| Summary: | In the palm oil industry, iodine value (IV) has become an important parameter in quality control that measures the degree of unsaturation of the oils. However, it is difficult to obtain the IV chemically. In other hand, the use of instrumental analysis in IV determination accurately needs suitable data pre-processing. In this study, we proposed the strategy for pre-processing the FT-NIR and FTIR spectra data in analyzing the IV of non-fried and fried palm oils. The utility and effectiveness of four data pre-processing which are column standardization, mean centre and combination of row scaling with column standardization and mean centre were applied. The effect of data splitting methods which are duplex and kenstone was also investigated in the Partial Least Squares (PLS) regression model of palm oils. From the result, the use of different data pre-processing provides different quality of prediction model. Either the application of the row scaling and column scaling individually or combination of both methods may improve the quality of the model. It is concluded that the data pre-processing is context dependent which is depend on the nature of the dataset and there can be no single method for general use. © 2014 IEEE.
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