Rapid Infrared Spectroscopy for Assessing Beef Quality and Farming Intensification in Europe

European livestock sector shows remarkable diversity in beef production systems with different intensification levels, including mountain systems based on grasslands. Different production practices lead to different beef quality grades, creating market differentiation opportunities but also fraud potential. Within the EU-funded INTAQT project, we evaluated performances of different visible/NIR spectral devices, portable and benchtop, coupled to chemometrics for beef authentication and quality in relation to the farming system. Results showed good discrimination among samples according to intensification degree using spectral data from both devices through a Covariate Selection-Linear Discriminant Analysis approach, with the portable tool showing comparable to higher performances than the benchtop (Accuracy>0.72). Highest classification rate was obtained for the classes associated to the outmost intensification degrees (i.e. extensive with exclusive pasture grazing vs indoor with high concentrates proportion in the diet). Prediction of meat quality traits through Partial Least Square regression showed different results depending on the spectral device, with the highest overall performances for the benchtop one. Best regressions were obtained for contents of total and heme iron, moisture, protein, intramuscular fat, many saturated and unsaturated fatty acids contents (R²≥0.70). Unsatisfactory results were obtained for other quality traits such as marbling score, hardness, TBARs, carnosine, anserine and vitamin B content (R²<0.40). Our results showed that the portable device tested in our study can achieve performances comparable to the benchtop one for certain authentication tasks, such as classification based on the intensification of the farming system, supporting the potential for implementing accessible field technologies to enhance authentication practices in the beef sector. At the same time, other applications, such as calibration for prediction of quality traits, still require further development.