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.
Lingua originale | English |
---|---|
pagine (da-a) | 105045-105045 |
Numero di pagine | 1 |
Rivista | International Dairy Journal |
Volume | 118 |
DOI | |
Stato di pubblicazione | Pubblicato - 2021 |
Keywords
- metabolomics
- milk quality