Multivariate Correlation of the Physicochemical and Sensory Profile of Milk Quality from Small Producers in Barranca, Lima-Peru

The comprehensive quality assessment of raw milk from small-scale producers remains essential for improving dairy sector competitiveness. This study employed a multivariate approach to correlate the physicochemical, colorimetric, and sensory profiles of raw milk from eleven producers in the town of Supe, Barranca, Lima, Peru. Milk samples were analyzed using a Lactoscan MCC ultrasonic analyzer, CIEL*a*b* colorimetry, and the Flash Profile sensory method. Data integration and interpretation were performed using Analysis of Variance (ANOVA), Generalized Procrustes Analysis (GPA) and Hierarchical Multiple Factor Analysis (HMFA). The results revealed significant heterogeneity, identifying two distinct producer groups. A high-quality group (DF7, DF10, DF11) presented adequate physicochemical parameters: high fat content (>3.77%), total solids (>12.06%), normal freezing point (≈−0.53 °C), creamy color (high L* and b*), and positive sensory attributes (“fatty”, “creamy”). In contrast, a low-quality group (DF4, DF5, DF8, DF9) showed evidence of water adulteration (12–16%), reflected in an elevated freezing point (up to −0.44 °C), low solids-not-fat, and defective sensory profiles (“tasteless”, “salty”). The HMFA demonstrated a strong concordance between instrumental and sensory data sets, identifying water adulteration and fat content as the primary drivers of quality variation. This integrated methodology provides a robust diagnostic tool for quality-based payment systems and targeted technical assistance, offering a replicable model for enhancing quality control and valorizing raw milk in smallholder dairy systems.