Department of Chemistry, Oklahoma State University, Stillwater, OK 74078, USA.
Appl Spectrosc. 2012 Aug;66(8):917-25. doi: 10.1366/11-06576.
Pattern recognition methods have been used to develop search prefilters for infrared (IR) library searching. A two-step procedure has been employed. First, the wavelet packet tree is used to decompose each spectrum into wavelet coefficients that represent both the high and low frequency components of the signal. Second, a genetic algorithm for pattern recognition analysis is used to identify wavelet coefficients characteristic of functional group. Even in challenging trials involving carboxylic acids, compounds that possess both carbonyl and hydroxyl functionalities can be readily differentiated from carboxylic acids. The proposed search prefilters allow for the use of more sophisticated and correspondingly more time-consuming algorithms in IR spectral library matching because the size of the library can be culled down for a specific match using information from the search prefilter about the presence or absence of specific functional groups in the unknown.
模式识别方法已被用于开发用于红外（IR）库搜索的搜索预滤波器。采用了两步法。首先，小波包树用于将每个光谱分解为表示信号高频和低频分量的小波系数。其次，使用遗传算法进行模式识别分析，以识别特征官能团的小波系数。即使在涉及羧酸的具有挑战性的试验中，也可以很容易地将同时具有羰基和羟基官能团的化合物与羧酸区分开来。所提出的搜索预滤波器允许在 IR 光谱库匹配中使用更复杂和相应更耗时的算法，因为可以使用搜索预滤波器中有关未知物中特定官能团存在或不存在的信息来缩小库的大小以进行特定匹配。
