非线性色谱中保留值定性方法的评价

色谱分析中最常用的定性方法是利用保留数据,当色谱行为表现为非线性时,就会出现保留值随进样量而变化的现象,这给用保留值定性带来很大困难。在气固色谱与气液色谱中,严格说来,大多数情况下其分配等温线均呈非线性;但在气液色谱中,当试样浓度限制在一定范围内时,可近似地看作线性色谱来处理。     

色谱柱极性和柱温对氯苯类物质保留时间及灵敏度的影响

考察了使用强极性的色谱柱DB-WAXTRE和弱极性的色谱柱DB-5分离3种氯代苯类物质时,其色谱峰保留时间与柱温的关系,建立了3种氯苯在不同的色谱柱间柱温变化相同幅度时保留时间的函数方程式.对二氯苯的柱温-灵敏度曲线峰值中线两侧几乎对称,但每种组分的灵敏度峰值出现所需的柱温不同,在不同的色谱柱上也不同.采用弱极性的DB-5色谱柱,程序升温时氯苯、二氯苯、三氯苯检出限可分别达到0.05、0.08、0.04 ng(进样量为1μL).     

非线性色谱的非平衡热力学理论Ⅰ.非线性-非理想-平衡色谱过程的局域Lagrangian方法

提出非线性-非理想-平衡色谱过程的局域Lagrangian(LLA)方法的矩阵形式。基于Lagrangian描述、局域平衡假设和热力学状态函数等基本物理原理,设计了局域热力学路径(LTP),采用LTP获得了完全热力学状态递推方程的矩阵形式。该递推方程具有Markov特性。对基于LTP的LLA方法的收敛性、相容性和稳定性进行了理论分析和数值实验,给出LLA的稳定性条件。以矢量形式表示了该LLA计算机程序,并模拟了空间分布、轴向扩散和进样量等因素对洗脱曲线的影响。在遍历空间中,建立了离散时间形式的溶质带演化轨线和离散时间控制矢量之间的对应关系。按Bellman动态规划思想,给出对于非线性-非理想-平衡色谱进行优化控制的多段决策问题的简明算法,以此可获得状态矢量和控制矢量的优化轨线。该LLA的矩阵形式消除了制备色谱理论和Markov决策过程或其他基于离散时间状态的现代控制方法之间的鸿沟。     

一个描述液相色谱体系中溶质进样量与保留值关系的方程

通过考虑溶质和溶剂在液相色谱体系中的相互作用,从理论上给出了一个描述溶质在液相色谱体系中进样量与保留值之间关系的方程。由方程可以证明,当进样量趋于零时,溶质的保留值为一定值,当进样量无限大时,溶质的保留值趋于零,且随着进样量的增加,溶质的保留值必然减小。通过方程的线性形式,可以获得两个描述色谱体系特征的重要参数：一个是溶质与固定相相互作用的平衡常数Ｋ,另一个是假想的分布在固定相表面上的活性点总数。     

气相色谱中保留值与进样量的关系

提出了测量气相色谱中吸附等温线的新方法，用不同进样量下测定的容量因子计算平衡时气-固两相组分浓度。利用建立的方法分别测量了正-己烷、正-壬烷和正-十一烷在邻苯二甲酸二辛酯(DOP)、阿皮松-L(Apiezon)和β，β′-氧二丙腈(ODPN)柱上的吸附等温线，讨论了色谱峰形状与吸附等温线的对应关系。研究了气相色谱中进样量对保留值的影响，利用佛伦德利希(Freundlich)吸附等温式导出了描述保留值与进样量关系的数学表达式。理论和实验证明，保留值与进样量具有良好的对数线性关系。     

阴离子交换色谱柱保留特性的变化规律及应对措施

选择对二甲苯氧化体系的高效液相色谱分析为研究对象,详细考察了阴离子交换色谱柱保留特性的变化,在整个色谱柱寿命期间(进样超过1 000次),全程采集了保留时间、分离度等数据.在此基础上,得出色谱柱保留特性的变化规律,并提出了相应的应对措施,把整个色谱柱寿命分成3个阶段,分别采用不同条件的流动相.     

毛细管区带电泳中场增强进样柱内富集的非线性特征

直接柱头场效应进样是一种毛细管区带电泳柱内富集,其进样过程中样品在柱内的分布可分为两部分,即在运行缓冲溶液中的堆积区段和由电渗流引入的样品溶液区段.通过对溶质输运行为的研究表明:两区段长度与进样时间之间并非简单的线性关系,因此进样量与进样时间的关系也非线性,且与溶质淌度有关;进样量的增加并不能导致富集倍数的同步增加,由于层流的作用使得场效应进样柱内富集效果降低.为了在保持柱效基本不变情况下得到好的富集效果,除需使溶质在运行缓冲溶液和样品溶液中的电导率比极大外,进样时间也应与之匹配.     

基于大体积循环进样的低丰度蛋白质富集

发展了一种大体积循环进样方法,用于富集低丰度蛋白质。在优化的色谱分离条件下,通过增加蛋白质样品的上样体积提高低丰度蛋白质的绝对含量;进一步采用增加样品进样循环次数的方法提高蛋白质的富集效率。以猪肝提取蛋白质为样品,每次上样量500 μL的大体积11次循环进样。根据色谱峰的信号强弱,选择了在原始谱图中看不到色谱峰、有较少小峰和有较多小峰出现的时间段等有代表性的馏分进行研究。在中等极性的组分中,保留时间为11.38 min和12.58 min组分的富集效率分别提高了52倍和61倍,实验结果与理论富集效率相近。所发展的方法为生物蛋白质样品研究提供了一种新的富集制备及检测方法。     

超临界流体色谱对吴茱萸中吲哚类生物碱的快速分析

建立了超临界流体色谱快速分析吴茱萸中吲哚类生物碱的方法。以标准品混合物和复杂样品为对象比较4种色谱柱的分离效果,进行色谱柱的筛选;考察了进样体积、改性剂、添加剂、温度和背压对保留行为的影响。结果表明,进样体积对峰形影响显著;添加剂对保留时间和色谱峰形影响有限;改变改性剂能使保留时间显著改变;降低温度,升高背压,保留时间减小。经过优化,确定采用Waters ACQUITY UPC² BEH色谱柱,以甲醇为改性剂,在35 ℃柱温和2.07×10⁷ Pa背压条件下,15 min内完成复杂样品的分析。同时采用超高效液相色谱完成复杂样品的快速分析。结果表明,超临界流体色谱可用于天然产物的高效快速分析,同时该方法与超高效液相色谱在分离选择上的差异有助于天然产物分析方法的拓展。     

色谱累加进样分离实验条件的研究

在采用反相液相色谱或亲和色谱完成蛋白质等大分子分离时,根据溶质保留值随溶剂梯度变化曲线上突变点的差别,可以通过累加进样分离法进行样品制备或直接柱内富集分析,但这一方法并非在任意条件下、对任何样品都适用。该文根据不同形式的保留值方程从理论上探讨了样品保留值与进样时间差、梯度洗脱速率等实验条件之间的关系;结果表明：两次进样的出峰时间差与进样时间间隔成正比关系,也与其在等度情况下的容量因子有关。样品中的两种组分在间隔进样时的流出时间差主要由两组分的容量因子决定,当样品中存在两种以上保留性能相近组分时,若保证指定的分离度,进样时间间隔存在一极大值,超出该范围,分离条件将不能满足     

Prediction of Programmed-temperature Retention Values of Naphthas by Artificial Neural Networks

Abstract

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.     

Prediction of programmed-temperature retention values of naphthas by artificial neural networks

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.     

中药色谱指纹图谱相似度计算中保留时间校正方法的研究

从保留时间校正方法对线性推移、非线性推移和时间延迟的校正效果方面探讨了保留时间校正方法的适用范围。认为一点法校正仅能校正线性推移;两点法可以校正时间延迟和线性推移,并能部分校正非线性推移。多点法使用分段线性逼近的方法对非线性推移有良好的校正效果。     

Foundations of retention in partition chromatography

The connection between the observable output in column chromatography (retention time, retention volume, retention factor, separation factor, etc.) and system properties (hold-up volume, pressure, temperature, isotherm behavior, etc.) is discussed from a practical and mechanistic perspective for gas–liquid chromatography, reversed-phase liquid chromatography, supercritical fluid chromatography, micellar electrokinetic chromatography, and capillary electrochromatography. The unifying feature of these techniques is that retention can be described by a partition model, although not always exclusively. When over simplistic system models are used to explain variation in retention parameters they frequently mask the true reasons for poor repeatability and difficulties in transfer between system. Methods employing relative retention afford higher precision but may contain residual uncorrected errors. For those systems with several separate mechanisms contributing to retention the effective retention parameters can no longer be interpreted by simple partition models. The broadly based and practically focused material in this article affords an illustration of the often complicated relationship between system properties and retention, and the dangers that lurk in simplified retention models if the validity of their underlining approximations is not appropriate for the system under study.     

色谱保留时间在蛋白质组研究中的应用

液相色谱与串联质谱联用(LC-MS/MS)技术是蛋白质组学研究中的常见方法。保留时间作为独立于质谱信息的参数已经被用于蛋白质的鉴定和定量工作中。在多肽鉴定领域,多肽的色谱保留时间预测与常规的二级串联质谱数据库搜索算法结合可以提高鉴定的可信度。鉴定的灵敏度也可以通过匹配多次LC-MS实验中具有相同精确质量数和保留时间的峰而提高。另一方面,由于色谱条件的微小改变即会引起保留时间的变化,因此对多次实验结果进行保留时间比对是进行非标记定量的不可或缺的步骤。另外,联合保留时间偏移和质量数信息还可以进行蛋白质翻译后修饰(post-translational modification, PTM)的鉴定。     

Separation and identification of phenolic compounds in Bidens pilosa L. by ultra high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry

A validated method based on ultra‐performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry was established to separate and identify phenolic compounds in Bidens pilosa L. Mass spectrometry experiments were performed both in positive and negative ion modes. A total of 35 compounds were detected, and 26 phenolic compounds were unequivocally identified or tentatively assigned based on retention time, maximum UV absorption, molecular formula, and fragments. The ultra high performance liquid chromatography method was validated and showed good linearity (R² ≧ 0.9996) over the test range. The limits of detection and quantification were above 0.072 and 0.162 μg/mL, respectively. The relative standard deviations of intraday and interday precision were below 0.3 and 1.6%, respectively.     

Reliability of the retention factor estimations in liquid chromatography

The retention factor is one of the most universally used parameters in chromatography. However, large differences in the experimental retention factor values are observed when the same compound is injected in a given stationary/mobile phase system under intermediate precision conditions. Conventional protocols for estimating retention factors have problems that mainly arise from difficulties in the hold-up time measurements and the omission of the existence of extra-column times by practicing chromatographers. In the present paper, three different approaches for estimating retention factors are tested: (i) classical retention factor estimations based on the gross hold-up time, (ii) based on the real hold-up time (taking into account the extra-column time), and (iii) a new approach that uses 'relative' retention factors based on the use of an external standard. Assays are performed in micellar liquid chromatography (MLC) under intermediate precision conditions (different days, equipments, columns lengths, and mobile phase flow rates). The reliability of the three approaches tested is evaluated by means of precision studies, analysis of factors affecting retention factors, and uncertainty calculations. The approach based on 'relative' retention factors was found to be the most precise, reliable, and robust strategy for estimating retention factors.     

A rapid method for simultaneous determination of 52 marker compounds in Xiao‐Qing‐Long‐Tang by ultra high performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry†

Xiao‐Qing‐Long‐Tang (XQLT) is a classical Chinese medicine formula. It is generally used for the treatment of common cold, bronchial asthma, and allergic rhinitis in Asia. In this study, a multicomponent quantification fingerprinting approach based on ultra high performance liquid chromatography coupled with quadrupole time‐of‐flight mass spectrometry has been developed for the analysis of compounds in XQLT in 14.5 min. A total of 52 compounds were identified by co‐chromatography of sample extract with authentic standards and comparing the retention time, UV spectra, molecular ions and characteristic fragment ions with those of authentic standards, or tentatively identified by MS^E determination along with Mass Fragment software. Moreover, the method was validated for the simultaneous quantification of 16 components in XQLT commercial products. The method is practical for comprehensive standardization of XQLT and holistic comparison of its commercial products from different manufacturers.     

单硫醚气相色谱保留指数拓扑化学研究

在分子拓扑化学理论的基础上,根据分子中原子的特性,用分子中原子的平衡电负性对分子图进行着色,在距离矩阵的基础上结合分子中各原子的支化度构建一组新的拓扑指数NPm(m=1,2,3),利用多元线性回归技术将单硫醚在4种极性固定相的气相色谱保留指数与NPm(m=1,2,3)建立相应的定量结构-保留相关关系模型(QSRR),并用这种模型对单硫醚的气相色谱保留指数进行预测,结果表明,预测结果和实验值吻合较好。     

基于深度学习的保留时间预测方法的研究进展及应用

在基于液相色谱-质谱联用的蛋白质组学研究中,肽段的保留时间作为有效区分不同肽段的特征参数,可以根据肽段自身的序列等信息对其进行预测。使用预测得到的保留时间辅助质谱数据鉴定肽段序列可以提高鉴定的准确性,因此对保留时间预测的工作一直受到领域内的广泛关注。传统的保留时间预测方法通常是根据氨基酸序列计算肽段的理化性质,进而计算肽段在特定色谱条件下的保留时间。近年来,深度学习方法取得了极大的进展,在蛋白质组学研究中发挥着越来越重要的作用。目前已发展出了多种基于深度学习的保留时间预测方法,与传统的保留时间预测方法相比有着更高的准确度,易于跨平台使用,并且能对修饰肽段的保留时间进行预测。但对某些复杂的修饰,如糖基化修饰等的预测结果还不够准确。如何进一步提高对修饰肽段预测的准确性是基于深度学习的保留时间预测方法的重要研究方向。这些预测的保留时间被应用于肽段鉴定的质量控制和方法评估,以及与预测的二级质谱谱图结合,建立模拟谱图库等方面。该文综述了深度学习方法在保留时间预测领域的最新研究进展以及应用成果,同时对其发展趋势和未来的应用方向进行了展望,以期为保留时间预测研究以及蛋白质组鉴定工作提供参考。