Integrating LASSO and Extreme Gradient Boosting for Optimal Multiple Linear Regression Modeling of Milk Color Traits in Relation to Somatic Cell Count and Milk Composition in Dairy Cows

Milk color reflects the optical output of a complex colloidal system governed by protein micelles, fat globules, and serum phase interactions. In this study, we evaluated whether CIE Lab* color parameters can explain variation in milk composition and somatic cell count (SCC) using Lasso-based multiple linear regression and Extreme Gradient Boosting (XGBoost). A total of 119 Holstein milk samples were analyzed for fat, protein, lactose, dry matter, electrical conductivity, freezing point, and SCC, and five color indices (L*, a*, b*, Hue, and Chroma) were used as predictors. Model robustness was evaluated using 10-fold cross-validation and an independent 80/20 train–test split. In regression analyses, Lasso explained 32.7% of protein variation (R2 = 0.327), 26.3% of dry matter (R2 = 0.263), 22.8% of lactose (R2 = 0.228), and 19.1% of fat (R2 = 0.191). Spectral tone parameters (a*, Hue, and Chroma) were consistently retained as key predictors, whereas L* showed a limited contribution. SCC exhibited weak direct associations with color traits but was significantly related to electrical conductivity (p < 0.05), indicating inflammation-driven ionic changes rather than pigment effects. In classification analysis (SCC ≥ 200,000 cells/mL), the XGBoost model achieved 74% accuracy and an AUC of 0.69 in the independent test set, with Chroma and electrical conductivity identified as the most influential features. These findings suggest that, among the evaluated color variables, Chroma provided the most relevant information for discriminating SCC status, whereas the overall contribution of milk color traits to compositional prediction remained moderate. Therefore, color-derived measurements should be interpreted as instrument-based optical indicators that may complement, but not replace, conventional milk quality assessments.