Analytical performance parameters Accuracy

A number of experimental considerations must be addressed in order to use XRF as a quantitative tool, and these have been discussed at length [75,76]. The effects on the usual analytical performance parameters (accuracy, precision, linearity, limits of detection and quantitation, and ruggedness) associated with instrument are usually minimal. 

The United States Pharmacopoeia (U.S.P.) [5] in a chapter on validation of compendial methods, defines analytical performance parameters (accuracy, precision, specificity, limit of detection, limit of quantitation, linearity and range, ruggedness, and robustness) that are to be used for validating analytical methods. A proposed United States Pharmacopeia (U.S.P.) general chapter on near-infrared spectrophotometry [6] addresses the suitability of instrumentation for use in a particular method through a discussion of operational qualifications and performance verifications. 

NIR spectroscopy became much more useful when the principle of multiple-wavelength spectroscopy was combined with the deconvolution methods of factor and principal component analysis. In typical applications, partial least squares regression is used to model the relation between composition and the NIR spectra of an appropriately chosen series of calibration samples, and an optimal model is ultimately chosen by a procedure of cross-testing. The performance of the optimal model is then evaluated using the normal analytical performance parameters of accuracy, precision, and linearity. Since its inception, NIR spectroscopy has been viewed primarily as a technique of quantitative analysis and has found major use in the determination of water in many pharmaceutical materials. 

The following analytical performance parameters were included into the validation process selectivity stability during chromatograidiic development and in solution spot stability prior to the run and after development linearity and range precision reproducibility limit of quantitation limit of detection accuracy. The definitions used for the performance parameters the methods applied to determine them and the acceptance criteria were also described. Therefore, these papers can be recommended to be used by practicising chromatographers. 

For non-compendial procedures, the performance parameters that should be determined in validation studies include specificity/selectivity, linearity, accuracy, precision (repeatability and intermediate precision), detection limit (DL), quantitation limit (QL), range, ruggedness, and robustness [6]. Other method validation information, such as the stability of analytical sample preparations, degradation/ stress studies, legible reproductions of representative instrumental output, identification and characterization of possible impurities, should be included [7], The parameters that are required to be validated depend on the type of analyses, so therefore different test methods require different validation schemes. 

In evaluation of the performance characteristics of a candidate method, precision, accuracy (trueness), analytical range, detection limit, and analytical specificity are of prime importance. The sections in this chapter on method evaluation and comparison contain a detailed outline of these concepts and their assessment. The estimated performance parameters for a method can then be related to quality goals that ensure acceptable medical use of the test results (see section on Analytical Goals), From a practical point of view, the ruggedness of the method in routine use is of importance. Reliable performance when used by different operators and with different batches of reagents over longer time periods is essential. 

The dynamic range of a mass spectrometer is defined as the range over which a linear response is observed for an analyte as a function of analyte concentration. It is a critical instrument performance parameter, particularly for quantitative applications, because it defines the concentration range over which analytes can be determined without sample dilution or preconcentration, which effects the accuracy and precision of an analytical method. Dynamic range is limited by physiochemical processes, such as sample preparation and ionization, and instrumental design, such as the type of mass analyzer used and the ion detection scheme. 

Knowing the most influential parameters of a specific biosensor architecture is the basis to understand and fine tune the performance of these devices in a rational manner. Figure 1.8 summarizes the key features of typical biosensors and lists several that are of additional importance for commercial devices. Among these, selectivity, sensitivity, accuracy, response, and recovery time as well as operating lifetime are some of the most important key factors. Keeping in mind the needs of the specific analytical task of interest, it seems to be necessary to characterize at least the key parameters mentioned in Figure 1.8 in order to specify the analytical performance of a biosensor design. 

In all analyses, there is uncertainty about the accuracy of the results that may be dealt with via sensitivity analyses [1, 2]. In these analyses, one essentially asks the question What if These allow one to vary key values over clinically feasible ranges to determine whether the decision remains the same, that is, if the strategy initially found to be cost-effective remains the dominant strategy. By performing sensitivity analyses, one can increase the level of confidence in the conclusions. Sensitivity analyses also allow one to determine threshold values for these key parameters at which the decision would change. For example, in the previous example of a Bayesian evaluation embedded in a decision-analytic model of pancreatic cancer, a sensitivity analysis (Fig. 24.6) was conducted to evaluate the relationship... 

In tandem MS mode, because the product ions are recorded with the same TOF mass analyzers as in full scan mode, the same high resolution and mass accuracy is obtained. Isolation of the precursor ion can be performed either at unit mass resolution or at 2-3 m/z units for multiply charged ions. Accurate mass measurements of the elemental composition of product ions greatly facilitate spectra interpretation and the main applications are peptide analysis and metabolite identification using electrospray iomzation [68]. In TOF mass analyzers accurate mass determination can be affected by various parameters such as (i) ion intensities, (ii) room temperature or (iii) detector dead time. Interestingly, the mass spectrum can be recalibrated post-acquisition using the mass of a known ion (lock mass). The lock mass can be a cluster ion in full scan mode or the residual precursor ion in the product ion mode. For LC-MS analysis a dual spray (LockSpray) source has been described, which allows the continuous introduction of a reference analyte into the mass spectrometer for improved accurate mass measurements [69]. The versatile precursor ion scan, another specific feature of the triple quadrupole, is maintained in the QqTOF instrument. However, in pre-... 

It is an essential condition of biological assay methods that the tests on the standard preparation and on the sample whose potency is being determined should be carried out at the same time and, in all other respects, under strictly comparable conditions. The validation of microbiological assay method includes performance criteria (analytical parameters) such as linearity, range, accuracy, precision, specificity, etc. 

Tire performance of an analytical method and its inherent reliability are characterized by a set of quality parameters that determine its applicability and its usefulness, b or a quantitative method, the most notable parameters are its precision, accuracy, and limit of detection. For a qualitative method, the most important characteristic is its reliability in the identification of the analyte. Since there may be found in the literature an enormous set of different methods for analyzing a particular analyte in a particular matrix, it must be decided which method is the most appropriate for the analysis. 

Acceptability is determined for each parameter by comparison of the standard deviation (s) and the average recovery (X) with the corresponding acceptance criteria for precision and accuracy as published in the method for the analytes of interest. If s and X for all parameters of interest meet the acceptance criteria, the system performance is acceptable and analysis of actual samples may begin. If any individual s exceeds the precision limit, or if any individual X falls outside the range for accuracy, the system performance is unacceptable for that parameter. The analyst must locate and correct the source of the problem and repeat the test for all parameters that failed. 

Another important test of the accuracy of the superposition approximation is the diffusion-controlled A + B — 0 reaction. For the first time it was computer-simulated by Toussaint and Wilczek [27]. They confirmed existence of new asymptotic reaction laws but did not test different approximations used in the diffusion-controlled theories. Their findings were used in [28] to discuss divergence in the linear and the superposition approximations. Since analytical calculations [28] were performed for other sets of parameters as used in [27], their comparison was only qualitative. It was Schnorer et al. [29] who first performed detailed study of the applicability of the superposition approximation. 

Accuracy (absence of systematic errors) and uncertainty (coefficient of variation or confidence interval) as caused by random errors and random variations in the procedure are the basic parameters to be considered when discussing analytical results. As stressed in the introduction, accuracy is of primary importance however, if the uncertainty in a result is too high, it cannot be used for any conclusion concerning, e.g. the quality of the environment or of food. An unacceptably high uncertainty renders the result useless. When evaluating the performance of an analytical technique, all basic principles of calibration, of elimination of sources of contamination and losses, and of correction for interferences should be followed (Prichard, 1995). 

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