Discrimination of edible oil products and quantitative determination by Fourier transform near-infrared spectroscopy

This work demonstrates the application of partial least squares (PLS) analysis as a discriminant as well as a quantitative tool in the analysis of edible fats and oils by Fourier transform near-infrared (FT-NIR) spectroscopy. Edible fats and oils provided by a processor were used to calibrate a FT-NIR spectrometer to discriminate between four oil formulations and to determine iodine value (IV). Samples were premelted and analyzed in glass vials maintained at 75 degrees C to ensure that the samples remained:liquid. PLS calibrations for the prediction of IV were derived-for each oil type by using a subset of the samples provided: as the PLS training set. For each oil formulation (type), discrimination-criteria were established based on the IV range, spectral;residual, and PLS factor scores output from the PLS calibration model. It was found that all four oil types could be clearly: differentiated from each other, and all the validation samples, including a set of blind validation samples provided by the processor, were correctly classified. The PLS-predicted IV for the validation samples were in good agreement with the gas chromatography IV values provided by the processor. Comparable predictive accuracy was obtained from a calibration derived by Combining samples of all four oil types in the training set as well as a global IV calibration supplied by the instrument manufacturer.. The results of this study demonstrate that by combining the rapid and convenient analytical capabilities of FT-NIR spectroscopy with the discriminant and predictive power of PLS, one can both identify oil type as well as predict IV with a high degree of confidence. These combined capabilities provide processors with better control over their process.