ATR-FTIR Spectroscopy and Chemometric Modelling for the Authentication of Canestrato di Castel del Monte Cheese

Canestrato di Castel del Monte (CCM) is a traditional sheep cheese from the Abruzzo region of Italy, strongly linked to local pastoral practices and characterized by high cultural and commercial value. Ensuring its authenticity is therefore essential to protect both producers and consumers. In this study, Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modelling was investigated for the classification of traditional sheep cheeses. A dataset of approximately 2000 spectra obtained from Canestrato di Castel del Monte (CCM), low-ripening CCM, and Pecorino Toscano was analyzed using different modelling strategies. Partial Least Squares Discriminant Analysis (PLS-DA) and Sequential Preprocessing through Orthogonalization combined with Linear Discriminant Analysis (SPORT-LDA) were first applied to simultaneously separate the three categories. Subsequently, a class-modelling approach based on Soft Independent Modelling of Class Analogy (SIMCA) was used to authenticate CCM and low-ripening cheeses. The discriminant models achieved excellent classification performance: accuracies close to 100% for CCM and low-ripening CCM and around 95% for Pecorino Toscano. SIMCA provided reliable rejection of non-target samples, although with lower sensitivity compared to discriminant approaches. Overall, the results demonstrate that ATR-FTIR spectroscopy coupled with appropriate chemometric modelling represents a powerful strategy for the authentication and classification of traditional sheep cheeses.