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

Russo, L; Albanese, D; Siettos, CI; di Matteo, M; Crescitelli, S

dc.contributor.author	Russo, L	en
dc.contributor.author	Albanese, D	en
dc.contributor.author	Siettos, CI	en
dc.contributor.author	di Matteo, M	en
dc.contributor.author	Crescitelli, S	en
dc.date.accessioned	2014-03-01T02:07:23Z
dc.date.available	2014-03-01T02:07:23Z
dc.date.issued	2012	en
dc.identifier.issn	09505423	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29552
dc.subject.other	Computational approach	en
dc.subject.other	Design variables	en
dc.subject.other	Extraction time	en
dc.subject.other	Food science and technology	en
dc.subject.other	Global optimum	en
dc.subject.other	Multi-criteria	en
dc.subject.other	Neuro-Fuzzy	en
dc.subject.other	Neuro-fuzzy modelling	en
dc.subject.other	Optimisations	en
dc.subject.other	Parameter values	en
dc.subject.other	Sensory profiles	en
dc.subject.other	Sensory qualities	en
dc.subject.other	Soft computing methods	en
dc.subject.other	Simulated annealing	en
dc.subject.other	Soft computing	en
dc.subject.other	Coffee	en
dc.title	A neuro-fuzzy computational approach for multicriteria optimisation of the quality of espresso coffee by pod based on the extraction time, temperature and blend	en
heal.type	journalArticle	en
heal.identifier.primary	10.1111/j.1365-2621.2011.02916.x	en
heal.identifier.secondary	http://dx.doi.org/10.1111/j.1365-2621.2011.02916.x	en
heal.publicationDate	2012	en
heal.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 30s), 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 22s and 94°C or 90% Arabica and 10% Robusta extracted at 25s 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 15s around 93°C. © 2012 The Authors. International Journal of Food Science and Technology © 2012 Institute of Food Science and Technology.	en
heal.journalName	International Journal of Food Science and Technology	en
dc.identifier.doi	10.1111/j.1365-2621.2011.02916.x	en
dc.identifier.volume	47	en
dc.identifier.issue	4	en
dc.identifier.spage	837	en
dc.identifier.epage	846	en