Predictive ANN models for the optimization of extra virgin olive oil clarification by means of vertical centrifugation

Abstract

AbstractEight artificial neural networks were developed as predictive models for regulated and nutritional parameters of oils obtained from vertical centrifugation. The networks were designed considering the NIR spectra of oily must at its exit from the horizontal decanter centrifuge, and the flows and temperatures of the oil and of the addition water. The results obtained in all the networks designed indicated the good creative capacity of the neural models through their quality indicators (RER and RPD). The correlations between the real data and those predicted by the networks were within the range of 0.76–0.99, the UV absorption networks (K232 and K270) being those which gave lower correlations. For polyphenols, tocopherols and carotene and chlorophyll pigments, the te‐Student external validation test makes a reliable prediction of these parameters. With predictive models, the vertical centrifuge can be modeled and regulated as well as monitored on‐line to control and optimize the clarification phase within the extraction process of extra virgin olive oil (EVOO).Practical applicationsThe predictive ANN can use to regulate and model the vertical centrifuge to obtain an EVOO of a maximum regulated and nutritional quality possible, reducing water and energy consumption. Besides, with on‐line prediction of oil characterization, it will be possible to perform a correct classification prior to its storage in the cellar.