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Mateo, F., Gadea-Gironés, R., Mateo, E.M. and Jiménez, M. (2011) Multilayer Perceptron Neural Networks and Radial-Basis Function Networks as Tools to Forecast Accumulation of Deoxynivalenol in Barley Seeds Contaminated with Fusarium culmorum. Food Control, 22, 88-95.
http://dx.doi.org/10.1016/j.foodcont.2010.05.013

has been cited by the following article:

• TITLE: A Review: Artificial Neural Networks as Tool for Control Food Industry Process

  AUTHORS: Estrella Funes, Yosra Allouche, Gabriel Beltrán, Antonio Jiménez

  KEYWORDS: Artificial Neural Networks, Olive Oils, Sensor On-Line, Process Control

  JOURNAL NAME: Journal of Sensor Technology, Vol.5 No.1, February 28, 2015

  ABSTRACT: In the last year, interest in using Artificial Neural networks as a modeling tool in food technology is increasing because they have found extensive utilization in solving many complex real world problems. Due to this and as previous step at development of some project, this paper intends to introduce the reader inside neural networks: general characteristics of the ANN, their architectures, their rules of learning, types of networks and ANN’s create process. Also this paper presents a comprehensive review of food industrial applications of artificial neural networks in the last year. ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers with except the applications in the olive oil industry that are described with special emphasis.