PGI Chianina meat traceability by means of multivariate HRMAS-NMR data analysis

Abstract

Food quality is a crucial issue for producers and consumers, either dealing with commodities according to basic standards or with top quality products. Among the parameters contributing to quality, the place of origin is considered to be one of the most relevant, especially for protected denomination of origin and protected geographical indication foods, PDO and PGI, respectively. These labels have been designed by the EU to protect and valorise high quality foodstuff produced in limited areas and to ensure higher incomes to farmers. Such economic interest has prompted the use of several analytical techniques for the traceability issue. Here we proposed the multivariate HRMAS-NMR (i.e. High-Resolution Magic Angle Spinning-Nuclear Magnetic Resonance) data analysis for the traceability of PGI Chianina meat, specifically for the semitendinosus muscle. The metabolic profile of Chianina meat assessed by HRMAS-NMR spectroscopy was analysed by means of PCA (Principal Component Analysis), PLS-DA (Partial Least Square-Discriminant Analysis) and OPLS-DA (Orthogonal Partial Least Square-Discriminant Analysis) in order to classify samples according to the geographical origin. The built models provided an excellent separation between PGI and non-PGI, and the use of the VIP (Valuable Influence on Projection) values allowed us to identify metabolites contributing significantly to classification. Specifically, we found molecules such as amino acids, carnosine, some nucleosides, and fatty acids to be responsible for the discrimination: the fatty acid profile of meat is affected by the different feeding systems, while the other metabolites are involved in the ageing process of meat (ATP degradation during post mortem and proteolysis).