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Iberian pigs fed on acorns and pasture were slaughtered from January until March of 2018 and 2019. The meat from those Iberian pigs is a seasonal food that only can be found fresh, at the marketplace, during a limit period of the year. Selling frozen-thawed meat is a legal practice, but consumers must be informed about it on the product label. However, to declare as fresh meat, meat previously frozen, is one of the most frequent meat frauds. The present study compares the performance of two rather different Near Infrared Spectroscopy instruments, based on Fourier Transform and Linear Variable Filter technologies, for the in-situ detection of fresh and frozen-thawed acorns-fed Iberian pig loins using Partial Least Discriminant Analysis (PLS-DA). The performance of the models developed for both instruments offered a very high discriminant ability. Furthermore, the models showed consistent results and interpretation when were evaluated with several scalars and graphical methods.

Conventional chemical analyses of meat products are time-consuming, expensive and destructive. The advantages of NIR spectroscopy are its speed, portability, suitability for both at-line and on-line analysis, low cost and the possibility of simultaneously measuring many different parameters in a large number of samples. The purpose of this study was to develop and validate calibrations for the prediction of moisture, protein and fat in Iberian pig pork loins using an FT-NIR instrument coupled to a 5-m fibre optic sensor head. The best equations obtained for intact loin in both modes of analysis (full and optimal spectral range) displayed Standard Error of Cross-Validation (RMSECV) of 1.06% and 1.09% and Determination Coefficient of Cross-Validation (RCV2) of 0.69 and 0.77 for fat: RMSECV of 0.87% and 0.77% and RCV2 of 0.67 and 0.73 for moisture; while for protein, the RMSECV values were 0.51% and 0.49% and the RCV2 values were 0.66 and 0.70.