THE USE OF MATHEMATICAL MODELLING TO ESTABLISH THE INTERCALIBRATION INTERVAL OF MEASURING LABORATORY INSTRUMENTS OF GRAIN LABORATORIES

Calibration of measuring instruments is a critical aspect of ensuring the accuracy, reliability, and standardization of quality and safety assessments in grain laboratories. Precise measurements are essential for evaluating key agricultural product parameters, including moisture content, protein concentration, and contaminant levels, which directly influence market value, food safety, and regulatory compliance. Regular calibration of laboratory equipment is mandated by ISO/IEC 17025:2017, which establishes general requirements for the competence of testing and calibration laboratories. This standard ensures measurement traceability, harmonization with international benchmarks, and reproducibility of analytical results. However, frequent calibration procedures present a significant financial burden for laboratories, particularly those operating under budget constraints. The challenge lies in balancing the need for rigorous quality control with cost-effective calibration management. This study explores the optimization of intercalibration intervals by leveraging mathematical modelling, specifically the application of control charts and statistical regression techniques. The research analyzes calibration data from two categories of instruments: simple measuring devices, represented by glass hygrometers (229 calibration certificates), and complex precision equipment, exemplified by automatic pipettes (79 calibration certificates). A comparative assessment of eight mathematical models, including linear, quadratic, cubic, and exponential regression functions, was conducted to determine the most suitable approach for approximating error progression over time. The findings indicate that while linear regression models provide robust and interpretable predictions for simple instruments, nonlinear models, particularly cubic regression, demonstrated superior predictive accuracy for complex equipment, explaining up to 94–96% of the variance in calibration errors. However, while nonlinear models offer enhanced precision in tracking measurement deviations, they necessitate larger datasets to mitigate the risk of overfitting and erroneous extrapolations. The study underscores the importance of selecting appropriate statistical modelling techniques tailored to the specific characteristics of the instrument being calibrated. By integrating data-driven approaches, laboratories can establish optimal calibration intervals, thereby enhancing measurement reliability while reducing unnecessary calibration costs. The proposed methodology provides a framework for achieving a balance between accuracy, compliance, and economic feasibility in laboratory calibration management.