Abstract

A combination of Fourier transform infrared (FT-IR) spectroscopy and microscopy, FT-IR microspectroscopy, has been used to characterize sections of human colorectal adenocarcinoma. In this report, a database of 2601 high quality FT-IR point spectra from 26 patient samples and seven different histological structures was recorded and analyzed. The computer-based analysis of the IR spectra was carried out in four steps: (1) an initial test for spectral quality, (2) data pre-processing, (3) data reduction and feature selection, and (4) classification of the tissue spectra by multivariate pattern recognition techniques such as hierarchical clustering and artificial neural network analysis. Furthermore, an example of how spectral databases can be utilized to reassemble false color images of tissue samples is presented. The overall classification accuracy attained by optimized artificial neural networks reached 95%, highlighting the great potential of FT-IR microspectroscopy as a potentially valuable, reagent-free technique for the characterization of tissue specimens. However, technical improvements and the compilation of validated spectral databases are essential prerequisites to make the infrared technique applicable to routine and experimental clinical analysis.

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