Replications, quality control

Consider a laboratory measuring perchlorate (CIO4) in human urine. For quality assurance, n = 5 replicate quality control samples made from synthetic urine spiked with perchlorate are measured every day. The control chart shows the mean value of the five samples observed each day over a series of days. The spike contains (x = 4.92 ng/mL, and the population standard deviation from many analyses over a long time is a = 0.40 ng/mL. 

Control chart. The graph in Box 5-1 shows mean values for five replicate quality control samples measured each day. The standard operating procedure calls for stopping work to identify the source of error if the mean daily quality control result is outside the action lines ( 3a/V7i). This condition does not occur in Box 5-1. Are any other rejection conditions from Box 5-1 observed in this data ... 

Untreated (control) soil is collected to determine the presence of substances that may interfere with the measurement of target analytes. Control soil is also necessary for analytical recovery determinations made using laboratory-fortified samples. Thus, basic field study design divides the test area into one or more treated plots and an untreated control plot. Unlike the treated plots, the untreated control is typically not replicated but must be sufficiently large to provide soil for characterization, analytical method validation, and quality control. To prevent spray drift on to the control area and other potential forms of contamination, the control area is positioned > 15 m away and upwind of the treated plot, relative to prevailing wind patterns. 

Briefly, to assure quality assurance and quality control, samples are analyzed using standard analytical procedures. A continuing program of analytical laboratory quality control verifies data quality and involves participation in interlaboratory crosschecks, and replicate sampling and analysis. When applicable, it is advisable, even insisted upon by the EPA, that analytical labs be certified to complete the analysis requested. However, in many cases, time constraints often do not allow for sufficient method validation. Many researchers have experienced the consequences of invalid methods and realized that the amount of time and resources required to solve problems discovered later exceeds what would have been expended initially if the validation studies had been performed properly. 

Analytical quality control (QC) efforts usually are at level I or II. Statistical evaluation of multivariate laboratory data is often complicated because the number of dependent variables is greater than the number of samples. In evaluating quality control, the analyst seeks to establish that replicate analyses made on reference material of known composition do not contain excessive systematic or random errors of measurement. In addition, when such problems are detected, it is helpful if remedial measures can be Inferred from the QC data. 

These applications demonstrate that pattern recognition techniques based on principal components may be effectively used to character zate complex environmental residues. In comparisons of PCBs in bird eggs collected from different regions, we demonstrated through the use of SIHCA that the profiles in samples from a relatively clean area differed in concentration and composition from profiles in samples from a more highly contaminated region. Quality control can be evaluated by the proximity of replicate analysis of samples in principal components plots. 

In excerpts 3Q and 3R, the authors describe their QA/QC procedures in separate subsections, complete with their own subheadings (Method Performance and Quality Assurance/Quality Control). Results of the QA/QC procedures (e.g., relative standard deviation across replicate samples, recoveries, and accuracy) are commonly described in the Methods section, rather than in the Results section. 

Even once a method is standardized, erroneous results can still be generated. As a result, it is critical to have robust quality control procedures in place. Here, careful attention should be paid to identify opportunity for in-process control measures such as internal standards, calibration, control plates, replicates and so on as opposed to post-processing data review steps. Inline QC approaches allow sources of error to be identified and remedied much more rapidly and help limit costly re-tests, or the possibility of erroneous data leaving the laboratory. 

As part of this field study, relevant quality assurance/quality control (QA/QC) criteria and guidelines (SETAC, 1993 JAMP, 1998a,b) have to be set to insure the quality of data generated during the assessments. The development of QA/QC criteria for this study involved conducting a series of replicate bioassays with each of the methods. Samples tested included a control sediment, contaminated sediments and reference toxicants. Based on the results of the bioassay replicates, the variability associated with the tests was quantified and we were able to determine what we considered acceptable QA/QC criteria for these methods. 

The environment in which a method is used changes significantly when the method is transferred to a quality control laboratory at the manufacturing site. The method may be replicated in several laboratories, multiple analysts may use it, and the method may be one of many methods used in the laboratory daily. The developing laboratory must therefore be aware of the needs of the receiving... 

Together, raw data and results from calibration checks, spike recoveries, quality control samples, and blanks are used to gauge accuracy. Analytical performance on replicate samples and replicate portions of the same sample measures precision. Fortification also helps ensure that qualitative identification of analyte is correct. If you spike the unknown in Figure 0-5 with extra caffeine and the area of a chromatographic peak not thought to be caffeine increases, then you have misidentified the caffeine peak. 

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. 

Quality assurance refers to activities that demonstrate that a certain quality standard is being met. This includes the management process that implements and documents effective QC. Quality control refers to procedures that lead to statistical control of the different steps in the measurement process. So QC includes specific activities such as analyzing replicates, ensuring adequate extraction efficiency, and contamination control. 

An important part of the sampling methodology relates to the use of control samples in order that the data can be quality controlled and quality assured (see Johnson et al., 2008). While it is only duplicates that are created during the sampling process, these, along with replicates, blanks (for waters) and secondary reference materials need to be assigned sample numbers so they are included as part of the routine sample submission and are blind to the analyst. Duplicate samples are collected from a... 

These blind control samples are in addition to any primary reference materials (PRM) that the laboratory may also analyse. For the G-BASE project, the BGS laboratories usually insert a PRM at the beginning and end of each batch of 500 samples. As G-BASE generally collects and analyses 2000—3000 samples each field campaign 8% of the samples is more than adequate to carry out quality control procedures. However, if sample numbers are <500, then it is recommended that the number of duplicates and replicates per hundred samples should be doubled. 

A key to quality control of environmental analyses is the insertion of blind (hidden) control samples (duplicates, replicates and reference materials) among the routinely collected samples. The control samples need to be allocated sample numbers that make them indistinguishable from the normal samples when submitted for analysis. This can be achieved by the use of sample number list sheets such as that illustrated in Fig. 5.3. 

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