Being a biological fluid, the composition of milk depends on several factors such as breed, individual metabolism, season, animal health, nutrition, or milking habits. However, in this strong chemical variability, some common features (patterns), correlated to each of the above mentioned factors, are present. The recognition of these patterns, in the chemical composition of different kinds of milk, can be used for the identification and the classification of the milk samples and can be eventually used to establish a correlation between the chemical profile and the factors influencing its variability. Chemical profiling, associated with multivariate statistical techniques, is often used to perform the abovementioned pattern recognition and then for the identification of milk.

Raffaele Lamanna, ENEA Research Center of Trisaia, Rotondella, Italy, and colleagues used nuclear magnetic resonance profiling combined with a single-layer artificial neural network for the evaluation of the content of mixtures of different kinds of milk. In particular, aqueous fractions of cow and sheep milk mixtures are analyzed by ¹H NMR. The spectral differences are highlighted by an analysis of the variance and a principal component analysis. The species classification problem is solved by a linear discriminant analysis. The quantification of the relative amount of the milk of two different species is then achieved by solving the appropriate multilinear problem.
The relative amount of sheep and cow milk is evaluated, by this multilinear regression of a selected set of NMR variables, with a precision of about 10 %.

• Identification of milk mixtures by ¹H NMR Profiling,
  Raffaele Lamanna, Angela Braca, Elvio Di Paolo, Giovanna Imparato,
  Magn. Reson. Chem. 2011, 49, S22-S26.
  DOI: 10.1002/mrc.2808