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Advisor(s)
Abstract(s)
Artificial 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.
Description
Keywords
Algorithm Food processsing Neural network Food modeling Prediction
Citation
Guiné, R.P.F. (2019). The use of artificial neural networks (ANN) in food process engineering. International Journal of Food Engineering, 5 (1), 15-21. doi: 10.18178/ijfe.5.1.15-21