A time-domain algorithm for NMR spectral normalization

被引:17
|
作者
Romano, R
Santini, MT
Indovina, PL
机构
[1] Univ Naples Federico II, Dipartimento Sci Fis, Ist Nazl Fis Mat, Unita Napoli, I-80126 Naples, Italy
[2] Ist Super Sanita, Ultrastrutture Lab, I-00161 Rome, Italy
关键词
NMR; algorithm; normalization; NMR of cells; time-domain algorithm;
D O I
10.1006/jmre.2000.2102
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Recently, a new method for quantitatively comparing NMR spectra of control and treated samples, in order to examine the possible occurring variations in cell metabolism and/or structure in response to numerous physical, chemical, and biological agents, was proposed. This method is based upon the utilization of the maximum superposition normalization algorithm (MaSNAl) operative in the frequency domain and based upon maximizing, by an opportune sign variable measure, the spectral region in which control and treated spectra are superimposed. Although the frequency-domain MaSNAl algorithm was very precise in normalizing spectra, it showed some limitations in relation to the signal-to-noise ratio and to the degree of diversity of the two spectra being analyzed. In particular, it can rarely be applied to spectra with a small number of visible signals not buried in the noise such as generally in vivo spectra. In this paper, a time-domain normalization algorithm is presented. Specifically, it consists in minimizing the rank of a Hankel matrix constructed with the difference of the two free induction decay signals. The algorithm, denoted MiRaNAl (minimum rank normalization algorithm), was tested by Monte Carlo simulations as well as experimentally by comparing two samples of known contents both with the new algorithms and with an older method using a standard. Finally, the algorithm was applied to real spectra of cell samples showing how it can be used to obtain qualitative and quantitative biological information. (C) 2000 Academic Press.
引用
收藏
页码:89 / 99
页数:11
相关论文
共 50 条
  • [21] A Spectral Time-Domain Method for Computational Electrodynamics
    Lambers, James V.
    NUMERICAL MATHEMATICS AND ADVANCED APPLICATIONS 2009, 2010, : 561 - 569
  • [22] A Spectral Time-Domain Method for Computational Electrodynamics
    Lambers, James V.
    IMECS 2009: INTERNATIONAL MULTI-CONFERENCE OF ENGINEERS AND COMPUTER SCIENTISTS, VOLS I AND II, 2009, : 2111 - 2116
  • [23] Numerical stability analysis of the pseudo-spectral analytical time-domain PIC algorithm
    Godfrey, Brendan B.
    Vay, Jean-Luc
    Haber, Irving
    JOURNAL OF COMPUTATIONAL PHYSICS, 2014, 258 : 689 - 704
  • [24] Terahertz time-domain spectral hierarchical thickness measurement based on integer genetic algorithm
    Sun, Xiaodong
    Zhang, Yuqi
    Liu, Yali
    Zhang, Hanming
    Sun, Yue
    Qu, Qiuhong
    Zhu, Di
    Zhang, Yizhu
    NONDESTRUCTIVE TESTING AND EVALUATION, 2024,
  • [25] Equivalence of the Time-Domain Matched Filter and the Spectral-Domain Matched Filter in One-Dimensional NMR Spectroscopy
    Spencer, Richard G.
    CONCEPTS IN MAGNETIC RESONANCE PART A, 2010, 36A (05) : 255 - 265
  • [26] SELECTIVE DETECTION IN NMR BY TIME-DOMAIN DIGITAL FILTERING
    ROSEN, ME
    JOURNAL OF MAGNETIC RESONANCE SERIES A, 1994, 107 (01) : 119 - 125
  • [27] Time-Domain NMR Techniques in Cellulose Structure Analysis
    Leonid Grunin
    Maria Ivanova
    Veronika Schiraya
    Tatiana Grunina
    Applied Magnetic Resonance, 2023, 54 : 929 - 955
  • [28] Water/moisture and fat analysis by time-domain NMR
    Todt, H
    Guthausen, G
    Burk, W
    Schmalbein, D
    Kamlowski, A
    FOOD CHEMISTRY, 2006, 96 (03) : 436 - 440
  • [29] Low-field, time-domain NMR of biochar
    Elder, Thomas
    Labbe, Nicole
    Kim, Pyoungchung
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2013, 246
  • [30] Time-Domain NMR Techniques in Cellulose Structure Analysis
    Grunin, Leonid
    Ivanova, Maria
    Schiraya, Veronika
    Grunina, Tatiana
    APPLIED MAGNETIC RESONANCE, 2023, 54 (10) : 929 - 955