When detection of dairy food fraud fails: An alternative approach through proton nuclear magnetic resonance spectroscopy

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.

Automated summary

This study investigated the limitations of the current method for determining the fatty acid profile of milk fats from proton nuclear magnetic resonance data. It was found that the current approach may lead to misclassification, and new descriptors based on proton nuclear magnetic resonance were proposed for fraud detection.