Analytical method reliability index (AMRI) as an innovative tool and software for the assessment of analytical procedure

The reliability of an analytical method is paramount, especially in high-stakes fields like forensic science, where results can directly affect legal outcomes. While traditional method validation establishes a baseline for acceptability, it often fails to differentiate between methods that merely meet minimum criteria and those that demonstrate superior and robust performance. To address this gap, the Analytical Method Reliability Index (AMRI) was introduced, an innovative scoring tool and software (available at bit.ly/AMRI2026 ) designed specifically to provide a standardized, quantitative assessment of analytical method reliability. The AMRI framework builds upon international validation guidelines (e.g., ICH, FDA) and employs a tailored scoring system for key parameters, including linearity, range, accuracy, precision, selectivity, robustness, stability, and application to real samples. Each parameter is evaluated against application-specific acceptance criteria, rewarding methods that exceed minimum requirements. The composite AMRI score facilitates objective comparison and selection of the most dependable analytical procedures. The utility of AMRI is demonstrated through several case studies involving pharmaceutical analysis in biological fluids, where it successfully differentiated the different methods in the case studies with scores ranging from 36 to 82, clearly reflecting their relative reliability. AMRI serves as a crucial complementary tool to validation, enhancing transparency, supporting defensible decision-making, and ensuring analytical methods are not only valid on paper but also robust and trustworthy in practice. • RAMI provides a quantitative framework to assess analytical reliability. • The metric aligns with ICH, FDA, EMA, and other validation guidelines. • Tiered scoring rewards methods exceeding minimum performance criteria. • Free open-source software offers visual and numerical RAMI outputs. • Case studies confirm RAMI distinguishes reliable analytical methods.