本文關(guān)鍵詞： 氣相色譜-質(zhì)譜聯(lián)用儀 定性分析 色譜分辨率 純凈質(zhì)譜圖提取 質(zhì)譜譜庫檢索　出處：《天津大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：氣相色譜-質(zhì)譜聯(lián)用儀(GC-MS)是對(duì)復(fù)雜混合物樣品進(jìn)行定性分析的重要工具。它融合了氣相色譜和質(zhì)譜兩種儀器單獨(dú)使用時(shí)的優(yōu)點(diǎn),具有更大的優(yōu)勢,廣泛應(yīng)用于環(huán)境、食品、地質(zhì)檢測等各個(gè)領(lǐng)域。 原始GC-MS數(shù)據(jù)是GC-MS進(jìn)行定性分析的基礎(chǔ),但它會(huì)包含來自儀器噪聲、色譜柱流失物、基質(zhì)等的干擾。同時(shí),若混合物樣品中包含保留時(shí)間接近的成分,得到的色譜圖中會(huì)出現(xiàn)譜峰重疊的現(xiàn)象,一種成分的質(zhì)譜圖會(huì)包含來自其它物質(zhì)離子的干擾。若直接應(yīng)用原始GC-MS數(shù)據(jù)對(duì)混合物樣品進(jìn)行定性分析,產(chǎn)生的分析結(jié)果是不可靠的。因此,在定性分析之前,需要從原始的GC-MS數(shù)據(jù)中提取干凈的譜圖,以提高定性分析的準(zhǔn)確性。最后,將得到的純凈質(zhì)譜圖與標(biāo)準(zhǔn)數(shù)據(jù)庫中的譜圖匹配,以完成定性分析。未知物質(zhì)譜圖與標(biāo)準(zhǔn)質(zhì)譜圖匹配采用計(jì)算機(jī)輔助的形式,其中,檢索算法的實(shí)現(xiàn)是完成譜圖匹配的關(guān)鍵。 本論文以提高和改善純凈譜圖提取算法與檢索算法的性能為目標(biāo),以得到正確可靠的定性結(jié)果為目的,主要開展了兩方面的工作。一是GC-MS純凈譜圖提取算法的研究。本文首先將GC-MS數(shù)據(jù)表示成矩陣形式,并在此基礎(chǔ)上提出了基于backfolding的純凈譜圖提取算法。該算法旨在去除噪聲的干擾,并從重疊的色譜峰中提取出獨(dú)立的成分,最終得到純凈成分的質(zhì)譜圖,為下一步定性分析奠定基礎(chǔ)。本論文的另外一個(gè)主要工作是質(zhì)譜圖檢索算法的研究,提出了基于向量模型的檢索算法,首先將未知物質(zhì)譜圖與標(biāo)準(zhǔn)質(zhì)譜圖表示成向量形式,通過比較向量間的相似程度來確定未知物質(zhì)譜圖與標(biāo)準(zhǔn)質(zhì)譜圖的相似性,最終實(shí)現(xiàn)可靠的定性分析。 通過實(shí)驗(yàn)驗(yàn)證,采用本論文提出的純凈譜圖提取算法與檢索算法對(duì)原始GC-MS數(shù)據(jù)進(jìn)行定性分析,得到的結(jié)果與現(xiàn)有商用軟件的處理結(jié)果一致。本文提出的算法具有較好的性能,能夠?qū)崿F(xiàn)快速可靠的定性分析。
[Abstract]:Gas chromatography-mass spectrometry (GC-MS) is an important tool for qualitative analysis of complex mixture samples. It combines the advantages of gas chromatography and mass spectrometry when they are used alone and has more advantages. Widely used in the environment, food, geological detection and other fields. The original GC-MS data is the basis for qualitative analysis of GC-MS, but it will contain interference from instrument noise, chromatographic column loss, matrix, etc. At the same time. If the mixture contains components with close retention time, there will be overlapping peaks in the chromatogram obtained. The mass spectrogram of a component may contain interference from other material ions. If the original GC-MS data are directly used for qualitative analysis of the mixture sample, the analytical results are unreliable. In order to improve the accuracy of qualitative analysis, it is necessary to extract the clean spectrum from the original GC-MS data before qualitative analysis. Finally, the pure mass spectrum is matched with the spectrum in the standard database. In order to complete qualitative analysis, the matching of unknown matter spectrum and standard mass spectrum is in the form of computer aided, among which, the realization of retrieval algorithm is the key to complete spectrum matching. The aim of this thesis is to improve the performance of pure spectrum extraction algorithm and retrieval algorithm, and to obtain correct and reliable qualitative results. Two aspects of work are mainly carried out. One is the research of GC-MS pure spectrum extraction algorithm. Firstly, the GC-MS data is expressed in matrix form. On this basis, a pure spectrum extraction algorithm based on backfolding is proposed, which aims to remove noise interference and extract independent components from overlapping chromatographic peaks. Finally, the mass spectrogram of pure components is obtained, which lays a foundation for the next qualitative analysis. Another main work of this paper is the research of the mass spectrum retrieval algorithm, and a vector model-based retrieval algorithm is proposed. First, the unknown matter spectrum and the standard mass spectrum are expressed as vector forms, and the similarity between the unknown matter spectrum and the standard mass spectrum is determined by comparing the similarity between the vectors, and the reliable qualitative analysis is achieved. Through experimental verification, the pure spectrum extraction algorithm and retrieval algorithm proposed in this paper are used to qualitatively analyze the original GC-MS data. The proposed algorithm has good performance and can realize fast and reliable qualitative analysis.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R318.51

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