Charts, quality assurance

The focus of this chapter is on the two principal components of a quality assurance program quality control and quality assessment. In addition, considerable attention is given to the use of control charts for routinely monitoring the quality of analytical data. 

Control charts were originally developed in the 1920s as a quality assurance tool for the control of manufactured products.Two types of control charts are commonly used in quality assurance a property control chart in which results for single measurements, or the means for several replicate measurements, are plotted sequentially and a precision control chart in which ranges or standard deviations are plotted sequentially. In either case, the control chart consists of a line representing the mean value for the measured property or the precision, and two or more boundary lines whose positions are determined by the precision of the measurement process. The position of the data points about the boundary lines determines whether the system is in statistical control. 

Using Control Charts for Quality Assurance Control charts play an important role in a performance-based program of quality assurance because they provide an easily interpreted picture of the statistical state of an analytical system. Quality assessment samples such as blanks, standards, and spike recoveries can be monitored with property control charts. A precision control chart can be used to monitor duplicate samples. 

Once a control chart is in use, new quality assessment data should be added at a rate sufficient to ensure that the system remains in statistical control. As with prescriptive approaches to quality assurance, when a quality assessment sample is found to be out of statistical control, all samples analyzed since the last successful verification of statistical control must be reanalyzed. The advantage of a performance-based approach to quality assurance is that a laboratory may use its experience, guided by control charts, to determine the frequency for collecting quality assessment samples. When the system is stable, quality assessment samples can be acquired less frequently. 

There should be an organization chart for the company showing individual functions and to whom they are responsible. Job descriptions should clearly indicate responsibilities for quality. The assessor should read the job description of the quality assurance/improvement coordinator as well as of a few managers and supervisors to establish the answer to the question. 

Fig. 14-9. Wet/dry precipitation collector and flow chart for analysis of samples. (DI HjO distilled water). Source "NADP Quality Assurance Report," Central Analytical Laboratory, Illinois Institute of Nafural Resources, Champaign, 111., March 1980.

The organization chart shown in Figure 6.3-2 shows the Quality Assurance Manager high in the organization, to achieve independence of QA as required by 10CFR50 Appendix B. 

Organisation chart showing the arrangements for quality assurance, including production and quality control. 

The program must require the vendors to measure a number of reference samples and/or duplicates submitted in a planned sequence. It should require prompt measurement and reporting of these data and should maintain the results in a control chart format. Prompt feedback and follow-up of any apparent data discrepancies and reconciliation of the results with control charts maintained by the vendors are required to minimize the length of uncertain performance. The quality assurance plan should include random sampling of the vendors data for their validity and conformance with quality assurance requirements. If quality assurance is properly practiced at all levels, an inspection of 5 percent of the total data output should be adequate. 

Validation of extraction procedures is frequently lacking. A good assessment of quality assurance implies that the extraction recoveries are verified, e.g. by spiking of standard addition. A major drawback is that the spike is not always bound the same way as the compounds of interest. For the development of good extraction methods, materials with an incurred analyte (i.e. bound to the matrix in the same way as the unknown), which is preferably labelled (radioactive labelling would allow verification of the recovery), would be necessary. Such materials not being available, the extraction method used should be validated by other independent methods, e.g. by verification against known samples and by use of a recovery SPC chart. A mere comparison of extraction methods is no validation. 

The validation process begun in Phase I is extended during Phase II. In this phase, selectivity is investigated using various batches of drugs, available impurities, excipients, and samples from stability studies. Accuracy should be determined using at least three levels of concentration, and the intermediate precision and the quantitation limit should be tested. For quality assurance evaluation of the analysis results, control charts can be used, such as the Shewart-charts, the R-charts, or the Cusum-charts. In this phase, the analytical method is refined for routine use. 

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. 

The previous chapters of this book have discussed the many activities which laboratories undertake to help ensure the quality of the analytical results that are produced. There are many aspects of quality assurance and quality control that analysts carry out on a day-to-day basis to help them produce reliable results. Control charts are used to monitor method performance and identify when problems have arisen, and Certified Reference Materials are used to evaluate any bias in the results produced. These activities are sometimes referred to as internal quality control (IQC). In addition to all of these activities, it is extremely useful for laboratories to obtain an independent check of their performance and to be able to compare their performance with that of other laboratories carrying out similar types of analyses. This is achieved by taking part in interlaboratory studies. There are two main types of interlaboratory studies, namely proficiency testing (PT) schemes and collaborative studies (also known as collaborative trials). 

Analytical laboratories, especially quality assurance laboratories, will often maintain graphical records of statistical control so that scientists and technicians can note the history of the device, procedure, process, or method at a glance. The graphical record is called a control chart and is maintained on a regular basis, such as daily. It is a graph of the numerical value on the y-axis vs. the date on the x-axis. The chart is characterized by five horizontal lines designating the five numerical values that are important for statistical control. One is the value that is 3 standard deviations from the most desirable value on the positive side. Another is the value that is 3 standard deviations from the most desirable value on the negative side. These represent those values that are expected to occur only less than 0.3% of the time. These two numerical values are called the action limits because one point outside these limits is cause for action to be taken. 

Sampling is just the beginning of the analytical process. On the way from sampling to the test report a lot of different requirements for high quality measurements have to be considered. There are external quality assurance requirements on the quality management system (e.g. accreditation, certification, GLP), internal quality assurance tools (e.g. method validation, the use of certified reference material, control charts) and external quality assurance measures (e.g. interlaboratoiy tests). 

Traditionally, the education that chemists and chemistry laboratory technicians receive in colleges and universities does not prepare them adequately for some important aspects of the real world of work in their chosen field. Today s industrial laboratory analyst is deeply involved with such job issues as quality control, quality assurance, ISO 9000, standard operating procedures, calibration, standard reference materials, statistical control, control charts, proficiency testing, validation, system suitability, chain of custody, good laboratory practices, protocol, and audits. Yet, most of these terms are foreign to the college graduate and the new employee. 

The equipment sterilization charts are included in the batch production record. The equipment sterilization charts for stability batch are produced in support of this submission. These sterilization charts shall be reviewed by Quality Assurance for adherence to the sterilization cycles specified in the batch records. 

Analytical method validation forms the first level of QA in the laboratory. Analytical quality assurance (AQA) is the complete set of measures a laboratory must undertake to ensure that it is able to achieve high-quality data continuously. Besides the use of validation and/or standardized methods, these measures are effective IQC procedures (use of reference materials, control charts, etc.), with participation in proficiency testing schemes and accreditation to an international standard, normally ISO/IEC 17025 [4]. Method validation and the different aspects of QA form the subject of Section 8.2.3. 

One way to use a CuSum chart to understand such changes is to plot a regular analysis of a reference material (which does not have to be a certified reference material, rather a material which is the same and stable over time) and note any obvious changes to the system on the chart. The quality assurance manager can then evaluate any effects due to the changes and take... 

The advantage of the following methods of graphical representation is the clear and simple presentation of the essential facts. Simple charts, like bar charts, x-y scatter diagrams or pie diagrams, which are also available in 3D-form are also suited to visual representation of data. They are not described because this section is devoted to treatment of multivariate data. Graphs for control charts, particulary for quality assurance and control, can be found in [FUNK et al., 1992 AQS, 1991]. 

Quality assurance (QA) measurements also are performed with a set of QA gamma-ray sources to confirm that the radiation detection instrument is functioning normally. The measurements are performed at regular intervals and the results are plotted to show the mean value and random deviations by 1 and 2 standard deviations (cr and 2cr). The factors that are considered include the count rate at characteristic control source peaks, the resolution of these peaks, and the background radiation shown by the detector. Any significant deviations beyond the 2-sigma values on the control charts require a repeated measurement and - if confirmed - corrective actions before further measurements are performed. 

A Gantt chart or similar tool for managing tasks and milestones against a time line Measures and metrics to track progress Identification of quality assurance reviews... 

Independent monitoring systems used to implement the key qnality assurance controls must be validated (whether they are complex Supervisory Control and Data Acqnisition (SCADA) systems, or simple chart recorders). For an independent system to be accepted as a validated alternative in the monitoring of critical parameters, the system must be able to manage key quality assurance functions. Such functions inclnde, but are not necessarily limited to ... 

Organization Is there a formal quality assurance department (ask for an organization chart) ... 

Figure 19-11 Operating specifications chart for an analytical quality requirement of 10% (T o) and 90% analytical quality assurance for systematic error. Allowable inaccuracy is plotted on the y-axis versus allowable imprecision on the x-axis.

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