Blank value chart

The Blank Value Chart is also very important. This is a special form of a Shewhart chart where the direct measurements (e.g. in Volts) from the analyses of blank samples are used. From this chart information can be received about contamination of reagents e g. from the enviromnent and the state of the analytical system. 

Different Control Charts Blank Value Chart... 

X-/mean-charts 3 Blank value chart 3 Range chart with absolute ranges 3 Range chart with relative ranges 3 Recovery rate chart 3 Differences chart... 

Mean value charts (charts on x, median charts, blank charts)... 

Proportional systematic errors are detected with a Recovery Rate Chart, but not constant systematic errors (e.g. too high blank values). Additionally the spiked analyte might be bound to the matrix differently. This possibly results in a higher recovery rate for the spike than for the originally bound analyte. 

We can convert all types of classical control chart (X-chart, blank value, recovery, range control chart etc.) into target control charts. 

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. 

Operation of a recovery control chart, when systematic errors from matrix interferences are expected Measurement of two blank solutions at the beginning and at the end of a batch in order to identify contamination of reagents, of the measurement system and instrumental faults and documentation of the blank values on a blank control chart... 

The reagent and system blanks must be recorded at specified intervals. These intervals may be related to the number of samples processed or may be fixed periods of time. Changes in the analytical system, including replacement of parts of the detector and new sets of reagents, should always be followed by blank controls. The blank values should be monitored using control charts. 

Here the concentration range of the analyte in the ran is relatively small, so a common value of standard deviation can be assumed. Insert a control material at least once per ran. Plot either the individual values obtained, or the mean value, on an appropriate control chart. Analyse in duplicate at least half of the test materials, selected at random. Insert at least one blank determination. 

Longer (e g. n > 20) frequent runs of similar materials Again a common level of standard deviation is assumed. Insert the control material at an approximate frequency of one per ten test materials. If the run size is likely to vary from run to run it is easier to standardise on a fixed number of insertions per run and plot the mean value on a control chart of means. Otherwise plot individual values. Analyse in duplicate a minimum of five test materials selected at random. Insert one blank determination per ten test materials. 

Here we cannot assume that a single value of standard deviation is applicable. Insert control materials in total numbers approximately as recommended above. However, there should be at least two levels of analyte represented, one close to the median level of typical test materials, and the other approximately at the upper or lower decile as appropriate. Enter values for the two control materials on separate control charts. Duplicate a minimum of five test materials, and insert one procedural blank per ten test materials. 

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]. 

To plot the points, select the xy data (cells B2 D6) from the original worksheet. Cliek on the Chart Wizard icon shown in the margin. Select XY(Scatter) from the standard types list and click on Next>. When the Step 2 of 4 window appears, click on Next> again. Click on the Gridlines tab, and check Major gridlines under Value (X) axis. Then click on the Titles tab, and enter x in the Value (X) axis blank and y in the Value (Y) axis blank. Finally, click on Finish to produce the following graph of the data. 

For ease of reading of the chart in Is ifigurel-19.xls, alternate cells of columns C and D are left blank [delete after filling down ] so that the CONDITIONAL FORMATTING option in the FORMAT dialogue box can be set to particular patterns for entries greater than zero values as in Figures 1.19a and b. 

Here A is the measured sample activity and udA) is its combined standard uncertainty. Acceptance limits for Z b may be analogously to 3a control limits on a control chart or at other values based on the desired false rejection rate they could, for example, be based on the estimated number of effective degrees of freedom for the combined standard uncertainty udA), described in the note in Section 10.3.2. Thus, the result of the reagent blank analysis A is found to be acceptable if its absolute value does not exceed a specified multiple of its combined standard uncertainty udA). 

Statistical control applies to all parts of the analytical system - sampling process, the calibration, the blank, and the measurement. Statistical control is attained by the quality control of the entire system and Involves maintenance of realistic tolerances for all critical operations. A system of control charts is the best way to demonstrate attainment of statistical control and to evaluate the appropriate standard deviations. In the simplest form, the results of measurement of a stable check sample, obtained over a period of time, are plotted. Statistical control is demonstrated when the values are randomly distributed around their average value." Control limits are often taken as 2 or 3 standard deviation units of these replicates. Dr. Taylor also adds, "Even the ranges of duplicate measurements of the actual samples tested can be plotted in a similar manner to demonstrate a stable standard deviation. In either case, the statistics of the control charts are the best descriptors of the variability of the measurement process."... 

Probably one of the most common charts is the Average and Range (3c and R) chart and an excellent blank chart is shown in Figure 18.19. Using the figures in Table 18.4 the central values are obtained from the equations ... 

The bands of figures in Table 4-12 represent a balance between chain and shaft sizes. When the hub class and bore intersect in the blank space below and to the left of the band, it means that the chain and sprocket will not transmit the fuU torque value of the shaft. When the intersection is above and to the right of the band, the chain and sprocket are stronger than the shaft the shaft may fail, or the designer has selected a heftier (and more expensive) chain than is needed. Thus the charts can also be used as a design check on the size of the shaft. 

The quality of the mirror blanks was controlled in various inspection procedures. The GTE values were measured according to the sampling plan shown in Fig. 4.44. Sample rods with a length of 100 mm were cut from ce-ramized Zerodur and measured with a high-precision dilatometer system. In Fig. 4.45 the results for mirror 2 are represented in a bar chart. 

Weigh about 12 mg of barium chloride crystals into an empty sample tube and obtain chromatograms as indicated under Procedure. Repeat with 9. 6. 3 and 1 mg amounts of barium chloride crystals. Add the values for the two peaks obtained for water in each determination and correct for the blank. Construct a calibration graph relating milligrams of water to the corrected total peak height (or peak area) measured from the recorder chart. 

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