Comparison of Near-Infrared and Mid-Infrared spectroscopy for the identification and quantification of argan oil adulteration through PCA, PLS-DA and PLS

Abstract

Argan oil, a rare and luxury oil, is often adulterated with cheaper vegetable oils to make profits. Therefore, in this study, the potential of Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy, along with chemometrics, for the rapid identification and quantification of argan oil adulteration, was investigated. First, the authentication of pure and adulterated samples was visually explored by Principal Component Analysis. MIR and NIR spectra allowed an excellent distinction between pure oil samples. Next, Partial Least Squares - Discriminant Analysis (PLS-DA) modelling was applied to discriminate between pure and adulterated argan oils. PLS-DA classification figures of merit, in terms of sensitivity, specificity, and accuracy, were very good for both NIR and MIR datasets. Finally, Partial Least Squares regression was used to model and predict the level of adulterant. The developed models showed a good performance, with RMSE values below 1.7% and coefficients of determination higher than 98% for both techniques.