Positively FALSE!
People often ask, “Why should I have my data validated if the laboratory is accredited and has internal quality assurance/quality control (QA/QC) measures in place?” The recent occurrence of two laboratories producing low-level false positive results underscores the significance of robust QA programs and the pivotal role of third-party data validation in upholding data integrity and ensuring that data used for decision-making is high quality.
Quality Assurance Challenges
Reviews of environmental data reported by two analytical laboratories uncovered instances of false positive results. In one case, a laboratory reported false positive results for several volatile organic compounds (VOCs) in water samples. During Stage 4 data validation, it was observed that the qualitative identification criteria were not met for several low-level reported positive results. The samples were analyzed using a gas chromatograph with mass spectrometer (GC/MS), and the mass spectrum for the sample did not match the mass spectrum of the reference standard due to out-of-ratio mass ions and/or secondary ions that were not present. Missing the qualitative review step resulted in the laboratory reporting analytes as positive when they weren’t, necessitating a comprehensive review and correction of affected results dating back several months.
In another example, a laboratory reported false positive results for a VOC analyte in air samples. These samples were analyzed on a GC with dual-column reporting. The laboratory noted a peak on one column but disregarded the confirmation column due to a QC failure, reporting the result as positive without the appropriate dual-column confirmation that is required per the analytical method and the laboratory standard operating procedure (SOP).
The Importance of a Thorough Quality Assurance Program
These incidents highlight the importance of a well-structured QA program within analytical laboratories. Key components of an effective QA program include:
- Internal Data Review: Laboratories should implement thorough internal reviews to identify and correct anomalies or inconsistencies in data reporting, ensuring that all analytical results meet the established qualitative and quantitative criteria before being reported.
- Training and Competency: Regular training for Analysts and peer reviewers is essential and should include the interpretation of spectral data and the handling of QC samples.
- Standard Operating Procedures: Adherence to well-defined SOPs helps ensure that all aspects of sample analysis, from preparation to reporting, are conducted consistently and accurately. Deviations from these procedures can lead to errors in data reporting and interpretation.
- Root-Cause Investigation: Laboratories should perform detailed root-cause investigations and corrective actions when issues are identified.
The Role of Third-Party Data Validation
In addition to internal QA, third-party data validation plays a critical role in ensuring the integrity of environmental data by:
- Identifying Errors: Third-party validators can detect errors or inconsistencies that may have eluded detection during laboratory analysis and reporting, preventing the reporting of inaccurate or misleading data.
- Enhancing Transparency: Third-party validation adds an additional layer of scrutiny, enhancing the transparency and credibility of the data, particularly in high-stakes environmental assessments where data accuracy is paramount.
- Improving Practices: Insights gleaned from third-party reviews can provide valuable guidance into areas for improvement within laboratory practices and QA programs, leading to the implementation of corrective actions and enhancements in data management procedures.
Looking Ahead
These steps reinforce the reliability of environmental data. A well-structured QA program combined with vigilant third-party validation not only helps in detecting and correcting errors but also in fostering confidence in the data utilized for critical environmental assessments and decision-making.
As we continue to address challenges in environmental analysis, these lessons emphasize the importance of diligence and continuous improvement in our QA practices.
