Evaluating Quality Assurance Data: Prescriptive Approach

Evaluating Quality Assurance Data

In the previous section we described several internal methods of quality assessment that provide quantitative estimates of the systematic and random errors present in an analytical system. Now we turn our attention to how this numerical information is incorporated into the written directives of a complete quality assurance program. Two approaches to developing quality assurance programs have been described9: a prescriptive approach, in which an exact method of quality assessment is prescribed; and a performance-based approach, in which any form of quality assessment is ac- ceptable, provided that an acceptable level of statistical control can be demonstrated.

Prescriptive Approach

With a prescriptive approach to quality assessment, duplicate samples, blanks, stan- dards, and spike recoveries are measured following a specific protocol. The result for each analysis is then compared with a single predetermined limit. If this limit is exceeded, an appropriate corrective action is taken. Prescriptive approaches to qual- ity assurance are common for programs and laboratories subject to federal regula- tion. For example, the Food and Drug Administration (FDA) specifies quality as- surance practices that must be followed by laboratories analyzing products regulated by the FDA.

A good example of a prescriptive approach to quality assessment is the protocol outlined in Figure 15.2, published by the Environmental Protection Agency (EPA) for laboratories involved in monitoring studies of water and wastewater.10 Indepen- dent samples A and B are collected simultaneously at the sample site. Sample A is split into two equal-volume samples, and labeled A₁ and A₂. Sample B is also split into two equal-volume samples, one of which, B_SF, is spiked with a known amount of analyte. A field blank, D_F, also is spiked with the same amount of analyte. All five samples (A₁, A₂, B, B_SF, and D_F) are preserved if necessary and transported to the laboratory for analysis.

The first sample to be analyzed is the field blank. If its spike recovery is unac- ceptable, indicating that a systematic error is present, then a laboratory method blank, D_L, is prepared and analyzed. If the spike recovery for the method blank is also unsatisfactory, then the systematic error originated in the laboratory. An ac- ceptable spike recovery for the method blank, however, indicates that the systematic error occurred in the field or during transport to the laboratory. Systematic errors in the laboratory can be corrected, and the analysis continued. Any systematic er- rors occurring in the field, however, cast uncertainty on the quality of the samples, making it necessary to collect new samples.

If the field blank is satisfactory, then sample B is analyzed. If the result for B is above the method’s detection limit, or if it is within the range of 0.1 to 10 times the amount of analyte spiked into B_SF, then a spike recovery for B_SF is determined.

An unacceptable spike recovery for B_SF indicates the presence of a systematic error in- volving the sample. To determine the source of the systematic error, a laboratory spike, B_SL, is prepared using sample B and analyzed. If the spike recovery for B_SL is acceptable, then the systematic error requires a long time to have a noticeable effect on the spike recovery. One possible explanation is that the analyte has not been properly preserved or has been held beyond the acceptable holding time. An unac- ceptable spike recovery for B_SL suggests an immediate systematic error, such as that due to the influence of the sample’s matrix. In either case, the systematic errors are fatal and must be corrected before the sample is reanalyzed.

If the spike recovery for B_SF is acceptable, or if the result for sample B is below the method’s detection limit or outside the range of 0.1 to 10 times the amount of analyte spiked in B_SF, then the duplicate samples A₁ and A₂ are analyzed. The results for A₁ and A₂ are discarded if the difference between their values is excessive. If the difference between the results for A₁ and A₂ is within the accepted limits, then the results for samples A₁ and B are compared. Since samples collected from the same sampling site at the same time should be identical in composition, the results are discarded if the difference between their values is unsatisfactory, and accepted if the difference is satisfactory.

This protocol requires four to five evaluations of quality assessment data before the result for a single sample can be accepted; a process that must be repeated for each analyte and for each sample. Other prescriptive protocols are equally demand- ing. For example, Figure 3.7 shows a portion of the quality assurance protocol used for the graphite furnace atomic absorption analysis of trace metals in aqueous solutions. This protocol involves the analysis of an initial calibration verifi- cation standard and an initial calibration blank, followed by the analysis of samples in groups of ten. Each group of samples is preceded and followed by continuing cal- ibration verification (CCV) and continuing calibration blank (CCB) quality assess- ment samples. Results for each group of ten samples can be accepted only if both sets of CCV and CCB quality assessment samples are acceptable.

The advantage to a prescriptive approach to quality assurance is that a single con- sistent set of guidelines is used by all laboratories to control the quality of analytical results. A significant disadvantage, however, is that the ability of a laboratory to pro- duce quality results is not taken into account when determining the frequency of col- lecting and analyzing quality assessment data. Laboratories with a record of producing high-quality results are forced to spend more time and money on quality assessment than is perhaps necessary. At the same time, the frequency of quality assessment may be insufficient for laboratories with a history of producing results of poor quality.