Functionality-driven food product formulation - An illustration on selecting sustainable ingredients building viscosity

Abstract

Currently, food industries typically favour formulation of food products using highly refined techno-functional ingredients of high purity. However, there is a growing interest in less pure techno-functional ingredients with a lower degree of refining as they deliver the same functional properties with reduced environmental impact. We propose that instead of selecting formulations based on purity, they should be selected based on their techno-functional properties. This article illustrates that the shift in perspective may increase the sustainability of food production. The functionality-driven product formulation is explored through a case study in which yellow pea ingredients are selected to increase the viscosity of a salad dressing. The relation between the ingredients (in terms of composition; protein, starch fibre, and a residual fraction) and the final viscosity was quantified and validated using multiple linear regression. The model described the observations well: the final viscosity is mostly dominated by the starch content; protein content has only a marginal impact; and dietary fibre contributes to viscosity with an antagonistic effect with starch. Based on the multiple linear regression model and further formulation optimisation, we identified various combinations of ingredients (with either a high or low degree of refining) that would result in the target final viscosity. An evaluation of the global warming potential of all blends showed that the desired viscosity could be achieved using only isolates, as well as by using only mildly refined fractions. The latter is associated with a global warming potential that is 80% lower than the one based on isolates. This case study demonstrates the proof of concept for this approach, showing it can aid in identifying alternative product formulations with similar techno-functional properties but with a higher sustainability.

Keywords: Functional ingredients; Mild fractionation; Regression models; Techno-functionality.