A procedure for calibration transfer between near-infrared instruments--a worked example using a transmittance single tablet assay for piroxicam in intact tablets.

A procedure was developed for different modes of calibration transfer in near-infrared (NIR) spectroscopy, which included a method for the selection of a subset of samples appropriate for transfer. As a worked example, these guidelines were applied to the transfer of a multivariate calibration model, representing a validated NIR single tablet assay for the active within an intact pharmaceutical product, between three equivalent dispersive NIR transmission instruments. Transfer was first evaluated between two instruments, representing the situation where both were available during calibration development. A spectral correction method alone, applied to the transfer instrument, was not sufficient to facilitate transfer, with further optimisation of the calibration model using a novel wavelength selection algorithm necessary to remove regions of the spectral range that resulted in skewed predictions on the second instrument. Through this approach, a single calibration model was found to be equally accurate and precise on the two instruments. A procedure, using the Kennard-Stone algorithm, is described for determining a reduced number of samples as a transfer set using only the spectral information from the original instrument. The purpose of the subset was to permit transfer to a new instrument where that instrument was not available until after calibration development or where it was undesirable to re-measure the full sample set (i.e. due to excessive reference chemistry). Utilising the transfer set, transfer to a third instrument was evaluated. The calibration model, optimised between the first two instruments, was not directly applicable for the third instrument, with further wavelength selection required to remove a small region of spectral data. On completion, using a full statistical evaluation, a single calibration model was found to be equally accurate and precise on all three instruments.

Optimisation of partial least squares regression calibration models in near-infrared spectroscopy: a novel algorithm for wavelength selection.

A novel optimisation algorithm is presented for full spectrum calibration models in near-infrared (NIR) spectroscopy. The algorithm is used to investigate the affect of removing continuous spectral regions on parameters critical to the validity of the model (e.g. explained variance, bias etc.) and ultimately identify and remove problem areas of the spectrum. As an example of its application, this paper shows how to optimise partial least squares regression (PLSR) calibration models for predicting moisture content within an intact pharmaceutical product and how problems due to changes in the nature of samples since setting up the original model may be eliminated. On application of two validated calibration models to a new set of samples unacceptable results were obtained for bias (-0.26 and -0.21% m/m moisture content) between the NIR predicted values and the true values (Karl Fischer analysis). The optimisation algorithm identified small regions of the spectrum, which if included in development of the models contributed significant bias to the final prediction. On removal of these problem regions the calibration models were found to be equally accurate and precise, but with the added advantage of robustness to a variable region of the sample spectrum (bias reduced to -0.05 and -0.09% m/m).