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A Practical Approach for Determination of Mass Spectral Baselines
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  • 作者:Kui Yang (1)
    Xiaoling Fang (2)
    Richard W. Gross (1)
    Xianlin Han (2)
  • 关键词:Baseline correction ; Mass spectrometry ; Shotgun lipidomics
  • 刊名:Journal of The American Society for Mass Spectrometry
  • 出版年:2011
  • 出版时间:November 2011
  • 年:2011
  • 卷:22
  • 期:11
  • 页码:2090-2099
  • 全文大小:355KB
  • 参考文献:1. Han, X., Gross, R.W.: Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of the cellular lipidomes directly from crude extracts of biological samples. / Mass Spectrom. Rev. 24, 367鈥?12 (2005) CrossRef
    2. Norris, J.L., Cornett, D.S., Mobley, J.A., Andersson, M., Seeley, E.H., Chaurand, P., Caprioli, R.M.: Processing MALDI mass spectra to improve mass spectral direct tissue analysis. / Int. J. Mass Spectrom. 260, 212鈥?21 (2007) CrossRef
    3. Satten, G.A., Datta, S., Moura, H., Woolfitt, A.R., Carvalho Mda, G., Carlone, G.M., De, B.K., Pavlopoulos, A., Barr, J.R.: Standardization and denoizing algorithms for mass spectra to classify whole-organism bacterial specimens. / Bioinformatics 20, 3128鈥?136 (2004) CrossRef
    4. Ivanova, P.T., Milne, S.B., Byrne, M.O., Xiang, Y., Brown, H.A.: Glycerophospholipid Identification and quantitation by electrospray ionization mass spectrometry. / Methods Enzymol. 432, 21鈥?7 (2007) CrossRef
    5. Han, X., Yang, J., Cheng, H., Ye, H., Gross, R.W.: Towards fingerprinting cellular lipidomes directly from biological samples by two-dimensional electrospray ionization mass spectrometry. / Anal. Biochem. 330, 317鈥?31 (2004) CrossRef
    6. Han, X., Yang, K., Gross, R.W.: Microfluidics-Based electrospray ionization enhances intrasource separation of lipid classes and extends identification of individual molecular species through multi-dimensional mass spectrometry: development of an automated high throughput platform for shotgun lipidomics. / Rapid Commun. Mass Spectrom. 22, 2115鈥?124 (2008) CrossRef
    7. Williams, B.; Cornett, S.; Dawant, B.; Crecelium, A.; Bodenheimer, B.; Caprioli, R. M. An Algorithm for Baseline Correction of Maldi Mass Spectra. / Proceedings of the 43聽rd Annual Southeast Regional Conference; Kennesaw, GA, 2005 Guimaracs, M. Ed. Association for Computing Machinery, New York, NY, 2005; pp. 137鈥?42.
  • 作者单位:Kui Yang (1)
    Xiaoling Fang (2)
    Richard W. Gross (1)
    Xianlin Han (2)

    1. Division of Bioorganic Chemistry and Molecular Pharmacology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
    2. Sanford-Burnham Medical Research Institute, 6400 Sanger Road, Orlando, FL, 32827, USA
文摘
Precise determination of the baseline levels of mass spectra is critical for identification and quantification of analytes. Herein, we present a practical approach for determination of the baselines of mass spectra acquired under differential conditions. The baseline determined by this approach was the sum of baseline drift and noise level. The baseline drift was determined by averaging a number of lowest ion intensities. The noise level was determined based on the fact that an accelerated intensity change exists from noise to signal. This change was best revealed by the established accumulative layer thickness curve that was derived from the thicknesses of individual deducted layers. Deductions were performed sequentially layer by layer, each of which has a thickness of averaged lowest ion intensities from existing spectral data. The layer where the accelerated intensity change occurred was defined as a transition layer, which was determined from the polynomial regression in the sixth order of the accumulative layer thickness curve followed by resolving the roots of its fourth derivative. We validated the presence of this transition layer through determination of its convergence from various accumulative layer thickness curves generated by varying either the ending or the fineness of the sequential layer deductions. This simple, practical, program-based baseline determination approach should greatly increase the accuracy and consistency of identification and quantification by mass spectrometry, and facilitate the automation of data processing, thereby increasing the power of any high throughput methodology in general and of shotgun lipidomics in particular.

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