Artificial neural network software for real-time estimation of olive oil qualitative parameters during continuous extraction

Abstract

This work provides a software based on the combination of a artificial neural network (ANN) approach and a numerical method (called "refining algorithm") for the estimation of the acidity level and of peroxides number of olive oil extracted by a continuous extraction process. The estimation is achieved through the measurement of some agronomical and technological parameters commonly measured by the technicians working at the oil mills. The ANN based approach is able to perform a rough prediction of the two parameters; this prediction is then refined by means of the numerical approach. The devised software, The mean error in estimation of the parameters with the combination of the ANN approach and the refining algorithm, in comparison with the standard chemical analysis, is in the range 6-8% for an oil extraction with a three phases decanting system and about 6-7% for an oil extraction with a two phases decanting system. The maximum error affecting the method (ANN + Refining) described in this work is about 15% for the first case and about 11% for the second one. The system developed in the present work is actually running on the oil mill "TEM Toscana Enologica Mori" of Florence, Italy.The present work has been financed by the Tuscany Regional Agricultural Development and Innovation Office (ARSIA: Azienda Regionale per lo Sviluppo e l'Innovazione dell'Agricoltura) and is a part of a 3-year project whose objective is to create an entirely software + hardware controlled oil mill.

Model based predictive control of an olive oil mill

“…Fig. 1 shows the most important phases of the process, whose description can be found in Civantos (1999), Furferi, Carfagni, andDaoub (2007) or Piacquadio, De Stefano, and Sciancalepore (1998).…”

“…The amount of water that is introduced in the decanter must be constant; that is, the sum of the vegetation water of the olive plus the added water must be constant. The raw material does not contain a homogeneous moisture (Furferi et al, 2007), which forces that the water flow must be continually adjusted in order to obtain the maximum oil in the decanter. The temperature of this water also influences extraction.…”

“…Real-time software for the estimation of acidity level and of peroxides number is presented in Furferi et al (2007). Although these on-line analytical methods are usually employed for quality analysis and detection of potential adulterants, they can be helpful for feedback control.…”

Neural network as tool for virgin olive oil elaboration process optimization

“…In this sense, the use of neural networks can contribute and it can facilitate this predictive regulation work, since it is a very appropriate technique for the multirelational non-linear treatment, used in the optimization of processes (Rebién et al, 1992; Negro et al, 2006;Magali et al, 2003), which has begun to be used lately in this sector of the olive oil to predict characteristics of obtained oils (Furferi et al, 2007) and for the development of virtual olive pomace sensors (Jiménez et al, 2008).…”

Evaluation of Artificial Neural Network in Determining the Quality and Classification of Edible Oil

“…This innovative software has proved that it is capable of accurate estimation of qualitative parameters which are oil acidity and peroxide value. And if this is the goal of the factory to increase the quality of the extracted oil plant, using this software is suggested to optimize some technical parameters [3]. In another study conducted in 2007 by AndrásFülöp et al, different modeling methods for determining vegetable oils quality were compared.…”