Comparison of Calibration Methods with and without Feature Selection for the Analysis of HPLC Data.
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- VÁZQUEZ Piedad Parrilla
- Department of Hydrogeology and Analytical Chemistry, University of Almeria
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- GALERA María Martínez
- Department of Hydrogeology and Analytical Chemistry, University of Almeria
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- FRENICH Antonia Garrido
- Department of Hydrogeology and Analytical Chemistry, University of Almeria
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- VIDAL José L. Martínez
- Department of Hydrogeology and Analytical Chemistry, University of Almeria
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Abstract
A comparison of two multivariate calibration methods, partial least squares (PLS) and principal component regression (PCR), applied to high-performance liquid chromatography (HPLC) data, is presented for the resolution of a pesticide mixture. The data set showed both coeluted peaks and overlapped absorption spectra. Besides, there is an additional overlapping between the signal of the mobile phase and that of some pesticide. Multivariate calibration models were evaluated using different criteria to choose the optimum number of latent variables. It is shown that PLS yields the best predictive models. Furthermore, two methods for selecting regions were applied with the goal to achieve an improved prediction ability in the present multicomponent determination by HPLC-DAD (diode array detector) with PLS. The selection of regions associated with a large correlation to the concentration and with large values in loading-weighs (from PLS) were considered. It is concluded that feature selection can also improve the multivariate calibration results using chromatographic data.
Journal
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- Analytical Sciences
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Analytical Sciences 16 (1), 49-55, 2000
The Japan Society for Analytical Chemistry
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Keywords
Details 詳細情報について
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- CRID
- 1390282679231992320
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- NII Article ID
- 10004704653
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- NII Book ID
- AA10500785
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- COI
- 1:CAS:528:DC%2BD3cXos1ekuw%3D%3D
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- ISSN
- 13482246
- 09106340
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- NDL BIB ID
- 4966630
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed