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Is core consistency a too conservative diagnostic?

  • Published In: Journal of Chemometrics, 2023, v. 37, n. 5. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Halberg, Helene Fog Froriep; Bevilacqua, Marta; Rinnan, Åsmund 3 of 3

Abstract

Fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) has successfully been applied for the analysis of food and beverages containing numerous autofluorescent compounds. For the decomposition of such data, it is crucial to establish the PARAFAC model complexity. This is not a trivial matter, especially when the sample complexity increases. Diagnostics are available for assisting the choice of the number of PARAFAC components, such as the core consistency. In this short communication, we show that when it comes to real (complex) data, the core consistency is too conservative and other diagnostic tools must be taken into account. We emphasize that it is imperative to inspect the PARAFAC excitation and emission loadings and assess whether these are chemically meaningful. Fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) has successfully been applied for the analysis of food and beverages containing numerous autofluorescent compounds. An essential step of the data analysis is the PARAFAC model complexity estimation, where the commonly used core consistency diagnostic appear too conservative. We emphasize that the PARAFAC excitation and emission loadings should be inspected and accompanied by an assessment of whether these are chemically meaningful. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Chemometrics. 2023/05, Vol. 37, Issue 5, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2023
  • ISSN:0886-9383
  • DOI:10.1002/cem.3483
  • Accession Number:163742895
  • Copyright Statement:Copyright of Journal of Chemometrics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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