Characterization and rapid detection of adulterations in sesame oil using FT-MIR and PCA-LDA

Sesame oil has an important place in human nutrition, medicinal, pharmaceutical, industrial and agricultural uses. The objective of this research work was to use Attenuated total reflectance-Fourier transform mid-infrared (ATR-FTMIR) spectroscopy coupled with chemometrics for the detection of the adulteration of sesame oil. Adulteration with sunflower oil, soybean oil or colza oil is one of the most difficult to detect due to the similar composition of them and sesame oil. Adulterations of sesame oil with different percentages of sunflower oil, soybean oil and colza oil were measured using ATR-FTMIR spectroscopy. The spectral data were subjected to a preliminary derivative elaboration based on the Savitzky-Golay algorithm to reduce the noise and extract a largest number of analytical information from spectra. Linear discriminant analysis (LDA) was adopted as classification method, and Principle component analysis (PCA) was employed to compress the original data set into a reduced new set of variables before LDA. The detection results indicated that the discriminant model built by PCA-LDA method could identify sesame oil adulterations in the 0-40% weight ratio range of edible oils, with an accuracy value of 94.64%. This work shows that PCA and LDA are useful chemometric tools for the multivariate characterization and discrimination of sesame oil adulteration with seed oils.