Bioanalytical calibration curves: variability of optimal powers between and within analytical methods.

The objective of this study was to investigate the variability of optimal power models in contrast to common regression models within and between analytical methods, as well as the frequency of outlier rejection. This was done by fitting the power model to calibration curve data using the minimum sum of squared residuals as a curve selection criterion. The jackknife percent deviation was used for detecting outliers. The data were obtained from 2087 analytical batches for 91 projects using various analytical techniques. The most frequent regression model varied between analytical techniques while the median and interquartile range of the optimal powers were stable. Outlier rejection is highest in GC and LCMS in which the Wagner (Quadratic, log-log) is the most frequent model. These results suggest that the greatest source of variability in the ideal transformation may not be the analytical technique but other within-lab sources. Outlying values may be due to these other sources of variability as suggested by the outlier rejection profile.