## 1 Introduction  
Milk and dairy products are among the essential food sources worldwide, and the quality of these products is critically important for consumer health and food safety. Quality control in the dairy industry is indispensable for extending product shelf life, reducing contamination risk, and preserving nutritional values [1]. The effectiveness of quality control processes plays a key role in ensuring producers comply with legal regulations and in enhancing consumer confidence [2].  
Milk quality control is a critical aspect of dairy processing, ensuring both safety and consistency in production. Advances in microbiological considerations, as highlighted by [PERSON] [3], have laid the foundation for developing robust quality assurance frameworks in the dairy industry. Additionally, in-line technologies for analyzing critical milk parameters during milking have been extensively reviewed, showcasing their potential to enhance real-time monitoring and operational efficiency [4]. Moreover, a detailed exploration of dairy processing standards and quality monitoring emphasizes the importance of integrating innovative approaches to maintain product integrity and comply with regulatory requirements [5]. These studies collectively underline the necessity for continued technological advancements in quality control processes, which this research aims to address.  
The application of edge computing in the dairy industry could significantly improve quality control processes. Edge computing, a technology enabling data processing at or near the data source rather than relying on centralized systems, enhances response times and reduces bandwidth usage, especially in applications requiring real-time analysis. For instance, edge devices could perform on-site quality control in milk production facilities, thereby reducing reliance on laboratory tests and providing real-time feedback [6]. This also offers advantages in terms of data security and privacy, as data are processed locally and only necessary information is transmitted to the central system [7].  
Edge computing also provides significant benefits in terms of energy efficiency and reducing operational costs [8]. These advantages are particularly valuable in energy-intensive sectors like the dairy industry [9]. Edge technology can also work compatibly with other IoT devices in milk production facilities, allowing for the creation of an integrated quality control and monitoring system [10].  
The primary aim of this study is to develop a multi-spectral sensor-based edge sensor node to improve quality control processes in the dairy industry and evaluate its performance. This system utilizes edge computing to enable rapid and accurate detection of milk components in industrial facilities, drawing on the XGBoost machine learning model previously implemented by [PERSON] et al. [11]. Machine Learning (ML) involves algorithms and models that learn patterns from data to make predictions or decisions without explicit programming for every task. XGBoost, or extreme gradient boosting, is a powerful ML algorithm known for high performance in structured data and efficient handling of complex relationships. The Gradient Boosting Machine (GBM) is an ML technique that builds models sequentially to improve predictive accuracy, especially in complex data structures.  
The scope of the study includes analyzing spectral data from milk samples, evaluating the model's performance, and assessing the device's applicability in the field. This research makes a significant contribution to the existing literature and presents a new technological approach for optimizing quality control in the dairy industry.