Mid-Infrared Spectroscopy for Predicting Goat Milk Coagulation Properties

The assessment of milk coagulation properties (MCPs) is crucial for enhancing goat cheese production and quality. In this study, 501 bulk goat milk samples were collected from various farms to evaluate the MCPs. Traditionally, cheesemaking aptitude is evaluated using lactodynamographic analysis, a reliable but time-consuming laboratory method. Mid-infrared spectroscopy (MIRS) offers a promising alternative for the large-scale prediction of goat milk's technological traits. Reference MCP measurements were paired with mid-infrared spectra, and prediction models were developed using partial least squares regression, with accuracy evaluated through cross- and external validation. The ability of MIRS to classify milk samples by coagulation aptitude was evaluated using partial least squares discriminant analysis. Only the model for rennet coagulation time obtained sufficient accuracy to be applied for screening (R²_CrV = 0.68; R²_Ext = 0.66; RPD = 2.05). Lower performance was observed for curd-firming time (R²_CrV = 0.33; R²_Ext = 0.27; RPD = 1.42) and curd firmness (R²_CrV = 0.55; R²_Ext = 0.43; RPD = 1.35). Classification of high coagulation aptitude achieved balanced accuracy values of 0.81 (calibration) and 0.74 (validation). With further model refinement and larger calibration datasets, MIRS may become a resource for the dairy-goat sector to monitor and improve milk suitability for cheesemaking.