TY - JOUR
T1 - 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
AU - Bhumireddy, Sudarshana Reddy
AU - Rocchetti, Gabriele
AU - Pallerla, Pavankumar
AU - Lucini, Luigi
AU - Sripadi, Prabhakar
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - metabolomics
KW - milk quality
KW - metabolomics
KW - milk quality
UR - http://hdl.handle.net/10807/178997
U2 - 10.1016/j.idairyj.2021.105045
DO - 10.1016/j.idairyj.2021.105045
M3 - Article
SN - 0958-6946
VL - 118
SP - 105045
EP - 105045
JO - International Dairy Journal
JF - International Dairy Journal
ER -