Use of local rank‐based spatial information for resolution of spectroscopic images

Spectroscopic images are singular chemical measurements that enclose chemical and spatial information about samples. Resolution of spectroscopic images is focused on the recovery of the pure spectra and distribution maps of the image constituents from the sole raw spectroscopic measurement. In image resolution, constraints are generally limited to non‐negativity and the spatial information is generally not used. Local rank analysis methods have been adapted to describe the local spatial complexity of an image, providing specific pixel information. This local rank information combined with reference spectral information allows the identification of absent compounds in pixels with low compound overlap. The introduction of this information in the resolution process under the form of constraints helps to increase the performance of the resolution method and to decrease the ambiguity linked to the final solutions. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  R. Tauler.  Multivariate curve resolution applied to second order data , 1995 .

[2]  R. Manne.  On the resolution problem in hyphenated chromatography , 1995 .

[3]  Margaret Werner-Washburne,et al.  BMC Bioinformatics BioMed Central Methodology article Multivariate curve resolution of time course microarray data , 2006 .

[4]  Anna de Juan,et al.  Soft Modeling of Analytical Data , 2006 .

[5]  Ji-Hong Wang,et al.  Discriminant image resolution: a novel multivariate image analysis method utilizing a spatial classification constraint in addition to bilinear nonnegativity , 2005 .

[6]  Sophie Sobanska,et al.  Chemistry at level of individual aerosol particle using multivariate curve resolution of confocal Raman image. , 2006, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[7]  Nathalie Dupuy,et al.  Reliability of the contribution profiles obtained through the SIMPLISMA approach and used as reference in a calibration process: Application to Raman micro-analysis of dust particles , 2003 .

[8]  Leszek Wojnar,et al.  Image Analysis , 1998 .

[9]  J. J. Andrew,et al.  Rapid Analysis of Raman Image Data Using Two-Way Multivariate Curve Resolution , 1998 .

[10]  R. Tauler.  Calculation of maximum and minimum band boundaries of feasible solutions for species profiles obtained by multivariate curve resolution , 2001 .

[11]  M. Maeder.  Evolving factor analysis for the resolution of overlapping chromatographic peaks , 1987 .

[12]  P. Hopke,et al.  Application of modified alternating least squares regression to spectroscopic image analysis , 2003 .

[13]  I. E. Clark,et al.  Raman Imaging of Emulsion Systems , 1998 .

[14]  Boiana O. Budevska,et al.  Application of Multivariate Curve Resolution for Analysis of FT-IR Microspectroscopic Images of in Situ Plant Tissue , 2003, Applied spectroscopy.

[15]  Romà Tauler,et al.  Spectroscopic imaging and chemometrics: a powerful combination for global and local sample analysis , 2004 .

[16]  S. Wold,et al.  Local principal component models, rank maps and contextuality for curve resolution and multi-way calibration inference , 1987 .

[17]  W. Windig,et al.  Interactive self-modeling mixture analysis , 1991 .

[18]  Peihua Qtu.  Multivariate Image Analysis , 2000, Technometrics.

[19]  S. Sonja Sekulic,et al.  Multivariate data analysis for Raman imaging of a model pharmaceutical tablet , 2005 .

[20]  Romà Tauler,et al.  Local rank analysis for exploratory spectroscopic image analysis. Fixed Size Image Window-Evolving Factor Analysis , 2005 .

[21]  Romà Tauler,et al.  Multivariate Curve Resolution (MCR) from 2000: Progress in Concepts and Applications , 2006 .

[22]  H. R. Keller,et al.  Peak purity control in liquid chromatography with photodiode-array detection by a fixed size moving window evolving factor analysis , 1991 .

[23]  Joseph P. Hornak,et al.  Encyclopedia of imaging science and technology , 2002 .

[24]  Romà Tauler,et al.  Chemometrics applied to unravel multicomponent processes and mixtures: Revisiting latest trends in multivariate resolution , 2003 .

[25]  Cyril Ruckebusch,et al.  Multivariate Curve Resolution Methods in Imaging Spectroscopy: Influence of Extraction Methods and Instrumental Perturbations , 2003, J. Chem. Inf. Comput. Sci..

[26]  Andreas T. Ernst,et al.  ICE: a statistical approach to identifying endmembers in hyperspectral images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[27]  B. Kowalski,et al.  Selectivity, local rank, three‐way data analysis and ambiguity in multivariate curve resolution , 1995 .

[28]  Romà Tauler,et al.  A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB , 2005 .