Comparison of calibration models based on near infrared spectroscopy data for the determination of plant oil properties

The aim of this study was to compare the prediction efficiency of different types of linear calibration models using near infrared absorbance spectral data of vegetable oils. The applied model types were PCA-MLR (Principal Component Analysis-Multiple Linear Regression), PLS (Partial Least Squares regression), PCA-ANN (Principal Component Analysis-Artificial Neural Network) and GA-ANN (Genetic AlgorithmArtificial Neural Network). The calibrations were carried out on the models for determination of the concentration of oleic acid of vegetable oils and the performances of the different models were determined using external validation (Kim et al., 2007). In external validation the constructed models were tested with vegetable oil samples the oleic acid contents of which were known and were not included in the calibration sample set. The models were compared on the basis of the accuracy of the prediction.