Comparison of Calibration Methods with and without Feature Selection for the Analysis of HPLC Data

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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.

収録刊行物

  • Analytical sciences : the international journal of the Japan Society for Analytical Chemistry  

    Analytical sciences : the international journal of the Japan Society for Analytical Chemistry 16(1), 49-55, 2000-01-10 

    社団法人 日本分析化学会

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各種コード

  • NII論文ID(NAID)
    10004704653
  • NII書誌ID(NCID)
    AA10500785
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09106340
  • NDL 記事登録ID
    4966630
  • NDL 雑誌分類
    ZP4(科学技術--化学・化学工業--分析化学)
  • NDL 請求記号
    Z54-F482
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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