A discussion on the use of prediction uncertainty estimation of NIR data in partial least squares for quantitative pharmaceutical tablet assay methods

Abstract Out-of-scope sample detection is a key element in successful pharmaceutical quality monitoring by multivariate models and spectroscopic measurements. It is of importance that the spectral data for new measurements are spectrally equivalent to that used in developing the calibration set to ensure that the predicted values will be accurately expressed. In the present work we evaluated prediction uncertainty approaches as applied to partial least squares (PLS) models that were applied to a pharmaceutical tablet assay method incorporating near infrared (NIR) spectroscopy. During the method implementation with TQ Analyst software, the algorithm was compared with the Unscrambler software, the SIMCA-P + software, Error-in-Variable (EIV) approach, and verified by the use of PLS_Toolbox software. It is found that the uncertainty values are very close between TQ Analyst and the Unscrambler though the algorithms are quite different. The magnitude of the uncertainty can be sensitive to variations in instrument performance and tablet composition. Confidence limit setting for the prediction uncertainty is discussed with consideration of confidence limits used in Hotelling-T 2 and Q-residual statistics and multiple linear regression.

[1]  Howard Mark,et al.  Evaluation of the conformity index and the mahalanobis distance as a tool for process analysis: A technical note , 2003, AAPS PharmSciTech.

[2]  D. Massart,et al.  The Mahalanobis distance , 2000 .

[3]  David J. Olive.  Prediction intervals for regression models , 2007, Comput. Stat. Data Anal..

[4]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[5]  Nicolaas M. Faber,et al.  Comparison of two recently proposed expressions for partial least squares regression prediction error , 2000 .

[6]  Tormod Næs,et al.  Leverage and influence measures for principal component regression , 1989 .

[7]  Stefan Andersson-Engels,et al.  Scatter correction of transmission NIR spectra by photon migration data: quantitative analysis of solids , 2005, SPIE Optics East.

[8]  Bruce R. Kowalski,et al.  PREDICTION ERROR IN LEAST SQUARES REGRESSION : FURTHER CRITIQUE ON THE DEVIATION USED IN THE UNSCRAMBLER , 1996 .

[9]  C. Braak,et al.  Prediction error in partial least squares regression: a critique on the deviation used in The Unscrambler , 1995 .

[10]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[11]  James K. Drennen,et al.  Process analytical technology case study, part III: Calibration monitoring and transfer , 2005, AAPS PharmSciTech.

[12]  Desire L. Massart,et al.  Estimation of partial least squares regression prediction uncertainty when the reference values carry a sizeable measurement error , 2003 .

[13]  Harald Martens,et al.  REVIEW OF PARTIAL LEAST SQUARES REGRESSION PREDICTION ERROR IN UNSCRAMBLER , 1998 .

[14]  Theodora Kourti,et al.  Optimization of Batch Operating Policies. Part I. Handling Multiple Solutions , 2006 .

[15]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[16]  Salvador García-Muñoz,et al.  A comparison of different methods to estimate prediction uncertainty using Partial Least Squares (PLS): A practitioner's perspective , 2009 .

[17]  Douglas C. Montgomery,et al.  Applied Statistics and Probability for Engineers, Third edition , 1994 .

[18]  Alexander Penlidis,et al.  An approach to interval estimation in partial least squares regression , 1993 .

[19]  Rasmus Bro,et al.  Standard error of prediction for multiway PLS 1 : background and a simulation study , 2002 .

[20]  Pierre Dardenne,et al.  Validation and verification of regression in small data sets , 1998 .