A systematic study on the effects of multi-set data analysis on the range of feasible solutions

Masoumeh Alinaghi, R. Rajkó, Hamid Abdollahi

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The objective of self modeling curve resolution (SMCR) methods is to decompose a second-order bilinear data matrix into a range of chemically meaningful matrices without any knowledge about the chemical or physical model describing the considered system. In addition, SMCR methods are efficient approaches to deeply investigate data structures by finding not only one of the solutions but all possible ones.Multi-set data analysis can be a powerful tool to decrease the range of feasible solutions in the absence of appropriate conditions for unique resolution. Using SMCR methods, we have investigated the impact of multi-set data analysis on the accuracy of soft modeling results. Interestingly, the feasible regions of individual and simultaneous analysis are compared in a common abstract space. It is demonstrated how such global analysis can result in the reduction of rotational ambiguity in soft modeling analysis. Moreover, as a systematic study, different factors are considered in order to discover the advantages and limitations of multi-set data analysis and lead to a proper design for more accurate results.

Original languageEnglish
Pages (from-to)22-32
Number of pages11
JournalChemometrics and Intelligent Laboratory Systems
Volume153
DOIs
Publication statusPublished - Apr 15 2016

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Data structures

Keywords

  • Global analysis
  • Lawton-Sylvestre method
  • Multi-set data analysis
  • Rotational ambiguity
  • Self modeling curve resolution (SMCR)
  • Simultaneous analysis

ASJC Scopus subject areas

  • Analytical Chemistry
  • Computer Science Applications
  • Software
  • Process Chemistry and Technology
  • Spectroscopy

Cite this

A systematic study on the effects of multi-set data analysis on the range of feasible solutions. / Alinaghi, Masoumeh; Rajkó, R.; Abdollahi, Hamid.

In: Chemometrics and Intelligent Laboratory Systems, Vol. 153, 15.04.2016, p. 22-32.

Research output: Contribution to journalArticle

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