Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.19/4931
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dc.contributor.authorGuiné, Raquel-
dc.identifier.citationGuiné, R.P.F. (2018, May). The Use of Artificial Neural Networks (ANN) in Food Process Engineering. In Abstract Book and Proceedings of 4th International Conference on Food and Agricultural Engineering (pp.34-41), Lisbon, Portugal.pt_PT
dc.description.abstractArtificial neural networks (ANN) aim to solve problems of artificial intelligence, by building a system with links that simulate the human brain. This approach includes the learning process by trial and error. The ANN is a system of neurons connected by synaptic connections and divided into incoming neurons, which receive stimulus from the external environment, internal or hidden neurons and output neurons, that communicate with the outside of the system. The ANNs present many advantages, such as good adaptability characteristics, possibility of generalization and high noise tolerance, among others. Neural networks have been successfully used in various areas, for example, business, finance, medicine, and industry, mainly in problems of classification, prediction, pattern recognition and control. In the food industry, food processing, food engineering, food properties or quality control, statistical tools are frequently present, and ANNs can process more efficiently data comprising multiple input and output variables. The objective of this review was to highlight the application of ANN to food processing, and evaluate its range of use and adaptability to different food systems. For that a systematic review was undertaken from the scientific literature and the selection of the information was based on inclusion criteria defined. The results indicated that ANN is widely used for modelling and prediction in food systems, showing good accuracy and applicability to a wide range of situations and processes in food engineering.pt_PT
dc.subjectFood processingpt_PT
dc.subjectFood modellingpt_PT
dc.titleThe Use of Artificial Neural Networks (ANN) in Food Process Engineeringpt_PT
degois.publication.title4th International Conference on Food and Agricultural Engineering (ICFAE 2018)pt_PT
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