VaMPIS - Validation of Measurement Procedures that Include Sampling

Importance of the topic

Reliable analytical outcomes depend not only on laboratory procedures but also on the quality of primary sampling. Traditional method validation often omits the sampling step, yet sampling variability can dominate overall measurement uncertainty. Integrating sampling into the validation framework ensures that reported results truly reflect the entire measurement process.

Objectives and overview

The VaMPIS guidance extends conventional method validation to encompass both sampling and analysis, treating them as a unified measurement procedure or in a sequential manner when the analytical method has been previously validated. It provides a structured approach for both ex situ and in situ scenarios to judge fitness for purpose based on a target measurement uncertainty.

Methodology and instrumentation

The VaMPIS approach comprises eleven key steps:

• Specify measurand and define sampling target.
• Identify the combined measurement procedure (analytical procedure + sampling procedure).
• Design validation experiments for the entire procedure.
• Implement the selected sampling and analytical methods.
• Apply analytical quality control (AQC) to ex situ samples as needed.
• Estimate overall measurement uncertainty (MU), including the sampling contribution (UfS), using the Duplicate Method and ANOVA.
• Compare the actual MU to a pre-defined target MU, set externally or via the Optimum Uncertainty method.
• If MU exceeds the target, iteratively refine sampling or analytical steps based on their contributions to MU and cost.
• For sequential validation, reassess the previously validated analytical method within the full procedure.
• Establish integrated measurement quality control (IMQC) for ongoing performance monitoring.

Instrumentation example:

• Portable X-ray fluorescence (pXRF) for in situ soil lead analysis.

Main results and discussion

By centering validation on measurement uncertainty, VaMPIS provides a single metric to assess the impact of random and systematic effects across all stages. Worked examples include nitrate determination in composite lettuce samples (ex situ) and lead quantification in soil using pXRF (in situ). The guidance emphasizes the importance of IMQC and highlights practical challenges such as coordinating field sampling teams with laboratory analysts.

Benefits and practical applications of the method

The integrated framework enables:

• Comprehensive validation that accounts for sampling variability.
• Informed decision-making for regulatory compliance and product quality.
• Cost-effective improvements by targeting the most influential steps.
• Applicability across environmental monitoring, food safety, and industrial QA/QC.

Future trends and opportunities

Advances in portable and in situ technologies will broaden VaMPIS adoption. Digital workflows, automated data capture, and AI-driven sampling design may streamline uncertainty estimation. Harmonized guidelines and regulatory acceptance of integrated validation will further support standardized measurement quality assurance.

Conclusion

VaMPIS delivers a robust, uncertainty-based framework for validating entire measurement procedures, bridging sampling and analysis. Its iterative, quality-focused approach and emphasis on ongoing control foster reliable, fit-for-purpose results across diverse analytical applications.

Reference

1. Ramsey M. H., Ellison S. L. R., Rostron P. (eds.) Eurachem/EUROLAB/CITAC/Nordtest/AMC Guide: Measurement Uncertainty Arising from Sampling: a Guide to Methods and Approaches, 2nd ed., Eurachem, 2019
2. Cantwell H. (ed.) Eurachem Guide: The Fitness for Purpose of Analytical Methods – A Laboratory Guide to Method Validation and Related Topics, 3rd ed., Eurachem, 2025
3. Ramsey M. H., Rostron P. D., Raposo F. C. (eds.) Eurachem/EUROLAB/CITAC/Nordtest/AMC Guide: Validation of Measurement Procedures that Include Sampling, Eurachem, 2024