METHOD OF OPERATION OF A CYBER-PHYSICAL SYSTEM FOR DETERMINING THE LEVEL OF CREATINE IN FOOD PRODUCTS

Abstract

The article is devoted to the development of a theoretical basis for an innovative cyber-physical system (CPS) for determining the level of creatine in food products to monitor the nutritional status of patients with neurodegenerative diseases, in particular spinal muscular atrophy (SMA). The key role of creatine in restoring muscle memory and cellular energy metabolism, which is critical for patient rehabilitation, is substantiated. Since about half of the daily requirement of this substance must come from food, there is an urgent need to verify the creatine content directly in food products. An analysis of existing methods revealed their high cost and complexity, which makes rapid testing in everyday conditions impossible. Requirements for the new system have been defined, the main ones being inclusiveness for people with visual impairments and automation of result interpretation. The mathematical support of the developed CPS is based on the Beer-Lambert-Barr law and digital color analysis models, which allow the transformation of optical signals into quantitative indicators of the concentration of creatinine picrate formed during the Jaffe reaction. The proposed method of operation of the cyber-physical system for determining the level of creatine in food products integrates the physicochemical preparation of liquid extracts of products using impregnated test strips with cybernetic reading of the color signal through budget RGB sensors. The method involves automatic normalization of scanning conditions, verification of data against a reference knowledge base, and output of voice or text status about the creatine level in the analyzed food product. The practical significance of the study lies in the creation of an affordable tool, where the cost of a single test is minimal (0.04 UAH), allowing for regular monitoring of all segments of the population. Despite restrictions on temperature control, the system minimizes the influence of the human factor and ensures high reliability of results. The creation of a collective cloud-based knowledge base lays the foundation for intelligent monitoring of nutritional quality, which is essential for improving the quality of life of patients with systemic muscle disorders.