The saponification value of fats and oils is one of the most common quality indices, reflecting the mean molecular weight of the constituting triacylglycerols. Proton nuclear magnetic resonance (¹H-NMR) spectra of fats and oils display specific resonances for the protons from the structural patterns of the triacylglycerols (i.e., the glycerol backbone), methylene (-CH₂-) groups, double bonds (-CH=CH-) and the terminal methyl (-CH₃) group from the three fatty acyl chains. Consequently, chemometric equations based on the integral values of the ¹H-NMR resonances allow for the calculation of the mean molecular weight of triacylglycerol species, leading to the determination of the number of moles of triacylglycerol species per 1 g of fat and eventually to the calculation of the saponification value (SV), expressed as mg KOH/g of fat. The algorithm was verified on a series of binary mixtures of tributyrin (TB) and vegetable oils (i.e., soybean and rapeseed oils) in various ratios, ensuring a wide range of SV. Compared to the conventional technique for SV determination (ISO 3657:2013) based on titration, the obtained ¹H-NMR-based saponification values differed by a mean percent deviation of 3%, suggesting the new method is a convenient and rapid alternate approach. Moreover, compared to other reported methods of determining the SV from spectroscopic data, this method is not based on regression equations and, consequently, does not require calibration from a database, as the SV is computed directly and independently from the ¹H-NMR spectrum of a given oil/fat sample.

This paper investigated the limits of the current approach for the determination of the fatty acids profile of milk fats from proton nuclear magnetic resonance data based on the hypothesis that the signal at 0.96 ppm, currently assigned in the literature as a marker for the "short chain fatty acids," is generated only by the butyric moiety (not by all of the short-chain fatty acids, which also include C6:0-caproic acid). The hypothesis was tested and experimentally confirmed. Moreover, the triplet at 0.96 ppm can also be due to n-3 fatty acids such as linolenic acid (C18:3); therefore, a previously reported methodology for the fatty acids profiling of dairy products-considered as general in the literature-cannot be used in fraud-detection approaches because it allows linolenic acid to be mistaken for butyric acid, consequently leading to misclassification of adulterated samples as nonadulterated. To support our opinion, we have applied the current literature approach for the determination of the fatty acids composition of 3 synthetic nondairy fat blends and have obtained fatty acid compositions similar to milk fats, allowing for their misclassification as genuine milk fats. However, in reality, the blends had very different compositions, as confirmed by gas chromatography. Consequently, we have highlighted the weaknesses of the existing methodology for the detection of dairy food adulteration. In return, new proton nuclear magnetic resonance descriptors based on various integral ratios of signals associated with CH2 moiety versus signals associated with butyric and n-3 fatty acids were proposed to detect adulterations.