Author: MX
Last saved by: Saritas, Sevim
Company: I

Using a Sensor-software based on Artificial Neural Network for Real-Time Characterization of Olive Oils from Olive Paste
Yosra Allouche, Estrella Funes López, Gabriel Beltrán Maza, Antonio Jiménez Márquez
IFAPA Centro 'Venta del Llano', Junta de Andalucía, P.O. Box 50, Mengíbar, Jaén E-23620, Spain

This study aimed to provide an automatic sensor-software based on artificial neural network (SS-ANN) for real-time prediction of potential polyphenol and pigment contents in olive oils from olive paste. These estimations used as inputs the technological variables of the extraction process: type of hammer crusher (single grid, double grid and Listello), sieve diameter (4, 5, 6 and 7 mm), and hammer rotation speed (from 2000 to 3000 rpm), the variables: olive fruits temperature before crushing and mill room temperature, and the wavelet pretreated near infrared spectra from the on-line scanned freshly crushed olive paste through a sensor for measuring reflectance set at an average height of the first body of the malaxer. Inputs data were collected throughout four consecutive crop years. The obtained results showed a very good predictive capacity of the three-layer ANN models on basis of the maximum correlation coefficient (r) and minimum root mean of squared error of prediction (RMSEP). For polyphenols, a good lineal correlation, in the interval 130-1228 mg/kg, was obtained with r = 0.9509 and RMSEP = 61.5 mg/kg. Chlorophylls, in the range 1.5-21.0 mg/kg, showed r = 0.9576 and RMSEP = 0.96 mg/kg. In the interval 3.0-14.9 mg/kg r and RMSEP values were respectively 0.9623 and 0.58 mg/kg for carotenes.