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1. Introduction
Cancer is a disease that does great harms to the health of human beings. The survival of patients depends largely on the detection of cancer at an early stage. It is of great importance to explore the early cancer diagnosis method. But when the changes in morphology can be seen under light microscope, there have been millions of cancer cells. In the process of carcinogenesis, nuclear acids, proteins, carbohydrates, and other biomolecules generate significant changes in their molecular structures. Fourier transform infrared (FT-IR) spectroscopy is a powerful tool to detect the changes of molecular structure and composition [1–3]. Therefore, it is possible for the FT-IR spectral analysis technology to become a rapid, noninvasive, and convenient method to detect tumors at the precarcinogenesis stage [4, 5]. At present, with the development of biospectroscopy and spectral analysis technology, the application of FT-IR spectroscopy in distinguishing malignant tissues from normal ones has become a focus [6–10]. Also, great progresses have been made in the research of cancer detection using FT-IR spectroscopy [11–17].
FT-IR spectroscopy can effectively provide chemical variation information about the structure and the composition of biological materials at molecular level. FT-IR technology makes it possible to detect inflammatory and cancer of the enteroscopic biopsies. It indicated that FT-IR method has the opportunity to develop as a new technique for enteroscope examination. We believe that noninvasive, rapid, accurate, and convenient analysis of colon tissues can be performed with Fourier-transform midinfrared spectroscopy if the mid-infrared fiber optics and colon endoscopy technologies can be combined successfully. The fundamental study on the application of chemometrics to the identification of colon biopsies, obtained from enteroscopy detection and measured in vitro using FT-IR spectrometer, was performed in this paper.
2. Theory
2.1. Principal Component Analysis
One of the difficulties in spectral analysis is that spectral data usually has too many variables. Fortunately, in spectrum data sets, groups of variables often move together. The absorption bands in neighborhood are related to each other [18]. Thus, here is plenty of redundancy of information in spectrum data set.
Principal component analysis (PCA) is a quantitatively mathematical procedure for achieving simplification. The method generates a new set of variables, called principal components. Each principal component is a linear combination of the original variables. All...