To optimize the sensory product attributes by changing the processing conditions or product recipe was the main objective of this research. A mathematical framework was built containing process and sensory models. First, a typical mayonnaise production line was modeled. The line consisted of two mixing steps; in mixer 1 the objective was to prepare an oil-in-water emulsion; while in mixer 2 the required product specifications had to be reached. The droplet size and emulsion viscosity were coupled in the processing model for mixer 2. The physicochemical emulsion properties were subsequently correlated to the sensory attributes with a Neural Network. This allowed us to estimate panel scores on sensory attributes. Second, an optimization case study was formulated with the objective to increase mayonnaise creaminess while fixing the oil concentration to a minimal value of 0.65 w/w. The overall result was that the creaminess could be increased by 22 %, but at the expense of other sensory attributes.
|Number of pages||6|
|Journal||Computer Aided Chemical Engineering|
|Publication status||Published - 1 Jan 2015|
|Event||12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering - Copenhagen, Denmark|
Duration: 31 May 2015 → 4 Jun 2015
- Sensory attributes