TY - JOUR
T1 - Robust calibrations on reduced sample sets for API content prediction in tablets
T2 - Definition of a cost-effective NIR model development strategy
AU - Pieters, Sigrid
AU - Saeys, Wouter
AU - Van den Kerkhof, Tom
AU - Goodarzi, Mohammad
AU - Hellings, Mario
AU - De Beer, Thomas
AU - Heyden, Yvan Vander
N1 - Funding Information:
Sigrid Pieters and Wouter Saeys are respectively funded as aspirant and postdoctoral fellow of the Research Foundation – Flanders (FWO) .
PY - 2013/1/25
Y1 - 2013/1/25
N2 - Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34-40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.
AB - Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34-40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.
KW - Augmentation methods
KW - Near infrared spectroscopy
KW - Orthogonal projection methods
KW - Prior spectral information
KW - Tablets
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U2 - 10.1016/j.aca.2012.11.034
DO - 10.1016/j.aca.2012.11.034
M3 - Article
C2 - 23312315
AN - SCOPUS:84872162159
SN - 0003-2670
VL - 761
SP - 62
EP - 70
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
ER -