Artificial neural networks (ANN) are computationally based mathematical tools inspired by the fundamental cell of the nervous system, the neuron. ANN constitute a simplified artificial replica of the human brain consisting of parallel processing neural elements similar to neurons in living beings. ANN is able to store large amounts of experimental information to be used for generalization with the aid of an appropriate prediction model. ANN has proved useful for a variety of biological, medical, economic and meteorological purposes, and in agro-food science and technology. The olive oil industry has a substantial weight in Mediterranean's economy. The different steps of the olive oil production process, which include olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in depth with a view to their optimization, and so have the authenticity, sensory properties and other quality-related properties of olive oil. This paper reviews existing literature on the use of bioinformatics predictive methods based on ANN in connection with the production, processing and characterization of olive oil. It examines the state of the art in bioinformatics tools for optimizing or predicting its quality with a view to identifying potential deficiencies or aspects for improvement.

Predictive models based on artificial neural networks have been developed for the percolation threshold of AOT based microemulsions with addition of either glymes or polyethylene glycols. Models have been built according to the multilayer perceptron architecture, with five input variables (concentration, molecular mass, log P, number of C and O of the additive). Best model for glymes has a topology of five input neurons, five neurons in a single hidden layer and one output neuron. Polyethylene glycol model's architecture consists in five input neurons, three hidden layers with eight neurons in both first two and five in the last, and a neuron in the last output layer. All of them have a good predictive power according to several quality parameters.

A series of models, based on artificial neural networks, of the percolative behaviour of AOT microemulsions in the presence of crown ethers as additives have been developed. Input variables, related to the chemical structure of crown ethers and their packing with surfactant film, have been selected. As a result, a model has been chosen with a good forecast capability for percolation threshold of such microemulsions.

Since their development in 1943, artificial neural networks were extended into applications in many fields. Last twenty years have brought their introduction into winery, where they were applied following four basic purposes: authenticity assurance systems, electronic sensory devices, production optimization methods, and artificial vision in image treatment tools, with successful and promising results. This work reviews the most significant approaches for neural networks in winemaking technologies with the aim of producing a clear and useful review document.

Different Artificial Neural Network architectures have been assayed to predict percolation temperature of AOT/i-C8/H2O microemulsions. A Perceptron Multilayer Artificial Neural Network with five entrance variables (W value of the microemulsions, additive concentration, molecular weight of the additive, atomic radii and ionic radii of the salt components) was used. Best ANN architecture was formed by five input neurons, two middle layers (with eleven and seven neurons respectively) and one output neuron. Root Mean Square Errors (RMSEs) are 0.18°C (R = 0.9994) for the training set and 0.64°C (R = 0.9789) for the prediction set.