A variation on external preference mapping

Abstract

A method for product optimization based on external preference mapping (EPM) is proposed and tested on a publically available data set. As with EPM an individual’s preferences are modeled using latent variables (LVs) derived from trained panelists’ sensory evaluations. The method differs from traditional EPM. First, it is advised that more LVs be used in modeling with the hope of improving model fit. Second it is suggested that a robust criterion be used in model selection and culling in order to achieve “better” models. Finally, standard, sophisticated optimization approaches are advised for estimating product optima. The method gives an estimate of the optimal product profile in the original sensory space for a given consumer group. A number of comparisons are made, e.g. modeling with LVs derived from partial least squares (PLS) regression vs. those derived from PCA, scaling vs. not scaling preference data, model selection with a genetic algorithm vs. backward selection, modeling with 1st order terms vs. 1st and 2nd order terms and optimization using two different sets of optimization constraints. Given enough latent variables, the method retained a large proportion of the individuals in the given population and fit preference data well in both a calibration and a validation sense. However, evaluating a previously published optimum with the new method underestimated a validated result. When 6, 7, 8 and 9 LVs were used, the associated optima were relatively consistent within a given approach.