气相色谱—质谱定性定量分析新方法研究
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摘要
气相色谱质谱法是一种常用的仪器分析方法,它结合气相色谱高效分离能力和质谱可提供丰富的结构信息的优点,是分析挥发性或半挥发性小分子化合物的重要分析方法。目前,在气相色谱质谱数据的处理中,定性分析主要依赖于基于相似度匹配的库搜索或保留指数数据库查询的方法,并使用标准品进行验证。由于数据库内存在大量相似的质谱,搜索结果中可能包含相似度匹配非常接近的一组化合物。在实际的定性过程中,分析工作者只能选择相似度最高的备选结构作为定性结果,大大降低了分析结果的准确度。更加重要的是,一些未收录于标准物质质谱数据库的化合物基本无法定性。针对这种困境,作者提出了三种解决的新思路：1)挖掘质谱特征和保留指数规律,并利用质谱特征和保留指数的互补性进行定性；2)建立专用质谱和保留指数数据库；3)消除仪器间质谱差异,实现高相似异构体的定性分析。在定量分析上,利用质谱数据离散性的特征,提出了两种识别特征离子的方法,并与迭代目标转换因子分析法结合发展了半自动化的多元分辨方法。本论文的主要内容包括：
一、提出利用中性丢失和质谱特征离子构建质谱分类器和预测结构特征的新方法。本文运用数据挖掘和逐步求精的方法确定中性丢失和特征离子,并构建质谱分类器的方法；提出了差异性分析的方法从原始质谱数据中提取特征离子和中性丢失信息,并构建未知化合物的结构特征的预测机。与模式识别方法比较发现,由中性丢失和特征离子构建的质谱分类器和结构特征预测机具有以下优点：a)能取得更好的分类或预测效果；b)在逐步求精过程中选出的变量得到经典质谱裂解理论的解释,变量具有较强的可解释性,模型也更具有拓展性；c)中性丢失被巧妙的利用作为一个变量用于质谱分类和特征提取。(第二章)
二、运用差异分析提取质谱特征,并结合保留指数发展新的定性分析方法。首先,运用数据挖掘和逐步求精的方法对NIST05标准物质质谱数据中的烷烃进行了挖掘,建立了烷烃和直链饱和烷烃的识别方法；接着,利用差异分析对数据中90单甲基烷烃的标准质谱进行了挖掘,得到了质谱局部特征并建立局部特征与甲基位置之间的关系。结合保留指数和分子离子峰的信息,建立了自动化识别单甲基烷烃的方法。该方法利用互补的左右凹陷,保留指数和分子离子峰的信息相互支撑,相互验证,为提高准确的定性结果提供了保障。最后,对蜡脂质谱和保留指数进行深入的挖掘,根据不同不饱和度蜡脂质谱谱图地貌的差异把蜡脂的质谱分成三类。针对每类质谱,提出了利用特征离子计算蜡脂中脂肪酸部分和脂肪醇部分碳原子数和不饱和度的公式。通过模拟数据的演示,实际数据和标准品数据的验证,说明差异分析可以有效的提取出质谱特征,也说明质谱特征结合保留指数的方法能够更加准确的定性样本中的未知化合物。(第三、四章)
三、提出了建立专有保留指数和质谱数据库的方法。结合质谱特征和化学计量学方法建立无需标准品的定性新方法,并用于五种食用植物油,杜仲果油和人类血浆样本中脂肪酸进行了定性定量分析。本方法利用固定的色谱条件测定所有样本,保证利用数据库内保留指数定性的准确度；利用化学计量学多元分辨的方法弥补因固定色谱条件造成的色谱峰重叠的不足；利用通过质谱特征识别出的样品内部的直链饱和脂肪酸甲酯的保留时间信息计算其它脂肪酸甲酯的等效链长,减少因不同进样批次引起的保留指数误差,从而实现在不使用标准品的情况下对脂肪酸甲酯进行准确定性。通过标准品混合物的验证,证明该方法在无需标准品的情况用于常规脂肪酸的检测分析。分析结果表明杜仲果油确实具有开发制作营养品的价值；同时该方法为利用人体血清中脂肪酸轮廓诊断疾病提供了坚实的定性定量分析方法学基础。(第五章)
四、发展了两种异源质谱的校正新方法,并用于质谱和保留指数都极相似异构体的识别。首先,提出了加权平均相对强度比向量的方法。该方法直接计算训练集中每对异源质谱中相对强度大于阈值的质谱峰的强度比,所有质谱得到的强度比的平均值作为最终的比值向量。相对强度小于阈值的质谱峰不参与强度比计算和平均值计算,训练集中所有相对强度都小于阈值的质荷比视为未校正,设其强对比为1。所有的质荷比的强度比组成最终的加权平均相对强度比向量,又称校正向量。其次,发展了分段直接标准校正法(最小二乘回归)的质谱校正方法。训练集和两类测试集的检验证明了这两种方法不仅能够很好把训练集中的质谱转化为目标条件下的质谱,且适用于具有不同的支链,官能团,双键位置和顺反异构的脂肪酸甲酯的质谱。最重要的是,它们可以消除仪器或条件引起的质谱差异,提高高相似质谱定性的准确度,能够在基本解决质谱定性的问题。(第六章)
五、提出了两种潜在特征离子识别的方法,并用于迭代目标转换因子分析法中浓度矢量的初始化。第一种方法是通过比较渐进因子分析法所确定得组份起始点和结束点,并与单个质荷比所对应的色谱图中的组份起始点和结束点进行比较分析,从而到达识别各组份的潜在选择性离子；第二种方法通过比较不同流出区域的质谱差异,以识别潜在选择性离子。对于多组份体系提出了分割法的策略,即从多组份体系中依次提取当前组份的流出窗口,分别获得各组份的浓度曲线,然后利用最小二乘回归方法计算得到纯物质的质谱,最后由标准质谱同样利用最小二乘回归方法计算出最终的各组份的浓度矢量。与HELP法的结果比较说明,这两种方法能够快速的获得高质量的解析结果。通过低信噪比数据体系,墨旱莲挥发油,97#汽油样本的数据的测试,结果证明这两种方法能够满足常规分析检测的需要。(第七章)
Gas chromatography mass spectrometry (GC-MS), which combines the high-performance separation capability of gas chromatography and abundant structural information from mass spectrometer, has been widely used to analyze the volatile or semi-volatile compounds. So far, in the processing of the GC-MS data, the main methods of qualitative analysis are similarity search in the reference mass spectral library and/or comparison of the retention index with standards or ones in library. Since there are many highly similar mass spectra in the reference mass spectral library, it is very difficult to determine the correct result from hit list. In this case, the accuracy of identification greatly reduces for most of analysts simply take the first candidate with the highest similarity as the correct result. More importantly, the method of library searching does not apparently well work for the unknown compounds out of library. To address these questions, three strategies are proposed as follows:1) mine the reference mass spectra and retention indices to obtain the mass spectral characteristics and elution features, and then combine these two information to determine the structure for unknown compounds; 2) establish the special library of mass spectra and retention indices for in-house analysis; 3) develop calibration model of mass spectra from different sources and distinguish isomers with highly similar mass spectra and highly similar retention indices. In quantitative analysis, with the help of discreteness of mass spectra, we propose two methods to identify the potential selective ions, whose chromatogram could be used to initialize concentration vector for iterative target transformation factor analysis. The main contents of this thesis are:
1. With the help of data mining and stepwise refinement, the mass spectral classifier is built by the neutral mass loss and characteristic ions. Additionally, dissimilarity analysis is developed and employed to build the prediction machine of number of branches in 2-heptene and 2-octene. This method calculates the difference spectrum of the unknown spectra and the corresponding spectra of normal 2-heptene or 2-octene, and then selects the most abundant peak in the difference spectrum as important ion, which is used to calculate the neutral loss. Finally, the neutral loss and some characteristic ions are emplyed to build the prediction machine. Through comparison with the chemometric methods of pattern recognition, the results indicate that the classifier and structure prediction machine built by neutral loss and characteristic ions have the following advantages:a) the better classification or prediction results can be achieved; b) the model can easily be expanded to similar compounds, due to important variables have been explained by the classical theory of fragmentation; c) neutral loss is ingeniously described and employed in mass spectrometry classification and feature extraction for the first time. (Chapter II)
2. After the mass spectral characteristics are obtained with the help of dissimilarity analysis, they are used to combine the retention indices of monomethylalkane to develop new method of qualitative analysis. In this thesis, firstly, mass spectral characteristics of alkane are summarized using the data mining and stepwise refinement and employed to establish model to identify the normal alkane and other alkanes; secondly, dissimilarity analysis of standard mass spectrometry of 90 monomethyl-alkanes is carried out to discover the local features of mass spectra, which could be employed to deduce the methyl position. Combining these features with retention index and the molecular ion peak, an automated method of identifying monomethylalkane is developed. In this method, complementary left and right concave, retention index and molecular ion peak could work together to improve the accuracy of the qualitative results; thirdly, through data mining of reference mass spectra of wax ester, the several mass spectral characteristics are summarized and explained by classic theory of fragmentation. According to the profiling of the whole spectra, the wax could be classified into three categories in the light of unsaturation degree in two moieties. For these three kinds of mass spectra, the calculation formulas are designed by mass of characteristic ions. Tested by the simulated data, the real data and validated by standards, the results indicate that the dissimilarity analysis is an effective method to mine the mass spectral characteristics and the combination of mass spectral characteristics and retention index could improve the accuracy of qualitative analysis for unknown compounds. (Chapter III and IV)
3. The strategy of special database is proposed and demonstrated by a simple library of fatty acids. To avoid the chromatographic shift, the fixed experimental conditions are used to analyze all samples. The chemometrics methods are employed to solve the overlapping peaks to overcome the shortage of fixed conditions. After the establishment of retention indices and mass spectra database, a method combining the mass spectral characteristics, retention indices and chemometrics is proposed to analyze the fatty acids in biological samples without standards. Furthermore, five edible vegetable oils, Eucommia ulmoides seed oil and fatty acids in human plasma samples are analyzed by this method. The saturated fatty acids are firstly identified by mass spectral characteristics and then used to calculate the equivalent chain length (ECL) values for other wax esters. Finally, the comparison with the ECL values in the special data is carried out to identify the unsaturated fatty acids. The results show that Eucommia ulmoides seed oil has nutritional value and this method could provide the fatty acid profiling of human serum for the diagnosis of disease with a solid basis for qualitative and quantitative analysis. (Chapter V)
4. Two calibration methods to transfer the mass spectra detected in different instruments or under different conditions to target are developed and employed to distinguish isomers with highly similar mass spectra and highly similarly retention indices. Two methods are the vector of calibration ratios and piecewise direct standardization. The first one mainly computes the weighted average of the ratio of relative abundance, while the second one is the piecewise direct standard with calibration method of least squares regression. After tested by a training set and two types of test sets, two methods are proved to be effective for not only for mass spectra in the training set, but also for fatty acids with different branch, functional groups, double bond position and the geometrical isomerisation of double bonds. For cis/trans isomers of polyunsaturated fatty acid methyl esters, the differences among their mass spectra are smaller than systematic difference from instrument or condition. After calibration, the most of the geometrical isomerisation of double bonds could be identified. (Chapter VI)
5. Two detectors of potential selective ion are proposed and used to initialize the concentration vector of iterative target transformation factor. The former method compares the start and end points determined from evolving factor analysis (EFA) and mass chromatograms, while the latter one analyzes the difference of mass spectra from different regions to identify potential selective ion. For multi-component system segmentation strategy is proposed to divide the whole data into several sub-windows, in which just one component mainly exists. The concentration profiles could be obtained from each subwindow and then be used to calculated mass spectra with the help of the least squares regression, whose standard spectra could be employed to compute the pure chromatographic curves by using the least squares regression. Through compared with previous method and tested by the low SNR data system, volatile oil of Eclipta prostrata,97# gasoline, the results suggest that the two methods could quickly get higher quality analytical results. (Chapter VII)
一、提出利用中性丢失和质谱特征离子构建质谱分类器和预测结构特征的新方法。本文运用数据挖掘和逐步求精的方法确定中性丢失和特征离子,并构建质谱分类器的方法；提出了差异性分析的方法从原始质谱数据中提取特征离子和中性丢失信息,并构建未知化合物的结构特征的预测机。与模式识别方法比较发现,由中性丢失和特征离子构建的质谱分类器和结构特征预测机具有以下优点：a)能取得更好的分类或预测效果；b)在逐步求精过程中选出的变量得到经典质谱裂解理论的解释,变量具有较强的可解释性,模型也更具有拓展性；c)中性丢失被巧妙的利用作为一个变量用于质谱分类和特征提取。(第二章)
二、运用差异分析提取质谱特征,并结合保留指数发展新的定性分析方法。首先,运用数据挖掘和逐步求精的方法对NIST05标准物质质谱数据中的烷烃进行了挖掘,建立了烷烃和直链饱和烷烃的识别方法；接着,利用差异分析对数据中90单甲基烷烃的标准质谱进行了挖掘,得到了质谱局部特征并建立局部特征与甲基位置之间的关系。结合保留指数和分子离子峰的信息,建立了自动化识别单甲基烷烃的方法。该方法利用互补的左右凹陷,保留指数和分子离子峰的信息相互支撑,相互验证,为提高准确的定性结果提供了保障。最后,对蜡脂质谱和保留指数进行深入的挖掘,根据不同不饱和度蜡脂质谱谱图地貌的差异把蜡脂的质谱分成三类。针对每类质谱,提出了利用特征离子计算蜡脂中脂肪酸部分和脂肪醇部分碳原子数和不饱和度的公式。通过模拟数据的演示,实际数据和标准品数据的验证,说明差异分析可以有效的提取出质谱特征,也说明质谱特征结合保留指数的方法能够更加准确的定性样本中的未知化合物。(第三、四章)
三、提出了建立专有保留指数和质谱数据库的方法。结合质谱特征和化学计量学方法建立无需标准品的定性新方法,并用于五种食用植物油,杜仲果油和人类血浆样本中脂肪酸进行了定性定量分析。本方法利用固定的色谱条件测定所有样本,保证利用数据库内保留指数定性的准确度；利用化学计量学多元分辨的方法弥补因固定色谱条件造成的色谱峰重叠的不足；利用通过质谱特征识别出的样品内部的直链饱和脂肪酸甲酯的保留时间信息计算其它脂肪酸甲酯的等效链长,减少因不同进样批次引起的保留指数误差,从而实现在不使用标准品的情况下对脂肪酸甲酯进行准确定性。通过标准品混合物的验证,证明该方法在无需标准品的情况用于常规脂肪酸的检测分析。分析结果表明杜仲果油确实具有开发制作营养品的价值；同时该方法为利用人体血清中脂肪酸轮廓诊断疾病提供了坚实的定性定量分析方法学基础。(第五章)
四、发展了两种异源质谱的校正新方法,并用于质谱和保留指数都极相似异构体的识别。首先,提出了加权平均相对强度比向量的方法。该方法直接计算训练集中每对异源质谱中相对强度大于阈值的质谱峰的强度比,所有质谱得到的强度比的平均值作为最终的比值向量。相对强度小于阈值的质谱峰不参与强度比计算和平均值计算,训练集中所有相对强度都小于阈值的质荷比视为未校正,设其强对比为1。所有的质荷比的强度比组成最终的加权平均相对强度比向量,又称校正向量。其次,发展了分段直接标准校正法(最小二乘回归)的质谱校正方法。训练集和两类测试集的检验证明了这两种方法不仅能够很好把训练集中的质谱转化为目标条件下的质谱,且适用于具有不同的支链,官能团,双键位置和顺反异构的脂肪酸甲酯的质谱。最重要的是,它们可以消除仪器或条件引起的质谱差异,提高高相似质谱定性的准确度,能够在基本解决质谱定性的问题。(第六章)
五、提出了两种潜在特征离子识别的方法,并用于迭代目标转换因子分析法中浓度矢量的初始化。第一种方法是通过比较渐进因子分析法所确定得组份起始点和结束点,并与单个质荷比所对应的色谱图中的组份起始点和结束点进行比较分析,从而到达识别各组份的潜在选择性离子；第二种方法通过比较不同流出区域的质谱差异,以识别潜在选择性离子。对于多组份体系提出了分割法的策略,即从多组份体系中依次提取当前组份的流出窗口,分别获得各组份的浓度曲线,然后利用最小二乘回归方法计算得到纯物质的质谱,最后由标准质谱同样利用最小二乘回归方法计算出最终的各组份的浓度矢量。与HELP法的结果比较说明,这两种方法能够快速的获得高质量的解析结果。通过低信噪比数据体系,墨旱莲挥发油,97#汽油样本的数据的测试,结果证明这两种方法能够满足常规分析检测的需要。(第七章)
Gas chromatography mass spectrometry (GC-MS), which combines the high-performance separation capability of gas chromatography and abundant structural information from mass spectrometer, has been widely used to analyze the volatile or semi-volatile compounds. So far, in the processing of the GC-MS data, the main methods of qualitative analysis are similarity search in the reference mass spectral library and/or comparison of the retention index with standards or ones in library. Since there are many highly similar mass spectra in the reference mass spectral library, it is very difficult to determine the correct result from hit list. In this case, the accuracy of identification greatly reduces for most of analysts simply take the first candidate with the highest similarity as the correct result. More importantly, the method of library searching does not apparently well work for the unknown compounds out of library. To address these questions, three strategies are proposed as follows:1) mine the reference mass spectra and retention indices to obtain the mass spectral characteristics and elution features, and then combine these two information to determine the structure for unknown compounds; 2) establish the special library of mass spectra and retention indices for in-house analysis; 3) develop calibration model of mass spectra from different sources and distinguish isomers with highly similar mass spectra and highly similar retention indices. In quantitative analysis, with the help of discreteness of mass spectra, we propose two methods to identify the potential selective ions, whose chromatogram could be used to initialize concentration vector for iterative target transformation factor analysis. The main contents of this thesis are:
1. With the help of data mining and stepwise refinement, the mass spectral classifier is built by the neutral mass loss and characteristic ions. Additionally, dissimilarity analysis is developed and employed to build the prediction machine of number of branches in 2-heptene and 2-octene. This method calculates the difference spectrum of the unknown spectra and the corresponding spectra of normal 2-heptene or 2-octene, and then selects the most abundant peak in the difference spectrum as important ion, which is used to calculate the neutral loss. Finally, the neutral loss and some characteristic ions are emplyed to build the prediction machine. Through comparison with the chemometric methods of pattern recognition, the results indicate that the classifier and structure prediction machine built by neutral loss and characteristic ions have the following advantages:a) the better classification or prediction results can be achieved; b) the model can easily be expanded to similar compounds, due to important variables have been explained by the classical theory of fragmentation; c) neutral loss is ingeniously described and employed in mass spectrometry classification and feature extraction for the first time. (Chapter II)
2. After the mass spectral characteristics are obtained with the help of dissimilarity analysis, they are used to combine the retention indices of monomethylalkane to develop new method of qualitative analysis. In this thesis, firstly, mass spectral characteristics of alkane are summarized using the data mining and stepwise refinement and employed to establish model to identify the normal alkane and other alkanes; secondly, dissimilarity analysis of standard mass spectrometry of 90 monomethyl-alkanes is carried out to discover the local features of mass spectra, which could be employed to deduce the methyl position. Combining these features with retention index and the molecular ion peak, an automated method of identifying monomethylalkane is developed. In this method, complementary left and right concave, retention index and molecular ion peak could work together to improve the accuracy of the qualitative results; thirdly, through data mining of reference mass spectra of wax ester, the several mass spectral characteristics are summarized and explained by classic theory of fragmentation. According to the profiling of the whole spectra, the wax could be classified into three categories in the light of unsaturation degree in two moieties. For these three kinds of mass spectra, the calculation formulas are designed by mass of characteristic ions. Tested by the simulated data, the real data and validated by standards, the results indicate that the dissimilarity analysis is an effective method to mine the mass spectral characteristics and the combination of mass spectral characteristics and retention index could improve the accuracy of qualitative analysis for unknown compounds. (Chapter III and IV)
3. The strategy of special database is proposed and demonstrated by a simple library of fatty acids. To avoid the chromatographic shift, the fixed experimental conditions are used to analyze all samples. The chemometrics methods are employed to solve the overlapping peaks to overcome the shortage of fixed conditions. After the establishment of retention indices and mass spectra database, a method combining the mass spectral characteristics, retention indices and chemometrics is proposed to analyze the fatty acids in biological samples without standards. Furthermore, five edible vegetable oils, Eucommia ulmoides seed oil and fatty acids in human plasma samples are analyzed by this method. The saturated fatty acids are firstly identified by mass spectral characteristics and then used to calculate the equivalent chain length (ECL) values for other wax esters. Finally, the comparison with the ECL values in the special data is carried out to identify the unsaturated fatty acids. The results show that Eucommia ulmoides seed oil has nutritional value and this method could provide the fatty acid profiling of human serum for the diagnosis of disease with a solid basis for qualitative and quantitative analysis. (Chapter V)
4. Two calibration methods to transfer the mass spectra detected in different instruments or under different conditions to target are developed and employed to distinguish isomers with highly similar mass spectra and highly similarly retention indices. Two methods are the vector of calibration ratios and piecewise direct standardization. The first one mainly computes the weighted average of the ratio of relative abundance, while the second one is the piecewise direct standard with calibration method of least squares regression. After tested by a training set and two types of test sets, two methods are proved to be effective for not only for mass spectra in the training set, but also for fatty acids with different branch, functional groups, double bond position and the geometrical isomerisation of double bonds. For cis/trans isomers of polyunsaturated fatty acid methyl esters, the differences among their mass spectra are smaller than systematic difference from instrument or condition. After calibration, the most of the geometrical isomerisation of double bonds could be identified. (Chapter VI)
5. Two detectors of potential selective ion are proposed and used to initialize the concentration vector of iterative target transformation factor. The former method compares the start and end points determined from evolving factor analysis (EFA) and mass chromatograms, while the latter one analyzes the difference of mass spectra from different regions to identify potential selective ion. For multi-component system segmentation strategy is proposed to divide the whole data into several sub-windows, in which just one component mainly exists. The concentration profiles could be obtained from each subwindow and then be used to calculated mass spectra with the help of the least squares regression, whose standard spectra could be employed to compute the pure chromatographic curves by using the least squares regression. Through compared with previous method and tested by the low SNR data system, volatile oil of Eclipta prostrata,97# gasoline, the results suggest that the two methods could quickly get higher quality analytical results. (Chapter VII)
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