Characterization of oils and fats by 1H NMR and GC/MS fingerprinting: Classification, prediction and detection of adulteration

Abstract

The correct identification of oils and fats is important to consumers from both commercial and health perspectives. Proton nuclear magnetic resonance (1H NMR) spectroscopy, gas chromatography–mass spectrometry (GC/MS) fingerprinting and chemometrics were employed successfully for the quality control of oils and fats. Principal component analysis (PCA) of both techniques showed group clustering of 14 types of oils and fats. Partial least squares discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) using GC/MS data had excellent classification sensitivity and specificity compared to models using NMR data. Depending on the availability of the instruments, data from either technique can effectively be applied for the establishment of an oils and fats database to identify unknown samples. Partial least squares (PLS) models were successfully established for the detection of as low as 5% of lard and beef tallow spiked into canola oil, thus illustrating possible applications in Islamic and Jewish countries.