Control 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. 

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. 

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. 

Control Charts may be the most powerful tool to demonstrate and to assure quality in ehemieal measurements. Therefore they are widely used in all kinds of laboratories and it is hard to imagine quality management systems in laboratories without eontrol eharts. 

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. 

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. 

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. 

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 ... 

A control chart is a sequential plot of some characteristic that is important in quality assurance. The chart also shows the statistical limits of variation that are permissible for the characteristic being measured. 

From a historical perspective, with the introduction of univariate control charts by Walter A. Shewhart [267] of Bell Labs, the statistical quality control (SQC) has become an essential element of quality assurance efforts in the manufacturing industry. It was W.E. Deming who championed Shew-hart s use of statistical measures for quality monitoring and established a series of quality management principles that resulted in substantial business improvements both in Japan and the U.S. [52]. 

The usage of quality control charts in the field of quality assurance is based on the assumption that the determined results are distributed normally. Typical control charts used in a LIMS for routine analysis are, for example, the Shewhart charts for mean and blank value control, the retrieval frequency control chart, and the range and single-value control chart [19]. Quality regulation charts can be displayed graphically in the system or exported to spreadsheet programs. 

As Experiments 12 and 13, this experiment supports a classroom discussion concerning significant figures, the metric system, and the measurement of physical properties. In addition, it introduces the concept of control charting, a popular and important means of monitoring the quality of measurements and products in quality assurance laboratories. It, thus, utilizes a familiar consumer product to demonstrate an activity of the chemist s real world of work and to tie in the classroom discussion. 

If you toured a lab facility, you probably toured a wet lab, a quality control lab, or perhaps a process development lab, or maybe all of the above and wondered what these terms meant. You probably saw a control chart and wondered what it was. You may have sat in on a meeting to prepare for the upcoming quality assurance audit and wondered what an audit actually was or what GLP, MSDS, and SOP meant. You may have heard someone talk about certified reference materials and wondered what that was. You may have encountered a formal means of disposing of chemical waste and said, Wow Or you may have noticed an experiment or an instrument that wasn t working properly and, subsequently, observed chemists and technicians teaming together for troubleshooting. 

Is there any evidence that the process is unstable with respect to the amount of variability inherent in the process Can the process be considered reliable to meet potency requirements From the control chart for subgroup standard deviation, the process appears stable. The control chart for the subgroup average certainly detected the potency differences among the batches. Whether these process shifts in potency are of practical significance to manufacturing and quality assurance may depend on the chemical stability of the product and the ability of the process to remain at the current level of variability, namely, the standard deviation of 3%. 

Quality Assurance and Quality Control. The terms "quality assurance"(QA) and "quality control"(QC) need to be defined. They are often used interchangeably, but to the professional they refer to two different activities. Quality control refers to those actions taken in the laboratory in an attempt to keep the measurement system in control. Examples would be running reference standards, calibrating Instruments, keeping quality control charts, etc. Quality assurance refers to the system or program whereby management assures itself (and its clients) that the quality control measures are being applied, and that the results reported do, in fact, refer to the sample that was submitted or collected by the laboratory. 

In many laboratories quality control is limited to the occasional analysis of a particular control material and plots of the results on conventional control charts. Such repetitive activity carried out without understanding or thought may give false comfort and assurance rather than truly objective assessment of the variability in results. 

Good laboratory—what it is, how to apply it, p. 125 e How to vahdate a method selectivity, hnearity, accuracy, precision, sensitivity, range, LOD, LOQ, ruggedness, p. 126 Quality assurance control charts, documenting, proficiency testing, p. 133 Electronic records, p. 135... 

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