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  • Achiral-chiral two-dimensional chromatography of free amino acids in milk: A promising tool for detecting different levels of mastitis in cows.

Achiral-chiral two-dimensional chromatography of free amino acids in milk: A promising tool for detecting different levels of mastitis in cows.

Journal of pharmaceutical and biomedical analysis (2015-01-27)
Federica Ianni, Roccaldo Sardella, Antonella Lisanti, Antimo Gioiello, Beniamino Terzo Cenci Goga, Wolfgang Lindner, Benedetto Natalini
ABSTRACT

In two-dimensional HPLC (2D-HPLC) "heart-cut" applications, two columns are connected in series via a switching valve and volume fractions from the "primary" column are re-injected on the "secondary" column. The heart-cut 2D-HPLC system here described was implemented by connecting a reversed-phase (RP) column (first dimension) to a chiral column (second dimension) containing a quinidine-based chiral stationary phase. The system was used to evaluate the change in the enantiomeric excess value of dansylated (Dns) amino acids (AAs) in milk samples from two cows with different "California Mastitis Test" scores: negative test for sample 1, positive for sample 2. Apart from the co-elution of Dns-Arg/Dns-Gly and the reduced chemoselectivity for Dns-Leu/Dns-allo-Ile, the optimized achiral RP method distinguished the remaining standard Dns-AAs. Dns-AAs were identified in the chromatograms of the real samples, and in higher concentration Dns-Ala, Dns-Arg, Dns-Asp, Dns-Glu, Dns-Ile, Dns-Leu, Dns-Phe and Dns-Val. Except Dns-Arg, the chiral column enabled the RP enantioseparation of all the other compounds (α and RS values up to 1.65 and 8.63, respectively, for Dns-Phe). In sample 2, the amounts of Dns-d-AAs were rather elevated, in particular for Dns-Ala and Dns-Asp. Instead, for sample 1, D-isomers were detected for Dns-Ala, Dns-Glu and Dns-Leu. The proposed 2D-HPLC method could be useful for the identification of clinical mastitis difficult to be diagnosed. Moreover, the eventual progressive reduction of D-AAs levels with the degree of sub-clinical mastitis could allow the building of mathematical models to use for the diagnosis of early stages of mastitis.

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