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• W2021796136 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." @default.
• W2021796136 created "2016-06-24" @default.
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• W2021796136 date "2000-01-01" @default.
• W2021796136 modified "2023-10-13" @default.
• W2021796136 title "Comparison of Calibration Methods with and without Feature Selection for the Analysis of HPLC Data" @default.
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• W2021796136 doi "https://doi.org/10.2116/analsci.16.49" @default.
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