Development of Partial Least Square (PLS) Prediction Model to Measure the Ripeness of Oil Palm Fresh Fruit Bunch (FFB) by Using NIR Spectroscopy

Abstract

In order to develop a model for predicting the oil palm Fresh Fruit Bunch (FFB) ripeness, a rapid and non-destructive method such as NIR spectroscopy is utilized. This method has shown its capability to determine the quality of some crops by predicting their internal chemical contents. The objective of the research is to investigate the feasibility of NIR spectroscopy to predict water and oil content in FFB by developing a calibration model. Sixty samples of FFB were scanned by using NIRFlex N-500 spectrometer ranging from 1000 to 2500nm. Water and oil content of samples were measured after scanned. To develop a calibration model, Partial Least Square (PLS) Regression and pre-processing were conducted using Unscrambler X 10.3. The results showed that PLS performs well to establish a calibration model to predict water content using MSC pre-processing with r2, factor, RSMECV, and RPD are 0.93, 3, 5.24, and 2, respectively. On the other hand, PLS could not be used well for establishing oil content calibration model because the result did not meet statistic parameters. For laboratory measurement, the model could predict water content of FFB; but it was limited to samples taken from the same variety and plantation. However, NIR Spectroscopy proposed a promising method to detect the ripeness of oil palm FFB.