论文标题
木糖葡萄糖寡糖的1H-NMR光谱的鉴定：使用非参数密度估计的人工神经网络和贝叶斯分类的比较研究
Identification of 1H-NMR Spectra of Xyloglucan Oligosaccharides: A Comparative Study of Artificial Neural Networks and Bayesian Classification Using Nonparametric Density Estimation
论文作者
论文摘要
质子核磁共振（1H-NMR）是一种用于化学结构分析的广泛使用的工具。但是，1H-NMR光谱遭受了自然畸变，这些畸变使计算机辅助的自动识别这些光谱很困难，而且有时是不可能的。以前的努力已成功实施了这些光谱的依赖或条件识别。在本文中，我们报告了第一个仪器独立的计算机辅助自动识别系统，该系统是一组称为木糖葡萄糖寡糖的复杂碳水化合物。开发的系统还在万维网（http://www.ccrc.uga.edu）上实施，作为称为CCRC-NET的身份套件的一部分，旨在识别这些结构的任何1H-NMR频谱，具有合理的信号与噪声比率，在任何500 MHz NMR仪器上都记录在任何500 MHz NMR仪器上。该系统使用人工神经网络（ANN）技术，对1H-NMR光谱的仪器和环境依赖性变化不敏感。在本文中，还提出了ANN引擎与多维贝叶斯分类器的比较结果。
Proton nuclear magnetic resonance (1H-NMR) is a widely used tool for chemical structural analysis. However, 1H-NMR spectra suffer from natural aberrations that render computer-assisted automated identification of these spectra difficult, and at times impossible. Previous efforts have successfully implemented instrument dependent or conditional identification of these spectra. In this paper, we report the first instrument independent computer-assisted automated identification system for a group of complex carbohydrates known as the xyloglucan oligosaccharides. The developed system is also implemented on the world wide web (http://www.ccrc.uga.edu) as part of an identification package called the CCRC-Net and is intended to recognize any submitted 1H-NMR spectrum of these structures with reasonable signal-to-noise ratio, recorded on any 500 MHz NMR instrument. The system uses Artificial Neural Networks (ANNs) technology and is insensitive to the instrument and environment-dependent variations in 1H-NMR spectroscopy. In this paper, comparative results of the ANN engine versus a multidimensional Bayes' classifier is also presented.
