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Download Hi-Res ImageDownload to MS-PowerPointCite This:Anal. Chem. 2010, 82, 4, 1462-1469
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Near-Infrared Hyperspectral Unmixing Based on a Minimum Volume Criterion for Fast and Accurate Chemometric Characterization of Counterfeit Tablets

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Instituto de Telecomunicações, Instituto Superior Técnico, Technical University of Lisbon, Avenida Rovisco Pais, 1049-001 Lisboa, Portugal, and Medicines Research Centre, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K.
* To whom correspondence should be addressed. Phone: +351218418387. Fax: +351218418472. E-mail: [email protected].
†Technical University of Lisbon.
‡GlaxoSmithKline.
Cite this: Anal. Chem. 2010, 82, 4, 1462-1469
Publication Date (Web):January 22, 2010
https://doi.org/10.1021/ac902569e
Copyright © 2010 American Chemical Society
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Abstract

A rapid detection of the nonauthenticity of suspect tablets is a key first step in the fight against pharmaceutical counterfeiting. The chemical characterization of these tablets is the logical next step to evaluate their impact on patient health and help authorities in tracking their source. Hyperspectral unmixing of near-infrared (NIR) image data is an emerging effective technology to infer the number of compounds, their spectral signatures, and the mixing fractions in a given tablet, with a resolution of a few tens of micrometers. In a linear mixing scenario, hyperspectral vectors belong to a simplex whose vertices correspond to the spectra of the compounds present in the sample. SISAL (simplex identification via split augmented Lagrangian), MVSA (minimum volume simplex analysis), and MVES (minimum-volume enclosing simplex) are recent algorithms designed to identify the vertices of the minimum volume simplex containing the spectral vectors and the mixing fractions at each pixel (vector). This work demonstrates the usefulness of these techniques, based on minimum volume criteria, for unmixing NIR hyperspectral data of tablets. The experiments herein reported show that SISAL/MVSA and MVES largely outperform MCR−ALS (multivariate curve resolution−alternating least-squares), which is considered the state-of-the-art in spectral unmixing for analytical chemistry. These experiments are based on synthetic data (studying the effect of noise and the presence/absence of pure pixels) and on a real data set composed of NIR images of counterfeit tablets.

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