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[Simultaneous spectrophotometric determination of three-component mixture by two partial least squares methods]
Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
|April 7, 2005
Summary
This study compares classical partial least squares (PLS) and kernel partial least squares (KPLS) for analyzing three-component mixtures. Both methods successfully determined components even with overlapping spectra, demonstrating their effectiveness.
Area of Science:
- Analytical Chemistry
- Chemometrics
Context:
- Simultaneous determination of multi-component mixtures presents analytical challenges, especially with spectral overlap.
- Partial Least Squares (PLS) regression is a widely used multivariate calibration technique.
Purpose:
- To evaluate and compare the performance of classical Partial Least Squares (PLS) and a kernel-based algorithm (KPLS) for the simultaneous determination of three-component mixtures.
- To assess the suitability of these methods for spectral data with overlapping signals.
Summary:
- Two distinct algorithms, SPGRPLS and SPGRKPLS, were developed based on PLS and KPLS, respectively, for analyzing three-component mixtures.
- Eight error functions were employed to determine the optimal number of factors for the models.
- KPLS demonstrated efficiency in handling large spectral matrices with fewer samples due to a smaller kernel matrix size.
Impact:
- Both PLS and KPLS methods proved successful in accurately determining components in mixtures with significant spectral overlap.
- The findings support the application of these chemometric methods for complex mixture analysis in analytical chemistry.