Design Of An IoT Based Model For Milk Quality Measurement Using Fringing Field Interdigital Capacitive Sensing and Dielectric Property Analysis

The proposed paper highlights the need for advanced milk quality measurement systems to enhance food safety and ensure quality assurance. Conventional methods are inefficient and incapable of monitoring real-time quality changes, thereby posing potential risks. This work focuses on a milk quality measurement system incorporating a fringing field interdigital capacitive sensor, consisting of three key models: Capacitive Sensing with signal conditioning, Dielectric Property Analysis, and Predictive Quality Assessment. The IoT-based Capacitive Sensing Model detects minute changes in milk quality by measuring capacitance variations caused by dielectric properties. It includes temperature compensation for accurate results. The Dielectric Property Analysis Model correlates dielectric constants with milk quality parameters such as fat, protein, and contamination levels. The Predictive Quality Assessment Model uses machine learning algorithms to detect trends and provide alerts for deviations. Together, these models enable real-time monitoring, enhancing accuracy, reliability, and predictive capabilities, significantly improving food safety and quality assurance practices.