基于遗传算法优化的约束背景双线性分解算法用于改进高效液相色谱灰色分析体系的校正结果

该文采用约束背景双线性分解算法(CBBL)对以高效液相色谱(HPLC)方法分离分析的灰色分析体系进行了多元校正研究。针对采用包括CBBL在内的矩阵校正方法处理HPLC灰色分析体系的固有缺陷,即在相关组分的色谱保留时间重现性较低的情形下多元校正的结果不理想,对CBBL方法进行了改进,即将待测组分的浓度与组分的色谱保留时间同时作为优化的参量引入CBBL,并采用遗传算法(GA)优化CBBL,对于模拟的组分保留时间飘移严重的HPLC灰色分析体系及保留时间重现性不佳的多种酚类化合物组成的实际HPLC灰色分析体系进行了多元校正分析,成功克服了经典CBBL的固有缺陷,取得了较理想的多元校正结果。另外,该研究所建议的方法的校正结果也显著优于传统的残差双线性分解法(RBL)以及秩消失因子分析法(RAFA)。     

子空间集成回归算法在多元校正中的应用

为提升多元校正模型的性能并简化其复杂度,遵循机器学习领域的集成思路,提出了一种基于子空间建模并以多元线性回归为基础的算法,多元校正算法,简写作SER-MLR。通过两个近红外光谱定量应用试验及与全谱偏最小二乘算法(PLS)的比较,验证了其优良的综合性能,该算法不仅容易解释,而且能够以更低的计算代价建立起简洁、稳健的校正模型,对过拟合也不敏感。     

校正变换矩阵法及其在五种芳香化合物同时测定中的应用

将目标变换因子分析中的自由浮动思想引入多元校正中,提出了一种新的多组分同时校正法——校正变换矩阵法。将该法应用于紫外分光光度法同时测定五种芳香族化合物,结果令人满意。与目标因子分析法和卡尔曼滤波法比较,本法具有更好的校正准确度。     

多元分析的稳健方法

对统计学和化学计量学中发展迅速的多元稳健方法进行了系统的评述。这些稳健方法可用于化学计量学中的多元校正和模式识别。在对一些重要方法的主要思路进行了回顾的基础上，对它们的适应性和局限性本文也给出了必要的讨论。     

叠加多元校正分析

基于多模型(模型融合)建模的思想,开发了两种新的叠加多元校正分析算法:叠加PCR(PLS)多元校正分析和叠加移动窗口PCR(PLS)多元校正分析。与一般的多模型建模方法不同的是其通过赋予光谱数据中的不同部分不同权重叠加子多元校正模型。因此,其可以通过权重调节或选择变量。在消除光谱数据中常见的冗余信息的同时,避免信息遗漏的缺点,并最终提高模型的稳健性,简化了模型。对于这两个新的算法,尽管其具体步骤不同,但仍取得了相似的预测结果。本文通过两套近红外光谱文献数据计算验证了这两个新方法的优越性。     

校正变换矩阵法及其在中芳香化合物同时测定中的应用

将目标变换因子分析中的自由浮动思想引入多元校正中，提出了一种新的多组分同时校正法－－校正变换矩阵法。将该法应用于紫外分光光度法同时测定五种芳香族化合物，结果令人满意。与目标因子分析法和卡尔曼波法比较，本法具有更好的校正准确度。     

近红外光谱法快速测定推进剂中N-甲基对硝基苯胺

采用近红外光谱法快速测定固体推进剂中N-甲基对硝基苯胺(MNA)的含量。评价了滤波平滑、一阶导数、二阶导数、多元散射校正(MSC)和标准正态变量校正(SNV)这5种不同光谱预处理方法的优化效果，基于建模参数优化结果建立了MNA定量模型，并对模型进行了准确性和重复性验证。结果表明，光谱最佳预处理方式是SNV，模型最佳主因子数为7，模型校正决定系数(R_C²)和验证决定系数(R_P²)分别为0.998 6和0.987 2，交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为0.012 0和0.009 8，重复性极差和绝对误差均低于0.2%。近红外光谱法与液相色谱法测定结果相比相对偏差在6%以内，经t检验，两种方法测定结果无显著性差异。近红外光谱法快速、准确，可用于推进剂老化进程监控。     

校正变换矩阵法及其在多组分直接同时测定中的应用

将目标变换因子分析中自由浮动技术引入多元校准中，提出了一种新的多组分同时校准法———校正变换矩阵法。该法用于四组分氨基酸人工混合样品分析，结果令人满意，通过与传统目标变换因子分析法比较研究，表明该法用于组分光谱严重共线的波长范围时，其校准能力大大优于传统目标变换法。     

模型诊断用于近红外光谱建模校正集中奇异样本的识别

由于校正集样本的质量决定校正模型的质量,校正集中奇异样本的检测在多元校正建模中具有非常重要的意义.本研究建立了一种用于近红外光谱多元校正建模时校正集中奇异样本的检测方法.本方法基于奇异样本的定义和偏最小二乘方法的原理,通过考察每个校正集样本在模型的每个因子(或主成分)中对模型的贡献,将与多数样本表现不同的样本识别为奇异样本.采用218个橘汁样本构成的近红外光谱数据进行了分析,结果表明,校正集中存在6个奇异样本,扣除奇异样本后,校正集的交叉验证均方根误差由16.870减小为4.809,预测集的均方根误差从3.688减小为3.332.     

神经网络扩展滤波算法及其多元光谱分辨应用

前馈神经网络(NN)误差反向传播算法(BP)应用较广, 但收敛较慢且易陷入局部极优, 针对这一不足, 本文提出了一种基于扩展滤波的快速学习新颖算法(EF)。与BP相比较, 该法不仅具有学习效率高, 收敛速度快, 所需学习次数少,数值稳定性好等优点, 而且所需调节参数少。由非线性系统建模与辨识的模拟结果表明, EF是一种有效的神经学习新算法。该法用于多元光谱校正与分辨,获得良好结果。     

序贯数论优化法用于“灰色”体系多组分同时定量分析

将序贯为九论优化法,结合采用约束背影双线性比模型用于多组分“灰色”体系同时测定,且对ＳＮＴＯ的收缩比和收敛判据进行了改进。结果表明,ＳＮＴＯ法是一种实用的全局优化方法,收缩比和收敛判据的改进可大大缩短计算时间。     

Robust linear regression taking into account errors in the predictor and response variables

We developed a robust regression technique that is a generalization of the least median of squares (LMS) technique to the field in which the errors in both the predictor and the response variables are taken into account. This simple generalization is limited in the sense that the resulting straight line is found by using only two points from the initial data set. In this way a simulation step is added by using the Monte Carlo method to generate the best robust regression line. We call this new technique 'bivariate least median of squares' (BLMS), following the notation of the LMS method. We checked the robustness of the new regression technique by calculating its breakdown point, which was 50%. This confirms the robustness of the BLMS regression line. In order to show its applicability to the chemical field we tested it on simulated data sets and real data sets with outliers. The BLMS robust regression line was not affected by many types of outlying points in the data sets.     

Robust curve fitting method for optical spectra by least median squares (LMedS) estimator with particle swarm optimization (PSO).

A curve fitting technique for optical spectra based on a robust estimator, least median squares (LMedS), is introduced in this study. For the effective calculation of LMedS, particle swarm optimization (PSO) is also introduced. Unlike a standard curve fitting method using least squares (LS) estimator, the method based on LMedS estimator is less influenced by outliers in experimental data. Two kinds of data sets, simulated data with outliers and temperature-dependent near-infrared (NIR) spectra of oleic acid (OA) are applied for the demonstration of the proposed method. The results clearly reveal that, compared with the LS estimator, the proposed method can effectively reduce undesirable effects of low SN ratio and can yield more accurate fitting results.     

An optimization procedure for determination of indomethacin and acemethacin by differential pulse adsorptive stripping voltammetry. Application on urine samples

Procedures for the determination of indomethacin and acemethacin by differential pulse adsorptive stripping voltammetry with a mercury electrode have been described and optimised. The selection and optimization of the experimental parameters was done using factorial and central composite designs. Indomethacin and acemethacin in urine were determined by this method with good results and without the need for tedious prior separation. For routine calibration and calculation of the ‘capacity to detect’, the robust regression method least median squares (LMS) has been proposed.     

动力学过程的精确模拟

采用加权最小二乘法更精确地模拟了化学动力学过程,结出了几种常见动力学模型的权重函数。用普通最小二乘法和加权最小二乘法对聚时苯二甲酸乙二酯的结晶动力学过程进行了处理。方差分析表明,两种加权最小二乘法的标准差均小于普通最小二乘法的标准差,所得斜率和截距也有明显不同,示出了模拟的相对结晶度及其导数曲线。     

Envirometrics. Part I: Modeling of water salinity and air quality data

Envirometrics utilises advanced mathematical, statistical and information tools to extract information. Two typical environmental data sets are analysed using MVATOB (Multi Variate Analysis TOol Box). The first data set corresponds to the variable river salinity. Least median squares (LMS) detected the outliers whereas linear least squares (LLS) could not detect and remove the outliers. The second data set consists of daily readings of air quality values. Outliers are detected by LMS and unbiased regression coefficients are estimated by multi-linear regression (MLR). As explanatory variables are not independent, principal component regression (PCR) and partial least squares regression (PLSR) are used. Both examples demonstrate the superiority of LMS over LLS.     

小波变换-偏最小二乘算法及其在复方甲硝唑注射液分析中的应用

提出了结合小波变换的偏最小二乘法（ＷＰＬＳ）,即先对光谱信号进行小波变换,去除噪声,再用偏最小二乘法对多组分同时测定。将该法用于模拟体系及复方甲硝唑注射液体系,结果表明,该法优于偏最小二乘法。     

Application of simulated standard spectra for the analysis of complex sample spectra from NaI(Tl) detectors

The most accurate method for the analysis of complex gamma ray spectra from scintillation detectors is least squares method. The major requirement of this method is individual standard spectra of all nuclides expected in the complex spectrum which is not possible and feasible for some nuclides. In the present work, an approach of using simulated standard spectrum of the radionuclides for the least squares analysis is studied. The paper describes the methodology used for the generation of simulated spectrum which is the main objective, and validation of results using standard sources in the Sodium Iodide (NaI(Tl)) based gamma ray spectrometer.