Applications of Modern Mass Spectrometry

Volume: 1

Chemometrics as a Powerful and Complementary Tool for Mass Spectrometry Applications in Life Sciences

Author(s): Yahya Izadmanesh and Jahan B. Ghasemi

Pp: 61-97 (37)

DOI: 10.2174/9789811433825120010005

* (Excluding Mailing and Handling)


Because of its unique capabilities, mass spectrometry is an indispensable part of life science research. In this chapter, a review is made on aids of chemometrics in life sciences applications of mass spectrometry. Because of the increasing complexity of biological samples and ongoing technological enhancements of mass spectrometers, huge sum of data are provided for each biological sample. If the routine exploratory tools are used for data exploration, much of the information is not extractable and hence it gets lost. However, chemometrics helps to explore data thoroughly and extract maximum amount of information. The most common aids of chemometrics in bio-based mass spectrometry data is for experimental design, noise reduction, classification, library search, identification of biomolecules, finding the biomarkers, data compression and data mining.

This chapter is focused on the different aspects of using chemometrics for the analysis of mass spectrometry data in omics and biomedical images. In the first part, chemometrics applications for mass data in omics sciences (metabolomics and proteomics) are revealed. The mass data in omics are mainly provided by hyphenation of mass spectrometry with chromatographic techniques, i.e., gas chromatography (GC), liquid chromatography (LC) and electrophoretic techniques. In the second part of the chapter, the benefits of using chemometrics for mass spectrometry images are revealed. The data of these images are gathered by mass spectrometer itself or hyphenation with chromatographic techniques. Since, hyphenated methods are used for both omics and biomedical imaging, some of the chemometrics methodologies used in these two disciplines may be the same.

Keywords: Biomolecules, Biomarker detection, Biomedical imaging, Chemometrics, Data analysis, Data binning, Data compression, Data mining, Experimental design, Genomics, Life science, Mass spectrometry, Metabolomics, Multivariate curve resolution-alternating least squares (MCR-LAS), Multivariate methods, OMICS, Proteomics, Regions of Interest (ROI), Statistics, Variable selection, XCMS software.

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