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Article Dans Une Revue Chemometrics and Intelligent Laboratory Systems Année : 2024

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

Résumé

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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hal-04625236 , version 1 (26-06-2024)

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Martina Beese, Tomass Andersons, Mathias Sawall, Cyril Ruckebusch, Gómez Sánchez Adrián, et al.. On the factor ambiguity of MCR problems for blockwise incomplete data sets. Chemometrics and Intelligent Laboratory Systems, 2024, Chemometrics Intell. Lab. Syst., 249, ⟨10.1016/j.chemolab.2024.105134⟩. ⟨hal-04625236⟩
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