Analytic practices recovery studies

The accuracy of a method is defined as the closeness of the value obtained to known or accepted values. Accuracy can be determined in a number of ways, depending on the nature of the CZE method and availability of orthogonal techniques to compare results. If practical, spike recovery studies (i.e., testing to determine whether recovery matches the amount of a known analyte or impurity spiked) are good alternatives to orthogonal assay comparisons. ICH guidelines also allow method accuracy to be inferred, once specificity, linearity, and precision are established. 

Two additional properties of filters, which are less apparent but still important in filter performance, are the extractables and affinity for the analyte. These filter properties are not as easy to predict for drug degradation products and impurities in that these compounds are often of unknown structure and poorly characterized. With regards to analyte affinity, it is useful to conduct a recovery study to ascertain the extent to which an analyte adheres to the membrane. This is not always practical if the prefiltrate is not suitable for direct chemical analysis or if the sample is precious. 

The recoveries obtained by the various laboratories were discussed and most were in the range 70 to 95% with standard deviations of 3 to 8%. It was agreed that the best practice was to conduct recovery studies alongside the analytical measurement. Trimethyllead in the spiked sample was determined as the difference between the spiked and the unspiked results. 

A recovery study determines the ability of a test to measure a known incremental amount of standard analyte from a sample matrix. Thus, in practice, a known amount of analyte A) is added to base B) and the recovery (C) is calculated as a concentration after performing the assay. The percentage of recovery is as follows ... 

If analytical methods are validated in inter-laboratory validation studies, documentation should follow the requirements of the harmonized protocol of lUPAC. " However, multi-matrix/multi-residue methods are applicable to hundreds of pesticides in dozens of commodities and have to be validated at several concentration levels. Any complete documentation of validation results is impossible in that case. Some performance characteristics, e.g., the specificity of analyte detection, an appropriate calibration range and sufficient detection sensitivity, are prerequisites for the determination of acceptable trueness and precision and their publication is less important. The LOD and LOQ depend on special instmmentation, analysts involved, time, batches of chemicals, etc., and cannot easily be reproduced. Therefore, these characteristics are less important. A practical, frequently applied alternative is the publication only of trueness (most often in terms of recovery) and precision for each analyte at each level. No consensus seems to exist as to whether these analyte-parameter sets should be documented, e.g., separately for each commodity or accumulated for all experiments done with the same analyte. In the latter case, the applicability of methods with regard to commodities can be documented in separate tables without performance characteristics. 

Several studies are devoted to the extraction of phenolic compounds. These compounds are particularly interesting from a practical viewpoint, as phenol derivatives are toxic pollutants that have marked detrimental effects on living organisms in general therefore, the development of effective methods of phenols recovery is a long-standing problem of analytical chemistry. To determine phenolic compounds at the trace level, typically preconcentration and separation from accompanying substances is required, but the extraction of phenolic compounds with conventional solvents is often not quantitative. From a more theoretical viewpoint, phenolic compounds exhibit a wide structural variability, thus, a study of their... 

As far as is practically possible, the selection and preparation of samples must take into account all possible variations in the matrix of the material to be analysed. The applicability of the method should be studied using various samples ranging from pure standards to mixtures with complex matrices as these may contain substances that interfere to a greater or lesser extent with the quantitative determination of an analyte or the accurate measurement of a parameter. Matrix effects can both reduce and enhance analytical signals and may also act as a barrier to recovery of the analyte from a sample. 

If the above approach creates practical problems, an alternative is to perform single analyses on a minimum of five test portions of the study sample. The standard deviation of replicate analysis results is an indicator of sample homogeneity and method precision. The disadvantage of this approach is that it does not provide a simultaneous measure of the analytical variance under the homogeneity test conditions. Analytical variance must be estimated from historical data (e.g. method validation) or spiked recoveries run with the homogeneity test samples. 

From a practical point of view, internal standard in a LC-MS/MS assay serves three distinct purposes in the analytical process. The first purpose is to compensate extraction recovery inconsistencies. The second purpose is to compensate injection volume variation. The third purpose is to compensate possible matrix effects during the MS ionization process as has already been discussed in detail above. In 2009, Tan A. et al. reported 12 case studies from incurred sample analyses using a wide variety of bioanalytical methods for the investigation of inconsistent internal standard response [23], For similar reasons, it has now become common for laboratory SOPs to contain specific requirements for the acceptable internal standard response of each individual sample within a sample batch during regulated bioanalysis. These requirements (e.g., 60-140 %, 50-150 % of the average internal standard area for all samples in the batch) ensure that the behavior of the internal standard, regardless of how well it tracks the analyte, is under control, and is consistent in all samples. 

It cannot be overemphasized that these values are for data from many laboratories in blind studies. They are useful for interpreting the results of analysis of unknown samples, as analyzed by a number of laboratories. They obviously do not correspond to the values for the repeatability (single laboratory) reported in the literature for standard solutions, recoveries of added analytes, and comparisons with other methods. Rather, the values in Figure 3 reflect the expected precision on real blind samples analyzed under somewhat ideal conditions. Analysis under practical conditions would be expected to be somewhat poorer analysis in a single laboratory by a single analyst would be expected to be considerably better. On balance, then. Figure 3 approximates what should be expected of methods operated at the indicated levels. 

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