Avoiding the pitfalls of data quality and integrity in the Pharma QC laboratory while running routine analyses is an ongoing challenge. Regulatory violations are frequently a result of a lack of complete data, suspect data,… Read more >
Bringing increased confidence to sample preparation workflows using automation, smart laboratory tools and electronic documentation for recording analyst activities Consistency, accuracy, compliance and data integrity can each be improved with automation of laboratory processes. We… Read more >
Enhancing performance, mitigating risk, and improving efficiency Today, Waters introduced the Arc HPLC, a modern liquid chromatography system that replicates established test methods while delivering improved performance. HPLC has been a cornerstone of routine analytical… Read more >
In an earlier blog, I discussed the concerns about extraneous peaks that might appear in LC separations. While there can be many sources for peaks unrelated to the test substances, how do you decide which… Read more >
Suppressing Peak Integration Being able to optimize peak integration and identification, whether by resetting the method RT settings, manually identifying peaks or suppressing integration of specific known peaks are certainly tools which, in the wrong… Read more >
How do data integration and reintegration affect data integrity?
What procedures can an analyst use to ensure that a chromatography system is ready to begin analyzing samples, without the fear that the system suitability tests would immediately fail?
Halting a chromatographic analysis where the system is not performing correctly or when another error is evident in the separation is a valuable and scientific way to avoid creating unusable data that needs to be subsequently invalidated following a defined analytical lab error/result SOP. But what do you do with that orphan data?
Removing the ability for individual users to either delete data or to disable audit trails is expected to be both implemented and validated for data integrity. But when changes are required, what editing tools should be accessible to an analyst?
Orphan data may exist for any number of scientific or operational reasons, but in today’s environment – until the regulators trust the companies again – each piece of orphan data is suspect and may contain evidence of data tampering. So can you delete it?