Linear multiple regression model of high performance liquid chromatography

  • Authors:

  • Stanislava Labátová

  • Affiliations:

  • Institute of Informatics, Department of discrete processes modeling and control, Slovak Academy of Sciences, Bratislava, Slovakia

  • Venue:
  • WSEAS Transactions on Information Science and Applications
  • Year:
  • 2010

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Abstract

The multiple regression analysis is proposed as method for presentation results of high performance liquid chromatography (HPLC), which simultaneously depends on several parameters. The coefficients of linear multiple regression model are assessed by the least square method. In this way dependent variable - retention volume - is expressed by more than one explanatory variable (i.e. by the molar mass, concentrations of eluent and temperature). The approach was to depict dependences of retention volumes on molar mass of polystyrene probes in tetrahydrofuran (THF) and in mixed eluents THF plus dimethylformamide eluents on the silica gel alkyl bonded column packing. The dependences retention volumes on molar mass, concentrations of eluent and are shown.