中文摘要：

相对一维光谱而言,二维相关谱技术具有较高的光谱分辨率,适合于相似样品的判别分析。由于二维相关谱是建立在一维光谱相关计算的基础上,因此一维谱上的噪声和虚假信息都会引起二维相关谱的变化,产生一些虚假的相关峰,特别是在对复杂生物体系进行分析时,这些虚假信息使得二维相关谱无法对待测物质的信息进行有效提取。本文依照朗伯-比尔定律,采用计算机模拟数据的方法,建立了二组分不重叠峰和重叠峰的光谱模型,研究了不同标准化方法对二维相关谱的影响,并进行了对比、分析。结果表明：无论分析体系中的特征峰是否重叠,选择合适的标准化方法进行预处理都非常重要,这既可有效地消除虚假的相关信息,又可充分提取待测物质的特征信息。

英文摘要：

Compared wifla one-dimensional （ 1 D） spectroscopy, two-dimensional （2D） correlation spectroscopy technology is of higher spectral resolution, which is suitable for the discriminant analysis of the similar samples. Since 2D correlation spectroscopy is calculated on the basis of the one-dimensional spectra, noise and false information on the 1D spectrum can all result in the change of the 2D correlation spectrum, which consequently generates some false correlation peaks. Due to these false information, especially for the analysis of complex biological system, it is difficult to extract useful information using 2D correlation spectroscopy. According to the Beer-Lambert law, two components spectral models are built without and with overlapped peaks using simulated spectra. The influences of different normalization methods on 2D correlation spectroscopy are studied. The results show that whether the characteristic peaks in analysis system are overlapped, the appropriate normalization method not only can effectively eliminate the false.information, but also can be sufficient to extract the feature information of the material under test.