期刊
JOURNAL OF NEAR INFRARED SPECTROSCOPY
卷 23, 期 2, 页码 111-121出版社
SAGE PUBLICATIONS LTD
DOI: 10.1255/jnirs.1155
关键词
olive fruit; oil; optimisation process; online control; sensor-software; artificial neural network
资金
- Ministry of Economy, Innovation and Science from Junta de Andalucia [P10-AGR 6429]
- Ministry of Science and Innovation
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, K-232 and K-270, 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, 5mm, 6mm and 7mm), 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 (RPM and coefficient of determination of validation (r(2)), 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 87.80mg 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.
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