Statistical quality control charts

Performance test results should be plotted on charts for trend analysis. Statistical quality control charting methods are used to detect statistically significant changes in instrument performance. Action limits can be set for test charts based on historical data so that appropriate repairs can be made when necessary. Examples of unacceptable instrument performance may be valuable in setting action limits for future performance tests. 

Method validation provides information concerning the method s performance capabilities and limitations, when applied under routine circumstances and when it is within statistical control, and can be used to set the QC limits. The warning and action limits are commonly set at twice and three times the within-laboratory reproducibility, respectively. When the method is used on a regular basis, periodic measurement of QC samples and the plotting of these data on QC charts is required to ensure that the method is still within statistical control. The frequency of QC checks should not normally be set at less than 5% of the sample throughput. When the method is new, it may be set much higher. Quality control charts are discussed in Chapter 6. 

Statistical Control for a New Method To implement a new method, a laboratory must produce a preliminary track record of its success so that quality control charts can be established and then maintained. Aside from acquiring the space, supplies, equipment, instrumentation, and manpower required, the method must be tested, modified, tested again, etc., until it is ready to go "online." Gillis and Callio (listed in Bibliography) recommend the following sequence for preparing an instrumental method for routine use. 

The traditional approach to quality control is to generate charts of various kinds to monitor the performance of a production unit. At a superficial level, statistical process control (SPC) and statistical quality control (SQC) [9] are terms used interchangeably to describe traditional... 

Retrospective validation involves using the accumulated in-process production and final product testing and control (numerical) data to establish that the product and its manufacturing process are in a state of control. Valid in-process results should be consistent with the drug products final specifications and should be derived from previous acceptable process average and process variability estimates, where possible, and determined by the application of suitable statistical procedures, that is, quality control charting, where appropriate. The retrospective validation option is selected when manufacturing processes for established products are considered to be stable and when, on the basis of economic considerations and resource limitations, prospective qualification and validation experimentation cannot be justified. 

Concurrent validation is conducted under a protocol during the course of normal production. The first three production-scale batches must be monitored as comprehensively as possible. The evaluation of the results is used in establishing the acceptance criteria and specifications of subsequent in-process control and final product testing. Some form of concurrent validation, using statistical process control techniques (quality control charting), may be used throughout the product manufacturing life cycle. 

Statistical process control (SPC), also called statistical quality control and process validation (PV), represents two sides of the same coin. SPC comprises the various mathematical tools (histogram, scatter diagram run chart, and control chart) used to monitor a manufacturing process and to keep it within in-process and final product specification limits. Lord Kelvin once said, When you can measure what you are speaking about and express it in numbers, then you know something about it. Such a thought provides the necessary link between the two concepts. Thus, SPC represents the tools to be used, while PV represents the procedural environment in which those tools are used. 

The Reliability of Measurements. The Analysis of Data. The Application of Statistical Tests. Limits of Detection. Quality Control Charts. Standardization of Analytical Methods. 

In industrial plants, large numbers of process variables must be maintained within specified limits in order for the plant to operate properly. Excursions of key variables beyond these limits can have significant consequences for plant safety, the environment, product quality and plant profitability. Statistical process control (SPC), also called statistical quality control (SQC), involves the application of statistical techniques to determine whether a process is operating normally or abnormally. Thus, SPC is a process monitoring technique that relies on quality control charts to monitor measured variables, especially product quality. 

The use of Shewart control charts is admirably documented in a number of statistical quality control books including those by Vardeman and Jobe,14 Wadsworth et al.,9 Duncan,11 Burr,10 Grant and Leavenworth,12 and Ott et al.13 Our purpose here is not to... 

The reliability of measurements. The arrptysis of data. The application of statistical tests. Limits of detection. Quality control charts. Standardization of analytical methods. Chcmometrics. 

A control chart (quality control chart) is a graphical record of the results of the analysis of a quality control sample using a particular method. Monitoring these results over a period of time is one of the most useful ways of determining whether or not a method is in statistical control, i.e. it is performing in a consistent manner. It helps to indicate the reliability of the results. There are many forms of control chart,one of the most commonly used is the Shewhart Chart (Figure 4). 

Indebtedness is expressed to Prof. R. A. Fisher and Dr. Frank A. Yates for permission to reprint Tables III-VI from their book Statistical Tables for Biological, Agricultural, and Medical Research (Oliver Boyd, Edinburgh and London), and to the British Standards Institution for permission to reprint certain factors for Quality Control Charts from B.S, 600R, Quality Control Charts. ... 

Statistical Quality Control (SQC) or Statistical Process Control (SPC) is an effective method of monitoring a process through control charts that enable the use of objective criteria for distinguishing background variation from events of significance based on statistical techniques. 

Quantitative methods are assays that result in meaningful numeric measurements for a characteristic of a product. Quantitative methods are used in assessing whether final product meets specifications. They are also used to measure product quality (or quantity) in various stages of manufactuiing and the results are often used in quality control charts. Validation is an objective process used to determine whether a quantitative method is performing as expected and is appropriate for its intended use. This chapter provides the motivation behind validation, some terms and definitions used in validation, a consolidated statistically sound approach to validation, along with appropriate statistical analysis, and reporting of validation results. A hypothetical but realistic example is presented and is used to illustrate the validation process. 

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]. 

In his chapter on Industrial Mixing in Small Particle Statistics (H2), Herdan covers ways of measuring degree of mixing, including the quality control chart. He also gives a discussion and explanation of the paper by Brothman, Wollan, and Feldman (B5). 

A quality control chart is a time plot of a measured quantity that is assumed to be constant (with a Gaussian distribution) for the purpose of ascertaining that the measurement remains within a statistically acceptable range. It may be a day-to-day plot of the measured value of a standard that is run intermittently with samples. The control chart consists of a central line representing the known or assumed value of the control and either one or two pairs of limit lines, the inner and outer control limits. Usually the standard deviation of the procedure is known (a good estimate of cr), and this is used to establish the control limits. 

The first data evaluation should regard the quality of data. Well-known methods are quality control charts (WHO, 1981). Another method is using outlier statistics. The first... 

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