Nine neural models were created to predict the characteristics of the extra virgin olive oil developed as a quality objective and by-products. These models are designed with the help of data of process variables from physical sensors such as temperature, flows, current intensity, etc. and physicochemical ones like the near infrared spectrum of the olive mass. The results obtained for the extractability of the process (fatty content and moisture) were highly significant correlations (r2≥0.90) and with similar prediction errors (root mean of squared error of prediction) relative to other analysis techniques which measure the by-product directly. For prediction the models gave correlations above 0.94, with the exception of ultraviolet absorption coefficients (0.72–0.84), with small prediction errors and the quality indicator relative error range with values above the optimal 10. The set of developed artificial neural networks models constitute the basis of the global ‘simulator’ tool of the extra virgin olive oil process. This simulator can perform a predictive optimization of the process to pre-adjust the process variables according to the goals marked in productivity or quality, from an near infrared spectral database or by real-time scanning. This simulator could be integrated into a control system that performs the function of a ‘virtual plant’ that allows the said system to adjust in real time the appropriate variables to meet the objectives.
© 2017 The Author(s)
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