OMB Meeting Book - January 8, 2015

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T he main purpose for method validation is to ensure that an analytical method designed and developed for a specific purpose can actually achieve an acceptable accuracy and precision.

Thompson also showed that the robust standard deviation for PT data can usu- ally be directly compared to the RSD (R) determined in a collaborative study (10). Ellison et al. identified some condi- tions that must be met in order for PT data to be used to assess reproducibil- ity: 1. collection of reproducibility data is designed into the PT scheme before the PT scheme is initiated; 2. the can- didate method should be characterized for precision and bias in a single-labo- ratory type of evaluation prior to being included in a PT validation project; 3. there must be a formal set of method instructions; 4. some minimum number of the laboratories participating in the PT program must use the candidate method under review; and 5. the PT scheme must include a range of materi- als covering the scope of the method. Using Intermediate Reproducibility to Determine Measurement Uncertainty Estimation of measurement uncer- tainty is an integral part of the modern accreditation process. ISO 17025 states that measurement uncertainty must be estimated and made avail- able if requested by the customer. The Codex Alimentarius Commission has guidelines that require laboratories involved in the import/export of foods to be accredited and report measurement uncertainty (11). Historically, AOAC has relied on the RSD (R) as an adequate estimate of the measurement uncer- tainty, and therefore AOAC does not require method developers to calculate or report measurement uncertainty as a part of the method evaluation process. However, measurement uncer- tainty can be estimated using many procedures which are described in the literature (12–14). In principle, two approaches may be used when calcu- lating the measurement uncertainty of a test result: the ‘Top-down’ or Type A approach which is based upon a statis- tical evaluation of the test results from samples that have undergone the entire analytical process; and the ‘Bottom-up’ or Type B approach in which all pos- sible sources of variation of the result are listed separately and the contribu-

uncertainty from single-laboratory vali- dation (SLV) data (17). Weitzel used accuracy, bias, precision, ruggedness, and intermediate reproducibility data to calculate the measurement uncertainty. In a separate communication, Weitzel also pointed out that that some AOAC method manuscripts already include a measurement uncertainty calculation (18). For example, in AOAC Official Method 2011.07, a method for the determination of vitamins A and E by UPLC-UV or FLD, the authors use a simplified approach described by Barwick and Ellison (19) to calculate measurement uncertainty using preci- sion and trueness study data. AOAC Official Method 2011.12, a method for the determination of vitamins D 2 and D 3 in food by UPLC/MS/MS, also includes an estimate of the measurement uncertainty calculated using a combination of preci- sion and analytical competence data. These method evaluations demon- strate that calculating measurement uncertainty from a variety of in-house or SLV data is a relatively trivial task if the evaluation studies are properly planned to consider the necessary data required to calculate measurement uncertainty. The main purpose for method vali- dation is to ensure that an analytical method designed and developed for a specific purpose can actually achieve an acceptable accuracy and precision. The main purpose for investigating the reproducibility of a method is to assess On-Site Verification

tion of each source to the measurement uncertainty is estimated. The Bottom-up approach to estimate the uncertainty of analytical results seems to be rather impractical for methods of analysis (15). In practice most laboratories have used the Top-down or Type A approach, estimating the measurement uncer- tainty using the data available from quality control, sample duplicates, and method validation, especially intermedi- ate reproducibility. There are three kinds of data that may be used to calculate the expanded uncertainty (U) using the Top-down approach: 1. Data from the original validation of the method 2. Data obtained from collaborative studies 3. Data obtained within a laboratory using the method (16) ISO Technical Standard 21748 “Guide to the Use of Repeatability, Reproducibility and Trueness Estimates in Measurements Uncertainty Estimation” provides several procedures for the estimation of the measurement uncertainty using repeatability and true- ness data. This would make it possible to determine measurement uncertainty using only in-house or single-laboratory data. This may be an attractive option in lieu of the difficulties in organizing collaborative studies. In a recent article entitled The Estimation and Use of Measurement Uncertainty for a Drug Test Procedure Validated According to USP <1225>, Weitzel illustrated with examples the procedures to determine measurement

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