Calibration model The choice of an appropriate calibration model is necessary for reliable quantification. Therefore, the relationship between the concentration of analyte in the sample and the corresponding detector response must be investigated. This can be done by analyzing spiked calibration samples and plotting the resulting responses versus the corresponding concentrations. The selleck Gemcitabine resulting standard curves can then be further evaluated by graphical or mathematical methods, the latter also allowing statistical evaluation of the response functions. Whereas there is a general agreement that calibration samples should be prepared in blank matrix and that their concentrations must cover the whole calibration range, recommendations on how many concentration levels should be studied with how many replicates per concentration level differ significantly.
In the Conference Report II, ��a sufficient number of standards to define adequately the relationship between concentration and response�� was demanded. Furthermore, it was stated that at least five to eight concentration levels should be studied for linear relationships and it may be more for nonlinear relationships. However, no information was given on how many replicates should be analyzed at each level. The guidelines established by the ICH and those of the Journal of Chromatography B also required at least five concentration levels, but again no specific requirements for the number of replicate set at each level were given. Causon recommended six replicates at each of the six concentration levels, whereas Wieling et al.
used eight concentration levels in triplicate. This approach allows not only a reliable detection of outliers but also a better evaluation of the behavior of variance across the calibration range. The latter is important for choosing the right statistical model for the evaluation of the calibration curve. The often used ordinary least squares model for linear regression is only applicable for homoscedastic data sets (constant variance over the whole range), whereas in case of heteroskedasticity (significant difference between variances at lowest and highest concentration levels), the data should mathematically be transformed or a weighted least squares model should be applied. Usually, linear models are preferable, but, if necessary, the use of nonlinear models is not only acceptable but also recommended.
However, more concentration levels are needed for the evaluation of nonlinear models than for linear models. After outliers have been purged from the data and a model has been evaluated visually and/or by, for example, residual AV-951 plots, the model fit should also be tested by appropriate statistical methods. The fit of unweighted regression models (homoscedastic data) can be tested by the analysis of variance (ANOVA) lack-of-fit test.