Noninvasive, quantitative analysis of drug mixtures in containers using spatially offset Raman spectroscopy (SORS) and multivariate statistical analysis

William J. Olds, Shankaran Sundarajoo, Mark Selby, Biju Cletus, Peter M. Fredericks, Emad L. Izake

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)530-537
Number of pages8
JournalApplied Spectroscopy
Volume66
Issue number5
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • spatially offset Raman spectroscopy
  • SORS
  • principal component analysis
  • PCA
  • partial least squares
  • PLS
  • pharmaceutical analysis
  • drug mixtures
  • forensic analysis
  • quantitative analysis

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