Peer-Reviewed Journal Details
Mandatory Fields
Li, B; Casamayou-Boucau, Y; Calvet, A; Ryder, AG.
2017
November
Analytical Methods
Chemometric approaches to low-content quantification (LCQ) in solid-state mixtures using Raman mapping spectroscopy.
Published
()
Optional Fields
Raman spectroscopy Pharmaceutical Analysis Chemometrics
9
6293
6301
The low-content quantification (LCQ) of active pharmaceutical ingredients or impurities in solid mixtures is important in pharmaceutical manufacturing and analysis. We previously demonstrated the feasibility of using Raman mapping of micro-scale heterogeneity of solid-state samples combined with partial least squares (PLS) regression for LCQ in a binary system.1 However, PLS is limited by the need for relatively high calibration sample numbers to attain high accuracy, and a rather significant computational time requirement for the large Raman maps. Here we evaluated alternative chemometric methods which might overcome these issues. The methods were: net analyte signal coupled with classical least squares (NAS-CLS), multivariate curve resolution (MCR), principal component analysis with CLS (PCA-CLS), and the ratio of characteristic analyte/matrix bands combined with shape-preserving piecewise cubic polynomial interpolation curve fitting (BR-PCHIP). For high (>1.0%) piracetam analyte content, all methods were accurate with relative errors of prediction (REP) of: <1.1%. For LCQ (0.05−1.0% w/w), three methods were able to predict piracetam content with reasonable levels of accuracy: 6.97% (PCA-CLS), 9.13% (MCR), and 12.8% (NAS-CLS). MCR offered the best potential as a semi-quantitative screening method as it was ~40% quicker than PLS, but was less accurate due to being more sensitive to spectral noise factors
http://pubs.rsc.org/en/Content/ArticleLanding/2017/AY/C7AY01778B#!divAbstract
https://doi.org/10.1039/C7AY01778B
Grant Details
Synthesis and Solid State Pharmaceutical Centre, funded by Science Foundation Ireland (Grant No: 12/RC/2275).
Publication Themes