Real-time monitoring of the current value of the cow’s milk condition.

The article discusses the development of a comprehensive integrated digital indicator of cow’s milk quality and a series of devices for its real-time identification and monitoring. Particular attention is given to the integral concept developed and proposed by Liubov Skokova, which combines artificial intelligence, artificial neural networks, and electromagnetic resonance spectroscopy for food quality control. Within this framework, parameters such as frequency, amplitude, resistance, inductance, electrochemical characteristics, and dynamic milk-flow properties are used as an integrated equivalent of the current content and concentration of milk components. The article describes compact, energy-efficient devices designed for operation on dairy farms, in field conditions, and in stationary installations, including versions powered by solar energy and intended for rapid analysis.

Special emphasis is placed on the selective real-time identification of somatic cell concentration, fat and fatty acid content, lactose concentration, traces of blood, and indicators associated with mastitis detection. The proposed systems incorporate resonant sensors, milk-flow transformation units, aerodynamic gas-removal mechanisms, and integrated monitoring modules capable of evaluating multiple milk-quality parameters simultaneously. The article also examines methodological approaches for the detection of subclinical and clinical mastitis through the combined assessment of somatic cell counts, electrical conductivity, pH, density, viscosity, and fat content. The presented devices, systems, software solutions, and associated methods are highlighted as innovative technological developments with significant practical potential for modern dairy production and quality assurance, while demonstrating strong prospects for patent protection through combinatorial and integrative patent applications.