holder

The following is an abstract for the selected article. A PDF download of the full text of this article is available here. Members may download full texts at no charge. Non-members may be charged a small fee for certain articles.


Noninvasive, Quantitative Analysis of Drug Mixtures in Containers Using Spatially Offset Raman Spectroscopy (SORS) and Multivariate Statistical Analysis

Volume 66, Number 5 (May 2012) Page 530-537

WILLIAM J. OLDS,* SHANKARAN SUNDARAJOO, MARK SELBY, BIJU CLETUS, PETER M. FREDERICKS, and EMAD L. IZAKE


In this paper, spatially offset Raman spectroscopy (SORS) is demonstrated for noninvasively investigating the composition of drug mixtures inside an opaque plastic container. The mixtures consisted of three components including a target drug (acetaminophen or phenylephrine hydrochloride) and two diluents (glucose and caffeine). The target drug concentrations ranged from 5% to 100%. After conducting SORS analysis to ascertain the Raman spectra of the concealed mixtures, principal component analysis (PCA) was performed on the SORS spectra to reveal trends within the data. Partial least squares (PLS) regression was used to construct models that predicted the concentration of each target drug, in the presence of the other two diluents. The PLS models were able to predict the concentration of acetaminophen in the validation samples with a root-mean-square error of prediction (RMSEP) of 3.8% and the concentration of phenylephrine hydrochloride with an RMSEP of 4.6%. This work demonstrates the potential of SORS, used in conjunction with multivariate statistical techniques, to perform noninvasive, quantitative analysis on mixtures inside opaque containers. This has applications for pharmaceutical analysis, such as monitoring the degradation of pharmaceutical products on the shelf, in forensic investigations of counterfeit drugs, and for the analysis of illicit drug mixtures which may contain multiple components.



Index Headings: Spatially offset Raman spectroscopy; SORS; Principal component analysis; PCA; Partial least squares; PLS; Pharmaceutical analysis; Drug mixtures; Forensic analysis; Quantitative analysis.