Multiple linear regression chromatography

V. Casal, P. J. Martin-Alvarez, T. Harraiz, Comparative prediction of the retention behaviour of small peptides in several Reversed-phase high-performance liquid chromatography columns by using partial least squares and multiple linear regression, Analytica Chimica Acta, 326 (1996), 77-84. 

These special cases of multiple linear regression analysis have been developed for the determination of the impact of individual molecular substructures (independent variables) on one dependent variable. Both techniques are similar yet, the Free-Wilson method considers the retention of the unsubstituted analyte as base, while Fujita-Ban analysis uses the less substituted molecule as reference. These procedures have not been frequently employed in chromatography only their application in QSRR studies in RP TLC and HPLC have been reported. 

Fig. 6. Results of multiple linear regression for description of polarity in gas chromatography. Predicted and measured McReynolds polarity values.

The experimental retention factors were determined by reversed-phase high-performance liquid chromatography HPLC). Stepwise multiple linear regression (MLR) and partial least square (PLS) methodology were used to investigate the correlation between the retention factor and a number of molecular descriptors for these compounds. The MLR and PLS equations were found to be useful for the estimation and comparison of retention factors k ) for new synthesized phosphoramidic acid derivatives using the selected molecular descriptors. More details are given in the section on HPLC. 

---