Improved prediction of tablet properties with near-infrared spectroscopy by a fusion of scatter correction techniques

Mishra, Puneet and Nordon, Alison and Roger, Jean-Michel (2020) Improved prediction of tablet properties with near-infrared spectroscopy by a fusion of scatter correction techniques. Journal of Pharmaceutical and Biomedical Analysis. ISSN 0731-7085 (In Press)

[img] Text (Mishra-etal-JPBA-2021-tablet-properties-with-near-infrared-spectroscopy-by-a-fusion-of-scatter-correction-techniques)
Mishra_etal_JPBA_2021_tablet_properties_with_near_infrared_spectroscopy_by_a_fusion_of_scatter_correction_techniques.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 7 October 2021.
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (1MB) | Request a copy from the Strathclyde author

    Abstract

    Near-infrared (NIR) spectra of pharmaceutical tablets get affected by light scattering phenomena, which mask the underlying peaks related to chemical components. Often the best performing scatter correction technique is selected from a pool of pre-selected techniques. However, the data corrected with different techniques may carry complementary information, hence, use of a single scatter correction technique is sub-optimal. In this study, the aim is to prove that NIR models related to pharmaceuticals can directly benefit from the fusion of complementary information extracted from multiple scatter correction techniques. To perform the fusion, sequential and parallel pre-processing fusion approaches were used. Two different open source NIR data sets were used for the demonstration where the assay uniformity and active ingredient (AI) content prediction was the aim. As a baseline, the fusion approach was compared to partial least-squares regression (PLSR) performed on standard normal variate (SNV) corrected data, which is a commonly used scatter correction technique. The results suggest that multiple scatter correction techniques extract complementary information and their complementary fusion is essential to obtain high-performance predictive models. In this study, the prediction error and bias were reduced by up to 15% and 57% respectively, compared to PLSR performed on SNV corrected data.