Meyer B, Hansen T, Nute D, Albersheim P, Darvill A, York W, Sellers J
Complex Carbohydrate Research Center, Athens, GA.
Science. 1991 Feb 1;251(4993):542-4. doi: 10.1126/science.1990429.
Artificial networks can be used to identify hydrogen nuclear magnetic resonance (1H-NMR) spectra of complex oligosaccharides. Feed-forward neural networks with back-propagation of errors can distinguish between spectra of oligosaccharides that differ by only one glycosyl residue in twenty. The artificial neural networks use features of the strongly overlapping region of the spectra (hump region) as well as features of the resolved regions of the spectra (structural reporter groups) to recognize spectra and efficiently recognized 1H-NMR spectra even when the spectra were perturbed by minor variations in their chemical shifts. Identification of spectra by neural network-based pattern recognition techniques required less than 0.1 second. It is anticipated that artificial neural networks can be used to identify the structures of any complex carbohydrate that has been previously characterized and for which a 1H-NMR spectrum is available.
人工网络可用于识别复杂寡糖的氢核磁共振（1H-NMR）光谱。具有误差反向传播的前馈神经网络能够区分仅相差二十分之一糖基残基的寡糖光谱。人工神经网络利用光谱强重叠区域（峰丘区域）的特征以及光谱解析区域（结构报告基团）的特征来识别光谱，即使光谱因化学位移的微小变化而受到干扰，也能有效识别1H-NMR光谱。基于神经网络的模式识别技术识别光谱所需时间不到0.1秒。预计人工神经网络可用于识别任何先前已表征且有1H-NMR光谱的复杂碳水化合物的结构。
