Real-Time Milk Quality Control Using Multi-Spectral Sensing and Edge Computing: Advancing On-Site Detection of Milk Components with XGBoost

This study explores the use of edge computing technologies to enhance the quality control processes in the dairy industry. Traditional milk quality control methods can be time-consuming and sometimes inadequate, whereas this new approach offers real-time data processing and rapid decision-making capabilities. The objective of the study is to assess the effectiveness of evaluating various spectral characteristics of milk in predicting critical parameters such as protein and fat content. In this research, a multi-channel sensor capable of collecting spectral data at various wavelengths was utilized. The collected data were processed using advanced machine learning models, where XGBoost and other regression models were assessed for their accuracy in predicting protein and fat content. The findings demonstrate the suitability of this technology for quality control in the dairy industry. The results reveal that edge computing-based systems can determine milk quality more quickly and accurately. This technology holds significant potential for overcoming the challenges faced in milk quality control, particularly in developing countries. This study provides valuable insights into how the use of edge computing can enhance operational efficiency and ensure product quality in the dairy industry. This research represents an important step towards developing more effective quality control mechanisms in the dairy industry and aims to establish a robust foundation for future studies. Recommendations focus on the adaptation of this technology to other food safety applications and its diversification for widespread industrial use.