曲线拟合和径向基函数神经网络方法定量解析分子荧光光谱及其分析应用研究
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摘要
随着现代分析技术的发展,巨量的分析数据的获得变得容易,数据的复杂性越来越强。获得的化学数据中不仅包含丰富的化学信息,而且还混杂有噪声、背景等干扰信息。传统的信息数据处理方法已经不能满足分析的需要。本文以“化学分离”代替“化学或物理分离”用于复杂体系重叠光谱的定量解析,使得多组分的分析变得简单、直接和快速。将曲线拟合和径向基函数神经网络方法用于分子荧光分析,为光谱的应用提供了新思路和新方法。
本文第一章绪论部分简要回顾了化学计量学历史和发展,对多元校正和分辨在光谱中的应用进行了综述。第二章就涉及的的相关算法的基本原理加以阐述。
第三章将毛细管电泳与(Genetic Algorithm, GA)优化输入变量下的径向基函数(Radial basic function, RBF)神经网络方法相结合用于定量解析重叠的毛细管电泳图,无需分离即可实现对难分离的苯二酚、苯酚和对硝基苯的同时、准确定量检测。
第四章和第五章将修正高斯模型为GA的适应性函数用于拟合荧光光谱图,有效地解决了分子荧光光谱法检测过程中荧光光谱相互干扰的间题。考察了不同尿液中内源性荧光物质对加替沙星(GFLX)荧光的干扰。应用拟合荧光光谱图可有效地消除了内源性荧光物质的干扰。在优化条件下,GFLX的浓度在0.06-3.5μg·mL-1范围内,与其荧光强度之间具有良好的线性,相关系数为0.9994。检出限为0.02μg·mL-1,回收率为99.2%～109.4%,相对标准偏差为1.3%-2.7%。在优化条件下,邻苯二酚、间苯二酚和对苯二酚的浓度分别在0.02-10μg·mL-1、0.01-10μg·mL-1和0.01-10μg·mL-1范围内,与其荧光强度之间具有良好的线性,相关系数分别为0.9920、0.9999和0.9996,其检出限分别为0.005μg·mL-1、0.003μg·mL-1和0.002μg·mL-1。该方法用于水中邻、间、对苯二酚含量的同时测定,其回收率分别为为84.0%-117%,其相对标准偏差(RSD)为0.3%-2.9%。本文提出的拟合同步荧光光谱法无需分离即可实现对尿液中的加替沙星和三种苯二酚同分异构体同时、准确、灵敏的定量检测,测定结果满意。
第六章在相关文献的基础上,通过模拟数据就图的噪声水平和子峰间的分离度与通用回归神经网络(Generalized redrevession neural networks, GRNN)和反向传播(BackPropogation,BP)神经网络建模的影响进行了系统研究。通过计算机模拟含有不同噪声和不同分离度的谱图,平均选取谱图上对应的数据点作为神经网络的输入变量,训练建模。对比神经网络的预测误差,讨论了识别能力与噪声及分离度之间的相关性,为两种神经网络的应用与研究提供一定的参考。研究结果显示,在提供的训练条件下,两种神经网络识别能力随着谱图的噪声水平的增大逐渐降低,以BP神经网络模型的识别能力随谱图中子峰间分离度的增大而提高。
第七章以GA优化RBF神经网络的输入变量,用于提高RBF神经网络重叠谱图的定量解析精度。该方法在一定程度上提高了RBF神经网络的预测能力,缓解了神经网络训练过程中的“过拟合”现象,简化了RBF神经网络模型的结构,提高了RBF神经网络的学习能力。本研究实现了对同步荧光光谱重叠谱图的有效定量解析,在理论和实验的结合上为RBF神经网络在光谱的应用提供了依据。
第八章将PCA用于优化RBF的输入变量方法用于提高神经网络的识别能力。通过PCA可去除沉余、不相关的数据点,在一定程度上提高神经网络模型的学习能力。应用本方法有效消除了尿液中内源性荧光物质对NOR荧光的干扰,建立了测定尿液中NOR的新方法,在优化条件下,神经网络模型NOR的预测误差为15.32%,网络结构为2：3：1。该方法快捷、方便,可实现尿液中NOR的无干扰测定。
第九章将PCA和GA两种数据压缩技术分别用于RBF神经网络输入变量的优化,以提高神经网络模型的识别能力。以这两种方法分别对相同的模拟数据和实验数据进行优化,用优化后的数据训练建模,计算比较了两种方法的神经网络模型的预测误差和神经网络结构。在优化的条件下,对于PCA优化后神经网络,平均预测误差为16.1%(模拟数据)和17.81%(实验数据)；网络结构为：7：14：3(模拟数据)和8：22：3(实验数据)。结果表明,以PCA优化能够改善神经网络结构,以GA优化能够提高神经网络的预测能力。
With the rapid development of modern analytical technology, analysts can now easily and quickly obtain more data points, and the data sets is becoming complex more and more. These data arrays not only contain amass of chemical information, but also involve some interferers including irrelevance chemical components, noise and background. The traditional methods of the processing data for analytical method can not meet the analysis needs. The use of mathematical seperation instead of "chemical or phisical seperation" for quantification resolution of overlapped spectra of complex matrices could make multi-component analysis with the characteristics of being simple, direct and faster. The curve fitting and radial rasis function neural network are used for molecular fluorescence analysis. It provides a new way and method for the application of spectroscopy.
First of all, it is described that the history and development of chemometrics, and the research advance of multivariate resolution and calibration in spectrum are reviewed. The basic principle of the related algorithms is introduced in the second chapter.
In Chapter3, the radial basis function artificial neural network based on genetic algorithm was combined with capillary electrophoresis for quantitative resolution of overlapped peaks in electrophoretogram. It is achieved that the simultaneous and accurate quantification of hardly separating dihydroxhenzene, ohenol and p-nitrophenol.
Chapters4and5used the exponentially modified Gaussian (EMG) model-based genetic algorithm as a fitness function for fitting fluorescence spectrogram. The method was effective for solving the interference of fluorescent substance in the course of the multi-component quantitative analysis. As a example, the interference of endogenous fluorophores in different urines on fluorescence of gatifloxacin (GFLX) was examined by using the fitting fluorescence spectrogram. Another example, the proposed method can effectively correct the overlapping interferences of fluorescence spectra of the three isomers. Under the optimized experimental conditions, the good linear relationship between the fluorescence intensity and concentration of GFLX concentration was obtained in the range of0.06μg·mL-1-3.5μg·mL-1with a correlation coefficient of0.9994. The detection limit and recovery were0.02μg·mL-1and99.2%-109.4%, respectively, with the relative standard deviation from1.3%to2.7%. In addition, the good linear relationship between the fluorescence intensity and concentration of catechol, resorcinol and hydroquinone was obtained in the range of0.02μg·mL-1-10μg·mL-1,0.01μg·mL-1-10μg·mL-1and0.01μg·mL-1-10μg·mL-1with a correlation coefficient of0.9920,0.9990and0.9996, respectively. Their detection limits were0.005、0.003and0.002μg·mL-1, respectively, and the recoveries were in the range of84.0%-117%with the relative standard deviation of0.3%-2.9%. The proposed fitting fluorescence spectrometric method was rapid, simple and highly sensitive for the determination of GFLX in different human urine and catechol, resorcinol and hydroquinone in water without preseparation. The results are satisfactory.
In Chapter6. the effects of noise level and resolution between sub-peaks on modeling of the General Regression Neural Network (GRNN) and Back Propogation (BP) artificial neural network were investigated in detail by simulated data based on involved references. The different noise and resolution of spectrums were simulated by computer. The data points corresponding spectrum selected evenly were used as input variables of the neural network, and neural network model was trained. Prediction error of neural network was compared and the relation of the recognition capability and noise and total resolution of overlapped spectroscopy was discussed to provide valuable reference for application of the neural network. It was shown that under the training conditions provided in this work the recognition capability of the two artificial neural networks reduced gradually with increasing noise level, and the accuracy of the quantification results by General Regression Neural Network model was improved with the increase of the total resolution.
In Chapter7, the genetic algorithm (GA) was used in optimizing input variables of radial basis function (RBF) artificial neural network to improve the precision of quantitative analysis of the unresolved spectra by artificial neural network. Using the proposed method can, in some extent, increase prediction capability, reduce "over-fitting" of the trained networks and structure of RBF artificial neural network, and improve learning ability of artificial neural network. Effective quantification analysis of overlaped synchronous fluorescence spectrms was achieved. This work provides a basis on the combination of theory and experiment for the application of RBF artificial neural network in spectroscopy.
In Chapter8, the principal components analysis (PCA) was used in optimizing input variables of the radial basis function artificial neural network to improve the recognition capability. The irrelevant data points were removed by PCA. Therefore learning ability of artificial neural network can be improved to some extent. Use of the presented method can eliminate effectively the interference of endogenous fluorophores in urines on fluorescence of Norfloxacin (NOR). A new fluorescence method for the determination of NOR in urines was developed. Under the optimized conditions, the prediction error of Neural Network model for NOR was15.32%, and Neural Network Structure was2:3:1。 The method is quick and convenient for the determination of Norfloxacin in urine without interference。
In Chapter9, the two kinds of data compression technology (PCA and GA) are widely used for feature extraction. The GA and PCA were used in optimizing same input variables of the radial basis function artificial neural network, respectively, to improve the recognition capability of neural network. The same simulated and experimental data were optimized by GA and PCA, and neural network models were trained. The prediction error of the two neural network models for three isomers and Neural Network Structure were calculated and compared. Under the optimized conditions, after obtimized with PCA, the prediction error is16.1%(simulated data) and17.81%(experimental data), and the structure of neural network model is7:14:3(simulated data) and8:22:3(experimental data). The results indicate that the use of PCA for the optimization of neural network has better neural network structure than use of GA, and the use of GA for the optimization of neural network has higher recognition capability than use of PCA.
本文第一章绪论部分简要回顾了化学计量学历史和发展,对多元校正和分辨在光谱中的应用进行了综述。第二章就涉及的的相关算法的基本原理加以阐述。
第三章将毛细管电泳与(Genetic Algorithm, GA)优化输入变量下的径向基函数(Radial basic function, RBF)神经网络方法相结合用于定量解析重叠的毛细管电泳图,无需分离即可实现对难分离的苯二酚、苯酚和对硝基苯的同时、准确定量检测。
第四章和第五章将修正高斯模型为GA的适应性函数用于拟合荧光光谱图,有效地解决了分子荧光光谱法检测过程中荧光光谱相互干扰的间题。考察了不同尿液中内源性荧光物质对加替沙星(GFLX)荧光的干扰。应用拟合荧光光谱图可有效地消除了内源性荧光物质的干扰。在优化条件下,GFLX的浓度在0.06-3.5μg·mL-1范围内,与其荧光强度之间具有良好的线性,相关系数为0.9994。检出限为0.02μg·mL-1,回收率为99.2%～109.4%,相对标准偏差为1.3%-2.7%。在优化条件下,邻苯二酚、间苯二酚和对苯二酚的浓度分别在0.02-10μg·mL-1、0.01-10μg·mL-1和0.01-10μg·mL-1范围内,与其荧光强度之间具有良好的线性,相关系数分别为0.9920、0.9999和0.9996,其检出限分别为0.005μg·mL-1、0.003μg·mL-1和0.002μg·mL-1。该方法用于水中邻、间、对苯二酚含量的同时测定,其回收率分别为为84.0%-117%,其相对标准偏差(RSD)为0.3%-2.9%。本文提出的拟合同步荧光光谱法无需分离即可实现对尿液中的加替沙星和三种苯二酚同分异构体同时、准确、灵敏的定量检测,测定结果满意。
第六章在相关文献的基础上,通过模拟数据就图的噪声水平和子峰间的分离度与通用回归神经网络(Generalized redrevession neural networks, GRNN)和反向传播(BackPropogation,BP)神经网络建模的影响进行了系统研究。通过计算机模拟含有不同噪声和不同分离度的谱图,平均选取谱图上对应的数据点作为神经网络的输入变量,训练建模。对比神经网络的预测误差,讨论了识别能力与噪声及分离度之间的相关性,为两种神经网络的应用与研究提供一定的参考。研究结果显示,在提供的训练条件下,两种神经网络识别能力随着谱图的噪声水平的增大逐渐降低,以BP神经网络模型的识别能力随谱图中子峰间分离度的增大而提高。
第七章以GA优化RBF神经网络的输入变量,用于提高RBF神经网络重叠谱图的定量解析精度。该方法在一定程度上提高了RBF神经网络的预测能力,缓解了神经网络训练过程中的“过拟合”现象,简化了RBF神经网络模型的结构,提高了RBF神经网络的学习能力。本研究实现了对同步荧光光谱重叠谱图的有效定量解析,在理论和实验的结合上为RBF神经网络在光谱的应用提供了依据。
第八章将PCA用于优化RBF的输入变量方法用于提高神经网络的识别能力。通过PCA可去除沉余、不相关的数据点,在一定程度上提高神经网络模型的学习能力。应用本方法有效消除了尿液中内源性荧光物质对NOR荧光的干扰,建立了测定尿液中NOR的新方法,在优化条件下,神经网络模型NOR的预测误差为15.32%,网络结构为2：3：1。该方法快捷、方便,可实现尿液中NOR的无干扰测定。
第九章将PCA和GA两种数据压缩技术分别用于RBF神经网络输入变量的优化,以提高神经网络模型的识别能力。以这两种方法分别对相同的模拟数据和实验数据进行优化,用优化后的数据训练建模,计算比较了两种方法的神经网络模型的预测误差和神经网络结构。在优化的条件下,对于PCA优化后神经网络,平均预测误差为16.1%(模拟数据)和17.81%(实验数据)；网络结构为：7：14：3(模拟数据)和8：22：3(实验数据)。结果表明,以PCA优化能够改善神经网络结构,以GA优化能够提高神经网络的预测能力。
With the rapid development of modern analytical technology, analysts can now easily and quickly obtain more data points, and the data sets is becoming complex more and more. These data arrays not only contain amass of chemical information, but also involve some interferers including irrelevance chemical components, noise and background. The traditional methods of the processing data for analytical method can not meet the analysis needs. The use of mathematical seperation instead of "chemical or phisical seperation" for quantification resolution of overlapped spectra of complex matrices could make multi-component analysis with the characteristics of being simple, direct and faster. The curve fitting and radial rasis function neural network are used for molecular fluorescence analysis. It provides a new way and method for the application of spectroscopy.
First of all, it is described that the history and development of chemometrics, and the research advance of multivariate resolution and calibration in spectrum are reviewed. The basic principle of the related algorithms is introduced in the second chapter.
In Chapter3, the radial basis function artificial neural network based on genetic algorithm was combined with capillary electrophoresis for quantitative resolution of overlapped peaks in electrophoretogram. It is achieved that the simultaneous and accurate quantification of hardly separating dihydroxhenzene, ohenol and p-nitrophenol.
Chapters4and5used the exponentially modified Gaussian (EMG) model-based genetic algorithm as a fitness function for fitting fluorescence spectrogram. The method was effective for solving the interference of fluorescent substance in the course of the multi-component quantitative analysis. As a example, the interference of endogenous fluorophores in different urines on fluorescence of gatifloxacin (GFLX) was examined by using the fitting fluorescence spectrogram. Another example, the proposed method can effectively correct the overlapping interferences of fluorescence spectra of the three isomers. Under the optimized experimental conditions, the good linear relationship between the fluorescence intensity and concentration of GFLX concentration was obtained in the range of0.06μg·mL-1-3.5μg·mL-1with a correlation coefficient of0.9994. The detection limit and recovery were0.02μg·mL-1and99.2%-109.4%, respectively, with the relative standard deviation from1.3%to2.7%. In addition, the good linear relationship between the fluorescence intensity and concentration of catechol, resorcinol and hydroquinone was obtained in the range of0.02μg·mL-1-10μg·mL-1,0.01μg·mL-1-10μg·mL-1and0.01μg·mL-1-10μg·mL-1with a correlation coefficient of0.9920,0.9990and0.9996, respectively. Their detection limits were0.005、0.003and0.002μg·mL-1, respectively, and the recoveries were in the range of84.0%-117%with the relative standard deviation of0.3%-2.9%. The proposed fitting fluorescence spectrometric method was rapid, simple and highly sensitive for the determination of GFLX in different human urine and catechol, resorcinol and hydroquinone in water without preseparation. The results are satisfactory.
In Chapter6. the effects of noise level and resolution between sub-peaks on modeling of the General Regression Neural Network (GRNN) and Back Propogation (BP) artificial neural network were investigated in detail by simulated data based on involved references. The different noise and resolution of spectrums were simulated by computer. The data points corresponding spectrum selected evenly were used as input variables of the neural network, and neural network model was trained. Prediction error of neural network was compared and the relation of the recognition capability and noise and total resolution of overlapped spectroscopy was discussed to provide valuable reference for application of the neural network. It was shown that under the training conditions provided in this work the recognition capability of the two artificial neural networks reduced gradually with increasing noise level, and the accuracy of the quantification results by General Regression Neural Network model was improved with the increase of the total resolution.
In Chapter7, the genetic algorithm (GA) was used in optimizing input variables of radial basis function (RBF) artificial neural network to improve the precision of quantitative analysis of the unresolved spectra by artificial neural network. Using the proposed method can, in some extent, increase prediction capability, reduce "over-fitting" of the trained networks and structure of RBF artificial neural network, and improve learning ability of artificial neural network. Effective quantification analysis of overlaped synchronous fluorescence spectrms was achieved. This work provides a basis on the combination of theory and experiment for the application of RBF artificial neural network in spectroscopy.
In Chapter8, the principal components analysis (PCA) was used in optimizing input variables of the radial basis function artificial neural network to improve the recognition capability. The irrelevant data points were removed by PCA. Therefore learning ability of artificial neural network can be improved to some extent. Use of the presented method can eliminate effectively the interference of endogenous fluorophores in urines on fluorescence of Norfloxacin (NOR). A new fluorescence method for the determination of NOR in urines was developed. Under the optimized conditions, the prediction error of Neural Network model for NOR was15.32%, and Neural Network Structure was2:3:1。 The method is quick and convenient for the determination of Norfloxacin in urine without interference。
In Chapter9, the two kinds of data compression technology (PCA and GA) are widely used for feature extraction. The GA and PCA were used in optimizing same input variables of the radial basis function artificial neural network, respectively, to improve the recognition capability of neural network. The same simulated and experimental data were optimized by GA and PCA, and neural network models were trained. The prediction error of the two neural network models for three isomers and Neural Network Structure were calculated and compared. Under the optimized conditions, after obtimized with PCA, the prediction error is16.1%(simulated data) and17.81%(experimental data), and the structure of neural network model is7:14:3(simulated data) and8:22:3(experimental data). The results indicate that the use of PCA for the optimization of neural network has better neural network structure than use of GA, and the use of GA for the optimization of neural network has higher recognition capability than use of PCA.
引文
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