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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.

Introduction

Background

Milk quality assessment is essential for ensuring food safety, economic value, and consumer trust in the dairy industry. While recent machine learning (ML) models—such as deep neural networks, gradient-boosted trees, and random forests—have achieved high predictive accuracy, they often lack interpretability. This "black-box" nature limits their usefulness for stakeholders (e.g., farmers, processors, regulators), who require clear, actionable insights to make informed decisions.

Objectives

The study aims to:

1. Develop and compare both complex (e.g., DNNs, GBT, RF) and interpretable (e.g., decision trees, logistic regression) ML models.

2. Provide explainable insights using tools like SHAP values and feature importance analysis to enhance transparency and trust.

Related Works

Numerous studies have applied ML to milk quality and adulteration detection:

• Spectroscopy-based methods ([1], [3]) and hardware-enabled systems ([5]) for real-time quality assessment.

• Advanced ML models ([2], [4], [8], [10]) focus primarily on predictive accuracy but neglect interpretability.

• Reviews ([6], [12], [14]) highlight ML's promise in dairy quality but acknowledge a gap in transparency.

This research addresses that gap by focusing on interpretable AI for practical and regulatory compliance.

Methodology

• Dataset: Manually curated with 7 features (pH, Temperature, Taste, Odor, Fat, Turbidity, Color) and 3 quality grades (Low, Medium, High).

• Preprocessing: Included mean/mode imputation, Min-Max normalization, binary encoding, and stratified data splitting.

• Model Types:

  • Complex: Deep Neural Networks (DNNs), Random Forests (RF), Gradient Boosted Trees (GBT)

  • Interpretable: Decision Trees, Logistic Regression, Linear Regression

• Evaluation Metrics:

  • Classification: Accuracy, Precision, Recall, F1-score

  • Regression: R², MAE

  • Interpretability: SHAP values, feature importance, partial dependence plots

Results

Classification Performance:

• DNNs: 92.4% accuracy (best overall)

• GBT: 91.2%

• RF: 89.7%

• Decision Tree: 83.5%

• Logistic Regression: 80.2%

Regression Performance:

• GBT: R² = 0.88, MAE = 0.35 (best)

• RF: R² = 0.85, MAE = 0.39

• Linear Regression: R² = 0.72, MAE = 0.54

Interpretability Insights:

• SHAP analysis identified pH, Fat, and Turbidity as the most influential features.

• Interpretable models trade off some predictive accuracy for transparency, which is critical for food safety and compliance.

Conclusion

In this study, we evaluated the performance of various machine learning models for predicting milk quality, focusing on classification and regression tasks. The Deep Neural Networks (DNNs) model outperformed others, achieving the highest accuracy of 92.4%, demonstrating its ability to effectively capture complex patterns within the data. Gradient Boosted Trees (GBT) also showed strong performance, particularly in regression tasks, where it achieved the best R² score and lowest Mean Absolute Error (MAE). These results emphasize the potential of machine learning models, particularly DNNs and GBT, in providing accurate and reliable milk quality predictions. Our research contributes to the growing body of work on machine learning applications in food quality assessment. By demonstrating the superior performance of DNNs, our study sets a new benchmark for predictive accuracy in this domain. Additionally, the feature importance analysis highlights key quality indicators such as pH, Fat, and Turbidity, which could inform quality control practices in dairy industries. Future work should explore further optimization of DNNs and GBT models, potentially incorporating additional features or advanced ensemble methods to enhance predictive accuracy. Moreover, real-time deployment of these models in dairy production environments could be investigated, aiming to improve operational efficiency and quality assurance in the industry.

References

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Copyright

Copyright © 2025 V T Ram Pavan Kumar M, Chandolu Manikanta, Arepalli Naga Swaroop, Kanumuri Gopi Chand, E Hareesh Babu, Kodi Venkat Prasad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.