Transforming Dairy Standards: Machine Learning in Milk Quality Prediction

Abstract: Accurate and interpretable milk quality prediction is critical for ensuring food safety and regulatory compliance in the dairy industry. While machine learning (ML) models like deep neural networks (DNNs) and gradient-boosted trees (GBT) achieve high predictive accuracy, their "black-box" nature limits stakeholder trust and actionable insights. This study bridges the gap between performance and interpretability by evaluating both complex and transparent ML models on a dataset of seven milk quality parameters (pH, temperature, taste, odor, fat, turbidity, color). We quantify feature contributions, revealing pH, fat, and turbidity as the most influential predictors. Our results show DNNs and GBTs achieve 92.4% and 91.2% classification accuracy, respectively, while interpretable models like decision trees (83.5%) provide rule-based insights. Regression analyses further highlight GBTs’ superiority (R²=0.88, MAE=0.35). By integrating high accuracy with explainability, this work enables dairy stakeholders to adopt ML-driven systems confidently, fostering real-time quality control and data-driven decision-making