复杂凝聚态体系中近红外光谱信号的信息提取及定量表示
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摘要
本论文采用二甲苯三种同分异构体混合物样品的近红外光谱数据,对近红外光谱模型建立中的信息提取和信息量的定量表示进行了系统性研究。该研究对近红外预测模型预测准确度的提高、近红外光谱中所含信息量的增强和信息量的定量表示具有重要的指导意义和应用价值。
本论文对比研究了近红外光谱信息提取中的几种连续波段的选择方法,指出了多重共线性对预测模型精确度的严重危害性。首次提出在同分异构体组分混合物的近红外光谱数据处理中使用差谱技术选择光谱波段,可以部分克服多重共线性问题,提高模型的预测准确度;特别对相关系数法进行了系统性研究,指出了该方法的优缺点,首次指明了相关系数与近红外光谱数据体系中经过PCA提取出来的主要的主成分载荷之间的相似关系,对主成分物理意义的解释有一定的指导意义。
首次深入研究了互相关分析方法在近红外光谱信息提取中的作用,理论推导出了近红外光谱信号经过互相关运算后,在一定的条件下变换后的光谱信号强度和目标组分的浓度呈线性关系,经过互相关分析后目标组分的光谱质量得到了提升;首次将互相关算法应用到近红外光谱模型的传递中,通过对不同类型近红外光谱仪器采集到的同一种混合物体系的近红外光谱进行互相关分析,突显出了不同光谱仪器间存在的固有差别,发现通过补偿的办法可以弥补这个差别,从而起到模型传递的作用。
首次将信息论的部分概念和观点引入到近红外光谱的分析领域,从信息论的角度,重新审视了近红外光谱的数据结构;首次采用组分熵对近红外光谱数据的重叠程度进行了定量表示,在对高度重叠的二甲苯三种同分异构体的近红外光谱数据的分析中取得了良好的效果;在改进多组分数据体系经PCA分解后的信息量增益公式的基础上,首次提出一种加权重的熵代数来定量表示多组分近红外光谱中所含有的信息量,并采用该代数来评价近红外光谱预处理操作对数据的变换品质,通过实验分析证实了该代数可以有效地表征近红外光谱数据中的信息增益,清晰地区分近红外光谱预处理对最终预测结果准确度的好坏影响。
This thesis has put the emphases on the information extraction and quantitative expression during the model building of near infrared spectrum using the spectral data sets of the dimethyl benzene’isomer. The research work may be important to the application and accuracy development of the NIR model.
In this thesis several kinds of wavelength selection methods, including Interval Partial Least-Squares Regression and Moving Window Partial Least-Square Regression, have been studied and compared. Both their merits and demerits have been pointed out. Specially, the method based on the correlation with concentration was carefully studied. The similarity between the PCs and the correlation coefficient was pointed out, and this will help us to better understand the meaning of PCs. The subtractive spectra technology was used to partly overcome the multicollinearity of the NIR spectrum data sets by wavelength selection.
The Cross-Correlation algorithm was introduced as spectral pretreatment in NIR field. The algorithm gave a substantial improvement of the performance when the NIR spectra with complex backgrounds and big noise. What is more, the Cross-Correlation algorithm was also applied to calibration transfer of the NIR spectra, and a good result was gotten.
The information theory was also introduced for analysis spectral data structure in the NIR field. The component entropy was used to evaluate the degree of overlapping of NIR spectra, and it gave a powerful solution for this problem. A new algorithm, named weighted Shannon’s entropy, stemming from the Shannon’s information, is used to assess the criteria for NIR spectral data optimization. The analysis results show that the quantitative analysis gives an accurate result and the algorithm works very well for assessing a right criterion for the NIR data pretreatment. Key words:
本论文对比研究了近红外光谱信息提取中的几种连续波段的选择方法,指出了多重共线性对预测模型精确度的严重危害性。首次提出在同分异构体组分混合物的近红外光谱数据处理中使用差谱技术选择光谱波段,可以部分克服多重共线性问题,提高模型的预测准确度;特别对相关系数法进行了系统性研究,指出了该方法的优缺点,首次指明了相关系数与近红外光谱数据体系中经过PCA提取出来的主要的主成分载荷之间的相似关系,对主成分物理意义的解释有一定的指导意义。
首次深入研究了互相关分析方法在近红外光谱信息提取中的作用,理论推导出了近红外光谱信号经过互相关运算后,在一定的条件下变换后的光谱信号强度和目标组分的浓度呈线性关系,经过互相关分析后目标组分的光谱质量得到了提升;首次将互相关算法应用到近红外光谱模型的传递中,通过对不同类型近红外光谱仪器采集到的同一种混合物体系的近红外光谱进行互相关分析,突显出了不同光谱仪器间存在的固有差别,发现通过补偿的办法可以弥补这个差别,从而起到模型传递的作用。
首次将信息论的部分概念和观点引入到近红外光谱的分析领域,从信息论的角度,重新审视了近红外光谱的数据结构;首次采用组分熵对近红外光谱数据的重叠程度进行了定量表示,在对高度重叠的二甲苯三种同分异构体的近红外光谱数据的分析中取得了良好的效果;在改进多组分数据体系经PCA分解后的信息量增益公式的基础上,首次提出一种加权重的熵代数来定量表示多组分近红外光谱中所含有的信息量,并采用该代数来评价近红外光谱预处理操作对数据的变换品质,通过实验分析证实了该代数可以有效地表征近红外光谱数据中的信息增益,清晰地区分近红外光谱预处理对最终预测结果准确度的好坏影响。
This thesis has put the emphases on the information extraction and quantitative expression during the model building of near infrared spectrum using the spectral data sets of the dimethyl benzene’isomer. The research work may be important to the application and accuracy development of the NIR model.
In this thesis several kinds of wavelength selection methods, including Interval Partial Least-Squares Regression and Moving Window Partial Least-Square Regression, have been studied and compared. Both their merits and demerits have been pointed out. Specially, the method based on the correlation with concentration was carefully studied. The similarity between the PCs and the correlation coefficient was pointed out, and this will help us to better understand the meaning of PCs. The subtractive spectra technology was used to partly overcome the multicollinearity of the NIR spectrum data sets by wavelength selection.
The Cross-Correlation algorithm was introduced as spectral pretreatment in NIR field. The algorithm gave a substantial improvement of the performance when the NIR spectra with complex backgrounds and big noise. What is more, the Cross-Correlation algorithm was also applied to calibration transfer of the NIR spectra, and a good result was gotten.
The information theory was also introduced for analysis spectral data structure in the NIR field. The component entropy was used to evaluate the degree of overlapping of NIR spectra, and it gave a powerful solution for this problem. A new algorithm, named weighted Shannon’s entropy, stemming from the Shannon’s information, is used to assess the criteria for NIR spectral data optimization. The analysis results show that the quantitative analysis gives an accurate result and the algorithm works very well for assessing a right criterion for the NIR data pretreatment. Key words:
引文
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