A sensor-software based on an artificial neural network (SS-ANN) was designed for real-time characterisation of olive fruit (pulp/stone ratio, extractability index, moisture and oil contents) and the potential characteristics of the extracted oil (free acidity, peroxide index, K232 and K270, pigments and polyphenols) in olive paste prior to the kneading step. These predictions were achieved by measuring variables related to olive fruit at the crushing stage, including the type of hammer mill (single grid, double grid and Listello), sieve diameter (4 mm, 5 mm, 6 mm and 7 mm), hammer rotation speed (from 2000 rpm to 3000 rpm), temperature before crushing and mill room temperature. These were related to the near infrared (NIR) spectra from online scanned freshly milled olive paste in the malaxer with data pretreated by either the moving average or wavelet transform technique. The networks obtained showed good predictive capacity for all the parameters examined. Based on the root mean square error of prediction ( RMSEP), residual predictive deviation ( RPD) and coefficient of determination of validation ( r2), the models that used the wavelet preprocessing procedure were more accurate than those that used the moving average. As examples, for moisture and polyphenols, RMSEP values were 1.79% and 87.80 mg kg−1, and 1.46% and 61.50 mg kg−1, respectively for the moving average and wavelet transform. Similar results were found for the other parameters. In conclusion, these results confirm the feasibility of SS-ANN as a tool for optimising the olive oil elaboration process.
A Portable in-situ Near-infrared LEDs-based Soil Nitrogen Sensor Using Artificial Neural Network