In a previous paper we reported that by using artificial neural networks (BNNs) to interpret data acquired using dual pulse staircase voltammetry (DPSV), it is possible to determine the concentrations of aliphatic compounds in mixtures. This paper extends that work by probing the relationship between the DPSV response and mixture composition by fitting the acquired data to a proposed model, using a method based on multiple linear regression (MLR) and analysis of variance (ANOVA). The results of this suggest that the system is predominantly a linear function of the analyte concentrations, with additional contributions from a non-linear ethanol term and a fructose-ethanol interaction term. The common multivariate calibration techniques of MLR, principal component regression (PCR) and linear and non-linear partial least squares (PLS) were subsequently evaluated for the calibration of DPSV voltammograms. These approaches are compared with the previously published artificial neural network (ANN) results. ANNs are found to give the lowest prediction errors, although PLS and PCR are only slightly worse - this is assumed to be due to their inability to model the fructose-ethanol interaction and non-linearity of the ethanol response.