title: Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy.
abstract: The objective of this contribution is to review the application of advanced multivariate data-analysis techniques in the field of mid-infrared (MIR) spectroscopic biomedical diagnosis. MIR spectroscopy is a powerful chemical analysis tool for detecting biomedically relevant constituents such as DNA/RNA, proteins, carbohydrates, lipids, etc., and even diseases or disease progression that may induce changes in the chemical composition or structure of biological systems including cells, tissues, and bio-fluids. However, MIR spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples. Consequently, MIR spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Hence, with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals. A large body of work relates to the combination of multivariate data-analysis techniques with MIR spectroscopy, and has been applied by a variety of research groups to biomedically relevant areas such as cancer detection and analysis, artery diseases, biomarkers, and other pathologies. The reported results indeed reveal a promising perspective for more widespread application of multivariate data analysis in assisting MIR spectroscopy as a screening or diagnostic tool in biomedical research and clinical studies. While the authors do not mean to ignore any relevant contributions to biomedical analysis across the entire electromagnetic spectrum, they confine the discussion in this contribution to the mid-infrared spectral range as a potentially very useful, yet underutilized frequency region. Selected representative examples without claiming completeness will demonstrate a range of biomedical diagnostic applications with particular emphasis on the advantageous interaction between multivariate data analysis and MIR spectroscopy.
authors: School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, 30332-0400, USA.
authors: Liqun
authors: L
authors: Wang
authors: Boris
authors: B
authors: Mizaikoff
mesh_terms: D000818:Animals; D015374:Biosensing Techniques / Q000295:instrumentation / Q000379:methods*; D001826:Body Fluids / Q000737:chemistry / Q000378:metabolism*; D002477:Cells / Q000737:chemistry / Q000166:cytology / Q000378:metabolism; D019411:Clinical Laboratory Techniques; D016000:Cluster Analysis; D006801:Humans; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013055:Spectrophotometry, Infrared / Q000295:instrumentation / Q000379:methods*; D014018:Tissue Distribution