A new conceptual approach to assigning biomass combustion-derived methoxyphenol structures by using a gas chromatographic retention index model

Abstract

A new conceptual approach towards iteratively constructing chromatographic retention time/index models is presented. The approach is applicable where there is potential structural uncertainty in a number of members of the dataset used in constructing the model, and where limited spectroscopic information is available to guide the process. The model is demonstrated on a suite of biomass combustion-derived methoxyphenols for which gas chromatographic polydimethylsiloxane retention index data was available in the literature, but where there was ambiguity regarding the identity of several members of the dataset. The retention property model is populated by sequentially screening a series of candidate structures that meet basic mass spectrometric requirements by using a multiple linear regression model containing molecular and physicochemical properties that have been previously shown to yield reliable predictions of chromatographic behaviour within a compound class. The criteria for deciding on the likely structure(s) out of a suite of candidate structures is based upon the improved quality of fit the most probable structure gives the regression model relative to other candidate structures.