​How to make the perfect mayonnaise – with a computer

What’s the secret to getting consistent quality in your mayonnaise production? How can you get that elusive balance between ingredients, shear force and mixing time just right every time? Here’s the answer:

To begin with, you need access to our prediction tool, which is a software programme based on the knowledge generated by a three-year research project into the production of cold emulsions. Secondly, you need one of our high shear mixers for cold emulsion products. It is designed to bring you unprecedented flexibility, consistent quality and maximum ingredient yield - all at the lowest possible cost. Together, the tool and the mixer ensure you get your mayonnaise just right.

Mayonaise ingredients.

No more guesswork

The prediction tool enables us to fine-tune mixing configuration and process parameters according to your recipes and requirements. By varying this palette of parameters, we can help you achieve your desired texture characteristics and other quality measures. This takes the guesswork out of making mayonnaise and emulsified sauces, as well as reducing the expensive and time-consuming process of physical trials.

What can you use it for?

  1. To replicate an existing product
    First we analyze the quality of your product and select the optimal mixing procedure and process parameters to replicate its texture, flavour, mouthfeel and appearance. You can then control the mixing process in such a way as to guarantee consistent product quality, regardless of the scale of your production.

  2. To develop new products, quickly and cost-effectively
    Based on input parameters such as batch size, mixing time, type and quantity of ingredients, the prediction tool makes it possible to accurately predict the end results. Using a simulation model significantly reduces the need for physical trials and improves time-to-market when launching new products.

Want to give it a try?

Just send us a sample of the product you want to replicate and we’ll analyze it, measuring quality parameters such as texture and droplet size. Based on our analysis, (and your input regarding ingredients) we will then use the prediction tool to determine the best mixing method and correct process parameters necessary to recreate your sample.

Contact our cold emulsion specialists. Please write in the comments field that you are interested in a product analysis using the prediction tool.

Research project

Across hundreds of trials, our food technologists analysed the emulsification process on a molecular level, while our process engineers studied flow patterns and mixing performance using Computational Fluid Dynamics. The results of this research project have been published in more than a dozen peer-reviewed articles:

  • Håkansson A., Mortensen H.-H., Andersson R.. Innings F.. (2017). Experimental investigations of turbulent fragmenting stresses in a Rotor-Stator Mixer. Part 1. Estimation of turbulent stresses and comparison to breakup visualizations. Accepted by Chemical Engineering Science.
  • Håkansson, A., Mortensen, H.-H., Andersson, R., Innings, F. (2017). Experimental investigations of turbulent fragmenting stresses in a Rotor-Stator Mixer. Part 2. Probability distributions of instantaneous stresses. Accepted by Chemical Engineering Science.
  • Håkansson A., Innings F., (2017) The dissipation rate of turbulent kinetic energy and its relation to pumping power in inline rotor-stator mixers, Advances in Engineering, https://advanceseng.com/chemical-engineering/dissipation-rate-turbulent-kinetic-energy-pumping-power-inline-rotor-stator-mixers/.
  • Håkansson A., Innings F., (2017). The dissipation rate of turbulent kinetic energy and its relation to pumping power in inline rotor-stator mixers. Chemical Engineering and Processing 115, pp. 46–55
  • Mortensen H—H., Innings F., Håkansson A., (2017). The effect of stator design on flowrate and velocity fields in a rotor-stator mixer—An experimental investigation; Chemical engineering research and design 121, p.245-254
  • Håkansson A., Arlov D., Carlsson F., and Innings F. (2016). Hydrodynamic Difference between Inline and Batch Operation of a Rotor-Stator Mixer Head - A CFD Approach. Can. J. Chem. Eng. 9999:1–11.
  • Håkansson A., Chaudhryb Z., Innings F. (2016). Model emulsions to study the mechanism of industrial mayonnaise emulsification; Food and bioproducts processing 98, pp. 189–195;
  • Håkansson A., Askaner M,. Innings F. (2016). Extent and mechanism of coalescence in rotor-stator mixer food emulsion emulsification; Journal of Food Engineering 175, pp. 127-135

For more details, take a look at our white papers ‘Navigating the mayonnaise maze' and “Optimize your mixing process and food quality by using CFD