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从多波长光谱数据计算平衡常数-IV 使用渐进因子分析的无模型最小二乘精修法

Calculation of equilibrium constants from multiwavelength spectroscopic data-IV Model-free least-squares refinement by use of evolving factor analysis.

作者信息

Gampp H, Maeder M, Meyer C J, Zuberbühler A D

机构信息

Institute of Inorganic Chemistry, University of Basel, CH-4056 Basel, Switzerland.

出版信息

Talanta. 1986 Dec;33(12):943-51. doi: 10.1016/0039-9140(86)80233-8.

Abstract

The newly developed algorithm of evolving factor analysis has been supplemented by iterative refinement. It allows the completely model-free calculation of concentration profiles and spectra from spectrophotometric and other spectroscopic data. Not even implicit use is made of the law of mass action. The results are practically identical with those based on a specific chemical model and classical least-squares refinement. Iterative evolving factor analysis is based on applying factor analysis successively to the set of the first 1,2 cdots, three dots, centered M spectra of a spectrometric titration. The analysis is repeated from the opposite end and the eigenvalues thus calculated are combined into "concentration profiles" of completely abstract "species". These "concentration profiles" are iteratively refined by normalization, calculation of the absorption spectra from the normalized concentrations and recalculation of the concentration profiles from the absorption spectra. Evolving factor analysis is not restricted to spectrometric titrations, and can also be applied to peak resolution in chromatography using a multiwavelength (diode array) photometric or mass-spectrometric detection system, or to any other ordered set of multichannel data.

摘要

新开发的渐进因子分析算法已通过迭代优化得到补充。它允许从分光光度法和其他光谱数据中完全无模型地计算浓度分布和光谱。甚至没有隐含地使用质量作用定律。结果与基于特定化学模型和经典最小二乘优化的结果几乎相同。迭代渐进因子分析基于将因子分析依次应用于光谱滴定的前1、2、…、n个中心M光谱集。从相反端重复分析,并将由此计算出的特征值组合成完全抽象的“物种”的“浓度分布”。这些“浓度分布”通过归一化、从归一化浓度计算吸收光谱以及从吸收光谱重新计算浓度分布进行迭代优化。渐进因子分析不限于光谱滴定,也可应用于使用多波长(二极管阵列)光度或质谱检测系统的色谱峰分辨率,或任何其他有序的多通道数据集。

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