Prediction of oil content in olive fruit using Fourier transformed infrared spectroscopy FT-IR coupled with partial least squares regression

A rapid Fourier transformed infrared (FT-IR) attenuated total reflectance (ATR) spectroscopic is applied to predict the quantity of oil in fresh olive in tree. The analytical method is evaluated by use of validation samples with nearly quantitative oil. 80 samples of olives in oil content, which varies between 8 and 21%, picked up in the Moroccan region, were subjected to infrared analysis. Analytical data were collected, by Fourier transform infrared spectroscopy (FT-IR) applied to the mesocarp of the fresh olives without any preliminary treatment. The objective of this study is to develop a calibration model for prediction of oil content in olive fruit by using FT-IR spectroscopy before harvest time. The transmission spectra of olive fruit were obtained in the wavelength range from 4000 to 600 cm^-1. The prediction models were developed by partial least square regression (PLS). The values obtained for correlation coefficient for oil content and root mean square errors of prediction (RMSEP) are 0.99 and 0.076 respectively. This show the capability of FTIR and the important role of chemometric in developing accurate models to predict oil content in olive fruit.