The ML procedure is designed to provide a means by which laboratories can demonstrate their performance with a method under routine laboratory operating conditions. All recently developed EPA CWA methods require that laboratories calibrate their instruments prior to analyzing environmental samples. The ML is defined as the lowest non zero standard in the laboratory's calibration, and therefore, reflects realistic expectations of laboratory performance with a given method under routine laboratory conditions ( i. e., under conditions of routine variability).

Also, the ML is based on the standard deviation of replicate analyses used to establish the MDL. As described in Section <IP_ADDRESS>.2, these analyses are performed to characterize laboratory and method performance, including routine variability, at low concentrations. When laboratories perform MDL studies and yield results that are approximately 3.18 times lower than the ML, they demonstrate that they can achieve expected levels of performance at the specified quantitation level.

EPA recognizes that one laboratory may obtain MDLs and MLs that are lower or higher than those in another laboratory. If the ML is being developed during method development, it is important to determine the ML at more than one laboratory to ensure that the published ML reflects demonstrated expectations of method performance in a community of laboratories. EPA does not believe this community should be so broad as to include the entire universe of possible laboratories that might desire to practice the method. Chapter 5 Draft Document for Peer Review  Do Not Circulate  August 2002 5 19 Rather, EPA believes that this community should include well operated laboratories that are experienced with the techniques used in the method and that have some familiarity with the method. See section <IP_ADDRESS>.2. for additional discussion of this topic.

<IP_ADDRESS>.3 Criterion 3: The quantitation limit concept must be supported by a practical and affordable procedure that a single laboratory can use to evaluate method performance.

The ML is designed for use by a single laboratory. The ML can be directly determined from the MDL, which is among the most affordable of procedures that have been suggested for determining detection limits ( see discussion in Section <IP_ADDRESS>.3 for additional details regarding affordability). As a result, the ML is among the most affordable of procedures for determining quantitation limits.

<IP_ADDRESS>.4 Criterion 4: The quantitation limit concept should identify a concentration at which the reliability of the measured results is consistent with the capabilities of the method when a method is performed by experienced staff in a well operated laboratory.

The ML meets this criterion. The ML is determined in individual laboratories each time they calibrate their instruments to perform a method, and the goal in doing so is to identify a concentration at which the reliability of the measured results is consistent with the capabilities of the method. EPA methods that employ the ML include criteria for demonstrating acceptable calibration performance, thereby ensuring that the ML is determined under controlled laboratory conditions. EPA methods that employ the ML also specify that the analytical method is to be performed by qualified staff who are experienced with the techniques used in the method.

<IP_ADDRESS>.5 Criterion 5: The quantitation limit concept must be applicable to the variety of decisions made under the Clean Water Act and should support state and local obligations to implement measurement requirements that are at least as stringent as those set by the Federal Government.

The ML meets this criterion. It has been successfully used to support State and local obligations under the Clean Water Act since 1984.

5.2.2 Assessment of the IQE The Interlaboratory Quantitation Estimate ( IQE) was developed by ASTM with support from members of the regulated industry in an attempt to provide a scientifically sound, comprehensive quantitation limit procedure that addresses the concerns of the regulated industry, of statisticians, and of analysts involved in ASTM Committee D 19 on water. A brief summary of the procedure for establishing and IQE is given in Section <IP_ADDRESS>. Section <IP_ADDRESS> presents EPA's assessment of the IQE against the five criteria established for evaluating quantitation limit concepts.

<IP_ADDRESS> Description of the IQE Concept and Procedure The ASTM Designation D 6512 is the Standard Practice Interlaboratory Quantitation Estimate. As stated in the practice:

" IQEZ % is computed to be the lowest concentration for which a single measurement from a laboratory selected from the population of qualified laboratories represented in an interlaboratory study will have an estimated Z % relative standard deviation ( Z % RSD, Assessment of Detection and Quantitation Concepts Draft Document for Peer Review  Do Not Circulate  August 2002 5 20 based on interlaboratory standard deviation), where Z is typically an integer multiple of 10, such as 10, 20, or 30, but Z can be less than 10."

The IQE is determined and verified using a procedure containing 5 major steps with approximately 46 substeps and conditions. The full text of the IQE procedure is available from ASTM International. The 5 major steps and their functions are given in Section 6 of the IQE procedure and are as follows:

1. Overview of the procedure.

2. IQE Study Plan, Design, and Protocol  in this section, the task manager ( study supervisor) chooses the analyte, matrix, and analytical method. Details are given for the appropriate range of study concentrations; the model of recovery vs. concentration; the study protocol ( ASTM Practice D 2777 is suggested); the instructions to be given to the participating laboratories, including reporting requirements; the allowable sources of variation; and the number of laboratories, analysts, measurement systems, and days, over which the study will be conducted.

3. Conduct the IQE Study, Screen the Data, and Choose a Model  after the study data are collected and screened according to ASTM Practice D 2777, the interlaboratory standard deviation ( ILSD) versus concentration data are tabulated and one of three models is fit to the data. The first attempt is at fitting a constant model. If the attempt fails, a straight line model is attempted. If the straight line model fails, a hybrid ( Rocke/ Lorenzato) model is fit. After fitting, the model is evaluated for reasonableness and lack of fit. If the model fails, the study supervisor determines if a subset of the data should be analyzed or if more data are needed.

4. Compute the IQE  the IQE is computed using the ILSD model selected in Step 3 to estimate the relative standard deviation as a function of concentration. The first attempt is at 10 % RSD ( IQE10%). If this attempt fails, IQE20% is tried, then IQE30%. IQEs greater than 30% are not recommended.

5. Nontrivial Amount of Censored Data  this section of the IQE procedure addresses the effect of " nondetects or " less thans." Suggestions are given to see if uncensored data can be obtained from the laboratories or if the study needs to be augmented with additional data. Suggestions are given for fitting a model to data that contain less than 10 % non detects or less thans to produce an IQE.

<IP_ADDRESS> Assessment of the IQE Against the Evaluation Criteria The following five subsections discuss the IQE concept and procedure in the context of the five evaluation criteria that concern detection limit concepts.

<IP_ADDRESS>.1 Criterion 1: The quantitation limit concept must be scientifically valid. ( Validity is comprised of five conditions.)

Condition 1: It can be ( and has been) tested. EPA is not aware of any organization, including ASTM, that has conducted a study to test the IQE procedure as written ( i. e., designed and implemented an interlaboratory study involving multi laboratory analysis of multiple concentrations of each matrix of interest). It has been tested by its developers using simulated datasets and on interlaboratory data sets that do not adequately characterize the low level region of interest. As part of this reassessment, EPA tested a variant of the IQE procedure on single laboratory data sets that were designed to characterize an analytical method in the region of detection and quantitation. Despite the lack of comprehensive testing performed to date, however, EPA believes that the IQE procedure can be tested if sufficient resources are invested. In other words, the IQE meets the condition that it ` can be' tested, but only partially meets the condition that it ` has been' tested. Chapter 5 Draft Document for Peer Review  Do Not Circulate  August 2002 5 21 Condition 2: It has been subjected to peer review and publication. Although the IQE has not been published in the peer reviewed scientific literature, the IQE has undergone extensive review and ballot by members of ASTM Committee D 19, many of whom are qualified peer reviewers. Therefore, although the IQE does not meet this condition in the sense of formal peer review and publication, EPA believes it does meet the intent of this condition ( i. e., submission to scrutiny of the scientific community).

Condition 3: The error rate associated with the procedure is either known or can be estimated. In theory, an expert statistician could estimate the error rate of the IQE. However, the IQE procedure is extremely complex from an analytical chemistry and statistical perspective. As a result, it is unlikely that the error rate could be estimated by the staff of an environmental testing laboratory. Moreover, in attempting to follow the IQE procedure during this reassessment, EPA found the procedure to be highly subjective, particularly with respect to selection of an appropriate model. The subjective nature of the procedure is likely to yield different IQEs from the same data set, depending on the staff involved in analyzing the data and performing the calculations. ( The likelihood of this problem is illustrated in Appendix C to this TSD.) EPA believes such conditions make it difficult if not impossible to estimate the actual error associated with the IQE. Therefore, the IQE fails this condition.

Condition 4: Standards exist and can be maintained to control its operation. The IQE concept and procedure is supported by a published procedure ( standard) to control its operation. The procedure gives the steps to be followed in determining the IQE and instructs the study supervisor how to gather the data and compute an IQE.

However, there are several " gray areas" in the published procedure. The most significant gray area is in model selection. The procedure provides insufficient guidance on the use of residual plots as a basis for selecting models and, as a result, selection of the model may be very subjective, especially if the number of concentrations is low. The discussion of what model to use after rejecting the hybrid and linear models also is very vague. The exponential model is mentioned, as well as models with more than one coefficient. Much of the data evaluated by EPA have tended to suggest the exponential model, based on the statistical tests discussed, but those data have almost always shown residual " patterns" when using this model, which would then lead to consideration of other models. In addition, fitting the " constant model" is never discussed in detail. Most likely, this is simply done by calculating a mean ( weighted if necessary) of the variances from the different concentrations, however this is never explicitly stated.

As discussed under Condition 4 of Section <IP_ADDRESS>.1 ( scientific validity of the IDE procedure), EPA also is concerned about inconsistencies between the IDE and IQE that suggest conceptual problems with these standards. Finally, EPA observed that the IQE contains statistical errors that if followed as written could produce inaccurate IQE values. For example, the computations for weighted least squares given in Table 1 of the procedure are incorrect. The formulas for the weighted means of the spike values and results given in Table 1 of D6512 would only be appropriate if the weighting were done based on the number of replicates per spike level, rather than on the estimated variance calculated using the chosen standard deviation model.

Based on these findings ( along with those discussed under Criterion 2 below), EPA believes that, although the IQE is supported by a published procedure, the procedure is not sufficient to control operation of the IQE because of the high degree of subjectivity involved in implementing the procedure, statistical errors in the procedure, and internal inconsistencies with the IDE. Therefore, the IQE fails this condition.

Condition 5: It has attracted widespread acceptance within a relevant scientific community. The IQE fails this condition because it is familiar to, and has been accepted only by, a very narrow segment of the scientific community. Although the IQE has been approved by ASTM for more than 2 years, EPA has not seen an IQE in the open literature or in an analytical method, including an ASTM method. Assessment of Detection and Quantitation Concepts Draft Document for Peer Review  Do Not Circulate  August 2002 5 22 <IP_ADDRESS>.2 Criterion 2: The quantitation limit concept must address demonstrated expectations of laboratory and method performance, including routine variability.

The IQE procedure is designed to reflect expectations of interlaboratory performance, including routine variability. The procedure contains extensive instructions for dealing with unusual conditions, including sources of variability. Based on studies of the single laboratory variant of the procedure in which the model selection proved to be highly subjective, EPA is skeptical about the procedure being able to demonstrate realistic expectations of laboratory and method performance.

The IQE procedure suggests attempting to fit study results to a constant, linear, or hybrid model. If all of these fail, the procedure suggests trying a different model, such as the exponential model. ( The exponential model figures more prominently in the IDE procedure, where it is one of the three main models discussed, replacing the Rocke and Lorenzato model.) Although the exponential model may be appropriate for the IDE ( which is not tied to a fixed RSD), it yields unacceptable results when applied to the IQE procedure. Under the exponential model, relative variability ( standard deviation divided by the true concentration) is a parabolic function, i. e., as concentration increases, relative variability decreases down to a specific percentage, and then begins to increase. This is not realistic of laboratory and method performance. In addition the exponential model will often result in having two possible values each for IQE10%, IQE20%, and IQE30%.

Another concern with the IQE procedure is that use of the non mandatory appendices in ASTM D 6512 to determine the fit of a model may produce results that differ from those that would be obtained by using the default procedures for testing model fit that are built into off the shelf statistical software.

Given the subjectivity and confusion involved in selecting the model, EPA tried using the same data set to calculate a single laboratory variant of the IQE with each of the available models and found that the calculated IQEs varied widely when different models were used.

Based on the problems described above, EPA believes the IQE fails this criterion.

<IP_ADDRESS>.3 Criterion 3: The quantitation limit concept must be supported by a practical and affordable procedure that a single laboratory can use to evaluate method performance.

The IQE procedure is neither practical nor affordable. It is designed for use by an ASTM study supervisor or task manager and not as a procedure that a single laboratory can use to evaluate method performance. EPA is aware that ASTM Committee D 19 is contemplating development of a withinlaboratory quantitation estimate ( WQE), but the WQE has not been approved through an ASTM ballot so it cannot be adequately evaluated at this time. The WQE may meet this criterion, but the IQE does not.

Regarding affordability, EPA estimates that the cost of implementing IQE procedure would be more than twice the cost of EPA's present implementation of the ML. The increased cost stems from the additional low level data required to assure that variability vs. concentration is being characterized in the region of detection and quantitation, challenges involved in applying the statistical procedures in the IQE, and because of the anticipated reanalysis and rework required if either the procedure failed to produce an IQE or if the resulting IQE failed to meet the specifications in the IQE procedure. Chapter 5 Draft Document for Peer Review  Do Not Circulate  August 2002 5 23 <IP_ADDRESS>.4 Criterion 4: The quantitation limit concept should identify a concentration at which the reliability of the measured results is consistent with the capabilities of the method when a method is performed by experienced staff in a well operated laboratory.

If the IQE were developed in an interlaboratory study that met the requirements of D 6512, the calculated IQE would likely be achievable by experienced staff in a well operated laboratory. Therefore, the IQE passes this criterion. However, EPA also notes that although it passes the criterion, the IQE also suffers from the likelihood that it may not identify the lowest concentration at which the reliability of the measured results is consistent with the capabilities of the method when it is performed by experienced staff in a welloperated laboratory. Also, EPA is concerned that the IQE is an interlaboratory concept and may be limiting in a single laboratory setting; i. e., the IQE may actually prevent a laboratory from demonstrating improved performance because any re development of the IQE would require an interlaboratory study which no single laboratory would undertake.

<IP_ADDRESS>.5 Criterion 5: The quantitation limit concept must be applicable to the variety of decisions made under the Clean Water Act and should support State and local obligations to implement measurement requirements that are at least as stringent as those set by the Federal Government.

Although the IQE could be applied to some decisions to be made under CWA, it may not support decisions when pollutant levels need to be protective of human health and the environment because the IDE may be considerably higher than these levels. At best, the IQE only partially passes this criterion.

5.2.3 Assessment of the ACS Limit of Quantitation The Limit of Quantitation ( LOQ) was developed by the Committee on Environmental Improvement of the American Chemical Society ( ACS) and published in the same two papers as the LOD.

<IP_ADDRESS> Description of the ACS LOQ Concept and Procedure The 1983 " Principles" define the LOQ as:

"... the level above which quantitative results may be obtained with a specified degree of confidence."

The same relationship used to define the LOD is used for the LOQ:

but the recommended minimal value for Kd be set at 10. Thus, the LOQ is 10F above the gross blank signal, Sb. According to the 1983 publication, the LOQ corresponds to an uncertainty of ± 30% ( 10F ± 3F). This uncertainty statement is based on F equal to 10% of the LOQ. Other statements of uncertainty are, of course, possible using knowledge of F and/ or the RSD.

Neither the 1980 nor 1983 ACS publications provide a specific procedure for estimating the LOQ, nor do they provide a minimum number of observations needed to estimate the gross blank signal or the variability term Fb. Assessment of Detection and Quantitation Concepts Draft Document for Peer Review  Do Not Circulate  August 2002 5 24 <IP_ADDRESS> Assessment of the ACS LOQ Against the Evaluation Criteria The following five subsections discuss the ACS LOQ concept and procedure in the context of the five evaluation criteria that concern detection limit concepts.

<IP_ADDRESS>.1 Criterion 1: The quantitation limit concept must be scientifically valid ( validity is comprised of five conditions)

Condition 1: It can be ( and has been) tested. Testing of the LOQ is hampered by 1) the lack of a supporting procedure for establishing and LOQ, and 2) it's conceptual dependence on the variability of blank measurements. If the blank measurements fail to produce a response, it is impossible to calculate an LOQ because the standard deviation of zero is zero. One solution for testing the concept is to assume that the LOQ is functionally equivalent to the ML as the blank signal approaches zero. EPA believes this is a reasonable assumption, and therefore, that the ML procedure is a viable means for testing the LOQ concept. Therefore, the LOQ meets this condition.

Condition 2: It has been subjected to peer review and publication. The ACS LOQ definition was published in the peer reviewed journal Analytical Chemistry in 1980 and 1983. Therefore, the ACS LOQ meets this condition.

Condition 3: The error rate associated with the procedure is either known or can be estimated. The definition of the LOQ specifically estimates the uncertainty associated with a concentration at the LOQ as ± 30% based on 10% RSD. Other valid statements in terms of % RSD may be made based on study requirements, policy judgments and/ or specific results. For example, the estimate of an uncertainty of ± 30% based on 10% RSD is inconsistent with EPA and ISO/ IUPAC estimations which place the uncertainty at ± 20% ( at ± 2F), and is inconsistent with the Episode 6000 data that place the median RSD at 7% and therefore the ± 2F uncertainty at approximately ± 14%.

Condition 4: Standards exist and can be maintained to control its operation. The ACS LOQ lacks a clearly defined procedure for estimating the important terms required to derive it. Although it may be possible to derive ACS LOQ values from data used to derive EPA MDL values, there is no discussion of using replicate blanks, replicate spiked samples, or a minimum recommendation for the number of replicates. Therefore, the ACS LOQ fails this condition.

Condition 5: It has attracted widespread acceptance within a relevant scientific community. Because the ACS does not develop and publish reference analytical methods, it is difficult to determine the degree of acceptance of the LOQ. EPA has not investigated the numbers of papers published in ACS journals that include LOQ values, but EPA's literature search for detection and quantitation concepts did not uncover a large number of citations that promote the LOQ in particular.

<IP_ADDRESS>.2 Criterion 2: The quantitation limit concept must address demonstrated expectations of laboratory and method performance, including routine variability.

The LOQ concept is designed to address demonstrated expectations of laboratory and method performance, including routine variability, so it appears to meet this criterion. Unfortunately, ACS has not published a procedure to implement the concept. In other words, the LOQ addresses demonstrated expectations of laboratory and method performance in theory, but in practice, provides no direct means for performing these demonstrations. Therefore, EPA believes the ACS LOQ partially meets this criterion. Chapter 5 Draft Document for Peer Review  Do Not Circulate  August 2002 5 25 <IP_ADDRESS>.3 Criterion 3: The quantitation limit concept must be supported by a practical and affordable procedure that a single laboratory can use to evaluate method performance.

The ACS LOQ concept is not supported by a clearly defined procedure for establishing the LOQ. Therefore, it fails this criterion.

<IP_ADDRESS>.4 Criterion 4: The quantitation limit concept should identify a concentration at which the reliability of the measured results is consistent with the capabilities of the method when a method is performed by experienced staff in a well operated laboratory.

Given the relationship of the ACS LOQ to the ML, EPA believes the LOQ meets this criterion for the reasons outlined in Section <IP_ADDRESS>.4, which discusses EPA's assessment of the ML against Criterion 4 for evaluating quantitation limit concepts.

<IP_ADDRESS>.5 Criterion 5  Must be applicable to the variety of decisions made under the Clean Water Act and should support State and local obligations to implement measurement requirements that are at least as stringent as those set by the Federal Government.

In the absence of a procedure for determining LOQ values, the ACS LOQ fails to meet this criterion because it cannot be used in a regulatory context. The LOQ passes only if it is assumed to be functionally equivalent to the ML ( i. e., the ML procedure is used to establish an LOQ).

5.2.4 Assessment of the IUPAC/ ISO Limit of Quantitation A similar LOQ concept was developed by IUPAC/ ISO and published in the same papers as the CRV and detection limit ( Sections 5.1.4 and 5.1.5).

<IP_ADDRESS> Description of the ISO/ IUPAC LOQ Concept The 1995 " Recommendations" define the LOQ as:

"... the ability of a CMP [ chemical measurement process] to adequately ` quantify' an analyte. The ability to quantify is generally expressed in terms of the signal or analyte ( true) value that will produce estimates having a specified relative standard deviation ( RSD), commonly 10 %."

The relationship used to define the LOQ is:

LQ = KQ × FQ The recommended value for KQ is 10. Thus, the LOQ is 10F above the blank signal, FQ.

<IP_ADDRESS> Assessment of the IUPAC/ ISO LOQ Against the Evaluation Criteria The following five subsections discuss the IUPAC/ ISO LOQ concept and procedure in the context of the five evaluation criteria that concern detection limit concepts.

<IP_ADDRESS>.1 Criterion 1: The quantitation limit concepts must be scientifically valid ( validity is comprised of five conditions) Assessment of Detection and Quantitation Concepts Draft Document for Peer Review  Do Not Circulate  August 2002 5 26 Condition 1: It can be ( and has been) tested. Testing of the IUPAC/ ISO LOQ is hampered by 1) the lack of a supporting procedure for establishing and LOQ, and 2) it's conceptual dependence on the variability of blank measurements. If the blank measurements fail to produce a response, it is impossible to calculate an LOQ because the standard deviation of zero is zero. One solution for testing the concept is to assume that the ISO/ IUPAC LOQ is functionally equivalent to the ML as the blank signal approaches zero. EPA believes this is a reasonable assumption, and therefore, that the ML procedure is a viable means for testing the LOQ concept. Therefore, the ISO/ IUPAC LOQ meets this condition.

Condition 2: It has been subjected to peer review and publication. The IUPAC/ ISO LOQ definition has been published in the peer reviewed journals Pure and Appl. Chem. in 1985; in Anal. Chim. Acta. and Chemometrics and Intelligent Lab. Systems in 1997; and in J. Radioanal. and Nuclear Chem. in 2000. Therefore, the IUPAC/ ISO LOQ meets this condition.

Condition 3: The error rate associated with the procedure is either known or can be estimated. EPA used data generated in the Episode 6000 study to obtain estimate the error rate associated with the LOQ. The Episode 6000 results show that the median error across all analytes and analytical techniques at 10F is approximately ± 14% with approximate 95 % confidence.

Condition 4: Standards exist and can be maintained to control its operation. The IUPAC/ ISO LOQ is lacks a clearly defined procedure for estimating the important terms required to derive it. Although it may be possible to derive IUPAC/ ISO LOQ values from data used to derive EPA MDL values, there is no discussion of using replicate blanks, replicate spiked samples, or a minimum recommendation for the number of replicates. Therefore, EPA believes that the IUPAC/ ISO LOQ fails this condition.

Condition 5: It has attracted widespread acceptance within a relevant scientific community. Acceptance by the scientific community is not known. Acceptance would be indicated by use of the LOD in ISO methods. EPA also did not perform a search of ISO methods because of copyright restrictions. However, EPA's literature search for detection and quantitation concepts in the open technical literature did not uncover a large number of citations that reference the LOQ. Therefore, it is difficult to determine if the ISO/ IUPAC LOQ meets this condition.

<IP_ADDRESS>.2 Criterion 2: The concepts must address demonstrated expectations of laboratory and method performance, including routine variability.

The most recent publication on the IUPAC/ ISO LOQ ( J. Radioanal. and Nuclear Chem., op. cit.) provides an insight into this issue through measurements of carbon 14 by accelerator mass spectrometry. Therefore, EPA believes that the IUPAC/ ISO LOQ passes this criterion.

<IP_ADDRESS>.3 Criterion 3: The concept must be supported by a practical and affordable procedure that a single laboratory can use to evaluate method performance.

The ISO/ IUPAC LOQ concept is not supported by a clearly defined procedure for establishing the LOQ. Therefore, it fails this criterion.

<IP_ADDRESS>.4 Criterion 4: The quantitation limit concept should identify a concentration at which the reliability of the measured results is consistent with the capabilities of the method when a method is performed by experienced staff in a well operated laboratory. Chapter 5 Draft Document for Peer Review  Do Not Circulate  August 2002 5 27 Given the relationship of the IUPAC/ ISO LOQ to the ML, EPA believes that the LOQ satisfies this criterion for the reasons outlined in Section <IP_ADDRESS>.4, which discusses EPA's assessment of the ML against Criterion 4 for evaluating quantitation limit concepts.

<IP_ADDRESS>.5 Criterion 5: The quantitation limit concept must be applicable to the variety of decisions made under the Clean Water Act and should support State and local obligations to implement measurement requirements that are at least as stringent as those set by the Federal Government.

In the absence of a procedure for determining LOQ values, the ISO/ IUPAC LOQ fails to meet this criterion because it cannot be used in a regulatory context. The ISO/ IUPAC LOQ passes only if the ML procedure is used to establish an LOQ. Draft Document for Peer Review  Do Not Circulate  August 2002 5 28 Table 5 1. Assessment of Detection Limit Concepts Against Evaluation Criteria Evaluation Criteria MDL IDE ACS LOD ISO/ IUPAC CRV ISO/ IUPAC MDV The detection limit concept must be scientifically valid:

° It can be ( and has been tested)

° Has undergone peer review and publication ° Has an error rate that is known or can be estimated ° Has standards that can be maintained to control its operation ° Has achieved widespread acceptance in a relevant scientific community Meets all 5 conditions for scientific validity with slight modifications noted to clarify understanding of error rate Meets 1, partially meets 1, and fails 3 of the 5 conditions for scientific validity.

° Can be, but has not been fully tested ( partial)

° Subjectivity makes calculation of error rate impossible ( fails)

° Has a standard but, due to the high degree of subjectivity,

errors, and conceptual inconsistency, it is unlikely to control its operation ( fails)

° Is familiar to and accepted by a very narrow segment of the scientific community ( fails) Meets 4 of the 5 conditions for scientific validity.

° No standards exist to control its operation Meets 3 of the 5 conditions for scientific validity.

° No standards exist to control its operation ° Degree of acceptance is unclear. Meets 3 of the 5 conditions for scientific validity.

° No standard exist to control its operation ° Degree of acceptance is unclear.

The detection limit concept must address demonstrated expectations of laboratory and method performance, including routine variability Can meet this criterion if properly applied Conceptually passes this criterion,

but fails in practice due to problems with model selection Partially meets the criterion. Concept meets the criterion but no procedure for implementing the concept is given.

Passes the criterion only if equivalency to the MDL is assumed. Partially meets this criterion. Concept meets the criterion but no procedure for implementing the concept is given.

Passes the criterion only if equivalency to the MDL is assumed. Could be used in planning method development and evaluation studies as recommended but not in operational detection decision making. Table 5 1. Assessment of Detection Limit Concepts Against Evaluation Criteria Evaluation Criteria MDL IDE ACS LOD ISO/ IUPAC CRV ISO/ IUPAC MDV Draft Document for Peer Review  Do Not Circulate  August 2002 5 29 The detection limit concept must be supported by a practical and affordable procedure that a single laboratory can use to evaluate method performance Meets this criterion. Procedure can be performed by a single laboratory during a single shift, or for method development by multiple labs in a single shift Fails this criterion. Requires interlaboratory study involving a reference lab or coordinating body, a minimum of 6 complete data sets,

and a skilled statistician. The cost of implementing this procedure would exceed most method development budgets. Fails this criterion. Fails this criterion. Fails this criterion.

The detection limit concept should result in a measured concentration at which there is 99%

confidence that a substance will be detected when the analytical method is performed by experienced staff in a welloperated laboratory. Meets this criterion When the allowance for false negatives and the prediction and tolerance intervals are taken into account, the resulting detection limit ( IDE) is raised to the point at which detection probability is estimated to be greater than 99.999999%; this yields numerical values that have no practical meaning as a detection standard. Therefore, the IDE fails this criterion. Meets this criterion. Meets this criterion. The MDV is a true concentration value not used in the actual detection decision. Does not meet the criterion.

The detection limit concept must be applicable to the variety of decisions made under the Clean Water Act,

and should support State and local obligations to implement measurement requirements that are at least as stringent as those set by the Federal Government Meets this criterion At best, only partially passes this criterion. Not likely to meet this criterion in instances in which a compliance limit is close to detection by a procedure such as the MDL. In the absence of a procedure for determining LOD values,

fails to meet this criterion. In the absence of a procedure for determining CRV values, fails to meet this criterion. In the absence of a procedure for determining MDV values, fails to meet this criterion Draft Document for Peer Review  Do Not Circulate  August 2002 5 30 Table 5 2. Assessment of Quantitation Limit Concepts Against Evaluation Criteria Evaluation Criteria ML IQE ACS LOQ ISO/ IUPAC LOQ The quantitation limit concept must be scientifically valid ° It can be ( and has been tested)

° Has undergone peer review and publication ° Has an error rate that is known or can be estimated ° Has standards that can be maintained to control its operation ° Has achieved widespread acceptance in a relevant scientific community Meets all 5 conditions for scientific validity,

though slight modification to the definition is suggested to improve operation when more than 7 replicates are used to estimate the ML. Meets 1 condition, partially meets 1 condition, and fails 3 conditions.

° Can be, but has not been fully tested ( partial)

° Error rate cannot be estimated due to problems with the procedure. ( fail)

° Standards are not likely to control its operation. ( fail)

° Has not achieved widespread acceptance. ( fail) Meets 3 of the 5 conditions for scientific validity.

° Lacks a standard to control its operation ° Difficult to determine the degree of acceptance. Meets 4 of the 5 conditions for scientific validity.

° Lacks a standard to control its operation ° Difficult to determine the degree of acceptance The quantitation limit concept must address demonstrated expectations of laboratory and method performance, including routine variability Meets this criterion.

Procedure can be performed by a single laboratory during a single shift, or for method development by multiple labs in a single shift. Fails this criterion due to subjectivity, errors, and theoretical inconsistencies in the procedure. Partially meets this criterion. The concept is designed to address these expectations but in practice,

there is no procedure for performing such demonstrations. Meets this criterion Table 5 2. Assessment of Quantitation Limit Concepts Against Evaluation Criteria Evaluation Criteria ML IQE ACS LOQ ISO/ IUPAC LOQ Draft Document for Peer Review  Do Not Circulate  August 2002 5 31 The quantitation limit concept must be supported by a practical and affordable procedure that a single laboratory can use to evaluate method performance Meets this criterion Fails this criterion. Requires interlaboratory study involving a reference lab or coordinating body, 6 complete data sets, and a highly skilled statistician.

The cost of implementing this procedure would exceed most method development budgets. Fails this criterion. Fails this criterion The quantitation limit concept should identify the concentration at which the reliability of the measured results is consistent with the capabilities of the method when the method is performed by an experienced analyst in a welloperated lab. Meets this criterion Meets this criterion, but is not likely to estimate the lowest level at which reliable measurements can be made by an experienced analyst in a well operated lab.. Meets this criterion Meets this criterion The quantitation limit concept must be applicable to the variety of decisions made under the Clean Water Act, and should support state and local obligations to implement measurement requirements that are at least as stringent as those set by the Federal Government Meets this criterion. At best, only partially passes this criterion. Fails for those instances in which the IQE limit is greater than an effluent limit or water qualitybased limit. Fails this criterion. In the absence of a procedure for determining ACS LOQ values, the ACS LOQ cannot be used in a regulatory context. Fails this criterion. In the absence of a procedure for determining LOQ values, the ISO/ IUPAC LOQ cannot be used in a regulatory context. Draft Document for Peer Review  Do Not Circulate  August 2002 6 1 Chapter 6 Conclusions EPA's assessment of detection and quantitation limit concepts was based on:

° Identification of relevant concepts to include in the assessment ( Chapter 2). ° Identification of issues that may be relevant to the assessment from an analytical chemistry, statistical, or regulatory perspective ( Chapter 3). ° Development of criteria that reflects EPA's views concerning these issues ( Chapter 4). These criteria formed the primary basis for evaluating the ability of each concept to meet EPA needs under the Clean Water Act. ° Assessment of how well each concept met the evaluation criteria ( Chapter 5). ° Use of real world data to evaluate both the theoretical and practical limitations of each concept ( Appendices B and C).

EPA evaluated four sets of detection and quantitation limit concepts advanced by EPA, ASTMInternational ACS, and ISO/ IUPAC. Of these, only the concepts advanced by EPA and ASTM International were supported by clearly defined procedures for implementing the concept. The lack of supporting procedures for the ACS and ISO/ IUPAC concepts is reflected in EPA's overall assessment.

EPA's overall assessment of each concept against each of the evaluation criteria suggests that 1) no single pair of detection and quantitation limit concepts perfectly meets EPA's criteria, and 2) the MDL and ML are closest to meeting EPA's criteria. EPA's assessment of the theoretical and practical applications of each concept ( see Appendices B and C) is summarized in Exhibit 6 1. This exhibit suggests that no concept produces the " right" answer, and that different concepts produce different detection and quantitation limits. Observed differences are largely due to different sources of variability accounted for among the concepts.

These findings suggest that the MDL and ML are at least as good, if not better, than other detection and quantitation limit concepts. EPA has suggested modifications to the MDL and ML to allow these concepts to fully meet EPA's evaluation criteria. ( A revised version of the MDL procedure, that reflects these modifications, is provided in Appendix D to this TSD. A revised version of the ML definition was given in Chapter 5.) As noted in Chapter 3, however, outside organizations use different detection and quantitation concepts that meet their own needs. Given the alternate needs and EPA's desire to 1) encourage the development of improved measurement techniques, and 2) provide the stakeholder community with a variety of options whenever possible, EPA believes it would impractical to require that all methods promulgated for use in Clean Water Act programs employ the MDL and ML concepts.

One possible alternative is for EPA to revise the current MDL and ML procedure as the standard concepts for use in CWA applications, and to allow alternate procedures for specific CWA applications as long as those procedures meet the needs of the specific application ( i. e., provide detection and quantitative capabilities that would protect human health and the environment). Assessment of Detection and Quantitation Concepts Draft Document for Peer Review  Do Not Circulate  August 2002 6 2 Exhibit 6 1: Theoretical and Practical Application of Each Concept Finding 1: Each concept yields different values.

Detection Limits Concepts ° The EPA MDL and ACS LOD concepts, which are functionally analogous, produced detection limits that are a median of 1.25 times higher than the CRV advanced by ISO and IUPAC. ° The Minimum Detectable Value ( MDV) advanced by ISO and IUPAC produced detection limits that are a median of 1.2 times higher than the MDL and LOD concepts. ° A single laboratory variant of the IDE ( the IDE has been advanced by ASTM International) produced detection limits that are a median of 2.9 times higher than the MDL and LOD concepts.

Quantitation Limit Concepts ° The EPA ML and the functionally equivalent ACS LOQ produced quantitation limits that are a median of 1.1 times higher than the LOQ advanced by ISO and IUPAC. ° A single laboratory variant of the IQE ( the IQE has been advanced by ASTM) produced median quantitation limits that are equivalent to the EPA ML and ACS LOQ.

Finding 2: More than the 5 levels specified by ASTM are required to produce a reliable IDE and IQE ° EPA found that the IDEs produced with a subset of data generated from the 5 concentrations recommended in the IDE procedure differed widely from the IDEs produced with a larger set of data involving 16 concentrations ( including the subset of 5). ° Findings suggest that more than 5 levels are needed to produce a reliable IDE due the limited power of the statistical tests for significant model parameters and the difficulty of drawing conclusions based on residual plots with only 5 points. ° Parallel reasoning can be applied to the IQE based on its similarity to the IDE.

Finding 3: The ML procedure yields quantitation limits that are generally in the range of the 10% RSD intended in the ML ( and the functionally analogous ACS LOQ) concept.

° EPA calculated the uncertainty associated with replicate measurements made at the ML for a large number of analytes and techniques. ° EPA found that on average, across all techniques tested, the RSD of replicate measurements at the ML was approximately 7%. Median RSDs calculated for each multi analyte method ranged from 6 14%, and RSD values calculated for each single analyte method ranged from 4  16%.

Finding 4: No single model adequately predicts the behavior of all analytes and all methods across the measurement range.

° EPA produced graphs representing hundreds of analyte/ method combinations. Selection of an appropriate model based on these graphs is highly subjective at best due to the lack of clear patterns and the residuals observed with each model applied. ° The IDE and IQE concepts are the only concepts other than the MDL and ML that are supported by a procedure for implementing the concept. The IDE and IQE procedures rely heavily on model selection, and the degree of subjectivity involved in selecting these models makes implementation of the IDE and IQE difficult.

Finding 5: Use of a recovery correction when establishing detection and quantitation limits may not be appropriate.

° EPA found that using a regression line to estimate a recovery correction at zero concentration causes great swings in the resulting detection and quantitation limits. ° Use of a recovery correction procedure also can result in ` double correcting' for recovery because 1) nearly all methods already contain specifications for acceptable recovery performance, and 2) some methods include recovery correction in the computation of results. Draft Document for Peer Review  Do Not Circulate  August 2002 R 1 References ASTM D6091, Standard Practice for 99 %/ 95 % Interlaboratory Detection Estimate ( IDE) for Analytical Methods with Negligible Calibration Error, 1991.

ASTM Practice D 2777, Standard Practice Interlaboratory Quantitation Estimate, 2000.

Clayton C. A., J. W. Hines, and P. D. Elkins ( 1987), Detection Limits with Specified Assurance Probabilities, Analytical Chemistry 59: 2506 2514.

Currie, Lloyd A. ( 1968), Limits for Quantitative Detection and Quantitative Determination, Analytical Chemistry 40: 586 593.

Currie, Lloyd A. ( 1995), Nomenclature in Evaluation of Analytical Methods including Detection and Quantification Capabilities, Pure and Appl. Chem. 67: 10, 1699 1722.

Currie, Lloyd A. ( 1999a), Nomenclature in Evaluation of Analytical Methods Including Detection and Quantification Capabilities ( IUPAC Recommendations 1995) Anal. Chim. Acta 391, 105 126.

Currie, Lloyd A. ( 1999b), Detection and Quantification Limits: Origins and Historical Overview, Anal. Chim. Acta 391, 127 134.

Currie, Lloyd A. ( 2000), Detection and quantification capabilities and the evaluation of low level data: Some international perspectives and continuing challenges, J. Radioanalytical and Nuclear Chem, Vol. 245, 145 156.

Daubert v. Merrell Dow Pharmaceuticals, 509 U. S. 579 ( 1993).

Federal Register ( 1979), Guidelines Establishing Test Procedures for the Analysis of Pollutants Under the Clean Water Act; Proposed Rule ( 44 FR 69463).

Federal Register ( 1984), Guidelines Establishing Test Procedures for the Analysis of Pollutants Under the Clean Water Act; Final Rule and Interim Final Rule, and Proposed Rule ( 49 FR 43234).

Federal Register ( 1987), National Primary Drinking Water Regulations  Synthetic Organic Chemicals Monitoring for Unregulated Contaminants; Final Rule, 52 FR 25699.

Federal Register ( 1992), National Primary Drinking Water Regulations; Synthetic Organic Chemicals and Inorganic Chemicals; Final Rule, 57 FR 31800.

Federal Register ( 1998), Federal Participation in the Development and Use of Voluntary Consensus Standards and in Conformity Assessment Activities, OMB Circular A 119, 63 FR 8546.

Federal Register ( 1999), Guidelines Establishing Test Procedures for the Analysis of Pollutants; Measurement of Mercury in Water ( EPA Method 1631, Revision B); Final Rule, 64 FR 30417.

Gibbons, R. D., F. H. Jarke, K. P. Stoub ( 1991), Detection Limits: For Linear Calibration Curves with Increasing Variance and Multiple Future Detection Decisions, Waste Testing and Quality Assurance: ASTM STP 1075, D. Friedman, Ed., American Society for Testing and Materials, Philadelphia 3: 337 390. References Draft Document for Peer Review  Do Not Circulate  August 2002 R 2 Gibbons, Robert D., David E. Coleman, and Raymond F. Maddalone ( 1997), Response to Comment on " An Alternative Minimum Level Definition for Analytical Quantification," Env. Sci. and Tech. 31: 7 2071 2077.

Glaser, John, Denis Foerst, Gerald McKee, Stephan Quave, William Budde ( 1981), Trace Analyses for Wastewaters, Env. Sci. & Tech 15: 1426 1435.

Hahn and Meeker, Statistical Intervals, Wiley, 1991.

Hubaux, A. and G. Vos ( 1970), Decision and Detection Limits for Linear Calibration Curves, Analytical Chemistry 42: 849 855.

Keith, Lawrence H., et. al. ( 1983), Principles of Environmental Analysis, Anal. Chem. 55: 14, 2210 2218.

Maddalone, Raymond , James Rice, Ben Edmondson, Babu Nott, & Judith Scott ( 1993), Defining Detection and Quantitation Levels, Water Environment and Technology 5, 41  44.

McDougal, Daniel, et al. ( 1980), Guidelines for Data Acquisition and Data Quality Evaluation in Environmental Chemistry, Anal. Chem. 52: 14, 2242 2249.

Patterson, Clair C. and Dorothy M. Settle, ( 1976), NBS Special Publication 422, Accuracy in Trace Analysis: Sampling, Sample Handling, and Analysis 1, 321.

Pratt and Gibbons, Concepts of Non parametric Theory, Springer Verlag, 1981.

Rocke, David and Stefan Lorenzato ( 1995) A Two Component Model for Measurement Error in Analytical Chemistry, Technometrics 37: 176 184.

U. S. Environmental Protection Agency ( 1991), Technical Support Document for Water Quality Based Toxics Control, EPA 505/ 2 90/ 001.

U. S. Environmental Protection Agency ( 1993), Guidance on Evaluation, Resolution, and Documentation of Analytical Problems Associated with Compliance Monitoring, EPA 821 B 93 001.

U. S. Environmental Protection Agency ( 1994), Draft National Guidance for the Permitting, Monitoring, and Enforcement of Water Quality based Effluent Limitations Set Below Analytical Detection/ Quantitation Levels.

U. S. Environmental Protection Agency ( 2001), EPA's Guidance for Implementation and Use of EPA Method 1631 for the Determination of Low Level Mercury, EPA 821 R 01 023.

Youden, W. J. and E. H. Steiner, Statistical Manual of the Association of Official Analytical Chemists AOACInternational 481 N. Frederick Ave., Suite 500, Gaithersburg, MD 20877 2417. Draft Document for Peer Review  Do Not Circulate  August 2002 A 1 Appendix A Literature Search Regarding Detection and Quantitation Limit Concepts Introduction Beginning in 2001, DynCorp staff conducted a search of published literature to identify articles that discuss detection and quantitation limit concepts. This literature search effort was conducted under EPA Contract No. 68 C 01 091 to support an evaluation of detection and quantitation limit concepts by the EPA Office of Water.

The principal goal of this literature search effort was to determine if any new detection or quantitation limit concepts had been published in the literature since an earlier search conducted for EPA by Science Applications International Corporation ( SAIC) in 1997 and 1998. That search resulted in an annotated bibliography developed by SAIC and delivered to EPA in 1998.

The results of the DynCorp literature search are summarized in the table at the end of this appendix.

How the search was conducted This search was conducted using two major techniques:

° a search of an on line citation index ( an index of articles cited by other authors) and ° a general on line search of literature.

On line citation index search Because the search was intended to identify detection and quantitation limit concepts and not specific numeric limits associated with a particular analytical method, DynCorp began by searching for references to the major concepts known to EPA. These included the Agency's method detection limit ( MDL) and any other terms that have been suggested to the Agency as alternative detection or quantitation limit concepts. In addition to searching for these concepts, DynCorp also searched the citation index to identify references to the original authors of these concepts and for any other authors who either cited the original concepts, the original papers underlying those concepts, or the authors of those concepts. DynCorp used a similar approach to find any papers that cited the references identified in the earlier literature search by SAIC.

DynCorp staff evaluated the full title of each identified citation to determine its relevance to EPA's objective. Where available electronically and at no additional cost, DynCorp staff also reviewed the abstract and/ or full paper to further characterize relevance. All papers that were determined to be relevant, or even possibly relevant, were obtained in hardcopy or electronic format for evaluation by EPA.

After reviewing all papers determined to be relevant to EPA's objective, DynCorp examined all of the references cited in those papers to identify additional papers of interest. These, too, were obtained in hardcopy or electronic format for evaluation by EPA, except where noted below General on line literature search DynCorp performed an on line direct search of published literature ( e. g., a literature database of published articles, not a citation index) using general terms such as " detection limit," " quantitation limit," or " calibration." As expected, this approach returned a very large numbers of papers that mention these terms, Draft Document for Peer Review  Do Not Circulate  August 2002 A 2 even if the focus of the paper was on something far removed from the development or assessment of concepts about detection and quantitation, and proved to be of limited value in serving EPA's objectives for the search. Therefore, DynCorp discontinued this effort and narrowed our on line literature search to a search for additional, uncited works by authors of the concepts known to EPA or identified through the citation index approach.

Papers determined to be relevant to EPA's objective were obtained in electronic or hardcopy format for evaluation by EPA, except where noted below.

How the results are presented DynCorp identified a total of 160 relevant publications using the approach described above. Thirtythree ( 33) of these publications were also identified in the earlier search by SAIC. Of the 127 remaining publications, 35 were published since the SAIC search was completed.

Each of the 160 publications identified in the search is listed in Attachment 1, which provides the title, year of publication, authors, and source citation. The citations for the 33 papers identified in the earlier search by SAIC are included in the attachment, and can be identified by the phrase " annotated only" in parentheses after the title of the paper.

The final column of the attached spreadsheet is labeled " Category." All of the citations identified in the SAIC literature search and the current search conducted by DynCorp were placed in one of the six following categories, based on the principal characteristic of the article:

° Background  The citation discusses background information ( including early works by Currie, Kaiser, and others). ° Calibration concept  The citation primarily deals with calibration of analytical instrumentation ° Critique  The major thrust of the citation is to critique one or more concepts, as opposed to introducing a new concept ° Multi laboratory concept  The citation describes an approach to developing detection and/ or quantitation limits that relies on multi laboratory measurements ° Single laboratory concept  The citation describes an approach to developing detection and/ or quantitation limits that relies on single laboratory measurements ° Single laboratory, multi level concept  The citation describes an approach to developing detection and/ or quantitation limits that relies on single laboratory measurements but explicitly includes multiple concentrations.

Although there is some degree of overlap between categories, and some papers could probably be classified in more than one category, each citation was classified into only one category for the purposes of this search.

A seventh category called " Not found" was used for three papers that were identified in the literature search, but copies of which could not readily be obtained. One paper is from a German journal that was not available via interlibrary loan. A second article was also not available via interlibrary loan. The third citation is an abstract by Currie, from 1983. Given that the work of Currie is well represented in the other citations and the fact that this citation appears to be only an abstract, additional efforts were not expended to obtain a copy.

The references presented in the table were sorted by category and year of publication and are displayed with the most recent citations in each category first. Appendix A Draft Document for Peer Review  Do Not Circulate  August 2002 A 3 Summary The principal goal of this literature search effort was to determine if any new detection or quantitation limit concepts had been published in the literature since the search by SAIC in 1997 1998. As anticipated, citations were identified that relate to the recent efforts of the International Organization for Standardization ( ISO), the International Union of Pure and Applied Chemists ( IUPAC), and the American Society for Testing and Materials ( ASTM). Additional articles critiquing various concepts were identified as well.

However, no previously unknown detection or quantitation limit concepts were uncovered as a result of this effort. Asessment of Detetion and Quantitation Concepts Draft Document for Peer Review  Do Not Circulate  August 2002 A 4 Results of the 2001 Literature Search Title Year Author Source Category Some Case Studies of Skewed ( and other ab normal) Data Distributions Arising in Low Level Environmental Research 2001 L. A. Currie Fresenius Journal of Analytical Chemistry 370: 705

718 Background Legislative Limits Below Detection Capability 2000 S. L. R. Ellison, V. J. Barwick, A. Williams Accreditation Quality Assurance 5: 308 313 Background International Recommendations Offered on Analytical Detection and Quantification Concepts and Nomenclature 1999 L. A. Currie Analytica Chimica Acta 391: 103 Background Detection and Quantifation Limits: Origins and Historical Overview 1999 L. A. Currie Analytica Chimica Acta 391: 127 134 Background 1996 ASMS Fall Workshop: Limits to Confirmation, Quantitation, and Detection 1997 R. Baldwin, R. A. Bethem, R. K. Boyd, W. L.

Budde, T. Cairns, R. D. Gibbons, J. D.

Henion, M. A. Kaiser, Journal of the American Society for Mass Spectrometry 8: 1180 1190 Background Measurement precision and 1/ f Noise in Analytical Instruments 1996 Y. Hayashi, R. Matsuda, R. B. Poe Journal of Chromatography A 722: 157 167 Background Fossil and Bio mass Combustion: C 14 for Source Identification, Chemical Tracer Development, and Model Validation 1994 L. A. Currie, G. A. Klouda, D. B. Klinedinst,

A. E. Sheffield, A. J. T. Jull, D. J. Donahue,

M. V. Connolly Nuclear Instr. And Methods in Physics Res. B 92:

404 409 Background Interlaboratory Comparison of Instruments Used for the Determination of Elements in Acid Digestates of Solids 1994 D. E. Kimbrough, J. Wakakuwa Analyst 119: 383 388 Background Throwaway Data 1994 L. H. Keith Environmental Science & Technology 28: 389A

390A Background EPA's Office of Water Surges Toward MDL Solution 1994 Larry Keith Radian Background In Pursuit of Accuracy: Nomenclature, Assumptions, and Standards ( annotated only) 1992 L. A. Currie Pure & Applied Chemistry 64: 455 472 Background Interlaboratory Aspects of Detection Limits Used for Regulatory and Control Purposes 1988 L. B. Rogers ACS Symposium Series 361: 94 108 Background Noise and Detection Limits in Signal Integrating Analytical Methods 1988 H. C. Smit, H. Steigstra ACS Symposium Series 361: 126 148 Background Effects of Analytical Calibration Models on Detection Limit Estimates 1988 K. G. Owens, C. F. Bauer, C. L. Grantr ACS Symposium Series 361: 194 207 Background Real World Limitations to Detection 1988 D. Kurtz, J. Taylor, L. Sturdivan, W. Crummett,

C. Midkiff, R. Watters Jr, L. Wood,

W. Hanneman, W. Horwitz ACS Symposium Series 361: 288 316 Background Detection Limits  A Systematic Approach to Detection Limits is Needed When Trace Determinations are to be Performed 1986 S. A. Borman Analytical Chemistry 58: A986 Background Chemometrics and Analytical Chemistry 1984 L. A. Currie Chemometrics 56: 115 146 Background Quality Control in Water Analyses 1983 C. Kirchmer ES& T 17: 174A 181A Background Title Year Author Source Category Draft Document for Peer Review  Do Not Circulate  August 2002 A 5 Validation of Analytical Methods 1983 J. K. Taylor Analytical Chemistry 55: 600A 602A, 608A Background Trace Analyses for Wastewaters  Author's response 1982 D. Foerst Envir. Sci. & Tech. 16: 430A  431A Background Zur Theorie der Eichfunktion bei der spektrochemischen Analyse 1982 V. H. Kaiser DK 535: 309 319 Background The Reliability of Detection Limits in Analytical Chemistry 1980 J. D. Winefordner, J. L. Ward Analytical Letters 13: 1293 1297 Background A Review and Tutorial Discussion of Noise and Sign to Noise Ratios in Analytical Spectrometry  I. Fundamental Principles of Signal to Noise Ratios 1978 C. T. J. Alkemade, W. Snelleman,

G. D. Boutilier, B. D. Pollard, J. D. Winefordner,

T. L. Chester, N. Omenetto Spectrochimica Acta 33B: 383 399 Background A Review and Tutorial Discussion of Noise and Sign to Noise Ratios in Analytical Spectrometry  II. Fundamental Principles of Signal to Noise Ratios 1978 G. D. Boutilier, B. D. Pollard, J. D. Winefordner,

T. L. Chester, N. Omenetto Spectrochimica Acta 33B: 401 415 Background A Tutorial Review of Some Elementary Concepts in the Statistical Evaluation of Trace Element Measurements 1978 P. W. J. M. Boumans Spectrochimica Acta 33B: 625 634 Background Analysis of Lead in Polluted Coastal Seawater 1976 C. Patterson, D. Settle, B. Glover Marine Chemistry 4: 305 319 Background Multielement Analysis with an Inductively Coupled Plasma/ Optical Emission System 1976 R. M. Ajhar, P. D. Dalager, A. L. Davison American Laboratory 72 78 Background Interlaboratory Lead Analyses of Standardized Samples of Seawater 1974 P. Brewer, N. Frew, N. Cutshall, J. J. Wagner,

R. A. Duce, P. R. Walsh, G. L. Hoffman,

J. W. R. Dutton, W. F. Fitzgerald Marine Chemistry 2: 69 84 Background Statistical and Mathematical Methods in Analytical Chemistry 1972 L. A. Currie, J. J. Filliben, J. R. DeVoe Anal. Chem. 44: 497R 512R Background Studies of Flame and Plasma Torch Emission for Simultaneous Multi Element Analysis I. Preliminary Investigations 1972 P. W. J. M. Boumans, F. J. De Boer Spectrochimica Acta 27B: 391 414 Background Quantitative Determination: Application to Radiochemistry 1968 Lloyd Currie Anal. Chem. 40: 586 593 Background Qualitative and Quantitative Sensitivity in Flame Photometry 1966 J. Ramirez Munoz Talanta 13: 87 101 Background The Limit of Detection of Analytical Methods 1962 J. B. Roos Analyst 87: 832 833 Background A Careful Consideration of the Calibration Concept 2001 S. D. Phillips, W. T. Estler, T. Doiron, K. R.

Eberhardt, M. S. Levenson Journal of Research of the National Institute of Standards and Technology 106: 371 379 Calibration Weighted Random Effects Regression Models with Application to Interlaboratory Calibration 2001 R. D. Gibbons, D. K. Bhaumik Technometrics 43: 192 198 Calibration Guidelines for Calibration in Analytical Chemistry Part I. Fundamentals and Single Component Calibration ( IUPAC recommendations 1998) 1998 K. Danzer, L. A. Currie Pure and Applied Chemistry 70: 993 1014 Calibration A Comparison of Uncertainty Criteria for Calibration ( annotated only) 1996 R. W. Mee, K. R. Eberhardt Technometrics 38: 221 229 Calibration Constant Width Calibration Intervals for Linear Regression ( annotated only) 1994 K. R. Eberhardt, R. W. Mee Journal of Quality Technology 26: 21 29 Calibration Regression and Calibration with Nonconstant Error Variance 1990 M. Davidian, P. D. Haaland Chemometrics and Intelligent Laboratory Systems 9:

231 248 Calibration Asessment of Detetion and Quantitation Concepts Title Year Author Source Category Draft Document for Peer Review  Do Not Circulate  August 2002 A 6 Calibration with Randomly Changing Standard Curves ( annotated only) 1989 D. F. Vecchia, H. K. Iyer, P. L. Chapman Technometrics 31: 83 90 Calibration Linear Calibration When the Coefficient of Variation is Constant ( annotated only) 1988 Y. C. Yao, D. F. Vecchia, H. K. Iyer Probability and Statistics: Essays in Honor of Franklin A. Graybill, 297 309 Calibration Analytical Method Comparisons by Estimates of Precision and Lower Detection Limit 1986 D. M. Holland, F. F. McElroy Environmental Science & Technology 20: 1157

1161 Calibration Design Considerations for Calibration ( annotated only) 1986 J. P. Buonaccorsi Technometrics 28: 149 155 Calibration Multivariate Calibration when the Error Covariance Matrix is Structured ( annotated only) 1985 T. Naes Technometrics 27: 301 311 Calibration An Implementation of the Scheffé Approach to Calibration Using Spline Functions,

Illustrated by a Pressure Volume Calibration ( annotated only) 1982 J. A. Lechner, C. P. Reeve, C. H.

Spiegelman Technometrics 24: 229 234 Calibration Measuring and Maximizing Precision in Analyses Based on Use of Calibration Graphs 1982 D. G. Mitchell, J. S. Garden Talanta 29: 921 929 Calibration Calibration in Quantitative Analysis: Part2. Confidence Regions for the Sample Content in the Case of Linear Calibration Relations 1981 J. Agterdenbos, F. J. M. J. Maessen, J. Balke Analytica Chimica Acta 132: 127 137 Calibration Design Aspects of Scheffe's Calibration Theory using Linear Splines ( annotated only) 1980 C. H. Spiegelman, W. J. Studden Journal of Research of the National Bureau of Standards 85: 295 304 Calibration Nonconstant Variance Regression Techniques for Calibration Curvev Based Analysis 1980 J. S. Garden, D. G. Mitchell, W. N. Mills Anal. Chem. 52: 2310 2315 Calibration Calibration in Quantitative Analysis 1979 J. Agterdenbos Analytica Chimica Acta 108: 315 323 Calibration Calibration Curves with Nonuniform Variance 1979 L. Schwartz Analytical Chem. 51: 723 727 Calibration Elimination of the Bias in the Course of Calibration ( annotated only) 1978 L. J. Naszódi Technometrics 20: 201 205 Calibration Optimal Designs for the Inverse Regression Method of Calibration ( annotated only) 1973 M. A. Thomas, R. H. Myers Communications in Statistics 2: 419 433 Calibration A Statistical Theory of Calibration ( annotated only) 1973 H. Scheffé The Annals of Statistics 1: 1 37 Calibration On the Problem of Calibration ( annotated only) 1972 G. K. Shukla Technometrics 14: 547 553 Calibration Statistical Processing of Calibration Data in Quantitative Analysis by Gas Chromatography 1970 P. Bocek, J. Novak J. Chromatog. 51: 375 383 Calibration Estimation of a Linear Function for a Calibration Line: Consideration of a Recent Proposal 1969 J. Berkson Technometrics 11: 649 660 Calibration A Note on Regression Methods in Calibration ( annotated only) 1969 E. J. Williams Technometrics 11: 189 192 Calibration Classical and Inverse Regression Methods of Calibration in Extrapolation ( annotated only) 1969 R. G. Krutchkoff Technometrics 11: 605 608 Calibration Title Year Author Source Category Draft Document for Peer Review  Do Not Circulate  August 2002 A 7 Optimal Experimental Designs for Estimating the Independent Variable in Regression ( annotated only) 1968 R. L. Ott, R. H. Myers Technometrics 10: 811 823 Calibration Classical and Inverse Regression Methods of Calibration ( annotated only) 1967 R. G. Krutchkoff Technometrics 9: 425 439 Calibration The Interpretation of Certain Regression Methods and their Use in Biological and Industrial Research ( annotated only) 1939 C. Eisenhart The Annals of Mathematical Statistics 10: 162 186 Calibration The Three " Rs" for Relevant Detection, Reliable Quantitation and Respectable Reporting Limits 2000 Ann Rosecrance Env. Testing & Anal. 9: 13,50 Critique Detection and Quantification Capabilities and the Evaluation of Low Level Data: Some International Perspectives and Continuing Challenges 2000 L. A. Currie Journal of Radioanalytical and Nuclear Chemistry 245: 145 156 Critique Realistic Detection Limits from Confidence Bands 1999 J. R. Burdge, D. L. McTaggart, S. O. Farwell Journal of Chemical Education 76: 434 439 Critique Response to Comment of " An Alternative Minimum Level Definition for Analytical Quantification" 1999 Henry Kahn, William Telliard, Chuck White Env. Sci. & Tech. 33: 1315 Critique Comment on " An Alternative Minimum Level Definition for Analytical Quantification" 1999 H. G. Rigo Env. Sci & Tech. 33: 1311 1312 Critique Response to Comment on " An Alternative Minimum Level Definition for Analytical Quantification" 1999 Robert Gibbons, David Coleman, Ray Maddalone Env. Sci. & Tech. 33: 1313 1314 Critique Comment on " An Alternative Minimum Level Definition for Analytical Quantification" 1998 Henry Kahn, William Telliard, Chuck White Envir. Sci & Tech 32: 2346 2348 Critique Response to Comment on " An Alternative Minimum Level Definition for Analytical Quantification" 1998 Robert Gibbons, David Coleman, Ray Maddalone Envir. Sci & Tech 32: 2349 2353 Critique A Discussion of Issues Raised by Lloyd Currie and a Cross Disciplinary View of Detection Limits and Estimating Parameters that are Often At or Near Zero ( annotated only) 1997 C. H. Spiegelman Chemometrics and Intelligent Laboratory Systems 37: 183 188 Critique A Mock Trial for Critical Values ( Detection Limits) ( annotated only) 1997 C. H. Spiegelman, P. Tarlow STATS: The Magazine for Students of Statistics 20:

13 16 Critique Comment on " An Alternative Minimum Level Definition for Analytical Quantification" 1997 David Kimbrough Envir. Sci. & Tech. 31: 3727 3728 Critique The Smallest Concentration 1997 R. F. Moran, E. N. Brown Clinical Chemistry 43: 856 857 Critique A Statistical Overview of Standard ( IUPAC and ACS) and New Procedures for Determining the Limits of Detection and Quantification: Application to Voltammetric and Stripping Techniques ( Technical Report) 1997 J. Mocak, A. M. Bond, S. Meitchell, G.

Scollary Pure and Applied Chemistry 69: 297 328 Critique Response to Comment on " An Alternative Minimum Level Definition for Analytical Quantification" 1997 R. D. Gibbons, D. E. Coleman, R. F.

Maddalone Envir. Sci. & Tech 31: 3729 3731 Critique Some Conceptual and Statistical Issues in Analysis of Groundwater Monitoring Data 1996 R. D. Gibbons Environmetrics 7: 185 199 Critique Some Statistical and Conceptual Issues in the Detection of Low Level Environmental Pollutants 1995 Robert Gibbons Environ. & Ecol. Statistics 2: 125 167 Critique Asessment of Detetion and Quantitation Concepts Title Year Author Source Category Draft Document for Peer Review  Do Not Circulate  August 2002 A 8 Comment on " Method Detection Limits in Solid Waste Analysis" 1995 D. E. Coleman Environmental Science & Technology 29: 279 280 Critique Comment on " Method Detection Limits in Solid Waste Analysis" 1995 Janice Wakakuwa, David Kimbrough Envir. Sci. & Tech. 29: 281 282 Critique " You Can't Compute With Less Thans" 1994 Ken Osborn, Ann Rosecrance East Bay Municipal Utility District, Core Laboratories Critique Limits of Detection 1994 N. Cressie Chemometrics Intelligent Laboratory Systems 22:

161 163 Critique Conflicting Perspectives About Detection Limits and About the Censoring of Environmental Data 1994 M. J. R. Clark, P. H. Whitfield Water Resources Bulletin 30: 1063 1079 Critique Limit of Discrimination, Limit of Detection and Sensitivity in Analytical Systems 1994 R. Ferrus, M. R. Egea Analytica Chimica Acta 287: 119 145 Critique Discussion of: A Study of the Precision of Lead Measurements at Concentrations Near the Method Limit of Detection 1994 B. R. Nott, R. R. Maddalone Water Environment Research 66: 853 854 Critique Limits of Detection Methodologies 1993 J. Lindstedt Plating and Surface Finishing 80: 81 86 Critique Method Detection Limits in Solid Waste Analysis 1993 David Kimbrough, Janice Wakakuwa Enviro. Sci. & Tech 27: 2692 2699 Critique Defining the Limits 1993 G. Stanko, W. Krochta, A. Stanley, T. Dawson,

K. Hillig, R. Javick, R. Obrycki, B. Hughes, F.

Saksa Environmental Lab 1: 16 20 Critique A Study of the Precision of Lead Measurements at Concentrations Near the Method Limit of Detection 1993 P. M. Berthouex Water Environment Research 65: 620 629 Critique Detection Limit Concepts: Foundations, Myths, and Utilization 1992 D. A. Chambers, S. S. Dubose, E. L.

Sensintaffar Health Phys. 63: 338 340 Critique Difficulties Related to Using Extreme Percentiles for Water Quality Regulations 1991 P. M. Berthouex, Ian Hau Research Journal WPCF 63: 873 879 Critique A Simple Rule for Judging Compliance Using highly Censored Samples 1991 P. M. Berthouex, Ian Hau Research Journal WPCF 63: 880 886 Critique Current Method for Setting Dioxin Limits in Water Requires Reexamination 1990 J. LaKind, E. Rifkin Env. Sci. & Tech 24: 963 965 Critique Kaiser 3 Sigma Criterion  A Review of the Limit of Detection 1990 L. S. Oresic, V. Grdinic Acta Pharmaceutica Jugoslavica 40: 21 61 Critique MCL Noncompliance: Is the Laboratory at Fault? 1990 Steven Koorse AWWA p53 58 Critique Qualitative or Quantitative Characterization of Spectrographic Methods? The Detection and Determination Limits 1990 Karol Florian Chemia Analityczna 35: 129 139 Critique False Positives, Detection Limits, and Other Laboratory Imperfections: The Regulatory Implications 1989 Steven Koorse Environmental Law Reporter 19: 10211 10222 Critique Evaluation of Detection Limit Estimators ( annotated only) 1988 F. C. Garner, G. L. Robertson Chemometrics and Intelligent Laboratory Systems 3:

53 59 Critique Title Year Author Source Category Draft Document for Peer Review  Do Not Circulate  August 2002 A 9 Chemometrics  Measurement Reliability 1988 K. Castaneda Mendez Clinical Chemistry 34: 2494 2498 Critique The Detection Limit 1988 P. S. Porter, R. C. Ward, H. F. Bell Environmental Science & Technology 22: 856 861 Critique Estimation of Detection Limits for Environmental Analytical Procedures  A Tutorial 1988 Cliff Kirchmer ACS Symposium Series 361: 78 93 Critique Limits of Detection 1984 J. K. Taylor Analytical Chemistry 56: 130A Critique Clarification of the Limit of Detection in Chromatography 1984 J. P. Foley, J. G. Dorsey Chromatographia 18: 503 511 Critique Limit of Detection: A Closer Look at the IUPAC Definition 1983 Gary Long, J. D. Winefordner Analytical Chem. 55: 712 724 Critique Trace Analyses for Wastewaters 1982 C. J. Kirchmer Envir. Sci. & Tech. 16: 430A Critique A comparison of statistical and empirical detection limits 1998 G. C. C. Su Journal of AOAC International 81: 105 110 Multilab Challenges in Regulatory Environmetrics 1997 C. B. Davis Chemometrics Intelligent Laboratory Systems 37:

43 53 Multilab Determining Quantitation Levels for Regulatory Purposes 1996 P. F. Sanders, R. L. Lippincott, A. Eaton Journal American Water Works Association 88: 104

114 Multilab Defining Detection and Quantitation Levels 1993 Raymond Maddalone, James Rice, Ben Edmondson, Babu Nott, Judith Scott Water Envir. & Tech. Jan. 93: 41 44 Multilab Concept 2000 A Statistical Approach for Analytical Practice  Part I: Limits of Detection,

Identification, and Determination 1999 Hadrich J et al Deutsche Lebensmittel Rundschau 1999, 95( 10),

428 436 not found Statistics and Environmental Policy: Case Studies from Long Term Environmental Monitoring Data 1999 Goudey R et al Novart FDN Sym 220; 144 157 not found The Many Dimensions of Detection in Chemical Analysis 1983 Currie LA Abstracts of Papers of the American Chemical Society, 185 ( Mar), 63 PEST not found A Practical Strategy for Determining and Verifying Detection Limits 2001 T. Georgian, K. E. Osborn Env. Testing & Analysis 10: 13 14 Single lab Review of the Methods of the US Environmental Protection Agency for Bromate Determination and Validation of Method 317.0 for Disinfection By Product Anions and Low  Level Bromate 2001 D. P. Hautman, D. J. Munch, C. Frebis, H. P.

Wagner, B. V. Pepich Journal of Chromatography A 920: 221 229 Single lab Comparison of Detection Limits in Environmental Analysis  Is it Possible? An Approach on Quality Assurance in the Lower Working Range by Verification 2001 S. Geib, J. W. Einax Fresenius Journal of Analytical Chemistry 370: 673

678 Single lab On the Assessment of Compliance with Legal Limits, Part I: Signal and Concentration Domains 2001 E. Desimoni, S. Mannino, B. Brunetti Accreditation Quality Assurance 6: 452 458 Single lab Capability of Detection  Part 2 2000 ISO ISO 11843 2 Single lab Nomenclature in Evaluation of Analytical Methods Including Detection and Quantifation Capabilities ( IUPAC Recommendations 1995) 1999 L. A. Currie Analytica Chimica Acta 391: 105 126 Single lab Asessment of Detetion and Quantitation Concepts Title Year Author Source Category Draft Document for Peer Review  Do Not Circulate  August 2002 A 10 New Reporting Procedures Based on Long Term Method Detection Limits and Some Considerations for Interpretations of Water Quality Data Provided by the U. S.

Geological Survey National Water Quality Laboratory 1999 C. J. Obinger Childress, W. T. Foreman, B.

F. Connor, and T. J. Maloney USGS Open File Report 99 193, 19 pages. Single lab Analyses of Polychlorinated Biphenyls and Chlorinated Pesticides in Biota: Method and Quality Assurance 1999 P. Cleemann, G. B. Paulsen Journal of AOAC International 82: 1175 1184 Single lab Detection Limits of Organic Contaminants in Drinking Water 1998 W. M. Draper, J. S. Dhoot, J. S. Dhaliwal,

J. W. Remoy, S. K. Perera, F. J. Baumann Journal of the American Water Works Association 90: 82 90 Single lab Detection: International Update, and Some Emerging Di lemmas Involving Calibration,

the Blank, and Multiple Detection Decisions ( annotated only) 1997 L. A. Currie Chemometrics and Intelligent Laboratory Systems 37: 151 181 Single lab Regulations  From an Industry Perspective or Relationships Between Detection Limits,

Quantitation Limits, and Significant Digits ( annotated only) 1997 D. Coleman, J. Auses, N. Grams Chemometrics and Intelligent Laboratory Systems 37: 71 80 Single lab Capability of Detection  Part 1 1997 ISO ISO 11843 1 Single lab Determination of Site Specific Effluent Detection Limits 1996 George Neserke, Harold Taylor Water Env. Res. 66: 115 119 Single lab Multivariate Detection Limits Estimators 1996 R. Boque, F. X. Rius Chemometrics and Intelligent Laboratory Systems 32: 11 23 Single lab Nomenclature in Evaluation of Analytical Methods including Detection and Quantification Capabilities 1996 Lloyd Currie Pure & Appl. Chem. 67: 1699 1723 Single lab Reporting Low Level Analytical Data Third Draft ( 1995 11 08)  New Project of Commission V. I., International Union of Pure and Applied Chemistry 1995 William Horwitz IUPAC Single lab IUPAC Recommendations for Defining and Measuring Detection and Quantification Limits 1994 LA Currie, W. Horwitz Analuses Magazine 22: 24 26 Single lab Recommendations for the Presentation of Results of Chemical Analysis ( annotated only) 1994 L. A. Currie, G. Svehla Pure & Applied Chemistry 66: 595 608 Single lab Detarchi A Program for Detection Limits with Specified Assurance Probabilites and Characteristic Curves of Detection 1994 L. Sarabia, M. C. Ortiz TRAC Trends in Analytical Chemistry 13: 1 6 Single lab Quality Control Level: An Alternaltive to Detection Levels 1994 D. E. Kimbrough, J. Wakakuwa Environmental Science & Technology 28: 338 345 Single lab Multivariate Decision and Detection Limits ( annotated only) 1993 A. Singh Analytica Chimica Acta 277: 205 214 Single lab A Model of Measurement Precision at Low Concentrations 1993 P. M. Berthouex, D. R. Gan Water Environment Research 65: 759 763 Single lab Robust Procedure for Calibration and Calculation of the Detection Limit of Trimipramine by Adsorptive Stripping Voltametry at a Carbon Paste Electrode 1993 M. C. Ortiz, J. Arcos, J. V. Jurarros, J. Lopez

Palacios, L. A. Sarabia Analytical Chemistry 65: 678 682 Single lab Nondetects, Detection Limits, and the Probability of Detection 1991 D. Lambert, B. Peterson, I. Terpenning JASA 86: 266 277 Single lab Title Year Author Source Category Draft Document for Peer Review  Do Not Circulate  August 2002 A 11 Detection Limits: For Linear Calibration Curves with Increasing Variance and Multiple Future Detection Decisions ( annotated only) 1991 R. D. Gibbons, F. H. Jarke, K. P. Stoub Waste Testing and Quality Assurance: ASTM STP 1075, D. Friedman, Ed., American Society for Testing and Materials, Philadelphia 3: 337 390 Single lab Limits of Detection in Multivariate Calibration 1991 G. Bauer, W. Wegscheider, H. M. Ortner Fresenius Journal of Analytical Chemistry 340: 135

139 Single lab Estimating Detection Limits in Ultratrace Analysis. Part I: The Variability of Estimated Detection Limits 1991 C. L. Stevenson, J. D. Winefordner Applied Spectroscopy 45: 1217 1224 Single lab Reporting Low Level Data for Computerized Data Bases 1988 M. Brossman, G. McKenna, H. Kahn, D.

King, R. Kleopfer, J. Taylor  ACS Symposium Series 361: 317 327 Single lab Detection Limits with Specified Assurance Probabilities 1987 C. A. Clayton, J. W. Hines, and P. D. Elkins Analytical Chemistry 59: 2506 2514 Single lab Limit of Detection and Limit of Determination  Application of Different Statistical Approaches to an Illustrative Example of Residue Analysis 1987 J. Vogelgesang Fresenius Zeitschrift Fur Analytsche Chemie 328:

213 220 Single lab Determining the Lowest Limit of Reliable Assay Measurement ( annotated only) 1983 L. Oppenheimer, T. P. Capizzi, R. M.

Weppelman, H. Mehta Analytical Chemistry 55: 638 643 Single lab Principles of Environmental Analysis 1983 L. H. Keith, W. Crummett, J. Deegan Jr, R. A.

Libby, J. K. Taylor, G. Wentler Analytical Chemistry 55: 2210 2218 Single lab Trace Analyses for Wastewaters 1981 John Glaser, Denis Foerst, Gerald McKee,

Stephan Quave, William Budde Env. Sci. & Tech 15: 1426 1435 Single lab Guidelines for Data Acquisition and Data Quality Evaluation in Environmental Chemistry 1980 ACS Committee of Environmental Improvement Anal. Chem. 52: 2242 2249 Single lab Sensitivity and Limit of Detection in Quantitative Spectrometric Methods ( annotated only) 1974 J. D. Ingle Jr. Journal of Chemical Education, 51, 100 105. Single lab Decision and Detection Limits for Linear Calibration Curves ( annotated only) 1970 A. Hubaux, G. Vos Analytical Chemistry 42: 849 855 Single lab Limits for Quantitative Detection and Quantitative Determination ( annotated only) 1968 L. A. Currie Analytical Chemistry 40: 586 593 Single lab A Statistical Method for Evaluation of Limiting Detectable Sample Concentrations 1967 P. A. St. John, W. J. McCarthy, J. D.

Winefordner Analytical Chem. 39: 1495 1597 Single lab Initial Evaluation of Quantitative Performance of Chromatographic Methods Using Replicates at Multiple Concentrations 2001 M. A. Castillo, R. C. Castells Journal of Chromatography A 921: 121 133 Single lab

multilevel Multivariate Detection Limits with Fixed Probabilities of Error 1999 R. Boque, M. S. Larrechi, F. X. Rius Chemometrics and Intelligent Laboratory Systems 45: 397 408 Single lab

multilevel Evaluation of Approximate Methods for Calculating the Limit of Detection and Limit of Quantification 1999 M. E. Zorn, R. D. Gibbons, W. C. Sonzogni Environmental Science & Technology 33: 2291

2295 Single lab

multilevel Asessment of Detetion and Quantitation Concepts Title Year Author Source Category Draft Document for Peer Review  Do Not Circulate  August 2002 A 12 Limits of Detection, Identification and Determination: A Statistical Approach for Practitioners 1998 J. Vogelgesang, J. Hadrich Accreditation Quality Assurance 3: 242 255 Single lab

multilevel Weighted Least Squares Approach to Calculating Limits of Detection and Quantification by Modeling Variability as a Function of Concentration ( annotated only) 1997 M. E. Zorn,, R. D. Gibbons, W. C. Sonzogni Analytical Chemistry 69: 3069 3075 Single lab

multilevel Detection Limits in GC MS Multivariate Analysis 1997 Boque R et al Quimica Analytica, 16( 2), 81 86 Single lab

multilevel An Alternative Minimum Level Definition for Analytical Quantification 1997 Robert Gibbons, David Coleman, Raymond Maddalone Environmental Science & Technology 31: 2071

2077 Single lab

multilevel A Two Component Model for Measurement Error in Analytical Chemistry 1995 David Rocke, Stefan Lorenzato Technometrics 37: 176 184 Single lab

multilevel Practical Quantitation Limits ( annotated only) 1992 R. D. Gibbons, N. E. Grams, F. H. Jarke, K. P.

Stoub Chemometrics and Intelligent Laboratory Systems 12: 225 235. Single lab

multilevel Experimental Comparison of EPA and USATHAMA Detection and Quantitation Capability Estimators 1991 C. L. Grant, A. D. Hewitt, T. F. Jenkins American Laboratory 23: 15 33 Single lab

multilevel High Pressure Liquid Chromatography Determination of the Intermediates Side Reaction Products in FD& C Red No. 2 and FD& C Yellow No. 5: Statistical Analysis of Instrument Response ( annotated only) 1978 C. J. Bayley, E. A. Cox, J. A. Springer J. Assoc. Off. Anal. Chem 61: 1404 1414. Single lab

multilevel Draft Document for Peer Review  Do Not Circulate  August 2002 B 1 Appendix B is not done yet, but it will go here when it is. It will be sent to the reviewers shortly. Draft Document for Peer Review  Do Not Circulate  August 2002 C 1 Appendix C is not done yet, but it will go here when it is. It will be sent tothe reviewers shortly. Draft Document for Peer Review  Do Not Circulate  August 2002 D 1 Appendix D Draft Revised MDL Procedure Definition and Procedure for the Determination of the Method Detection Limit, Revision 4.0, Draft, July 1, 2002 Definition The method detection limit ( MDL) is the measured concentration at which there is 99% confidence that a given analyte is present in a given sample matrix. The MDL is estimated from replicate analyses of a matrix containing the analyte.

Scope and Application This procedure is for determination of an MDL for a given analyte ( parameter) in a given matrix ( the component or substrate that contains the analyte) using a given test procedure ( analytical method). It is applicable to a wide variety of analytes, to matrices ranging from air through water to solid, and to a broad variety of physical, chemical, and biological methods. The MDL for a given analyte measured using a given analytical method may vary as a function of the matrix tested.

This procedure requires a complete, specific, and well defined analytical method. It is essential that all sample processing steps of the analytical method be included in determination of an MDL. The procedure is independent of the analytical system employed for the measurement.

The MDL must be determined in a reference matrix to demonstrate the capability of the analytical method in the absence of interferences. It may be determined in other matrices if desired.

Procedure 1. Estimate the MDL The experience of the laboratory is important to properly estimate the MDL. However, the laboratory must include the following considerations in producing this initial estimate:

1.1 The concentration of analyte that produces an instrument signal/ noise in the range of 2.5 to 5 for those instances in which an instrument is used for the determination.

1.2 The concentration equivalent to three times the standard deviation of replicate measurements of the analyte in a blank. If analysis of the blank produces no response ( zero), the concentration equivalent to three times the standard deviation of replicate measurements at the lowest concentration that produces a response.

1.3 A concentration in the region of constant or near constant standard deviation at low concentrations.

1.4 The lowest concentration that can be measured by analyzing samples containing successively lower concentrations of the analyte.

2. Select a reference matrix To establish the capability of an analytical method, the MDL must be determined in a matrix free of the analyte and free of matrix effects ( interferences). The most common reference matrix is reagent Assessment of Detection and Quantitation Concepts Draft Document for Peer Review  Do Not Circulate  August 2002 D 2 water. Reagent water is defined as water in which the analyte and interferences are not detected at the MDL or, if this is the initial estimate, detected at the MDL estimate produced in Step 1. An interference is defined as a systematic error in the measured analytical signal caused by the presence of a substance other than the analyte. Other common reference matrices are playground sand for soils, sediments, and other solid samples; and corn oil for tissue samples.

3. Establish the test concentration 3.1 Reference matrix   Spike the reference matrix with the analyte to produce a test concentration in the range of one to five times the MDL estimated in Step 1 or, if this is an iteration from Step 7, the last MDL calculated. A sufficient quantity of spiked reference matrix should be prepared for 7 analyses minimum. Proceed to Step 4.

3.2 Matrix other than reference matrix   Analyze a sample of the matrix in triplicate.

3.2.1 If the average measured concentration of the analyte in the matrix is in the range of one to five times the MDL determined in the reference matrix ( Steps 3.1 and 4  7), proceed to Step 4.

3.2.2 If the average measured concentration of the analyte in the matrix is less than the MDL determined in the reference matrix, spike a sample of the matrix to bring the concentration of the analyte to between one and five times the MDL determined in the reference matrix. Proceed to Step 4.

3.2.3 If the average measured concentration of the analyte in the matrix is greater than five times the MDL determined in the reference matrix, reduce the concentration of the analyte to between one and five times the MDL determined in the reference matrix using one of the following:

<IP_ADDRESS> Selectively remove the analyte from the matrix.

<IP_ADDRESS> Obtain another matrix with a lower concentration of the analyte.

<IP_ADDRESS> Dilute a sample of the matrix with the reference matrix. For example, if the matrix is aqueous, dilute the sample with reagent water.

3.3 If this is an iteration from Step 7, spike the matrix at the last MDL calculated.

4. Perform the analyses 4.1 It may be economically and technically desirable to evaluate the estimate of the MDL ( Step 1) before proceeding with determination of the MDL in Step 4.2. This will prevent repeating this entire procedure when the costs of analyses are high, and attempt to insure that the procedure is being conducted at the correct concentration. To evaluate the estimated MDL, proceed as follows:

4.1.1 Process three aliquots of the sample to be used to calculate the MDL ( Step 3) through the entire method per Step 4.2. Appendix D Draft Document for Peer Review  Do Not Circulate  August 2002 D 3 4.1.2 Calculate the standard deviation of results for the three aliquots as follows:

Where:

Xi; i= 1 to 3, are the analytical results in the method reporting units obtained from analysis of the 3 sample aliquots, and 2 is the degrees of freedom for the 3 measurements.

4.1.3 Calculate a preliminary MDL as follows:

Preliminary MDL = 7s where:

7 = the Students' t value appropriate for a 99% confidence level and a standard deviation estimate with 2 degrees of freedom ( 3 replicates). s = standard deviation of results of analyses of the 3 replicates.

4.1.4 If the Preliminary MDL is in the range of 0.2  1.0 times the concentration in the spiked sample ( Step 3), take four additional aliquots and proceed using the procedure in Step 4.2. Use all seven measurements for calculation of the MDL. Otherwise, produce a new sample per Step 3 with the analyte at the concentration of the Preliminary MDL and either repeat Step 4.1 or proceed to Step 4.2 for determination of the MDL.

4.2 Process seven aliquots of the test sample chosen in Step 3 or Step 4.1 through the entire analytical method. Make all computations according to the method with final results in the method reporting units. To obtain a valid MDL, the seven analytical results must all be positive numbers. If any of the seven results is negative or zero, increase the test concentration ( per Step 3) and repeat the MDL procedure.

5. Calculate the standard deviation ( s) from the last set of seven replicate analyses, as follows:

where :

xi, i= 1 to i= 7, are the results from the last ( kth) set of seven replicate analyses. Assessment of Detection and Quantitation Concepts Draft Document for Peer Review  Do Not Circulate  August 2002 D 4 6. Calculate the MDL where:

MDL = the method detection limit 3.14 = the Students' t value appropriate for a 99% confidence level and a standard deviation estimate with 6 degrees of freedom. s = standard deviation of analyses of the 7 replicates.

7. Iterate and verify the reasonableness of the MDL 7.1 If two MDL estimates have been produced, proceed to step 7.2; otherwise, return to step 3 and produce a second MDL estimate.

7.2 Calculate:

where:

s2 h = the variance estimate from the larger spike concentration in the last two iterations s2 l = the variance estimate from the smaller spike concentration in the last two iterations 7.3 If F > 3.05, return to step 3 and produce another MDL estimate; otherwise continue.

7.4 The final MDL is the last ( most recent) MDL calculated from two successive iterations that pass the F ratio test ( F # 3.05).

Reporting The following information must be reported to support the MDL for each analyte:

C Analytical method by number and title C Matrix C Mean recovery ( if a laboratory standard or a sample that contained a known amount of analyte was used for the MDL determination)

The following information must also be reported, and expressed in the appropriate method reporting units:

C Concentration at which the test was performed C The measured concentration in each aliquot for the iterations used to calculate the MDL ( 14 results) C The final MDL If the analytical method permits a change in analytical conditions that could affect the MDL, these conditions must be reported.