摘要
光谱分析技术具有快速、无损、高效和低成本等优势,已广泛应用于农业、化工、制药、纺织等各个领域。本文利用近红外和拉曼光谱分析技术为石油产品的种类、成品汽油的产地和牌号建立了定性分类模型。在定性分析过程中,运用各种局部策略,较好地解决了异常光谱的检测修复、定性模型参数调整以及不同类样本重叠情况下分类算法的改良问题。具体研究内容包括：
1.针对油品在线光谱中尖锐小峰(国外文献称之为spike)的干扰问题,提出了一种基于时域局部策略的spike检测及修复方法。该方法同时运用强度检测和滑动窗口的相关分析来确定在线拉曼光谱中是否存在spike,并采用微分谱图和局部线性拟合实现了光谱的较好修复。该算法已成功地应用于自主开发的在线拉曼光谱测量分析系统中,实际运行情况证明该算法简单有效。
2.针对基于近红外光谱的汽油牌号分类问题,将流型思想引入光谱的特征提取过程,提出了一种基于流型的局部分类算法。在该算法中,首先利用等度规映射(isometric mapping,Isomap)的流型方法对光谱进行降维处理,而后采用最为常见的K近邻法实现汽油牌号的识别分类。汽油牌号的分类测试实验结果表明Isomap的降维比主成分分析(principal component analysis,PCA)更适合于体现不同牌号汽油样本近红外谱图的特征信息。
3.为解决不同牌号汽油拉曼光谱重叠问题,提出了一种基于相关分析加权的LSSVM分类算法。它以LSSVM分类算法为基础,构建了局部策略方法以进一步提高分类的准确度。该局部策略选取邻近样本的标准同时包含了样本间的欧式距离和样本之间的相关程度。实验结果表明,与目前常用的分类算法相比,基于相关分析加权的LSSVM分类算法具有更好的分类准确度。
4.针对不同种类油品的分类问题,根据不同油品拉曼谱图形状差异性较大的实际,提出了一种基于相关分析的简单分类算法。该算法与其他分类算法相比,原理简单,无需复杂数学运算和人为设置参数,便于实际应用。
5.现有的最小二乘支持向量机(least squares support vector machine,LSSVM)参数调整大都采用搜索寻优策略,具有较大的盲目性,为此提出了一种基于邻域样本数的LSSVM参数调整方法。该算法以调整邻域样本的个数来调整LSSVM的预测性能,使得径向基核函数无量纲化并减小了调整范围,以特征值惩罚系数代替惩罚系数γ,只需对特征值惩罚系数作较宽范围内值的设定。实验结果表明：在同样的优化水平上,该方法概念更加清晰,调整策略更加简单,更便于编程实现。
In the methods of oil classification, spectral analysis gets more and more attention due to its rapid measurement, nondestruction to samples, high efficiency and low cost.This thesis aims to distinguish kinds of petroleum products and classify product gasoline by source and brand based on NIR spectroscopy and Raman spectroscopy. Local approach is used in spectral outlier detection and recovery, model parameter optimization and classification algorithm modification. The detailed research work is given as follows:
1. An improved algorithm based on local approach to remove cosmic spikes in Raman Spectra for online monitoring is proposed. In this algorithm, a new scheme composed of intensity identification and local moving window correlation analysis is introduced for cosmic spike detection; intensity identification based on derivative spectra and local linear fitting approximation are used for the recovery of cosmic spikes. The algorithm is proved to be simple and effective, which has been applied in an online Raman instrument.
2. A new classification method based on NIR spectroscopy for gasoline brand recognition is proposed. In this method, Isomap(a manifold learning algorithm) is used to reduce dimensionality of spectra, which is different with traditional dimensionality reduction by PCA, and K-nearest neighbor (KNN) algorithm is implemented after dimensionality reduction. Therefore, this method is called the Isomap-KNN algorithm. The classification experiment results show that the manifold learning algorithm can obtain more feature information of gasoline brand than PCA during dimensionality reduction.
3. A novel local weighted LSSVM algorithm based on Raman spectroscopy is proposed to classify gasoline samples by source and brand. The weight is constructed based on correlation coefficient R and this algorithm can be denoted as R-weighted LSSVM. In this algorithm, both of Euclidean distance and correlation coefficient are considered to select neighboring samples. Local approach is realized by the RBF kernel and the weight. LDA based on PCA, LSSVM, local LSSVM and R-weighted LSSVM are compared in the classification experiment. Experimental results show that Raman spectroscopy is an effective means to classify gasoline brand and origin, and the R-weighted LSSVM algorithm gives the best classification result.
4. A simple method based on correlation analysis for the classification of petroleum products is proposed. This method gives good classification results because of significant differences between Raman spectra of petroleum products. This method costs little calculation time and human interference. Moreover, it can be easily implemented in the practical application.
5. A simple method used to optimise the parameters of LSSVM is proposed. This method optimises the LSSVM model by adjusting the number of neighboring samples instead of adjusting the RBF kernel parameter. The range of adjustment is also reduced. The traditional punishment coefficient y is replaced by characteristic punishment coefficient which can be easily set in a wide range. Experiment results show that this method has similar optimization performance with other complex optimization methods.
引文
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