Enhanced Characterization of Naproxen Formulation by Near Infrared Spectroscopy

Abstract

Near infrared spectroscopy in combination with appropriate chemometric methods is an effective technique for quantitative analysis of parameters of interest for the pharmaceutical industry. In this study, the artificial neural network (ANN) was applied to monitor critical parameters (compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets) in the process of naproxen pharmaceutical preparation. The performance of ANN was compared to linear methods (partial least squares regression (PLS) and synergy interval partial squares (siPLS)). The ANN models for compression force, tablet hardness, mean particle size, and active pharmaceutical ingredient concentration of tablets yielded the low root mean square error of prediction (RMSEP) values of 0.936 KN, 0.302 kg, 4.49 mg, and 2.14 µm, respectively. The predictive ability of the PLS model was improved by siPLS with selection of spectral regions and the best performance among all calibration methods was showed by the nonlinear method (ANN). Effective models were built by using these approaches using near infrared spectroscopy.

Keywords:

• Artificial neural network
• NIR spectroscopy
• Partial least squares regression
• Quantitative analysis
• Synergy interval partial squares

ACKNOWLEDGMENTS

The authors are grateful for the support provided by Sichuan Huirui Pharmaceutical Co. and Thermo Fisher Scientific Co.

Notes

^a Input nodes is the dimension of the fourth wavelet approximation coefficients.

^b Hidden nodes is the number of neurons in the hidden.