Comparison of Calibration Models Based on Near Infrared Spectroscopy Data for the Determination of Plant Oil Properties

Abstract

The aim of this study was to compare the prediction efficiency of different type of linear calibration models using near infrared (NIR) absorbance spectral data of vegetable oils. The applied model types were the PCA-MLR (principal component analysis-multiple linear regression), the PLS (partial least squares regression), the PCA-ANN (principal component analysis-artificial neural network) and the GA-ANN (genetic algorithm-artificial neural network). The calibrations were executed on the models for the determination of the concentration of oleic acid of vegetable oils and the performances of the different models were determined using external validation. During external validation the built models were tested with vegetable oil samples of which oleic acid content was known and was not included in the calibration sample set. The comparison of the models was executed on the basis of the accuracy of the prediction.