Name
Title | ||
|---|---|---|
SPECTRAL UNMIXING VIA MINIMUM VOLUME SIMPLICES: APPLICATION TO NEAR INFRARED SPECTRA OF COUNTERFEIT TABLETS |
Abstract | ||
|---|---|---|
Counterfeit pharmaceutical products pose a serious public health problem. It is thus important not only to detect them, but also to identify their composition and assess the risk for the patient. Iden- tifying the spectral signatures of the pure compounds present in a (maybe counterfeit) tablet of unknown origin is clearly a hyperspec- tral unmixing problem. In fact, under a linear mixing model, the hy- perspectral vectors belong to a simplex whose vertices are the spec- tra of the pure compounds in the mixture. Minimum volume simplex analysis (MVSA) and minimum-volume enclosing simplex (MVES) are recently proposed algorithms, exploiting the idea of finding a simplex of minimum volume fitting the observed data. This work gives evidence of the usefulness of MVES and MVSA for unmix- ing near infrared (NIR) hyperspectral data of tablets of unknown composition. Experiments reported in this paper show that MVES and MVSA strongly outperform the state-of-the-art method in ana- lytical chemistry for spectral unmixing: multivariate curve resolu- tion - alternating least squares (MCR-ALS). These experiments are based on synthetic data (studying the effect of noise and of the pres- ence/absence of pure pixels) and on a real dataset composed of NIR hyperspectral images of counterfeit tablets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WHISPERS.2009.5289081 | WHISPERS |
Keywords | Field | DocType |
minimum volume simplex,near infrared imaging.,counterfeit tablets,index terms— hyperspectral unmixing,alternating least squares,source separation,analytical chemistry,materials,chemicals,public health,imaging,hyperspectral imaging,synthetic data,data mining,minimisation,linear mixed model,near infrared,pixel | Computer vision,Pattern recognition,Near-infrared spectroscopy,Simplex,Hyperspectral imaging,Synthetic data,Artificial intelligence,Pixel,Counterfeit,Spectral signature,Source separation,Mathematics | Conference |
Citations | PageRank | References |
2 | 0.39 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marta B. Lopesy | 1 | 2 | 0.39 |
| Jos ´ e | 2 | 83 | 9.14 |
| M. Bioucas-Diasz | 3 | 2 | 0.39 |
| M ´ ario | 4 | 82 | 2.91 |
| A. T. Figueiredoz | 5 | 2 | 0.39 |
| Jean-Claude Wolff | 6 | 2 | 0.39 |