Application of Genetic Algorithms to Spectral Quantification

✍ Scribed by Gregory J. Metzger; Maqbool Patel; Xiaoping Hu

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 82 KB
• Volume: 110
• Category: Article
• ISSN: 1064-1866
• DOI: 10.1006/jmrb.1996.0050

✦ Synopsis

Quantitative data analysis is an important step in the appli-where n(t) is assumed to be white Gaussian noise. F i (t) is specified as a function of the resonance frequency, n i , and cation of NMR spectroscopy. Despite an assortment of existing methods, difficulty still exists in accurate quantifica-decay rates, d i , according to the desired lineshape. The commonly used Lorentzian and Gaussian lineshapes are given, tion of spectral peaks, particularly for in vivo studies. In this Communication, the application of a genetic algorithm to respectively, as spectral fitting is described and verified by experimental re-

A number of methods have been developed for fitting

[3] peaks in NMR spectroscopy (1-13). These include those based on solving for Lorentzian peaks using linear prediction The goal of fitting is to solve for the amplitudes and the (1-4). They have the advantage of computational efficiency parameters in the functional form based on the measured and work well for high-resolution NMR. However, since data. Since the amplitudes are linear in Eq. [1], they can be they rely on the Lorentzian lineshape, they are not very derived separately, based on linear fitting as used in VAR-