Control charts Analysis

The control chart analysis indicated that the process was in a state of statistical control because none of the averages and ranges was outside its respective control limits. The data distribution (histogram) was deemed reasonably close to a normal distribution (the bellshaped curve superimposed on the histogram) because the cumulative data distribution was close to a straight line on the normal probability plot. 

Manual on Presentation of Data and Control Chart Analysis, 7th Ed. ASTM International, 2002. 

Assuming symmetrical limits are placed around the target hardness of 8 kp, the limits for individual target hardness should be 4-12 kp. We can use the upper and lower control limits from the control chart analysis to establish the average hardness range 6.5-93 kp. The normal probability plot for tablet hardness, Fig. 16, does not show any significant departure from normality, so the proposed limits for individual tablet hardness are consistent with our assumptions. 

A control chart is a statistical device used for the study and control of safety performance in the workplace. The basis of control chart analysis is the knowledge of chance variations in the data (Duncan 1974,375). If a series of safety measurements are plotted and the obtained measurements are truly random, the distribution would approximate the normal bell-shaped curve. Plotting the data on a control chart, one would obtain measurements over time that fall into the ranges depicted in Figure 4.1, with more measures occurring at or near the average more frequently and readings at the extreme ends of the possible measurements infrequently. 

ASTM MNL 7 Manual on the PresauatUm of Data Control Chart Analysis. 6tb ed., ASTM, 1990. 

The control chart is set up to answer the question of whether the data are in statistical control, that is, whether the data may be retarded as random samples from a single population of data. Because of this feature of testing for randomness, the control chart may be useful in searching out systematic sources of error in laboratory research data as well as in evaluating plant-production or control-analysis data. ... 

The principal tool for performance-based quality assessment is the control chart. In a control chart the results from the analysis of quality assessment samples are plotted in the order in which they are collected, providing a continuous record of the statistical state of the analytical system. Quality assessment data collected over time can be summarized by a mean value and a standard deviation. The fundamental assumption behind the use of a control chart is that quality assessment data will show only random variations around the mean value when the analytical system is in statistical control. When an analytical system moves out of statistical control, the quality assessment data is influenced by additional sources of error, increasing the standard deviation or changing the mean value. 

Construct a precision control chart using the following 20 ranges, each determined from a duplicate analysis of a 10-ppm calibration standard... 

Another important quality assessment tool, which provides an ongoing evaluation of an analysis, is a control chart. A control chart plots a property, such as a spike recovery, as a function of time. Results exceeding warning and control limits, or unusual patterns of data indicate that an analysis is no longer under statistical control. 

Laquer, F. C. Quality Control Charts in the Quantitative Analysis Laboratory Using Conductance Measurement, ... 

This experiment demonstrates how control charts and an analysis of variance can be used to evaluate the quality of results in a quantitative analysis for chlorophyll a and b in plant material. 

Statistical control of an analysis or instmment is best demonstrated by SQC of a standard sample analysis. The preferred approach to demonstrate statistical control is to use a reference sample of the subject material that has been carefully analyzed or, alternatively, to use a purchased reference standard. Either material must be stored so that it remains unchanged, eg, sealed in ampuls or septum capped bottles. Periodically a sample can then be reanalyzed by the technique used for routine analysis. These results are plotted in a control chart. Any change in the stabihty of the test in question results in a lack of... 

Fig. 2. An SPC control chart of the purity analysis of a reference standard where (—) represents the average value and UCL and LCL are the upper and...

Q7 PROCESS CHART. PARETO ANALYSIS, CAUSE AND EFFECT DIAGRAM, HISTOGRAM, CORRELATION DIAGRAMS, PROCESS CONTROL CHARTS, CHECK SHEETS... 

When data of a single type accumulate, new forms of statistical analysis become possible. In the following, conventional control and Cusum charts will be presented. In the authors opinion, newer developments in the form of tight (multiple) specifications and the proliferation of PCs have increased the value of control charts especially in the case of on-line in-process controlling, monitors depicting several stacked charts allow floor supervi-... 

DEGRAD STABILjcIs Section 1.8.4 The analysis of stability reports often suffers from the fact that the data for each batch of product is scrutinized in isolation, which then results in a see-no-evil attitude if the numerical values are within specifications. The analyst is in a good position to first compare all results gained under one calibration (usually a day s worth of work) irrespective of the products/projects affected, and then also check the performance of the calibration samples against experience, see control charts, Section 1.8.4. In this way, any analytical bias of the day will stand out. For this purpose a change in format from a Time-on-Stability to a Calendar Time depiction is of help. 

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. 

The analysis of quality control samples is an important activity for laboratories and to make the most of the data, control charts should be used. This chapter has discussed a number of common types of control chart and described how they are set up and interpreted. 

Control charts Routine charting of data obtained from the analysis of quality control materials to check that the results lie within predetermined limits. 

There are numerous approaches to the problem of capturing all the information in a set of multi endpoint data. When the data are continuous in nature, approaches such as the analog plot can be used (Chemoff, 1973 Chambers et al., 1983 Schmid, 1983). A form of control chart also can be derived for such uses when detecting effect rather than exploring relationships between variables is the goal. When the data are discontinuous, other forms of analysis must be used. Just as the control chart can be adapted to analyzing attribute data, an analog plot can be adapted. Other methods are also available. 

The results of the analysis of a control are often plotted on a control chart (Chapter 1) in order to visualize the history of the analysis in the laboratory so that a date and time can be identified as to when the problem was first detected. Thus, the problem can be traced to a bad reagent, instrument, or other component of the procedure if such a component was first put into use the day the problem was first detected. Your instructor may want you to use controls in various experiments in this text. 

Several method performance indicators are tracked, monitored, and recorded, including the date of analysis, identification of equipment, identification of the analyst, number and type of samples analyzed, the system precision, the critical resolution or tailing factor, the recovery at the reporting threshold level, the recovery of a second reference weighing, the recovery for the control references (repeated reference injections for evaluation of system drift), the separation quality, blank issues, out of spec issues, carry over issues, and other nonconformances. The quantitative indicators are additionally visualized by plotting on control charts (Figure 23). 

STL s quality-control programme includes the recovery of known additions of analyte, analysis of externally supplied standards, calibration, analysis of duplicates and control charting. Each analyte is monitored by analysing at least one AQC standard for every 20 samples. AQC results are plotted on control charts and action is taken if a point Hes outside +3 standard deviations (SD) or if two consecutive points He outside +2 SDs. 

Internal QC inclndes the nse of blanks, chemical calibrants, spiked samples, blind samples, replicate analysis, and QC samples. QC samples shotrld be homogeneous and stable. They shotrld be available in sufficient quantities. The use of control charts is recommended (see chapter 13). 

We are starting with the case where we have a control sample that covers the whole analytical process inclnding all sample preparation steps. The matrix of the control sample is similar to that of the routine samples. Then the standard deviation of the analysis of this sample (under between-batch conditions) can be used directly as an estimate for the reproducibility within the laboratory. The standard deviation can be taken directly from a control chart for this control sample (see chapterl3). In the table two examples are shown for different concentration levels. 

We have seen two different approaches to estimate the measurement uncertainty. One was using data from control charts, CRM analysis, PT results and/or recoveiy tests and sometimes maybe also experience of the analyst, the other was just using the reproducibility standard deviations from interlaboratory tests. In most cases the second method delivers higher estimates. 

The Cusum Control Chart is a very special chart from which a lot of information can be drawn. Cusum is the abbreviation for cumulative sum and means the sum of all differences from the target value. Every day the difference of the control analysis from the target value is added to the sum of all the previous ddferences. 

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