质谱数据挖掘及中药色谱指纹图谱评价新方法

【作者】 杨锦瑜；

【导师】 梁逸曾；

【作者基本信息】 中南大学 ， 分析化学， 2005， 硕士

【摘要】 现代分析科学和电子信息科学的发展提供了大量而又丰富的现代化学数据,在这些化学数据中蕴藏了大量的化学信息和知识,化学数据知识挖掘的目的就在于找到外在的化学量测表征与内在的化学结构之间的相互关系。如何有效的将这些蕴涵在大量的化学量测数据中的化学信息和知识提取出来,是分析化学工作者们所面临的巨大的挑战与极大的机遇。本文从当前化学学科的发展现状出发,从质谱数据库所包含的大量的质谱量测数据入手,力图建立起有效的提取质谱量测数据与化合物结构关系的化学计量学方法,同时对中药色谱指纹图谱的评价问题也进行了一定的研究。本文主要分为两个部分:质谱数据库的数据挖掘和中药色谱指纹图谱的评价方法。 一.质谱数据库的数据挖掘(第二章—第三章):这部分的主要目的是通过对质谱数据库的机器学习,来达到基于质谱量测数据对未知化合物的结构进行分类预测。第二章中考察了原始质谱数据、[0,1]一位编码数据、丰度对数分布转换数据、质谱特征光谱数据和组合峰数据等五种常见的用于质谱数据挖掘的数据形式对质谱数据挖掘的影响进行了研究,采用了常见的K最近邻(KNN)、支持向量学习机(SVM)和Boosting结合决策树算法对七类共4435种化合物的质谱数据进行分类,对于不同的分类算法对该五种数据形式存在着一定的选择性,但是总的说来,原始质谱数据形式以及特征光谱数据形式可以有效的提取保留质谱数据的信息从而使得它们具有更多还原

【Abstract】 The recent development of Analytical Chemistry, Instrument Science and Information Science spurs the accumulation of chemical data. Such data contains lots of chemical knowledge and information. The data mining in chemical data sets is to discover the hidden relationship between the chemical data and their chemical knowledge. How to efficiently extract the chemical information is a big challenge and opportunity for analytical chemists. Therefore, the aim of this thesis is to develop new methods in mass spectra data mining and evaluation of chromatographic fingerprint of herbal medicine. There are two main parts of this paper: Data mining in Mass spectra database and evaluation of chromatographic fingerprints of herbal medicines.1. Data mining in mass spectra database (chapter 2 to chapter 3): Purpose of this study is to classify and predict the structure of unknown compounds by machine learning base on Mass spectra database. In chapter 2, the influence of the different data modes for data mining of mass spectra had been studied. The origin mass spectral data mode, [0,1] single code data mode, prior logarithm normal distribution data mode, spectral features data mode and peak combination data mode had been classified with K-nearest neighbor (KNN), support vector machine (SVM) and Boosting classifiers, respectively. In general, the origin massspectral data mode and spectral features data mode have the best classification performance. For classification of mass spectra, the more complex the basic structure for one kind compound is, the better classification performance is. In chapter 3, the classification of multi-class mass spectral data had been studied at the first time. A novel sequential procedure assembling AT-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Boosting technique is developed for the classification of mass spectral data. The combined outputs of two sequential classifiers are able to yield the satisfactory classification of mass spectral data, which are better than those of any single classifier. Especially, the average correct rate of classification using the SVM-KNN sequential procedure reaches 80.1%. The proportional procedure for classification of mass spectral data is very encouraging.2. Evaluation of Chromatographic Fingerprint of Herbal Medicine (chapter 4): Modernization of Chinese traditional medicine mostly relies on quality control, and fingerprint technology is a powerful tool to nature product’s quality control. Correct evaluation of chromatographic fingerprint of Chinese traditional medicine is a fronting problem for analysts. Robust principal component regression based on principal sensitivity vectors (RPPSV) combined with Monte Carlo cross validation (MCCV) is developed to evaluate quality of chromatographic fingerprints in this study. Compared with correlation coefficient, RPPSV,which has a sound statistic background, seems to have a better ability to detect "outliers". Thus, the common patterns of different Chinese medicines could be better established after deleting the outliers detected by the proposed method. However, the evaluation results upon the fingerprints from both correlation coefficient and RPPSV are consistent with each other on the whole.更多还原