Background: Mayonnaise is a widely used emulsion-like food that is popular for its flavor properties. However, the modern trend of healthy eating requires a reduction in the calorie content of this product, which means a decrease in the oil content. Such emulsion systems require the solution of increased problems associated with the stabilization of their structure. It is known that the size of droplets as a microstructural characteristic depends on the stability of the emulsion and correlates with the rheological properties of emulsions. Thus, the study of these characteristics becomes one of the important factors in predicting the properties of emulsions being developed with different natures of the main ingredients.
Objective: The purpose of this study was a preliminary chemometric analysis of data on acidity, rheological and microstructural characteristics of commercial mayonnaises and mayonnaise sauces containing from 25 to 67% oils (sunflower, rapeseed and olive) in order to predict the effect of the main ingredients of the recipe on textural characteristics.
Methods: Microstructural and rheological characteristics of the samples were determined by laser diffraction and rotational viscometry with coaxial cylinders, respectively. Rheological data were analyzed within the framework of a structural representation based on the generalized Casson’s model. The nine standardized parameters were grouped using multivariate statistical methodology techniques such as principal component analysis and hierarchical cluster analysis.
Results: The experimental flow curves demonstrated pseudoplastic behavior, which is typical for such emulsion systems. The three factors of multivariate factor analysis can explain 72.5% of the variability. In the first factor, the most important variables (with the highest loads) were the Casson’s model coefficient of the aggregation degree, the static yield stress and the average droplet size. In the second factor, the highest loadings were the oil content and the Casson model coefficient, which indicates a tendency to form an infinitely large droplet aggregate. The pattern captured by PCA is confirmed by HCA analysis data. This approach made it possible to identify five clusters that unite objects that have a similar effect of acidity, rheological and microstructural characteristics on texture. The influence of the nature of the food ingredients of the mayonnaise-type emulsion on the results of multivariate analysis is discussed.
Conclusion: Rheology combined with microstructural characteristics can be used as a tool to evaluate the effect of ingredients in mayonnaises and mayonnaise sauces on textural properties. This information is important for formularies when using alternative ingredients.
