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

A neuro-fuzzy computational approach for multicriteriaoptimisation of the quality of espresso coffee by pod based on theextraction time, temperature and blend / L., Russo; D., Albanese; C. I., Siettos; M., Di Matteo; Crescitelli, Silvestro; Siettos, Constantinos. - In: INTERNATIONAL JOURNAL OF FOOD SCIENCE & TECHNOLOGY. - ISSN 0950-5423. - 47:4(2012), pp. 837-846. [10.1111/j.1365-2621.2011.02916.x]

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

CRESCITELLI, SILVESTRO;SIETTOS, Constantinos
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 (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
2012
A neuro-fuzzy computational approach for multicriteriaoptimisation of the quality of espresso coffee by pod based on theextraction time, temperature and blend / L., Russo; D., Albanese; C. I., Siettos; M., Di Matteo; Crescitelli, Silvestro; Siettos, Constantinos. - In: INTERNATIONAL JOURNAL OF FOOD SCIENCE & TECHNOLOGY. - ISSN 0950-5423. - 47:4(2012), pp. 837-846. [10.1111/j.1365-2621.2011.02916.x]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/436129
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