主成分分析同时单点R滴定法研究

将主成分分析用于单点Ｒ滴定法中，同时测定了镍矿中Ｎｉ，Ｃｕ，Ｃｏ含量，讨论了方法原理，指定电位的选择，建立了主成分分析常数矩阵，对２０个模拟样和矿样进行了分析，均获得满意结果。     

主成分分析同时单点pH滴定法研究

本文将主成分分析用于多组分同时单点ｐＨ滴定法中,讨论了方法原理,指定ｐＨ值的选择,建立了主成分分析常数矩阵,对四组分醇酮氧化酸试样及模拟样进行了多次测定,均获得了满意结果。     

一个基于诊断的稳健主成分分析方法

经典的主成分分析方法易受异常点影响。本文根据该方法的特点，提出一新的诊断方法，将多变量数据中异常剔除后再进行主成分分析，构成有效的稳健主成分分析法。用此法处理二组实际数据，结果令人满意。     

主成分分析同时测定多组分金属离子

将主成分分析用于同时单点pH络合滴定,可同时测定多组分金属离子。讨论了方法原理、pH值的选择,建立了主成分分析常数矩阵。对四元金属离子混合样进行了多次测定,均获得满意结果。     

基于小波变换平滑主成分分析

小波变换具有很强的信号分离能力,很容易把随机噪音从信号中分离出来,从而提高信号的信噪比。本文把小波变换引入到因子分析中,提出了基于小波变换平滑主成分分析,该算法既保留普通主成分分析的正交分解,又具备了小波变换的信号分离能力。模拟数据和实验数据的结果表明,该算法具有从低信噪比的数据中提取出有用信息,并提高信号的信噪比。迭代目标变换因子分析处理实验数据的结果表明,基于小波变换平滑主成分分析的处理结果优     

昆明西山植物微量元素主成分分析

用主成分软件分析昆明西山植物微量元素含量特征，元素Ｍｎ，Ｐｂ，Ｎｉ含量的累计贡献率达８４％，从而建立了以Ｍｎ，Ｐｂ，Ｎｉ为主导的三个主成分方程。     

中药微量元素的主成分分析

基于主成分分析的方法,研究了中药的相似性。主成分综合得分越近,中药就越相似。     

光谱结合主成分分析和模糊聚类方法的样品聚类与识别

针对紫外光谱结合化学计量学方法快速测定渣油烃族组成模型适应性问题,对渣油光谱进行主成分分析,以主成分得分作为聚类的特征变量进行模糊聚类,建立了光谱结合主成分分析和模糊聚类方法的样品聚类与识别方法和识别,为光谱结合化学计量分析方法中构正模型的正确选择提供了依据。     

降脂中药微量元素的主成分分析和聚类分析

探讨降脂中药中微量元素的种类和含量与其药用价值的相关性。以常用的10种降脂中药作为研究对象,采用原子吸收光谱法(AAS)对其所含微量元素进行测定,运用化学计量学方法中的主成分分析和聚类分析方法进行信息解析。主成分分析结果表明,前4个主因子含有降脂类中药微量元素含量88.96%的信息。根据主成分得分图和聚类分析谱图,可将10种中药分为两类。找出了与每类中药相关性密切的微量元素,解释了10种中药之间的相似性与差异。通过主成分分析和聚类分析初步揭示出10种降脂中药的微量元素与其功效存在相关性,为该类中药的开发提供了科学依据和理论基础。     

通草类中药微量元素的主成分分析和聚类分析

探讨通草类中药材中微量元素含量与其功效间的相关性。以微量元素含量为指标,运用主成分分析和聚类分析对11种通草类中药的微量元素进行分析。主成分分析结果表明前3个主因子含有通草类中药材微量元素含量84.50%的信息。利用3个主因子模型和聚类分析谱图,解释了11种通草类中药中药的相似性与差异。利用主成分分析和聚类分析法初步得出11种通草类中药的微量元素与其功效存在相关性,为该类中草药的开发利用提供了科学依据和理论基础。     

钙调蛋白抑制剂预测模型研究

选用30个结构多样的caM抑制剂分子作为数据集,采用多元线性回归(MLR)方法及主成分回归分析(PCA)方法对每个化合物的194个分子参数进行回归分析,分别建立了各自的最优预测模型.结果表明:多元线性回归分析方法所建模型与主成分回归所建模型相对比,发现逐步筛选法为最优建模方法?该方法所建模型统计结果良好(R2=0.952,SEE为0.289),应用于检验集时结果也比较令人满意(R2=0.941,SEP为0.295),模型表现出较强的可靠性和预测性.     

基于主成分分析的彩色扫描仪光谱特性化

为了实现扫描仪在不同光源、不同观察者条件下准确获取颜色信息,最大程度的避免同色异谱现象,本文采用光谱的方法对扫描仪进行特性化处理,通过多项式回归和BP神经网络分别与主成分分析法结合,首先对检测样本的光谱反射率进行主成分分析,提取主成分与主成分系数,通过实验得到主成分系数与多项式回归、BP神经网络结构之间的转换模型,实现了扫描仪低维RGB信号对原始光谱反射率信息的重构,进而实现扫描仪的光谱特性化.实验结果表明,多项式项数为19项时,达到训练样本的均方根误差为1.7%,检测样本的均方根误差为1.9%.而包含15个隐层节点的单隐层BP神经网络结构为比较合理的网络结构,达到训练样本的均方根误差为1.3%,检测样本的均方根误差为1.5%.对彩色扫描仪的特征化处理,采用多项式回归法得到光谱特性化精度较低,采用BP神经网络模型能够实现更高的光谱特性化精度.     

Principal and independent components analysis of overlapping spectra in the context of multichannel time-resolved absorption spectroscopy

The separation of overlapping absorption spectra in the context of multichannel time-resolved absorption spectroscopy and chemical kinetics is a particular case in the general problem of splitting the observed data into several linear components. Here, principal and independent components analysis are applied to kinetic data of iodine--ozone chemistry, which contains overlapping spectra of different absorbers. The objective of this work is to demonstrate a method which in spite of this overlap is able to extract separated time traces of such absorbers. These time traces are clearly a pre-requisite for any further accurate quantitative analysis. The statistical properties of data recordings obtained from flash photolysis of I(2) and O(3) have been studied to check if the requirements of the model are fulfilled. Results of separation in appropriate spectral windows displaying overlapped vibrational features are presented. Validation is made using prior information and conventional techniques.     

主成分-BP算法在近红外光谱法纸浆卡伯值测量中的应用研究

提出采用主成分-BP算法建立纸浆卡伯值近红外光谱法在线测量模型。结果表明,这种算法由于既考虑到了近红外光谱响应的非线性因素,又可防止BP算法在建模时出现“过拟合”的现象,利用该算法建立的纸浆卡伯值测量模型与一元回归,多元回归和主成分回归等线性方法相比,具有更高的预测精度。     

主成分回归残差神经网络校正算法用于近红外光谱快速测定汽油辛烷值

根据汽油辛值预测体系本身的非线性特点,提出主成分回归残差神经网络校正算法（principal component regression residual artificial neural network,PCRRANN)用于近红外测定汽油辛烷值的预测模型校正,该方法给合了主成分回归算法（PC）,与经典的线性校正算法（PLS（Partial Least Square),PCR, 以及非线性PLS（NPLS,Non-linear PLS)等相比,预测明显的改善,文中还讨论了PCR主成分数及训练参数对预则模可能的影响。     

Principal component analysis of polarity and interaction parameters in inverse gas chromatography

Inverse gas chromatography is used in the characterization of aliphatic-aromatic and aromatic ketones, their oximes, and ketone-oxime or oxime-oxime mixtures. All these organic materials are used as liquid stationary phases in gas chromatographic columns. A series of polarity and Flory-Huggins interaction parameters are determined and used to describe the physicochemical properties of examined materials, metal extractants, and products of their degradation. Principal component analysis (PCA) is performed on a data matrix consisting of polarity and interaction parameters for ketones, their oximes, and mixtures. The calculations are carried out on the correlation matrix. It is found that seven principal components account for more than 95% of the total variance in the data, indicating that the polarity (interaction) parameters are not correlating well. Physical meanings are attributed to the principal components, the most influential ones being that the first and the second principal components account for several Flory-Huggins interaction parameters, whereas the fifth is correlated with criterion "A". The plots of component loadings show characteristic groupings of polarity indicators, whereas that of component scores show several groupings of stationary phases. Cluster analysis provides mainly the same groupings. PCA allows for the grouping of polarity and solubility parameters based on the information carried within those parameters. There is no need to use more than one parameter from each cluster. McReynolds polarity and the partial molar excess Gibbs free energy of solution per methylene group carry the same information. The groups of ketones, oximes, and their mixtures can be distinguished with the use of PCA on the basis of the measured polarity, solubility parameters, or both.     

基于主成分和支持向量机浓度参量同步荧光光谱油种鉴别

基于浓度参量同步荧光光谱技术,对不同溢油类型不同油源原油样品集、引入外扰相似油源样品集进行光谱数据采集,获取其浓度同步荧光光谱矩阵Concentration-Synchronous-Matrix-Fluorescence(CSMF),利用主成分分析方法对两套不同层次的原油相关样品集进行了多类分类识别。结果表明:主成分载荷图可以很好地反映各个原油相关样品在油源上的相似程度,结合支持向量机可以实现不同溢油类型及不同油源原油的准确分类,对于引入风化和海水外扰相似油源溢油样品集,两类分类区分的结果远远高于多类分类识别的结果。通过详细的主成分分析讨论,为溢油油种鉴别提供了一种利用多类分类识别,逐步缩减嫌疑样本数量,最后通过两两分类实现溢油样品准确识别的新思路。     

Nondestructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enables nondestructive analysis of solid samples without time-consuming sample preparation methods. A new method for the nondestructive determination of compound amoxicillin powder drug via NIR spectroscopy combined with an improved neural network model based on principal component analysis (PCA) and radial basis function (RBF) neural networks is investigated. The PCA technique is applied to extraction relevant features from lots of spectra data in order to reduce the input variables of the RBF neural networks. Various optimum principal component analysis-radial basis function (PCA-RBF) network models based on conventional spectra and preprocessing spectra (standard normal variate (SNV) and multiplicative scatter correction (MSC)) have been established and compared. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations are also used, which are compared with PCA-RBF neural networks. Experiment results show that the proposed PCA-RBF method is more efficient than PCR and PLS multivariate calibrations. And the PCA-RBF approach with SNV preprocessing spectra is found to provide the best performance.     

A new regression method based on independent component analysis

Based on independent component analysis (ICA), a new regression method, independent component regression (ICR), was developed to build the model of NIR spectra and the routine components of plant samples. It is found that ICR and principal component regression (PCR) are completely equivalent when they are applied in quantitative prediction. However, independent components (ICs) can give more chemical explanation than principal components (PCs) because independence is a high-order statistic that is a much stronger condition than orthogonality. Three ICs are obtained by ICA from the NIR spectra of plant samples; it is found that they are strongly correlated to the NIR spectra of water, hydrocarbons and organonitrogen compounds, respectively. Therefore, ICA may be a promising tool to retrieve both quantitative and qualitative information from complex chemical data sets.     

径向基函数网络在近红外人体无创伤血糖浓度检测基础研究中的应用

在近红外无创伤血糖浓度检测的基础研究中，对于多组分的混合物的分析，常因光谱与样品浓度之间呈现非线性响应，使得基于线性模型的校正方法失效。本文讨论了非线性校正方法径向基函数神经网络( RBFN )的有效性，并与线性校正方法中的主成分分析和偏最小二乘法作了对比研究。验证实验所用样品为①葡萄糖水溶液②包含牛血红蛋白和白蛋白的葡萄糖水溶液，结果表明：在①实验中PLS模型和RBFN预测标准偏差分别为8．2、8．9；在②实验中分别为15．6、8．8。可见在样品组分增多时，RBFN算法较线性PLS方法建立的模型预测能力强。