Use of control sample for estimation of prediction error in multivariate determination of lidocaine solutions with...

The aim of this study was to investigate the ability of a control sample, of known content and identity, to diagnose and correct errors in the predictions when the same multivariate calibration model was used for analysis of new samples over time. A calibration set consisting of 16 samples with a known content of lidocaine was analysed and two external test sets, A and B, were used for the validation. Test set A contained 15 samples with different concentrations of lidocaine and test set B contained three samples with different lidocaine content, which were analysed six times in order to obtain a measure of repeatability. The multivariate calibration was done with PLS regression on UV spectra collected between 245 and 290 nm. A representative UV spectrum was exported from the collected DAD files by two methods, average spectrum over the whole file and average spectrum over the sample plug. Test set A was analysed further on another three occasions together with a control sample. The results showed that the control sample could be used to give a diagnosis and estimate of the prediction error. Moreover, the measured prediction error of the control sample could also be used to correct the predictions, thereby reducing the prediction error. Finally, some practical considerations regarding use of the proposed DAD method with a control sample are presented. The procedure suggested could lead to an efficient analytical approach where the same calibration model could be used over time without recalibration, which may be attractive in industrial quality control or screening analysis in pharmaceutical research.