Modern Analytical Cheymistry - Chapter 15 pps

CChhaapptteerr 1 5 705 Quality Assurance In Chapter 1 we noted that each field of chemistry brings a unique perspective to the broader discipline of chemistry. For analytical chemistry this perspective was identified as an approach to solving problems, which was presented as a five-step process: (1) Identify and define the problem; (2) Design the experimental procedure; (3) Conduct an experiment and gather data; (4) Analyze the experimental data; and (5) Propose a solution to the problem. The analytical approach, as presented thus far, appears to be a straightforward process of moving from problem-to-solution. Unfortunately (or perhaps fortunately for those who consider themselves to be analytical chemists!), an analysis is seldom routine. Even a well-established procedure, carefully followed, can yield poor data of little use. An important feature of the analytical approach, which we have neglected thus far, is the presence of a “feedback loop” involving steps 2, 3, and 4. As a result, the outcome of one step may lead to a reevaluation of the other two steps. For example, after standardizing a spectrophotometric method for the analysis of iron we may find that its sensitivity does not meet the original design criteria. Considering this information we might choose to select a different method, to change the original design criteria, or to improve the sensitivity. The “feedback loop” in the analytical approach is maintained by a quality assurance program (Figure 15.1), whose objective is to control systematic and random sources of error. 1 – 5 The underlying assumption of a quality assurance program is that results obtained when an analytical system is in statistical control are free of bias and are characterized by well-defined confidence intervals. When used properly, a quality assurance program identifies the practices necessary to bring a system into statistical control, allows us to determine if the system remains in statistical control, and suggests a course of corrective action when the system has fallen out of statistical control. The focus of this chapter is on the two principal components of a quality assurance program: quality control and quality assessment. In addition, considerable attention is given to the use of control charts for routinely monitoring the quality of analytical data. 1400-CH15 9/8/99 4:38 PM Page 705 706 Modern Analytical Chemistry 1 5 A Quality Control Quality control encompasses all activities used to bring a system into statistical control. The most important facet of quality control is a set of written directives de- scribing all relevant laboratory-specific, technique-specific, sample-specific, method-specific, and protocol-specific operations. 1,3,6 Good laboratory practices (GLPs) describe the general laboratory operations that need to be followed in any analysis. These practices include properly recording data and maintaining records, using chain-of-custody forms for samples that are submitted for analysis, specifying and purifying chemical reagents, preparing commonly used reagents, cleaning and calibrating glassware, training laboratory personnel, and maintaining the laboratory facilities and general laboratory equipment. Good measurement practices (GMPs) describe operations specific to a tech- nique. In general, GMPs provide instructions for maintaining, calibrating, and using the equipment and instrumentation that form the basis for a specific tech- nique. For example, a GMP for a titration describes how to calibrate a buret (if nec- 1. Identify the problem Determine type of information needed (qualitative, quantitative, characterization, or fundamental) Establish context of the problem 2. Design the experimental procedure Establish design criteria (accuracy, precision, scale of operation, sensitivity, selectivity, cost, speed) Identify interferents Select method Establish validation criteria Establish sampling strategy Quality assurance program 3. Conduct an experiment Calibrate instruments and equipment Standardize reagents Gather data 4. Analyze the experimental data Reduce or transform data Analyze statistics Verify results Interpret results 5. Propose a solution Conduct external evaluation Q u a l i t y C o n t r o l A s s e s s m e n t Q u a l i t y Figure 15.1 Schematic diagram of the analytical approach to problem solving, showing the role of the quality assurance program. quality control Those steps taken to ensure that an analysis is under statistical control. good laboratory practices Those general laboratory procedures that, when followed, help ensure the quality of analytical work. good measurement practices Those instructions outlining how to properly use equipment and instrumentation to ensure the quality of measurements. quality assurance The steps taken during an analysis to ensure that the analysis is under control and that it is properly monitored. 1400-CH15 9/8/99 4:38 PM Page 706 Chapter 15 Quality Assurance 707 standard operations procedure The procedure followed in collecting and analyzing samples and in interpreting the results of an analysis. essary), how to fill a buret with the titrant, the correct way to read the volume of titrant in the buret, and the correct way to dispense the titrant. The operations that need to be performed when analyzing a specific analyte in a specific matrix are defined by a standard operations procedure (SOP). The SOP describes all steps taken during the analysis, including: how the sample is processed in the laboratory, the analyte’s separation from potential interferents, how the method is standardized, how the analytical signal is measured, how the data are transformed into the desired result, and the quality assessment tools that will be used to maintain quality control. If the laboratory is responsible for sampling, then the SOP will also state how the sample is to be collected and preserved and the na- ture of any prelaboratory processing. A SOP may be developed and used by a single laboratory, or it may be a standard procedure approved by an organization such as the American Society for Testing and Materials or the Federal Food and Drug Ad- ministration. A typical SOP is provided in the following example. EXAMPLE 1 5 .1 Provide an SOP for the determination of cadmium in lake sediments by atomic absorption spectrophotometry using a normal calibration curve. SOLUTION Sediment samples should be collected using a bottom grab sampler and stored at 4 °C in acid-washed polyethylene bottles during transportation to the laboratory. Samples should be dried to constant weight at 105 °C and ground to a uniform particle size. The cadmium in a 1-g sample of the sediment is extracted by adding the sediment and 25 mL of 0.5 M HCl to an acid-washed 100-mL polyethylene bottle and shaking for 24 h. After filtering, the sample is analyzed by atomic absorption spectrophotometry using an air–acetylene flame, a wavelength of 228.8 nm, and a slit width of 0.5 nm. A normal calibration curve is prepared using five standards with nominal concentrations of 0.20, 0.50, 1.00, 2.00, and 3.00 ppm. The accuracy of the calibration curve is checked periodically by analyzing the 1.00-ppm standard. An accuracy of ±10% is considered acceptable. Although an SOP provides a written procedure, it is not necessary to follow the procedure exactly as long as any modifications are identified. On the other hand, a protocol for a specific purpose (PSP), which is the most detailed of the written quality control directives, must be followed exactly if the results of the analysis are to be accepted. In many cases the required elements of a PSP are established by the agency sponsoring the analysis. For example, labs working under contract with the Environmental Protection Agency must develop a PSP that addresses such items as sampling and sample custody, frequency of calibration, schedules for the preventive maintenance of equipment and instrumentation, and management of the quality assurance program. Two additional aspects of a quality control program deserve mention. The first is the physical inspection of samples, measurements and results by the individuals responsible for collecting and analyzing the samples. 1 For example, sediment sam- ples might be screened during collection, and samples containing “foreign objects,” such as pieces of metal, be discarded without being analyzed. Samples that are dis- carded can then be replaced with additional samples. When a sudden change in the protocol for a specific purpose A precisely written protocol for an analysis that must be followed exactly. 1400-CH15 9/8/99 4:38 PM Page 707 708 Modern Analytical Chemistry performance of an instrument is observed, the analyst may choose to repeat those measurements that might be adversely influenced. The analyst may also decide to reject a result and reanalyze the sample when the result is clearly unreasonable. By identifying samples, measurements, and results that may be subject to gross errors, inspection helps control the quality of an analysis. A final component of a quality control program is the certification of an ana- lyst’s competence to perform the analysis for which he or she is responsible. 7 Before an analyst is allowed to perform a new analytical method, he or she may be required to successfully analyze an independent check sample with acceptable accuracy and precision. The check sample should be similar in composition to samples that the analyst will routinely encounter, with a concentration that is 5 to 50 times that of the method’s detection limit. 1 5 B Quality Assessment The written directives of a quality control program are a necessary, but not a suffi- cient, condition for obtaining and maintaining an analysis in a state of statistical control. Although quality control directives explain how an analysis should be properly conducted, they do not indicate whether the system is under statistical control. This is the role of quality assessment, which is the second component of a quality assurance program. The goals of quality assessment are to determine when a system has reached a state of statistical control; to detect when the system has moved out of statistical control; and, if possible, to suggest why a loss of statistical control has occurred so that corrective ac- tions can be taken. For convenience, the methods of quality assessment are divided into two categories: internal methods that are coordinated within the laboratory and exter- nal methods for which an outside agency or individual is responsible. The incorpora- tion of these methods into a quality assurance program is covered in Section 15C. 1 5 B.1 Internal Methods of Quality Assessment The most useful methods for quality assessment are those that are coordinated by the laboratory and that provide the analyst with immediate feedback about the sys- tem’s state of statistical control. Internal methods of quality assessment included in this section are the analysis of duplicate samples, the analysis of blanks, the analysis of standard samples, and spike recoveries. Analysis of Duplicate Samples An effective method for determining the precision of an analysis is to analyze duplicate samples. In most cases the duplicate samples are taken from a single gross sample (also called a split sample), although in some cases the duplicates must be independently collected gross samples. The results from the duplicate samples, X 1 and X 2 , are evaluated by determining the difference, d, or the relative difference, (d) r , between the samples d = X 1 – X 2 and comparing the results with accepted values, such as those shown in Table 15.1 for the analysis of waters and wastewaters. 7 Alternatively, the results for a set of n duplicates are combined to estimate the standard deviation for the analysis () ()/ d d XX r = + × 12 2 100 quality assessment The steps taken to evaluate whether an analysis is under statistical control. duplicate samples Two samples taken from a single gross sample and used to evaluate an analytical method’s precision. 1400-CH15 9/8/99 4:38 PM Page 708 Chapter 15 Quality Assurance 709 Table 1 5 .1 Selected Quality Assessment Limits for the Analysis of Waters and Wastewaters Limits for Spike Recovery (d) r When [Analyte] < 20 × MDL (d) r When [Analyte] > 20 × MDL Analyte (%) (±%) (±%) acids 60–140 40 20 anions 80–120 25 10 bases or neutrals 70–130 40 20 carbamate pesticides 50–150 40 20 herbicides 40–160 40 20 metals 80–120 25 10 other inorganics 80–120 25 10 volatile organics 70–130 40 20 Abbreviation: MDL = method’s detection limit. where d i is the difference between the ith pair of duplicates. The degrees of freedom for the standard deviation is the same as the number of duplicate samples. If dupli- cate samples from several sources are combined, then the precision of the measure- ment process must be approximately the same for each. The precision obtained is then compared with the precision needed to accept the results of the analysis. EXAMPLE 1 5 .2 To evaluate the precision for the determination of potassium in blood serum, duplicate analyses were performed on six samples, yielding the following results. Duplicate X 1 X 2 1 160 147 2 196 202 3 207 196 4 185 193 5 172 188 6 133 119 Calculate the standard deviation for the analysis. SOLUTION The standard deviation is determined as follows. Duplicate d = X 1 – X 2 d 2 1 13 169 2–636 3 11 121 4–864 5 –16 256 6 14 196 s d n i = ∑ 2 2 1400-CH15 9/8/99 4:38 PM Page 709 The Analysis of Blanks The use of a blank was introduced in Chapter 3 as a means of correcting the measured signal for contributions from sources other than the analyte. The most common blank is a method, or reagent blank, in which an analyte-free sample, usually distilled water, is carried through the analysis using the same reagents, glassware, and instrumentation. Method blanks are used to identify and correct systematic errors due to impurities in the reagents and con- tamination in the glassware and instrumentation. At a minimum, method blanks should be analyzed whenever new reagents are used, although a more frequent analysis provides an ongoing monitoring of the purity of the reagents. A new method blank should also be run whenever a sample with a high concentration of the analyte is analyzed, because any residual carryover of the analyte may contami- nate the glassware or instrumentation. When samples are collected in the field, the method blank may be augmented with field and trip blanks. 8 A field blank is an analyte-free sample carried from the laboratory to the sampling site. At the sampling site the blank is transferred to a clean sample container, exposing it to the local environment, preserved, and trans- ported back to the laboratory for analysis. Field blanks are used to identify and correct systematic errors due to sampling, transport, and analysis. Trip blanks are analyte-free samples carried from the laboratory to the sampling site and returned to the laboratory without being opened. A trip blank is used to identify and correct systematic errors due to cross-contamination of volatile organic compounds during transport, handling, storage, and analysis. Analysis of Standards The analysis of a standard containing a known concentra- tion of analyte also can be used to monitor a system’s state of statistical control. Ide- ally, a standard reference material (SRM) should be used, provided that the matrix of the SRM is similar to that of the samples being analyzed. A variety of appropriate SRMs are available from the National Institute of Standards and Technology (NIST). If a suitable SRM is not available, then an independently prepared synthetic sample can be used if it is prepared from reagents of known purity. At a minimum, a standardization of the method is verified by periodically analyzing one of the cali- bration standards. In all cases, the analyte’s experimentally determined concentra- tion in the standard must fall within predetermined limits if the system is to be con- sidered under statistical control. Spike Recoveries One of the most important quality assessment tools is the recov- ery of a known addition, or spike, of analyte to a method blank, field blank, or sam- ple. To determine a spike recovery, the blank or sample is split into two portions, and a known amount of a standard solution of the analyte is added to one portion. The concentration of the analyte is determined for both the spiked, F, and unspiked portions, I, and the percent recovery, %R, is calculated as where A is the concentration of the analyte added to the spiked portion. %R FI A = − × 100 s = ++ ++ + == 169 36 121 64 256 196 26 842 12 84 ()() . 710 Modern Analytical Chemistry field blank A blank sample collected in the field. trip blank A blank prepared in the laboratory that accompanies a set of sample containers in the field and laboratory. spike recovery An analysis of a sample after spiking with a known amount of analyte. 1400-CH15 9/8/99 4:38 PM Page 710 Chapter 15 Quality Assurance 711 EXAMPLE 1 5 . 3 A spike recovery for the analysis of chloride in well water was performed by adding 5.00 mL of a 25,000-ppm solution of Cl – to a 500-mL volumetric flask and diluting to volume with the sample. Analysis of the sample and the spiked sample resulted in chloride concentrations of 183 ppm and 409 ppm, respectively. Determine the percent recovery of the spike. SOLUTION The concentration of the added spike is calculated by taking into account the effect of dilution. Thus, the spike recovery is Spike recoveries on method blanks and field blanks are used to evaluate the general performance of an analytical procedure. The concentration of analyte added to the blank should be between 5 and 50 times the method’s detection limit. Sys- tematic errors occurring during sampling and transport will result in an unaccept- able recovery for the field blank, but not for the method blank. Systematic errors occurring in the laboratory, however, will affect the recoveries for both the field and method blanks. Spike recoveries for samples are used to detect systematic errors due to the sample matrix or the stability of the sample after its collection. Ideally, samples should be spiked in the field at a concentration between 1 and 10 times the expected concentration of the analyte or 5 to 50 times the method’s detection limit, whichever is larger. If the recovery for a field spike is unacceptable, then a sample is spiked in the laboratory and analyzed immediately. If the recovery for the labora- tory spike is acceptable, then the poor recovery for the field spike may be due to the sample’s deterioration during storage. When the recovery for the laboratory spike also is unacceptable, the most probable cause is a matrix-dependent relationship be- tween the analytical signal and the concentration of the analyte. In this case the samples should be analyzed by the method of standard additions. Typical limits for acceptable spike recoveries for the analysis of waters and wastewaters are shown in Table 15.1. 7 1 5 B.2 External Methods of Quality Assessment Internal methods of quality assessment should always be viewed with some level of skepticism because of the potential for bias in their execution and interpretation. For this reason, external methods of quality assessment also play an important role in quality assurance programs. One external method of quality assessment is the certification of a laboratory by a sponsoring agency. Certification is based on the successful analysis of a set of proficiency standards prepared by the sponsoring agency. For example, laboratories involved in environmental analyses may be re- quired to analyze standard samples prepared by the Environmental Protection %.%R = − ×= 409 183 250 100 90 4 A =×=25 000 250, ppm 5.00 mL 500.0 mL ppm proficiency standard A standard sample provided by an external agency as part of certifying the quality of a laboratory’s work. 1400-CH15 9/8/99 4:38 PM Page 711 712 Modern Analytical Chemistry Agency. A second example of an external method of quality assessment is the volun- tary participation of the laboratory in a collaborative test (Chapter 14) sponsored by a professional organization such as the Association of Official Analytical Chemists. Finally, individuals contracting with a laboratory can perform their own external quality assessment by submitting blind duplicate samples and blind standard sam- ples to the laboratory for analysis. If the results for the quality assessment samples are unacceptable, then there is good reason to consider the results suspect for other samples provided by the laboratory. 1 5 C 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 described 9 : 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. 1 5 C.1 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 A 1 and A 2 . 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 (A 1 , A 2 , 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 1400-CH15 9/8/99 4:38 PM Page 712 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 A 1 and A 2 are analyzed. The results for A 1 and A 2 are discarded if the difference between their values is excessive. If the difference between the results for A 1 and A 2 is within the accepted limits, then the results for samples A 1 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. Chapter 15 Quality Assurance 713 D F recovery within limits D L recovery within limits B > MDL, or B > 0.1 × [spike], and B < 10 × [spike] Systematic error in laboratory Systematic error in field Ye sNo Ye sNo Immediate systematic errors Time- dependent systematic errors Ye sNo No A 1 – B within limits Poor Replication NoYe s A 1 – A 2  within limits B SF recovery within limits B SL recovery within limits Poor duplicate samples Ye s Ye s Ye s No No Data valid Figure 15.2 Example of a prescriptive approach to quality assurance. Adapted from Environmental Monitoring and Support Laboratory, U.S. Environmental Protection Agency, “Handbook for Analytical Quality Control in Water and Wastewater Laboratories,” March 1979. 1400-CH15 9/8/99 4:38 PM Page 713 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 in Chapter 3 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. 1 5 C.2 Performance-Based Approach In a performance-based approach to quality assurance, a laboratory is free to use its experience to determine the best way to gather and monitor quality assessment data. The quality assessment methods remain the same (duplicate samples, blanks, standards, and spike recoveries) since they provide the necessary information about precision and bias. What the laboratory can control, however, is the fre- quency with which quality assessment samples are analyzed, and the conditions in- dicating when an analytical system is no longer in a state of statistical control. Fur- thermore, a performance-based approach to quality assessment allows a laboratory to determine if an analytical system is in danger of drifting out of statistical con- trol. Corrective measures are then taken before further problems develop. 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 fundamen- tal 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. Control charts were originally developed in the 1920s as a quality assurance tool for the control of manufactured products. 11 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 represent- ing 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 measure- ment process. The position of the data points about the boundary lines determines whether the system is in statistical control. 714 Modern Analytical Chemistry control chart A graph showing the time-dependent change in the results of an analysis that is used to monitor whether an analysis is in a state of statistical control. 1400-CH15 9/8/99 4:38 PM Page 714 [...]... therefore, is only 0.26% The 1400-CH15 9/8/99 4:38 PM Page 719 Chapter 15 Quality Assurance Table 15. 3 719 Average Range for Duplicate Samples for Different Concentrations of Chromium in Water Cr (ppb) Number of Duplicate Samples – R 32 15 16 15 8 5 0.32 0.57 1.12 3.80 5.25 76.0 5 to . control is a set of written directives de- scribing all relevant laboratory-specific, technique-specific, sample-specific, method-specific, and protocol-specific operations. 1,3,6 Good laboratory. 0.32 10 to <25 15 0.57 25 to <50 16 1.12 50 to < ;150 15 3.80 150 to <500 8 5.25 >500 5 76.0 Range Sequence UCL (a) (b) (c) UCL UCL UWL UWL UWL Sequence Sequence 1400-CH15 9/8/99 4:38. 707) trip blank (p. 710) 1400-CH15 9/8/99 4:38 PM Page 721 722 Modern Analytical Chemistry Few analyses are so straightforward that high-quality results are easily obtained. Good analytical work requires