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5 - The importance of a quality assurance plan for method validation and minimizing uncertainties in the HPLC analysis of phytoplankton pigments
- Edited by Suzanne Roy, Carole A. Llewellyn, Plymouth Marine Laboratory, Einar Skarstad Egeland, Geir Johnsen, Norwegian University of Science and Technology, Trondheim
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- Book:
- Phytoplankton Pigments
- Published online:
- 05 March 2012
- Print publication:
- 27 October 2011, pp 195-256
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Summary
Introduction
A quality assurance plan (QAP) describes a process to ensure an analytical method fulfils the agreed upon accuracy objectives at all points during the analysis of samples. A QAP includes such things as standardized procedures, method validation, quality control (QC) measurements, and quality assessment (QA); the latter quantitatively describes how results of QC measurements are used to determine whether a method is performing within expectations. By analogy, a QAP describes what can be considered, in a more general sense, a ‘holistic’ approach to sample analysis, whereby the ‘whole is considered to be a result of the interdependence of all parts’. Here, the ‘whole’ represents the overall combined uncertainty of a final data product, and ‘the interdependence of all parts’ represents the uncertainties contributed by the many individual procedures required to produce that final data product.
An in depth discussion of uncertainty analysis in the chemical laboratory, given in EURACHEM (2000), is beyond the scope of this chapter, but the importance of a so-called holistic approach to sample analysis, whether this includes a formalized QAP or not, is necessary to provide knowledge of the uncertainties associated with measured values and, thus, facilitate confidence in the data products. Such knowledge is important, because pigment data are often compiled in increasingly large databases, and end users of the data are remote (in space and time) from the data providers. Knowing the accuracy of the data facilitates their wider utility, for both current and unanticipated future applications. This chapter describes components of a QAP in the context of knowledge gained during intercomparisons sponsored by the National Aeronautics and Space Administration (NASA). A primary objective of these activities was to determine if the myriad providers of HPLC analyses to NASA researchers were satisfying the accuracy requirements for field observations.
Appendix B - HPLC instrument performance metrics and validation
- Edited by Suzanne Roy, Carole A. Llewellyn, Plymouth Marine Laboratory, Einar Skarstad Egeland, Geir Johnsen, Norwegian University of Science and Technology, Trondheim
-
- Book:
- Phytoplankton Pigments
- Published online:
- 05 March 2012
- Print publication:
- 27 October 2011, pp 636-649
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- Chapter
- Export citation
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Summary
Currently, there are over 90 companies that offer HPLC hardware and accessories, and more than 30 that offer complete systems. Given the myriad choices available in the marketplace, the discerning chromatographer needs to approach equipment purchases with a critical mindset and a clear understanding of what they require from an HPLC system or component. This appendix covers some of the features available in HPLC autosamplers, pumps, detectors and ovens. It is not meant to be a definitive catalog of available HPLC hardware components and design elements. Instead, it is designed to call attention to some of the features available in specific HPLC hardware that the authors of this appendix have researched in the context of how these decisions can affect one's ability to produce consistent, high quality pigment results. A thorough review of the basics and advancements in HPLC hardware is covered in the third edition of Introduction to Modern Liquid Chromatography (Snyder et al., 2010).
To make informed decisions regarding one's needs in HPLC hardware, one must understand the component design (and software control thereof) from the perspective of its contribution to combined uncertainty. Uncertainties in pigment results related to hardware characteristics are most often associated with injectors and detectors, and to a lesser extent, column oven design and pump capabilities. The uncertainties of the latter are often related to implementation of a method (e.g. baseline disturbance).