标、相关系数、长轴斜率、偏度和峰度等7个特征参数,并作为不同油类的识别特征。对7个特征参数进行主成分分析(PCA),前3个主成分累计贡献率为88.79%,但经聚类分析发现5w-40型润滑油和15w-40型润滑油的主成分混叠较强,无法准确实现分类。将经PCA得到的3个主成分作为反向传输人工神经网络的输入量,将石油烃有机物的种类作为输出量,以进行油类识别,综合识别率达到95.6%。实验结果表明,基于三维荧光光谱方法直接从油污土壤中识别污染油可行,该方法为后续研究基于三维荧光光谱识别土壤中油类污染物提供了技术支持,具有较好的应用前景。
Abstract
This study focuses on selected soil samples containing different types of lubricating oil, engine oil, diesel oil, and gasoline. Three-dimensional (3D) fluorescence spectra are extracted from different soil samples, and 7 characteristic parameters are calculated for each of them, including the fluorescence intensity mean, standard deviation, transverse and longitudinal coordinates of center of gravity, correlation coefficient, long-axis slope, skewness, and kurtosis. Spectral data are used as identification characteristics for oil. Principal component analysis (PCA) is performed on the 7 characteristic parameters, and the feature vectors of the first 3 principal components after dimension reduction are extracted, accounting for a cumulative contribution rate of 88.79%. Clustering analysis reveals highly similar principal components of 5w-40 and 15w-40 lubricating oils; therefore, these oils can not be accurately classified. Subsequently, the first 3 principal components obtained by PCA are input into the back-propagation artificial neural network and the types of petroleum organic matter are used as outputs for oil identification, resulting in a 95.6% comprehensive recognition rate. Experimental results demonstrate the feasibility of identifying oil pollutants directly using 3D fluorescence spectroscopy of oily soil. Additionally, technical support is provided for subsequent research on oil pollutant identification in soil based on 3D fluorescence spectroscopy, indicating good application prospects.

左兆陆, 赵南京, 孟德硕, 黄尧, 殷高方, 刘建国, 谷艳红. 基于三维荧光光谱的土壤中石油类有机物分类识别[J]. 激光与光电子学进展, 2019, 56(22): 222601. Zhaolu Zuo, Nanjing Zhao, Deshuo Meng, Yao Huang, Gaofang Yin, Jianguo Liu, Yanhong Gu. Identification of Petroleum Organic Matter in Soil Based on Three-Dimensional Fluorescence Spectroscopy[J]. Laser & Optoelectronics Progress, 2019, 56(22): 222601.

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