DEVELOPMENT OF FINGERPRINTING APPROACH FOR IDENTIFICATION AND DETECTION OF ADULTERATION IN FOOD

Abstract

The recent intense media coverage on food frauds has heightened concerns among consumers. Instead of detecting specific compounds to assess food quality and safety, fingerprinting with chemometrics was applied for the identification and detection of adulteration in oils and fats, as well as herbs and spices using nuclear magnetic resonance (NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). Principal component analysis (PCA) of both techniques showed excellent differentiation between different types of oils and fats, as well as herbs and spices. Partial least squares-discriminant analysis (PLS-DA) has also demonstrated good classification. The supervised models with predictive ability had the potential to be a useful tool for establishing databases for quality control. NMR and GC/MS data with partial least squares (PLS) models were applied to detect animal fats spiked into canola oil. The models could detect adulteration of at least 5% (w/w) of animal fats in canola oils.