论块状不完整数据集 MCR 问题的因子模糊性

IF 3.7 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-04-27 DOI:10.1016/j.chemolab.2024.105134

Martina Beese , Tomass Andersons , Mathias Sawall , Cyril Ruckebusch , Adrián Gómez-Sánchez , Robert Francke , Adrian Prudlik , Robert Franke , Klaus Neymeyr

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引用次数: 0

摘要

多变量曲线解析（MCR）方法有时会遇到数据缺失或错误的情况，例如由于传感器饱和。在某些情况下，可以对缺失数据进行估算，但 MCR 通常使用最大的无缺失条目的子矩阵。这就忽略了数据矩阵中包含缺失值的所有行和列。Alier 和 Tauler（2013 年）提出了一种处理不完整数据多集的成功方法，但其中不包括因子模糊性分析。在这里，缺失数据问题将结合因子模糊性分析来解决。本文提出了一种方法，即使从光谱数据矩阵不完整的行和列中提取最大的光谱信息，也能最大限度地减少因子模糊性。该方法需要较高的信噪比。介绍了 UV/Vis 和 HSI 数据的应用。

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On the factor ambiguity of MCR problems for blockwise incomplete data sets

Multivariate curve resolution (MCR) methods are sometimes faced with missing or erroneous data, e.g., due to sensor saturation. In some cases, an estimation of the missing data is possible, but often MCR works with the largest submatrix without missing entries. This ignores all rows and columns of the data matrix that contain missing values. A successful approach to deal with incomplete data multisets has been proposed by Alier and Tauler (2013), but it does not include a factor ambiguity analysis. Here, the missing data problem is addressed in combination with a factor ambiguity analysis. An approach is presented that minimizes the factor ambiguity by extracting a maximum of spectral information even from incomplete rows and columns of the spectral data matrix. The method requires a high signal-to-noise ratio. Applications are presented for UV/Vis and HSI data.

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.