Abstract
Nowadays, people are increasingly interested in the food they consume. Authenticity and natural origin are amongst the most valued issues of food products by society. Although various national and international laws have been created for the regulation of labelling and trade of food, unfortunately, they are often not effective in avoiding food product fraud. The Iberian pig and the cured products obtained with this breed have a great international reputation due to their high quality and added value. However, the authentication of these pigs feeding regime is sometimes difficult. Therefore, the objective of this study was to use faecal volatilome information to differentiate the different feeding regimes which determine the final commercial category of Iberian products. Individual faeces samples were sampled on 10 farms from 133 Iberian pigs to evaluate their volatilome through gas chromatography (GC) coupled to ion mobility spectrometry (IMS). The intensity of GC-IMS plot features were extracted and chemometric tools were employed to develop two different models: one, focused on the discrimination between acorn-fed (completely natural diet grazed) and feed-fed samples, and another one for commercial category classification. Both models were carried out in duplicate, using spectral fingerprint information and a different approach studying specific markers. Good classification rates were obtained in both models: 92,3% and 96,3% were the rates obtained in acorn-fed vs feed-fed model with fingerprint and specific markers information, respectively; and the same classification success was also achieved with both approaches in the second model, focused on commercial category classification. The misclassified samples in both models, which belonged to acorn-fed pigs, may be related to the diet heterogeneity of these animals and the differences in natural resources foraged. The results of the present study highlight GC-IMS as an useful tool to carry out an in vivo authentication of Iberian pig feeding regime and the subsequent commercial category, as well as to avoid labelling fraud. Further studies including larger number of samples are needed in order to obtain more complex models to classify very different samples.
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