通用模拟退火用于稳健多元分析校正

模拟退火是一种全局优化算法，具有跨越局部最优点的机制，最小一乘是一种较常用的最小二乘更为稳健的优化准则，更适用于可能偏离正态分布的实际数据集，本文探讨了用最小一乘为准则并利用模拟退火方法同时测定多组分体系的可能性。应用于2－3组分药物体系分析，获得了满意的结果，本文还探讨了改变步长提高模拟退火算法优化精度的方法。     

K系数—多波长最小一乘回归分光光度法同时测定三组分的研究

将双波长Ｋ系分光光度法和多波长线性回归分光光度法相结合，并采用最小一乘法准则计算回归系数，提出了一种同时测定三组分的新方法，即Ｋ系数－多波长最小一乘回归分光光度法。     

K系数－多波长最小—乘回归分光光度法同时测定三组分的研究

将双波长Ｋ系数分光光度法和多波长线性回归分光光度法相结合，并采用最小一乘法准则计算回归系数，提出了一种同时测定三组分的新方法，即Ｋ系数－多波长最小一乘回归分光光度法。该法不需要吸收曲线交点和等吸收点，且能较好地克服异常点的影响，得到比最小二乘法更好的回归稳健性。方法用于测定模拟混合色素样品和饮料样品中的柠檬黄、胭脂红、果绿，相对误差为—３．６％～２．８％，回收率为９６．３％～１０４．８％，变异系数小于２．７９％。     

比值导数波谱-多波长最小一乘回归分光光度法同时测定饮料中糖精钠、苯甲酸和山梨酸

提出了比值导数波谱－多波长最小一乘回归分光光度法。该法能同时测定三组分混合物而毋需零交点波长和等吸收点，且回归稳健性好．可消除异常点的影响。方法用于合成样品和饮料中糖精钠、苯甲酸和山梨酸的同时测定，相对误差＜± ３． ５％，回收率为 ９６． ６％～１０４．４％，相对标准偏差为２．０％～３．１％．     

傅里叶变换用于铁和锌的同时光度测定

研究了傅里叶变换技术用于铁锌二组分的同时分光光度测定，采用傅里叶变换对吸光度数据进行预处理，再结合目标转换因子分析或偏最小二乘分析，结果较普通的目标转换因子分析或偏最小二乘法有显著改善。以傅里叶变换－偏最小二乘法就用于实际铝合金样品中铁和锌的同时测定，结果令人满意。     

非线性偏最小二乘法在速效伤风胶囊紫外分光光度分析中的应用

本文介绍了非线性偏最小二乘法的基本原理及算法。以速效伤风胶囊的紫外分光光度分析为例,扑热息痛、咖啡因、扑尔敏、人工牛黄四组分的测定结果满意,且明显好于线性偏最小二乘法。本法为多组分混合体系的分光光度分析提供了更为理想的新途径。     

动力学参数数目与法方程条件数的关系

多相催化反应动力学研究的基本问题是动力学方程中的参数估计，它一直是动力学研究中的活跃领域·在参数估计中最常用的方法是最小二乘法．早在1947年［刘开始用线性最小二乘法处理动力学模型的数据．1958年在线性最小二乘法中应用置信区间问题［21．1959年MarguardM出非线性最小二乘法问．前人提出如何求非线性最小二乘法的初值［4］，及加权最小二乘法的重要性问，并对最小二乘法在动力学中的应用做了综述卜］．在多相催化动力学研究中，关干线性最小二乘法遇到根本性困难的问题前人未涉及．我们曾在动力学研究中发现线性最小二乘法的法…     

PLS-ANN算法-NIR光谱非破坏性Norvasc药物有效成分的定量分析

采用偏最小二乘(PLS)结合人工神经网络(ANN)算法解析Norvasc(络活喜)药片的近红外(NIR)漫反射光谱, 实现了对其中有效成分苯磺酸氨氯地平的非破坏定量测定. 设计了最佳的PLS-ANN模型, 分别讨论了最佳波长范围、 导数光谱及输入层和隐含层节点数对预测结果的影响. 以HPLC法的测定结果作标准, 苯磺酸氨氯地平浓度预测值的相对误差RE＜3.5%, 该方法可用于Norvasc药品实际生产中的质量控制.     

多相多组元化学平衡相平衡通用计算方法的研究

本文工作基于自由能最小原理,以通用、可靠、高效率为目标,研究多相多组元化学相平衡新的通用计算方法。编制了通用算法SVMP的计算机程序;并在此基础上,对SVMP法进行了三项改进,改进后的算法称之为M-SVMP法。检验结果表明,对多数情况M-SVMP的性能优于原算法。     

LM优化反向传播网络测定多组分

为了提高此网络算法的学习效率及稳定性,在反向传播算法（backpropagation(BP)中引入了基于非线性最小二乘法的Levenberg-Marquart(LM)最优算法,替代原BP算法中的梯度下降法寻找最佳网络连接权值,LM优化算法其学习效率比带动量项的BP算法高一个数量级以上,值得推广应用,将其用于混合体系的多组份CAS－CTMAB显色体系光度法同时测定Ca,Mg,Fe,得到平均预测误差为2．6534mg/L,平均预测方差为1．9580,能够满足多组分测定的需要。     

一个多元校正的稳健诊断新方法

提出一种新的稳健诊断方法，与最小二乘估计结合进行混合物光谱中非线性点的诊断。文中探讨了该方法的性能，用计算机数字模拟及实际多组分光谱体系夺其进行检验，展示了此诊断方法在分析化学计量学中实际应用的可行性。     

稳健偏最小二乘光度法同时测定贵金属元素锇和钌

本文将稳健偏最小二乘法用于贵金属元素锇、钌的光度法同时测定，较好地解决了实际校准模型由于实验误差偏离正态分布使计算结果的精度遭到破坏的问题。     

Robust regression techniques A useful alternative for the detection of outlier data in chemical analysis

The validation of an analytical procedure means the evaluation of some performance criteria such as accuracy, sensitivity, linear range, capability of detection, selectivity, calibration curve, etc. This implies the use of different statistical methodologies, some of them related with statistical regression techniques, which may be robust or not. The presence of outlier data has a significant effect on the determination of sensitivity, linear range or capability of detection amongst others, when these figures of merit are evaluated with non-robust methodologies.In this paper some of the robust methods used for calibration in analytical chemistry are reviewed: the Huber M-estimator; the Andrews, Tukey and Welsh GM-estimators; the fuzzy estimators; the constrained M-estimators, CM; the least trimmed squares, LTS. The paper also shows that the mathematical properties of the least median squares (LMS) regression can be of great interest in the detection of outlier data in chemical analysis. A comparative analysis is made of the results obtained by applying these regression methods to synthetic and real data. There is also a review of some applications where this robust regression works in a suitable and simple way that proves very useful to secure an objective detection of outliers. The use of a robust regression is recommended in ISO 5725-5.     

Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine

A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis–NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x–y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard–Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.     

基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用

提出一种基于粒子群算法的最小二乘支持向量机(PSO-LS-SVM)方法,用于建立红花提取过程关键质控指标的定量分析模型.近红外光谱数据经波段选择、预处理和主成分分析(降维)后,利用粒子群优化(PSO)算法对最小二乘支持向量机算法中的参数进行优化,然后使用最优参数建立固含量和羟基红花黄色素A(HSYA)浓度的定量校正模型.将校正结果与偏最小二乘法回归(PLSR)和BP神经网络(BP-ANN)比较,并将所建的3个模型用于红花提取过程未知样本的预测.结果表明,BP-ANN校正结果优于PSO-LS-SVM和PLSR,但是对验证集和未知样品集的预测能力较差,而PSO-LS-SVM和PLSR模型的校正、验证结果相近,相关系数均大于0.987,RMSEC和RMSEP值相近且小于0.074,RPD值均大于6.26,RSEP均小于5.70％.对于未知样品集,pSO-LS-SVM模型的RPD值大于8.06,RMSEP和RSEP值分别小于0.07％和5.84％,较BP-ANN和PLSR模型更低.本研究所建立的PSO-LS-SVM模型表现出较好的模型稳定性和预测精度,具有一定的实践意义和应用价值,可推广用于红花提取过程的近红外光谱定量分析.     

小波变换结合多维偏最小二乘方法用于近红外光谱定量分析

将小波变换和多维偏最小二乘法相结合用于近红外光谱定量校正模型的建立。首先将原始光谱进行小波变换分解，得到系列小波细节系数，通过选取一组受外界因素少、信息强的小波系数组成三维光谱阵，然后再采用多维偏最小二乘法建立校正模型。实验结果表明，该方法所建近红外校正模捌的预测能力更强，并更具稳健性。     

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.     

稳健灰色模型在色谱保留值研究中的应用

本文提出了具有稳健性的ＧＭ（１，１）灰色模型，并用此模型对８种多环芳烃化合物容量因子与流动相组成间的关系作了研究，建立了其间的稳数学模型，结果表明，稳健ＧＭ（１，１）模型比常规ＧＭ（１，１）模型具有更好的抗干扰性能和受异常点影响小的优点，是一个值得推广的好方法。既丰富了灰色系统理论，又拓宽了灰色理论在分析化学中的应用范围。     

Robust partial least squares regression—part III,outlier analysis and application studies

This paper is the third part of the work on robust partial least squares (RPLS) regression. The paper focuses on implementation issues for outlier detection and diagnosis. Furthermore, the paper introduces a numerically more efficient algorithm for determining the Stahel–Donoho estimator (SDE). This has been identified as a potential drawback of the new proposed RPLS algorithm, detailed in Part II of this work. Finally, a total of three application studies are presented which involve data recorded from (i) a calibration experiment (similar number of variables/observations), (ii) a distillation process for purifying benzene (considerably more observations than variables) and (iii) an experiment of a multi‐component concentration determination using Raman spectroscopy (considerably more variables than observations). Copyright © 2008 John Wiley & Sons, Ltd.