We demonstrate how soft computing methods can be exploited to solve multicriteria quality optimisation problems in food science and technology. In particular, we link neuro-fuzzy modelling techniques with simulated annealing to optimise ⁄ design the quality of espresso coffee by pod. The design variables are the extraction time (ranging from 10 to 30 s), temperature (80–110 C) and blends (100% Arabica, 100% Robusta and Arabica Robusta: A20R80, A80R20 and A40R60); they are not the only variables that affect the sensory profile of a cup of espresso coffee, but have a strong impact on the sensory quality of the beverage. Based on the framework, we show that the particular problem is a nonlinear one. Hence, an espresso coffee characterised by a specific sensory profile can be extracted using different sets of parameter values. For example, the same sensory profile can be obtained using either pure Robusta extracted at 22 s and 94 C or 90% Arabica and 10% Robusta extracted at 25 s and 99 C. Yet, the global optimum with respect to the distance to the optimum sensorial values is obtained using 70% Arabica and 30% Robusta extracted at 15 s around 93 C.

A Neuro-Fuzzy Computational approach for Multi-criteria Optimisation of the Quality of Espresso Coffee by Pod based on the Extraction Time, Temperature and Blend

ALBANESE, DONATELLA;DI MATTEO, Marisa;
2012

Abstract

We demonstrate how soft computing methods can be exploited to solve multicriteria quality optimisation problems in food science and technology. In particular, we link neuro-fuzzy modelling techniques with simulated annealing to optimise ⁄ design the quality of espresso coffee by pod. The design variables are the extraction time (ranging from 10 to 30 s), temperature (80–110 C) and blends (100% Arabica, 100% Robusta and Arabica Robusta: A20R80, A80R20 and A40R60); they are not the only variables that affect the sensory profile of a cup of espresso coffee, but have a strong impact on the sensory quality of the beverage. Based on the framework, we show that the particular problem is a nonlinear one. Hence, an espresso coffee characterised by a specific sensory profile can be extracted using different sets of parameter values. For example, the same sensory profile can be obtained using either pure Robusta extracted at 22 s and 94 C or 90% Arabica and 10% Robusta extracted at 25 s and 99 C. Yet, the global optimum with respect to the distance to the optimum sensorial values is obtained using 70% Arabica and 30% Robusta extracted at 15 s around 93 C.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/3093921
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