Low-molecular-weight compounds in milk are of interest from both nutritional and technological perspectives. To analyse potential milk metabolites, acetonitrile extraction followed by ethyl chloroformate derivatisation was developed. A GC/MS (SIM) method was then applied to different animal milk samples (i.e., buffalo, bovine, and donkey) and processed milk samples (i.e., pasteurised and dried). The optimised extraction-derivatisation method is rapid and more comprehensive when compared with the other published methods. The supervised orthogonal projection to latent structure discriminant analysis (OPLS-DA) based on the amino acid profile showed clear discrimination as a function of animal origin and milk processing. In this regard, variable importance in projection (VIP) analysis following an OPLS-DA prediction model showed that aspartic acid and asparagine (VIP scores = 1.26 and 1.19, respectively) were the best markers of the milk origin, whilst proline and glycine (VIP scores = 1.30 and 1.28, respectively) mainly discriminated samples according to different processing conditions.
Bhumireddy, S. R., Rocchetti, G., Pallerla, P., Lucini, L., Sripadi, P., A combined targeted/untargeted screening based on GC/MS to detect low-molecular-weight compounds in different milk samples of different species and as affected by processing, <<INTERNATIONAL DAIRY JOURNAL>>, 2021; 118 (118): 105045-105045. [doi:10.1016/j.idairyj.2021.105045] [http://hdl.handle.net/10807/178997]
A combined targeted/untargeted screening based on GC/MS to detect low-molecular-weight compounds in different milk samples of different species and as affected by processing
Rocchetti, Gabriele;Lucini, Luigi;
2021
Abstract
Low-molecular-weight compounds in milk are of interest from both nutritional and technological perspectives. To analyse potential milk metabolites, acetonitrile extraction followed by ethyl chloroformate derivatisation was developed. A GC/MS (SIM) method was then applied to different animal milk samples (i.e., buffalo, bovine, and donkey) and processed milk samples (i.e., pasteurised and dried). The optimised extraction-derivatisation method is rapid and more comprehensive when compared with the other published methods. The supervised orthogonal projection to latent structure discriminant analysis (OPLS-DA) based on the amino acid profile showed clear discrimination as a function of animal origin and milk processing. In this regard, variable importance in projection (VIP) analysis following an OPLS-DA prediction model showed that aspartic acid and asparagine (VIP scores = 1.26 and 1.19, respectively) were the best markers of the milk origin, whilst proline and glycine (VIP scores = 1.30 and 1.28, respectively) mainly discriminated samples according to different processing conditions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.