摘要
拉曼光谱技术以其高灵敏性、快速性以及样本无需预处理等诸多优势,成为非接触式检测领域中的一种关键技术。然而,作为一种间接性的检测技术,拉曼定量分析系统的实际应用常会遇到光谱重叠、光谱非线性变化以及训练样本数量较少等问题。为此,本文在深入研究拉曼光谱分析技术原理的基础上,基于拉曼光谱解析建模的数学理论,针对实际问题提出了一系列创新性的拉曼光谱定量分析方法,具体内容包括：
1)光谱解析算法的首要任务是建立拉曼光谱的数学解析模型,将拉曼光谱表示为多个Voigt峰函数的叠加形式。光谱建模的精确程度直接影响定量分析模型的准确性,而这一过程往往是一个高维寻优问题,需要同时对几十个乃至几百个参数进行同时寻优,这导致巨大的运算开销并有可能令优化问题呈现“病态性”。为了降低优化问题维数,提高计算效率,提出了一种快速光谱解析算法,该算法通过对峰的重叠性进行判断,在迭代过程中大大减少了待优化的参数数量。实验结果表明快速解析算法与传统方法相比具有运行时间短、模型精确度高等优点。
2)针对混合溶液中不同成分的特征峰相互重叠问题,提出了一种基于Voigt谱峰函数的定量分析方法——直接硬建模算法(Direct hard modeling)。该方法首先将混合溶液光谱解析为Voigt峰的累加和形式,然后通过对峰高与待测成分浓度之间进行相关性分析找到待测成分在混合物中的特征峰,最后利用特征峰强度与待测成分浓度之间建立线性模型。以对二甲苯(PX)装置吸附塔进料成分的定量分析为例,进行了对比研究。实验结果表明,与传统建模方法相比,该算法具有训练样本数量少、回归模型的外推性强等优点。
3)针对待测成分在混合物拉曼光谱中可能出现的非线性变化以及混合物中可能存在其它未知成分等复杂情况,提出了一种基于光谱解析的定量分析算法。该算法首先将待测成分的拉曼光谱表示为数学解析形式,然后通过对解析参数的调整来拟合待测成分光谱在混合物中的非线性变化。当混合物中存在未知成分时,该算法利用多个峰函数的叠加形式对未知成分所产生的光谱形态进行拟合并将拟合结果从混合物光谱中扣除,消除了未知成分光谱贡献对定量分析的干扰作用。将该算法应用于甲醇汽油中甲醇浓度的定量分析,实验结果表明,在基础油种类已知和未知的两种情况下,均可得到较准确的分析结果。与偏最小二乘和最小二乘支持向量机算法相比,基于光谱解析模型的定量分析算法只需较少的训练样本即可建立预测精度较高的回归模型。
Raman spectroscopy has been revealed as an important technique in nondestructive testing field. This fact is associated with its main characteristics, such as analytical sensitive, high analytical speed and minimum sample preparation. However, there are still some difficulties in practical application of quantitative analysis based on Raman spectra, such as overlapping band of different components in a mixture, nonlinear mixture effects of spectra, shorting of training samples and so on. To overcome these problems, a series of Raman spectra quantitative analysis methods have been presented in this thesis, which were proposed based on the theory of spectral analytic model. The thesis specifically including:
1) The primary task of spectral hard modeling method is to represent the measured spectrum as a sum of Voigt peaks. The precision of the spectral model has immediate impact on the accuracy of the regression model. A spectrum often includes dozens or even hundreds of Voigt peaks, which mean that the spectral modeling process is an optimization problem with high dimensionality in fact. So, the computational process is time consuming and the results may not be the optimal solution due to ill-condition of the optimization problem. Herein, a rapid spectral modeling method was proposed, which reduces the dimensionality of optimization problem by only adjusting the parameters of overlapping peaks in stead of all peaks in the process of spectrum modeling. Experimental results showed that the spectral model built by the new method is more accuracy and need much shorter running time than the conventional method.
2) A new characteristic peaks extracting and quantitative analysis method (Direct hard modeling, DHM) based on Voigt function was proposed to overcome the problem of strong spectral overlap in mixture. By DHM algorithm, the spectra of mixtures were modeled as a sum of Voigt functions at first; and then the characteristic peaks of every pure component in mixture can be extracted by analyzing the linear correlation between the intensity of Voigt peaks and the concentration of components; a linear calibration model will be built based on the intensity of characteristic peaks and the concentration of pure-components at last. Comparative studies were taken on to quantitatively analysis the incoming stock components of paraxylene absorption tower. Experimental results showed that the DHM algorithm has the advantages of generalization and requiring less training samples compared to partial least squares algorithm.
3) The quantitative analysis based on Raman spectra will become very difficult if there are some unknown components in mixture and the spectral profile of tested component present nonlinear changes. To overcome these problems, a new quantitative analysis method based on spectral model was proposed. At first, the spectral profile of tested component is modeled mathematically, and then the nonlinear effect on spectrum of tested component can be handled by adjusting the parameters of its spectral model. If there are some unknown components in mixture, the spectrum produced by unknown components can be fitted by a sum of Voigt peaks during an iterative algorithm, and then the fitting spectral profile can be deducted to eliminate the undesired signal come from unknown components. This method was applied to determine the methanol concentration in methanol gasoline and the analysis results were satisfied no matter the type of base gasoline is known or unknown. Compared with partial least squares and least squares support vector machine algorithms, the new method has the advantages of accuracy of prediction and requiring less training samples.
引文
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