Fourier transform infrared spectroscopy combined with multivariate calibrations for the authentication of avocado oil

Abstract

Avocado oil is one of the functional oils having high quality and high price in the market. This oil shows many benefits for the human health and is applied in many cosmetic products. The authentication of avocado oil becomes very important due to the possible adulteration of avocado oil with other lower priced oils, such as palm oil and canola oil. In this study, Fourier transform infrared spectroscopy using attenuated total reflectance in combination with chemometrics techniques of partial least squares and principal component regression is implemented to construct the quantification and classification models of palm oil and canola oil in avocado oil. Partial least squares at the wavenumbers region of 1260-900 cm-1 revealed the best calibration models, having the highest coefficient of determination (R2 = 0.999) and the lowest root mean square error of calibration, 0.80%, and comparatively low root mean square error of prediction, 0.79%, for analysis of avocado oil in the mixture with palm oil. Meanwhile, the highest R2, root mean square error of calibration, and root mean square error of prediction values obtained for avocado oil in the mixture with canola oil at frequency region of 3025-2850 and 1260-900 cm-1 were 0.9995, 0.83, and 0.64%, respectively.