摘要
拉曼光谱技术是一种重要的分析手段,正在得到越来越广泛的应用。本文对拉曼光谱的标准化以及不同仪器之间的传递进行了深入研究,对传统定量分析方法进行了改进,相关研究成果被应用于分体式在线拉曼分析仪的数据分析软件。本文主要研究内容包括：
(1)色散型拉曼光谱仪容易受到环境温度的影响,使测得的光谱重复性变差,产生波数漂移以及分辨率变化的现象。为此,本文提出了一种基于高斯函数卷积的光谱校正方法。该方法利用标准物质获得光谱仪在不同温度下的仪器响应函数,并以此构造高斯函数,通过卷积运算对温度造成的波数漂移以及分辨率变化进行校正。该方法直接基于拉曼谱分析,机理性强,无需选择一组传递样本。实验结果表明,该方法能有效去除温度对色散型拉曼光谱仪的影响,使得不同温度下测得的光谱一致性大幅提高。
(2)本文研究了基于仪器信号响应函数的拉曼光谱传递,提出了一个将拉曼光谱从高分辨率传递至低分辨率光谱仪的简便方法。该方法无需选择一组有代表性的样本建立统计模型,只需构建一个高斯函数进行卷积运算,就能消除源机与目标机在分辨率上的区别；同时,结合现有的波数校正方法与相对拉曼强度校正方法,即可实现拉曼光谱的传递。实验结果表明,该方法借助于校正光源与标准荧光片,就能较好的实现拉曼光谱在一台仪器上或者不同仪器之间的传递。
(3)为了实现拉曼数据库在不同仪器之间的应用,本文提出了一种基于分段直接标准化(PDS)的拉曼光谱传递方法。该方法无需对光谱仪信号响应进行机理分析,只要通过少数传递样本建立统计模型,就能获得不同仪器间的拉曼光谱转换系数。对于传递样本的选择,使用了基于马氏距离的Kennard-Stone样本选择方法。实验将离线分析仪测得的汽油样本光谱传递至在线分析仪上,建立了辛烷值(RON)的拉曼分析模型。结果表明,传递模型达到了与重建模型相近的性能,基于PDS的拉曼光谱传递方法具有实际应用价值。
(4)针对常用软建模、硬建模方法的各自局限,本文提出了一种基于拉曼光谱合成的定量分析方法。该方法利用各组分光谱的线性叠加拟合混合物的拉曼光谱,并以此计算组分的浓度。建立定量模型时无需获得大量真实样本,计算方便,适合于工艺稳定、变化缓慢的分析对象。将拉曼光谱合成模型应用于甲醇汽油的甲醇含量分析,并与PLS方法进行了比较。结果表明,在建模样本数量有限时,拉曼光谱合成模型的整体预测性能与PLS模型接近,但在外推时具有更低的风险。
(5)本文介绍了一种基于拉曼光谱的在线分析仪表,适用于甲醇汽油的调和过程。在线分析仪的主机安置于远离现场的操作室,利用长光纤与工业现场的拉曼探头连接。采用分体式的设计使得工业现场无电气信号,主机无需防爆设计,大大简化了系统。分析软件中,结合了先进的光谱数据预处理以及定量分析算法,对油样进行质量分析。现场运行结果表明,该分析仪能对调和成品油中的甲醇含量进行连续监控,具有分析速度快、精度高、维护简便的优点,为企业稳定、优化生产提供了一个重要手段。
Raman spectroscopy is an important tool for chemical and physical analysis, and has been widely applied. The aim of this thesis is to research on Raman spectra standardisation or transfer between different instruments, and to make improvement on traditional quantitative analysis methods. Some achievements have been implemented in the software for our online Raman analyzer. The main contents of this thesis are:
1. Dispersive Raman spectrometer is sensitive to the temperature of environment, which affects the consistency of measured spectra. To solve the problem, a novel method is proposed for correcting the temperature effect on instruments by applying convolution with a Gaussian function. A standard sample is used to represent the instrumental response functions under various temperature conditions, and a Gaussian function for convolution is built, then wave number drift and resolution variation are corrected. Based on spectral analysis and curve fitting, this method does not need to measure a set of well-prepared samples. Results show that it is an effective method to reduce temperature influence on dispersive Raman spectrometer, and the consistency of Raman spectra measured at different temperatures is greatly improved after correction.
2. This thesis proposes a calibration transfer method based on the instrument response function, allowing transfer of Raman spectra from higher to lower resolution. No need to measure a set of well-prepared samples to construct a statistical transfer model, the method can correct the instrumental differences from source to target by convolution with a Gaussian function. Combined with exsiting methods for wavenumber calibration and relative intensity correction, an integrated procedure for Raman spectra transfer is presented. Experiment results show that, with a calibration source and a standard luminescence glass, this procedure can be applicable for all kinds of Raman spectra transfer.
3. To transfer well-established Raman database, a spectra transfer method based on piecewise direct standardization (PDS) is proposed. This method needs no analysis of instrumental response function but a few transfer samples, to build a transfer model linking two Raman spectrometers. A Mahalanobis distance based Kennard-Stone method is used for transfer samples selection. Gasoline sample spectra acquired by a laboratory spectrometer are transferred to an online spectrometer, and a quantitative model predicting RON number is built with transferred sample. The experiment results show that, the transfer model performs as well as the rebuilt model, which means this transfer method for Raman spectra has practical value.
4. A quantitative analysis method based on Raman spectra combination is proposed to avoid the limitation in commonly-used soft modeling and hard modeling methods. In this method, the mixture spectrum is represented by a linearly combination of each component spectrum, and each component content is determined by the weighting coefficient. Only a few real samples are needed for the calibration method, which is suitable to the analysis of sample which varies little. This method is used to predict methanol content in methanol gasoline, and the results are compared with a PLS model. It shows that, when the number of calibration samples is limited, the general performance is similar with PLS model, but the extrapolation risk is lower.
5. An online Raman analyzer is introduced for process monitoring of methanol gasoline blending. The Raman spectrometer is placed in an operating room far from the production unit, and a couple of long optic Fibers are used to connect with the probe on the sample line. With separated structure, there are no electrical signal in production unit and no explosion-proof equipment for spectrometer. The above spectral data processing and quantitative analysis algorithms are implemented in the analysis software. Application results show that, the online analyzer is capable of continuously monitoring the methanol content in the production oil. With the advantages of rapid analysis, high accuracy and less maintenance, this analyzer can provide an efficient tool to the enterprise for stable and optimal production.
引文
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