Quality assurance statistical evaluation

This chapter deals with handling the data generated by analytical methods. The first section describes the key statistical parameters used to summarize and describe data sets. These parameters are important, as they are essential for many of the quality assurance activities described in this book. It is impossible to carry out effective method validation, evaluate measurement uncertainty, construct and interpret control charts or evaluate the data from proficiency testing schemes without some knowledge of basic statistics. This chapter also describes the use of control charts in monitoring the performance of measurements over a period of time. Finally, the concept of measurement uncertainty is introduced. The importance of evaluating uncertainty is explained and a systematic approach to evaluating uncertainty is described. 

This chapter has considered two key aspects related to quality assurance - the use of control charts and the evaluation of measurement uncertainty. These activities, along with method validation, require some knowledge of basic statistics. The chapter therefore started with an introduction to the most important statistical terms. 

While the provision of suitably validated analytical methods is a necessary requirement for ensuring compliance with MRLs, the method alone is not sufficient to ensure creditable analytical measurements. In addition to selecting suitable methods, the analyst must demonstrate that the method is operating under statistical control in the laboratory and is performed to meet performance specifications as required by the analytical problem. This means that all methods should be applied in an environment with appropriate quality assurance procedures and performance evaluation checks. 

Quality assurance functions primarily to monitor the fact that the quality function is being performed. Its role in PV is readily associated with its main functions. For example, it performs the tests that demonstrate the product s content uniformity. It may also perform the statistical evaluation of the test results to show that the process is reproducible. Quality assurance initiates the action to dispose of nonconforming product. It implements the inspection criteria and sets the specifications for product approval or rejection. It analyzes the product complaints to learn how effective its test program has been in preventing reject-able product from reaching the marketplace. 

When the analytical laboratory is not responsible for sampling, the quality management system often does not even take these weak links in the analytical process into account. Furthermore, if sample preparation (extraction, cleanup, etc.) has not been carried out carefully, even the most advanced, quality-controlled analytical instruments and sophisticated computer techniques cannot prevent the results of the analysis from being called into question. Finally, unless the interpretation and evaluation of results are underpinned by solid statistical data, the significance of these results is unclear, which in turn greatly undermines their merit. We therefore believe that quality control and quality assurance should involve all the steps of chemical analysis as an integral process, of which the validation of the analytical methods is merely one step, albeit an important one. In laboratory practice, quality criteria should address the rationality of the sampling plan, validation of methods, instruments and laboratory procedures, the reliability of identifications, the accuracy and precision of measured concentrations, and the comparability of laboratory results with relevant information produced earlier or elsewhere. 

Quality control procedures are generally established to provide checks on the data that have been collected to evaluate whether in fact the quality assurance procedures were followed and whether the data meet agreed-upon norms. Otherwise, it is difficult for the user to judge the integrity of a data set per se, because there may be few ways to tell that procedures were not followed or values properly recorded. Quality control measures can be linked to the quality assurance procedures. In the example given above for use of field blanks, spikes and duplicate samples, laboratories must provide evidence that their analysis of these samples meets acceptable statistical guidelines for accuracy and precision. Quality control can also simply involve careful analysis of a data set to determine whether it is internally consistent. 

Assessment of the sampling pattern (i.e., random, systematic, or judgmental) Statistical evaluation of distribution parameters of the elements of interest Implementation of quality measures for the assurance of data quality Incorporation of a special depth function for element distribution, particularly for urban soils... 

The standard operating procedure (SOP) manual contains the procedures validated by the laboratory it is a complete set of instructions for pre-analytical, analytical and post-analytical methodology and also procedures for quality assurance/control, chain-of-custody and security. Each step in the handling of the specimen should be evaluated, optimized where possible and documented in the SOP. Important steps in the analytical process include collection, transport and accessioning of the specimen, sample preparation, isolation and detection of the analytes, production of the report and disposal of the specimen. This chapter focuses on the quality assurance and control issues for analytical method development and validation as well as statistical representation of the data. 

Before participating in interlaboratory studies the laboratory must have set-up all adequate internal quality assurance and quality control systems [7]. This also means that all basic investigations have been performed for possible mistakes, that these have been noticed and corrected. In other words the laboratory has validated the method to be applied. For a laboratory performance study, this also implies that in the laboratory the method is under statistical control for a given type of matrix, that this control is monitored and that results are evaluated. 

The final chapter (Chapter 10) focuses on web and sheet forming processes. It demonstrates how the statistical techniques can be applied to evaluate process and control performance for quality assurance and to acquire fundamental insight towards the operation of such processes. 

Keith, L.H. et al. (1983). Principles of environmental analysis, Anal. Chem. 55, 2210 Klich, H. and Walker, R, (1993). COMAR - The international database for certified reference materials, Fresenius J. Anal. Chem. 24S, 104 Kurfurst, U., Pauwels, J., Grobecker, K.-H., Stoeppler, M., Muntau, H. (1993). Micro-heterogeneity of trace elements in reference materials - determination and statistical evaluation, Fresenius J. Anal. Chem. 345.112 Parr, R.M. (1984). Quality assurance of trace element analysis, in Health Effects and Interactions of Essential and Toxic Elements (Proc. Symp. Lund, Sweden, June 1983) Nutrition Research, special supplement... 

It should be stressed that laboratories should have installed all necessary quality assurance and quality control systems prior to participation in an interlaboratory study, ie. the method(s) used should be validated and performed under statistical control for the particular matrix concerned by the study. In other words, interlaboratory studies should not serve the purpose of evaluating and7or optimizing a method in the course of its development. 

Statistical Analysis of Results and Performance Characteristics. A common problem with most external quality assurance systems has been the determination of the true value for a control sample. Because the true value is an ideal concept, the data are evaluated against the estimated true value, which may be established from (1) the amount of analyte spiked to the test samples (2) a group of reference laboratories using definitive or reference methods (3) the consensus mean, which is the mean of the results obtained by the participants after exclusion of outlier. It is the responsibility of the coordinators to ensure and demonstrate that the reference values are reliable [6]. 

If the subcontractor supplies statistical data from the manufacturing process that indicates that quality is being controlled, then an analysis of this data based on assurances you have obtained through site evaluation can provide sufficient confidence in part quality to permit release into the organization. 

The quality of measurements in the radioanalytical chemistry laboratory is evaluated by QC measures for both instrumental measurements and sample analysis, as outlined in Section 11.2.8. QC ensures that the measurement process remains in a state of statistical control so that uncertainty estimates are valid, and ideally should help to keep measurement uncertainties small. QC may also provide assurances that the quality of measurements does not decrease when there are changes in personnel, instrumentation, or methods. 

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