Quantitative structure-retention relationship (QSRR) models for predicting the GC retention times of essential oil components

Abstract

Multiple linear regression (MLR) and partial least squares (PLS) analysis have been used to model gas chromatographic retention times ( t R ) of 77 components of the essential oil of Ottonia martiana . The genetic algorithm (GA) was used to select the variables that resulted in the best-fitting models. Appropriate models with low standard errors and high correlation coefficients were obtained. MLR and PLS analysis were performed to derive the best quantitative structure-retention relationship (QSRR) models. The predictive quality of the QSRR models was tested for an external prediction set of 15 compounds, randomly chosen from the 77 compounds. Surprisingly, the results were of approximately the same quality for MLR and PLS modeling [squared regression coefficients ( R 2 ) of 0.964 and 0.968, respectively].