Precision control chart

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. 

Constructing a Precision Control Chart The most common measure of precision used in constructing a precision control chart is the range, R, between the largest and smallest results for a set of j replicate analyses on a sample. 

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

Example of the use of subrange precision control charts for samples that span a range of analyte concentrations. The precision control charts are used for... 

The same rules apply to precision control charts with the exception that there are no lower warning and lower control limits. 

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. 

Construct a precision control chart for these data, and evaluate the state of statistical control. 

There are two types of control charts accuracy chart and precision control chart. Accuracy control charts are prepared from the percent spike recoveries data obtained from multiple routine analysis. Precision control charts may be prepared from the relative percent difference (RPD) of analyte concentrations in the samples and their duplicate analytical data. Alternatively, RPDs are calculated for percent recoveries of the analytes in the matrix spike and matrix spike duplicate in each batch and twenty (or any reasonable number of data points) are plotted against the frequency or number of analysis. If the samples are clean and the analytes are not found, the aliquots of samples must be spiked with the standard solutions of the analytes and the RPD should be determined for the matrix spike recoveries. Ongoing data quality thus can be checked against the background information of the control chart. Sudden onset of any major problem in the analysis can readily be determined from the substantial deviation of the data from the average. 

Precision control charts may, alternatively, be constructed by plotting the RPDs of duplicate analysis measured in each analytical batch against frequency of analysis (or number of days). The mean and the standard deviation of an appropriate number (e g., 20) of RPDs are determined. The upper and lower warning limits and the uppper and lower control limits are defined at 2 and 3.v, respectively. Such a control chart, however, would measure only the quality of precision in the analysis. This may be done as an additional precision check in conjunction with the spike recovery control chart. 

Control charts often have a center line and two control lines with two pairs of limits a warning line at m 2s and an action line at m 3s. Statistics predict that 95.45% and 99.7% of the data will fall within the areas enclosed by the 2s and 3s limits. The center line is either the mean or the true value. In the ideal case, where unbiased methods are being used, the center line would be the true value. This would apply, for example, to precision control charts for standard solutions. 

Is precision of production measuring equipment routinely monitored (via control charts or other similar techniques) ... 

One can apply a similar approach to samples drawn from a process over time to determine whether a process is in control (stable) or out of control (unstable). For both kinds of control chart, it may be desirable to obtain estimates of the mean and standard deviation over a range of concentrations. The precision of an HPLC method is frequently lower at concentrations much higher or lower than the midrange of measurement. The act of drawing the control chart often helps to identify variability in the method and, given that variability in the method is less than that of the process, the control chart can help to identify variability in the process. Trends can be observed as sequences of points above or below the mean, as a non-zero slope of the least squares fit of the mean vs. batch number, or by means of autocorrelation.106... 

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. 

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). 

Real samples can be used for Range and Difference Charts. They deliver a rapid repeatabihty precision control, but no traeness check. If different matrices are analysed it might be useful to separate the results from different matrices in different charts. 

Precision data can be documented in bar charts or control charts such as Shewhart control charts (see the discussion of internal quality control in Section 8.2.3.5). Bar charts plot %RSD values with their corresponding confidence interval. Control charts plot the individual measurement results and the means of sets of measurements with their confidence level (or with horizontal lines representing limits, see below) as a function of the measurement number and the run number, respectively [15,55,56, 58,72, 85]. 

Minimum of three repeats per concentration level Calculate repeatability precision SDr, RSDr, r = 2.8 x SDr, C, Cl Calculate intermediate precision SD t, RSDbt, r = 2.8 x SD nt, C, Cl Calculate reproducibility precision SDR, RSDr, r = 2.8 x SDr, C, Cl Document in bar chart or control chart... 

Two aspects are important for IQC (1) the analysis of control materials such as reference materials or spiked samples to monitor trueness and (2) replication of analysis to monitor precision. Of high value in IQC are also blank samples and blind samples. Both IQC aspects form a part of statistical control, a tool for monitoring the accuracy of an analytical system. In a control chart, such as a Shewhart control chart, measured values of repeated analyses of a reference material are plotted against the run number. Based on the data in a control chart, a method is defined either as an analytical system under control or as an analytical system out of control. This interpretation is possible by drawing horizontal lines on the chart x(mean value), x + s (SD) and x - s, x + 2s (upper warning limit) and x-2s (lower warning limit), and x + 3s (upper action or control limit) and x- 3s (lower action or control limit). An analytical system is under control if no more than 5% of the measured values exceed the warning limits [2,6, 85]. 

The analysis of quality control (QC) samples with the construction of quality control charts has been suggested as another way of performing PQ. Control samples with known amounts are interdispersed among actual samples at intervals determined by the total number of samples, the stability of the system, and the precision specified. The advantage of this procedure is that the system performance is measured more or less continuously under conditions that are very close to the actual application. 

For quality assessment of an analytical process, a control chart could show the relative deviation of measured values of calibration check samples or quality control samples from their known values. Another control chart could display the precision of replicate analyses of unknowns or standards as a function of time. 

Specifications How good do the numbers have to be Write specifications Pick methods to meet specifications Consider sampling, precision, accuracy, selectivity, sensitivity, detection limit, robustness, rate of false results Employ blanks, fortification, calibration checks, quality control samples, and control charts to monitor performance Write and follow standard operating procedures... 

Frequently, however, the lack of specificity in an analytical technique can be compensated for with sophisticated data processing, as described in the chemometrics chapter of this text (Chapter 8). Quinn and associates provide a demonstration of this approach, using fiber-optic UV-vis spectroscopy in combination with chemometrics to provide realtime determination of reactant and product concentrations.23 Automatic window factor analysis was used to evaluate the spectra. This technique was able to detect evidence of a reactive intermediate that was not discernable by off-line HPLC, and control charting of residuals was shown to be diagnostic of process upsets. Similarly, fiber-optic NIR was demonstrated by some of the same authors to predict reaction endpoint with suitable precision using a single PLS factor.24... 

Step 9 Method maintenance Incorporate the new method or analyzer into the existing method maintenance systems for the site, to ensure that the method as practiced continues to meet the technical requirements for as long as it is in use. This is done by the receiver. Method maintenance systems may include check sample control-charting, intra-and/or inter-lab uniformity testing, on-site auditing, instrument preventive maintenance (PM) scheduling, control-charting the method precision and/or accuracy, etc. 

When conducting an inspection, several target areas must be evaluated. Control limits or "charts" are helpful and should be established by plotting the defined limits of acceptable quality control. These charts are important tools for assessing laboratory precision, accuracy, and reproducibility. They can be based on a curve established from the high, mid, and low concentrations of a standard analyte. Either the mid level or the average of the three concentrations then becomes the mid-line for the control chart. Acceptable levels of fluctuation for routine mid-level standards,... 

The laboratory quality control program has several components documentation of standard operating procedures for all analytical methods, periodic determination of method detection levels for the analytes, preparation of standard calibration curves and daily check of calibration standards, analysis of reagent blank, instrument performance check, determination of precision and accuracy of analysis, and preparation of control charts. Determination of precision and accuracy of analysis and method detection limits are described under separate subheadings in the following sections. The other components of the quality control plan are briefly discussed below. 

Thus, control charts measure both the precision and accuracy of the test method. A control chart is prepared by spiking a known amount of the analyte of interest into 4 to 6 portions of reagent grade water. The recoveries are measured and the average recovery and standard deviation are calculated. In routine analysis, one sample in a batch is spiked with a known concentration of a standard and the percent spike recovery is measured. An average of 10 to 20 such recoveries are calculated and the standard deviation about this mean value is determined. The spike recoveries are plotted against the frequency of analysis or the number of days. A typical control chart is shown below in Figure 1.2.2. 

Maintenance of control charts to determine the acceptance criteria for accuracy and precision... 

Laboratory control sample/laboratory control sample duplicate Organic and inorganic compounds recovery as determined by laboratory control charts typical precision is 30% ICP-AES 75 to 125% recovery 20% precision AA 80 to 120% recovery 20% precision... 

If project-specific matrix spike data are available, the chemist evaluates them to establish the effects of matrix interferences on the accuracy and precision of project sample analysis. Similar to LCS/LCSD recoveries, MS/MSD recoveries are monitored at the laboratory as control charts these recoveries, however, are not used as acceptance criteria for qualifying the data for the whole batch. The RPD between the MS and MSD results is an additional measure of analytical precision that may be used when the LCS/LCSD precision has failed or is not available. 

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