A neuro-fuzzy computational approach for multicriteria optimisation of the quality of espresso coffee by pod based on the extraction time, temperature and blend

作者：Russo Lucia; Albanese Donatella; Siettos Constantinos I^*; Di Matteo Marisa; Crescitelli Silvestro

来源：International Journal of Food Science and Technology, 2012, 47(4): 837-846.

DOI：10.1111/j.1365-2621.2011.02916.x

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 (80110 degrees 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 degrees C or 90% Arabica and 10% Robusta extracted at 25 s and 99 degrees 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 degrees C.

• 出版日期2012-4