光谱学与光谱分析, 2019, 39 (11): 3407, 网络出版: 2019-12-02  

数字图像识别在混合油类三维荧光光谱分析中的应用

The Application of Digital Image Recognition to the Analysis of Three-Dimensional Fluorescence Spectra of Mixed Oil
作者单位
1 燕山大学电气工程学院, 河北 秦皇岛 066004
2 Department of Telecommunications and Information Processing, Ghent University, B-9000 Ghent, Belgium
3 燕山大学信息科学与工程学院, 河北 秦皇岛 066004
4 北京化工大学信息科学与技术学院, 北京 100029
摘要
海上溢油已成为全球环境污染的重要问题之一, 溢油严重破坏了海洋生态的平衡, 并导致人类健康受到危害。 因此, 研究高效的溢油检测方法对保护海洋生态环境具有重要意义。 三维荧光光谱技术因能获得溢油的“指纹”图谱而成为溢油鉴别领域的有效分析手段, 其与平行因子分析算法相结合获得了良好的溢油鉴别效果。 但平行因子算法在使用过程中需要确定不同石油产品本身所适用的浓度范围, 且其对预估计组分数敏感, 组分数选择是否准确直接影响最终定性定量结果, 这些问题都会对油类检测造成使用上的限制。 油类组分极为复杂, 其中各组分间不存在统一的线性浓度范围, 其相互之间还受到荧光猝灭效应的影响。 直接对未经稀释的油类样本进行光谱数据采集, 所获得的三维荧光光谱会因样本中组分的种类及其含量不同而存在较大差异, 导致对三维荧光光谱数据进行解析的平行因子分析算法不再适用。 但组分的种类及含量相近的油样其光谱特征相似度较高, 并且随着特定组分及其含量的改变, 其光谱形状的变化规律也较为明显。 基于此, 将三维荧光光谱和数字图像识别相结合, 提出一种针对混合油类样本的辨识方法。 首先, 利用五种矿物油(汽油、 柴油、 航空煤油、 机油和润滑油)配制三类混合油样本, 其中每类混合油是用其中两种不同矿物油以不同体积比直接混合配制而成; 然后利用FS920荧光光谱仪获取样本的三维荧光光谱数据, 并对该数据进行求导及灰度化预处理, 进而得到三维荧光导数光谱灰度图; 其次提取样本三维荧光导数光谱灰度图的颜色、 纹理和形状等数字图像特征; 最后, 通过Fisher判别分析建立样本的分类模型, 采用逐步回归建立混合油样本各组分相对体积的定量模型。 分类模型对三类混合油样本的分类及识别效果良好。 所建立的定量模型的线性相关性R大于0.99, 显著性检验p值小于0.05。 研究结果表明, 三维荧光光谱的数字图像特征可以被本文所述方法有效提取并用于对油类样本的定性定量分析。 该研究为海面溢油检测提供了一种简单、 可靠的识别方法。
Abstract
Oil spill has become one of the most serious problems in global environmental pollution and brings a serious threat to the marine ecological balance and human health. Therefore, it is of great importance to study efficient oil spill detection methods to protect the marine ecological environment. As three-dimensional fluorescence spectra technology has advantages of getting oil spill fingerprints, it has become an important analytical method in the field of oil spill identification. A satisfactory oil spill identification effect was obtained by combining 3D fluorescence spectra technology with the parallel factor (PARAFAC) analysis algorithm. The applicable concentration range for different oils should be determined before the implementation of PARAFAC algorithm. Besides, PARAFAC is sensitive to number of components. The selection of number of components directly affects qualitative and quantitative analysis results. The method of 3D fluorescence spectra technology combined with PARAFAC is limited in real sea surface oil spill due to above reasons. The composition of oil spill is extremely complex, in which each component not only has a uniform concentration linear range but also is affected by the fluorescence quenching. Due to different content of components, the three-dimensional fluorescence spectra of the oil spill sample (sample is not diluted) are quite different. Some algorithms (such as parallel factor analysis) that resolve the three-dimensional fluorescence spectra are no longer applicable. With the change of the type and content of the sample components, the change rule of the three-dimensional fluorescence spectra image characteristics is also obvious. Therefore, a novel detection method for oil spill based on 3D fluorescence spectra technology and digital image recognition is proposed in this paper. Firstly, three types of mixed oil samples were formulated. Each type of mixed oil was directly mixed with two types of five mineral oils (gasoline, diesel, jet fuel, engine oil, lubricating oil) at different volume ratios. The three-dimensional fluorescence spectral of samples were obtained by FS920 fluorescence spectrometer. The corresponding three-dimensional fluorescence derivative spectral grayscale image was obtained by preprocessing of derivation and graying. Then, the digital image features such as color, texture and shape of three-dimensional fluorescence derivative spectral grayscale image were extracted. Finally, the classification and quantitative models of samples were established by fisher discriminant and stepwise regression respectively. The classification model has good classification and recognition effect on three types of mixed oil samples. The linear correlation coefficient R of the quantitative model is greater than 0.99. The significance test p-value of the quantitative model is less than 0.05. The results show that the digital image characteristics three-dimensional fluorescence spectral can be effectively extracted by our method and used for the qualitative and quantitative analyses of oil samples. The study provides a simple and accurate identification method for sea surface oil spill.

孔德明, 崔耀耀, 孔令富, 王书涛, 史慧超. 数字图像识别在混合油类三维荧光光谱分析中的应用[J]. 光谱学与光谱分析, 2019, 39(11): 3407. KONG De-ming, CUI Yao-yao, KONG Ling-fu, WANG Shu-tao, SHI Hui-chao. The Application of Digital Image Recognition to the Analysis of Three-Dimensional Fluorescence Spectra of Mixed Oil[J]. Spectroscopy and Spectral Analysis, 2019, 39(11): 3407.

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!