A Neural Network Application in the Design of Emulsion-Based Products

Arend Dubbelboer*, Edwin Zondervan, Jan Meuldijk, Hans Hoogland, Peter M.M. Bongers

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

2 Citations (Scopus)


In the design of emulsion-based products, consumer appreciation is the key aspect. While the most commonly applied product/process design strategies lack the incorporation of consumer desires, this contribution describes a design approach where consumer appreciation is the main objective. The first step of the design is to translate the consumer needs into quantifiable product attributes, e.g. creaminess and firmness for a mayonnaise. A tasting panel is employed to rate these attributes. A Neural Network (NN) is then applied to correlate these attributes with a characteristic product viscosity. The uncertainty of both the measurements and the panel ratings is included in the training and validation of the neural network. According to the Akaike information criterion NN is inferior to partial least squares regression but NN scores better on the validation test; RMSE = 10%.

Original languageEnglish
Pages (from-to)692-696
Number of pages5
JournalComputer Aided Chemical Engineering
Publication statusPublished - 2012
Externally publishedYes
Event22nd European Symposium on Computer Aided Process Engineering, ESCAPE 2012 - University College London, London, United Kingdom
Duration: 17 Jun 201220 Jun 2012
Conference number: 22


  • Neural network
  • Product-driven process synthesis
  • Structured emulsions

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