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Nuclear magnetic resonance profiling, combined with a single-layer artificial neural network, is used 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 (1) H 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.

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Nuclear magnetic resonance (NMR) profiling is used for characterization of monocultivar binary wine mixtures. Classification and quantification of the relative amount of wine in the mixture are made in two steps. First, each sample is classified as a mixture of a determined type by solving the appropriate classification problem using NMR profiles. The relative amount of the two corresponding monovarietal wines is then evaluated by multilinear regression of a selected set of NMR variables. Linear discriminant analysis (LDA), used in the classification step, gives a very good separation among the different mixture classes. On the other hand, a single layer artificial neural network, used to solve the multilinear problem, gives the relative amount of wine type in the mixture with a precision of about 10%.