Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

Abstract

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production, starting from the upstream and downstream. This content can be used to monitor the progress of the oil palm fresh fruit bunch (FFB) and be applied to identify product profitability. Based on the near-infrared (NIR) signals, this study proposes an empirical mode decomposition (EMD) technique to decompose signals and predict the oil content of palm fruit. First, 350 palm fruits with Tenera varieties (Elaeis guineensis Jacq. var. tenera), at various ages of maturity, were harvested from the Cikabayan Oil Palm Plantation (IPB University, Indonesia). Second, each sample was sent directly to the laboratory for NIR signal measurements and oil content extraction. Then, the EMD analysis and artificial neural network (ANN) were employed to correlate the NIR signals and oil content. Finally, a robust EMD-ANN model is generated by optimizing the lowest possible errors. Based on performance evaluation, the proposed technique can predict oil content with a coefficient of determination (R2) of 0.933 ± 0.015 and a root mean squared error (RMSE) of 1.446 ± 0.208. These results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly, without neither solvents nor reagents, which makes it environmentally friendly. Therefore, the proposed technique has a promising potential to be applied in the oil palm industry. Measurements like this will lead to the effective and efficient management of oil palm production.

Keywords

• Artificial neural network
• Empirical mode decomposition
• Oil palm
• Oil content prediction