Auto-mmBCFAs: A swift precision approach for determination of monomethyl branched-chain fatty acids

Abstract

Monomethyl-branched chain fatty acids (mmBCFAs) are prevalent in diverse food sources, garnering significant interest owing to their distinctive branched structure and biological activity. However, the conventional analytical methods suffer from the time consumption and lower accuracy. In this study, we developed a method named Auto-mmBCFAs integrated GC-MS and algorithms for rapid and accurate measuring mmBCFAs. This method utilizes a multivariate matching approach, significantly enhancing detection efficiency. Compared to NIST database, Auto-mmBCFAs exhibits higher detection rates and accuracy. The developed method was validated in terms of precision, sensitivity, and linearity, and further evaluated mmBCFAs profile in sheep milk from different regions and breeds. Our results demonstrated that Auto-mmBCFAs methods is a rapid and reliable method for the determination of mmBCFAs in complex biological matrices.