Unveiling Adulterated Cheese: A H-NMR-Based Lipidomic Approach.

The main objective of this research consists in finding a rapid method for cheese lipidomics based on NMR data. This study plays an important role in differentiation and characterization of cheese samples in accordance with fat composition, especially in the case of fat substitution with exogenous animal or vegetal fat. Our findings play an important role in relation to religious requirements regarding non-allowed foods (pork fat, for example, in some cultures) and in the correct characterization of foods according to their lipidic profile. The approach consists in establishing a fingerprint region (0.86-0.93 ppm from H-NMR spectra) and then creating a database of the results obtained. The evaluation of the long-chain saturated fatty acids and the saturated short-chain fatty acids (C4 to C8) was established with a newly developed set of equations that make the computation possible even when mixtures of fats from different sources are present. This was accomplished by developing a new method for quantification of the fatty acid composition of different types of cheese, based on H-NMR spectroscopy. Principal component analysis (PCA) was applied to 40 cheese samples with varying degrees (0%, 5%, 12%, or 15%) of milk fat substitution (pork fat, vegetable fat, hydrogenated oils) and different clotting agents (calcium chloride or citric acid). The best sample discrimination was achieved using fatty acid profiles estimated from H-NMR data (using a total of six variables), explaining 89.7% of the total variance. Clear separation was observed between samples containing only milk fat and those with added fats. These results demonstrate that the integration of H-NMR spectroscopy with principal component analysis (PCA) provides a reliable approach for discriminating cheese samples according to their fat composition.