ATR-FTIR spectroscopy and chemometric techniques for the identification of edible vegetable oils

Abstract

There is a large variety and trademarks of vegetable oils in Bangladesh. The oils have characteristics very similar to each other and often cannot be classified by the observation of color, odor or taste. This paper proposes a vibrational spectroscopic method like FTIR in association with chemometric techniques to classify vegetable oils like: sunflower, mustard, sesame, soybean, castor, olive and palm oils from different manufacturers. In the FTIR spectra of oil, as information about fatty acid composition is concentrated in the range of 4400-200 cm-1 principal component analysis (PCA) was applied on the standardized full FTIR spectral data of this region for vegetable oils to totally capture the FTIR spectral pattern; seven varieties of vegetable oils could be successfully classified from their PCA scores. PCA of FTIR spectra of different known vegetable oils is used to determine the identity of several unknown vegetable oils. The unknowns are then analyzed, plotted, and identified based on their proximity to the known in principal component space. For the multivariate analysis PCA and soft independent modeling of class analogy (SIMCA) and support vector machines (SVMs) were used. 85% and 14% variability of data was explained by PC1 and PC2 respectively. PC1 has strong positive correlation with soybean, sunflower, palm and olive oil while strong negative correlation with mustard, castor and sesame oil. Soybean oils are positively and sesame oils are negatively correlated with PC2. Unknown oil samples can be identified properly by used supervised methods i.e. SIMCA, SVM by developing model with the help of PCA. The major interest of this method using chemometric analysis of spectral data is in their rapidity, since no chemical treatment of samples is required.

Bangladesh J. Sci. Ind. Res. 50(4), 233-240, 2015