Trueness sampling

CsuRos M (1997) Environmental Sampling and Analysis. Laboratory Manual. CRC Press. Doerfeel K (1994) Assuring trueness of analytical results. Fresenius J Anal Chem 348 183-184... 

In summary, official German analytical methods for pesticide residues are always validated in several laboratories. These inter-laboratory studies avoid the acceptance of methods which cannot readily be reproduced in further laboratories and they do improve the ruggedness of analytical procedures applied. The recently introduced calibration with standards in matrix improves the trueness of the reported recovery data. Other aspects of validation (sample processing, analyte stability, extraction efficiency) are not considered. 

The reliability of multispecies analysis has to be validated according to the usual criteria selectivity, accuracy (trueness) and precision, confidence and prediction intervals and, calculated from these, multivariate critical values and limits of detection. In multivariate calibration collinearities of variables caused by correlated concentrations in calibration samples should be avoided. Therefore, the composition of the calibration mixtures should not be varied randomly but by principles of experimental design (Deming and Morgan [1993] Morgan [1991]). 

It should be noted that the term sensitivity sometimes may alternatively be used, namely in analytical chemistry and other disciplines. Frequently the term sensitivity is associated with detection limit or detection capability. This and other misuses are not recommended by IUPAC (Orange Book [1997, 2000]). In clinical chemistry and medicine another matter is denoted by sensitivity , namely the ability of a method to detect truly positive samples as positive (O Rangers and Condon [2000], cited according to Trullols et al. [2004]). However, this seems to be more a problem of trueness than of sensitivity. 

Limit of Quantification (LoQ) is the lowest concentration of analyte in a sample that can be determined with acceptable accnracy, i.e. with acceptable precision and acceptable trueness, nnder the stated conditions of the test. 

The recovery can be used as a measure of the trueness. Recoveries usually depend on sample matrix, sample preparation method and concentration present in the sample. The mean % recovery for a trace component (<... 

For trueness control synthetic samples with known content or RM/CRM samples may be analysed... 

Provided that standard solutions are completely independent from the calibration solutions they can be used to verify the calibration. But the influence of a sample matrix cannot be detected. Therefore only a limited precision check and only a very little control of trueness are possible. 

Trueness is expressed in terms of bias or percentages of error. Bias is the difference between the mean value determined for the analyte of interest and the accepted true value or known level actually present [87]. It represents the systematic deviation of the measured result from the true result. Method trueness is an indicator also for utility and applicability of that method with real samples [88]. 

One or more of these bias components are encountered when analyzing RMs. In general, RMs are divided into certified RMs (CRMs, either pure substances/solu-tions or matrix CRMs) and (noncertified) laboratory RMs (LRMs), also called QC samples [89]. CRMs can address all aspects of bias (method, laboratory, and run bias) they are defined with a statement of uncertainty and traceable to international standards. Therefore, CRMs are considered useful tools to achieve traceability in analytical measurements, to calibrat equipment and methods (in certain cases), to monitor laboratory performance, to validate methods, and to allow comparison of methods [4, 15, 30]. However, the use of CRMs does not necessarely guarantee trueness of the results. The best way to assess bias practically is by replicate analysis of samples with known concentrations such as reference materials (see also Section 8.2.2). The ideal reference material is a matrix CRM, as this is very similar to the samples of interest (the latter is called matrix matching). A correct result obtained with a matrix CRM, however, does not guarantee that the results of unknown samples with other matrix compositions will be correct [4, 89]. 

Two aspects are important for IQC (1) the analysis of control materials such as reference materials or spiked samples to monitor trueness and (2) replication of analysis to monitor precision. Of high value in IQC are also blank samples and blind samples. Both IQC aspects form a part of statistical control, a tool for monitoring the accuracy of an analytical system. In a control chart, such as a Shewhart control chart, measured values of repeated analyses of a reference material are plotted against the run number. Based on the data in a control chart, a method is defined either as an analytical system under control or as an analytical system out of control. This interpretation is possible by drawing horizontal lines on the chart x(mean value), x + s (SD) and x - s, x + 2s (upper warning limit) and x-2s (lower warning limit), and x + 3s (upper action or control limit) and x- 3s (lower action or control limit). An analytical system is under control if no more than 5% of the measured values exceed the warning limits [2,6, 85]. 

There are two major reasons why a traceability chain may be broken and trueness lost due to the introduction of bias insufficient commutability of a calibration material and non-specificity of a measurement procedure. The effect of these separate properties are often indiscriminately lumped together as matrix effect . Commutability refers to the ability of a material, here a calibrator, to show the same relationships between results from a set of procedures as given by routine samples [16, 17]. Analytical specificity refers to the ability of a measurement procedure to measure solely that quantity which it purports to examine [16, 18]. Discrepancies between results of a reference procedure and a routine procedure applied to routine samples are often caused by non-specificity of the routine procedure. The use of a set of human samples as a manufacturer s calibrator to eliminate so-called matrix effects should only be accepted if the relationship between the results from reference and routine procedures is sufficiently constant to allow explicit correction with consequent increased uncertainty of assigned values. 

If the laboratory develops the validation method in-house, there always needs to be some sample to be used for this purpose a sample that best mimics routine samples is the most suitable. The usual practice is that a routine sample is used for this purpose as knowledge of the true value is not a critical issue at this stage. Next, the trueness of a method is usually determined by analyzing an appropriate CRM and/or participating in an ILC, one with an externally defined reference value.10... 

As stated further above, under all circumstances trueness, accuracy and sensitivity of the assay should be demonstrated on a sufficient number of patient samples. In our view, however, the use of an additional, also easily realizable chromatographic dimension (online-SPE-LC-MS/MS) [67, 68] without a doubt represents the analytical state of the art in immunosuppressant TDM. Nowadays tandem MS instruments are used almost exclusively for the detection and quantification of analytes. The detection of analytes is generally performed in the selected reaction monitoring (SRM, synonym MRM) mode. Depending on which instrumentation is used, an analysis can be completed within two to four minutes. 

The other major use of CRMs is to ascertain the trueness of the results found. A common (but unregulated) practice is to assume that the results for the samples are correct if the result of the CRM, analyzed along with the samples, is within the 95 percent CL It should be kept in mind that the analyst knows the certified levels of the analytes in the CRM prior to the analysis, which unavoidably makes him/her biased. Therefore, good CRM results may indicate that the results of the samples the CRM was analyzed with are just as good, but are not useful as a guarantee for the trueness of results. 

Before assessing the other validation parameters (trueness, recovery, precision, selectivity, specificity, detection capability, stability, and applicability/mgged-ness), the appropriateness of the calibration model should be evaluated. The correctness of the analytical determination of elements in food and food products depends indeed on the choice and the evaluation of the calibration model. The calibration model gives the mathematical relationship between the signal of the measuring system and the concentration in the sample. Several authors have published guidelines concerning calibration in analytical chemistry [5-7]. 

The true value (see Section 6.2) of the amount of analyte in the sample matrix is not normally known. However, in some cases, certified reference materials (CRM) (see Section 3.1) are available. The analyst can perform an analysis on the CRM. The difference between the measured mean value and the stated value is a measure of the accuracy of the method (also known as trueness, bias). 

Reference materials, that is, materials of known composition of components (usually determined on the basis of interlaboratory tests), can be of great help in proper preparation of standard solutions. They usually serve for verification of trueness of a given analytical procedure and accuracy of the obtained analytical result. If the composition of a reference material matches that of the analyzed sample, and the substance to be analyzed is one of its components, then the material can be utilized for calibration in such a way that a series of standard solutions is prepared via addition of analyte in known quantities. 

There are no official guidehnes on the sequence of validation experiments and the optimal sequence can depend on the method itself. A potentially useful sequence for a liquid chromatographic method is 1) Selectivity of standards (optimizing separation and detection of standard mixtures) 2) precision of retention times and peak areas 3) linearity, limit of quantitation, hmit of detection, range 4) selectivity with real samples 5) trueness or accuracy, at different concentrations 6) ruggedness. 

The accuracy of an analytical method is the degree of closeness between the true value of analytes in the sample and the value determined by the method and is sometimes called trueness [78], Accuracy can be measured by analyzing samples with known concentrations and comparing the measured values with the true values. According to FDA [16] the accuracy for bioanalysis should be determined by a minimum of five determinations for at least... 

Due to the high workload of analysing such large series, trueness is usually not determined during method validation, but rather from the results of a great number of quality control (QC) samples during routine application or in interlaboratory studies. 

Accuracy/trueness Y, N Recovery 98-102% (individual) with 80, 100, 120% spiked sample ... 

In developing the protocol, the trueness of a method was considered in terms of recovery, i.e. the ratio of the observed value to the expected value. The evaluation of uncertainties associated with recovery is discussed in detail elsewhere [18, 19]. In general, the recovery, R, for a particular sample is considered as comprising three components ... 

An uncertainty evaluation must consider the full range of variability likely to be encountered during application of the method. This includes parameters relating to the sample (analyte concentration, sample matrix) as well as experimental parameters associated with the method (e.g. temperature, extraction time, equipment settings, etc.). Sources of uncertainty not adequately covered by the precision and trueness studies require separate evaluation. There are three main sources of information calibration certificates and manufacturers specifications, data published in the literature and spe-... 

We have applied this protocol to the evaluation of the measurement uncertainty for a method for the determination of three markers (Cl solvent red 24, Cl solvent yellow 124 and quinizarin (1,4-dihydroxyanthra-quinone)) in road fuel. The method requires the extraction of the markers from the sample matrix by solid phase extraction, followed by quantification by HPLC with diode array detection. The uncertainty evaluation involved four experimental studies which were also required as part of the method validation. The studies were precision, trueness (evaluated via the analysis of spiked samples) and ruggedness tests of the extraction and HPLC stages. The experiments and uncertainty calculations are described in detail in Part 2. A summary of the uncertainty budget for the method is presented in Fig. 3. 

In Part 1 [1] we described a protocol for the evaluation of measurement uncertainty from validation studies such as precision, trueness and ruggedness testing. In this paper we illustrate the application of the protocol to a method developed for the determination of the dyes Cl solvent red 24 and Cl solvent yellow 124, and the chemical marker quinizarin (1,4-dihydroxyanthra-quinone) in road fuel. The analysis of road fuel samples suspected of containing rebated kerosene or rebated gas oil is required as the use of rebated fuels as road fuels or extenders to road fuels is illegal. To prevent illegal use of rebated fuels, HM Customs and Excise require them to be marked. This is achieved by adding solvent red 24, solvent yellow 124 and quinizarin to the fuel. A method for the quantitation of the markers was developed in this laboratory [2]. Over a period of time the method had been adapted to improve its performance and now required re-validation and an uncertainty estimate. This paper describes the experiments under-... 

In addition, the results obtained from the replicate analysis of a sample of BP diesel, prepared for the trueness study (see below), were used in the estimate of uncertainty associated with method precision. 

The precision and trueness studies were designed to cover as many of the sources of uncertainty as possible (see Fig. 1), for example, by analysing different sample matrices and concentration levels, and by preparing new standards and HPLC mobile phase for each batch of analyses. Parameters which were not adequately var-... 

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