PREDICTION OF TEXTURE PERCEPTION OF MAYONNAISES FROM RHEOLOGICAL AND NOVEL INSTRUMENTAL MEASUREMENTS

Abstract

ABSTRACT Commercial and model mayonnaises varying in fat content and type and amount of thickener were characterized by sensory analysis, rheological measurements and novel instrumental measurements covering other physicochemical properties and/or reflecting changes of food properties during oral processing. Predictions of texture attributes by rheological measurements were analyzed and compared with predictions by rheological measurements combined with novel measurements. Most of the texture attributes were predicted well by rheological parameters alone. Parameters from other instrumental measurements played a small complementary role, except in the predictions of most of the afterfeel attributes. Most important were rheometry at large deformation and in the nonlinear regime of the dynamic stress sweep and two novel measurements reflecting the effect of saliva: turbidity of rinse water and viscosity with added saliva. Tanδat 500% strain, reflecting the fluid‐like character of the samples during high‐strain dynamic flow, relates best to creaminess and other texture attributes.PRACTICAL APPLICATIONSThis article describes how and how well the texture attributes of mayonnaises can be predicted from rheological and novel instrumental measurements. It shows that many texture attributes can be successfully predicted by bulk rheological properties alone, but that for some the quality of the predictions increases slightly when parameters from other instrumental measurements, such as those from turbidity measurements, those from viscosity measurements in the structure breakdown cell with saliva, or those from friction measurements, are added. These results are relevant not only to those investigating the mechanisms involved in the oral perception of texture in semisolids but also to those who want to perform quick screening of new samples without the use of time‐consuming and expensive sensory panels. Simulations can be performed using the models to predict how texture attributes are influenced by changes in the rheological characteristics of a product. The results also identify rheological measurements and novel instrumental measurements relevant for texture attributes of mayonnaise. This knowledge may help to improve the efficiency of product development in industry.