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Disease recognition by infrared and Raman spectroscopy
Author(s) -
Krafft Christoph,
Steiner Gerald,
Beleites Claudia,
Salzer Reiner
Publication year - 2009
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.200810024
Subject(s) - principal component analysis , linear discriminant analysis , artificial intelligence , pattern recognition (psychology) , computer science , support vector machine , partial least squares regression , raman spectroscopy , machine learning , physics , optics
Infrared (IR) and Raman spectroscopy are emerging biophotonic tools to recognize various diseases. The current review gives an overview of the experimental techniques, data‐classification algorithms and applications to assess soft tissues, hard tissues and body fluids. The methodology section presents the principles to combine vibrational spectroscopy with microscopy, lateral information and fiber‐optic probes. A crucial step is the classification of spectral data by a variety of algorithms. We discuss unsupervised algorithms such as cluster analysis or principal component analysis and supervised algorithms such as linear discriminant analysis, soft independent modeling of class analogies, artificial neural networks support vector machines, Bayesian classification, partial least‐squares regression and ensemble methods. The selected topics include tumors of epithelial tissue, brain tumors, prion diseases, bone diseases, atherosclerosis, kidney stones and gallstones, skin tumors, diabetes and osteoarthritis. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)