气相色谱与液相色谱保留值的换算方法

根据色谱热力学理论,在色谱保留值公式统一形式的基础上导出了气相色谱保留系数（I）与反相液相色谱保留公式参数a,c之间的关系式,证明结构类似化合物的a,c值与保留指数呈线性关系,同时存在氢键作用能、偶极矩作用的影响,从而提出了色相色谱与反相液相色谱保留值换算的方法;该理论为氯代芳烃的文献数据所证实。     

反相液相色谱保留值的预测模式

液相色谱保留值的预测一直是色谱领域里研究最为活跃的领域之一,寻找一种以最少系数求算和预测色谱保留值是色谱工作者的长期研究的目标,但目前,仍没有真正的预测方法。我们在液相色谱保留值基本方程的基础上,曾对不同C_(18)的担体上的保留规律进行过研究,并从中引入了溶质lnk′=A C·C_B BlnC_B (1)作用指数的概念,作用指数即为方程(1)中的参数C(参数B在反相色谱中忽略),CB为二元冲洗剂中强冲洗组分的浓度。本文从溶质的作用指数出发来探讨反相液相色谱保留值的预测模式。     

采用反相液相色谱作用指数进行已知保留值定性的研究

本文从理论上提出了采用反相液相色谱作用指数进行已知保留值定性的方法,该方法是建立在反相液相色谱保留值基本方程Ink′=a+cC_B的基础上,通过建立各类样品标准的a,c指数库来实现保留值的预测,从而达到定性的目的,在定性过程中利用了参数a,c之间的线性关系以及双柱之间的a-a线性关系,在此基础上给出了一些典型的实例来验证这种定性方法的正确性。     

疏水分配常数用于反相液相色谱保留值的预测

在反相液相色谱保留值基本方程log k_′=a+_cC_B的基础上,描述了采用疏水分配常数及氢键作用能来预测a、c参数的方法,并系统讨论了疏水分配常数对参数a、c的影响,借此对反相液相色谱宽浓度范围内的保留值进行了预测。     

反相液相色谱作用指数温度效应的研究

本文研究了反相液相色谱作用指数C的温度效应对给定溶质、温度升高,C值降低;并符合近似的状态方程。在此基础上预测了不同温度、不同冲洗剂浓度的溶质下保留值、预测值与实测值基本一致。     

从分子微观参数预测反相色谱保留值方程系数

在考虑氢键作用能随流动相组成改变的情况下,重新推导了液相色谱保留值方程,进而得到液相色谱保留值方程系数与分子微观参数之间的关系。在分子母体结构相同条件下,提出采用五个系数预测反相色谱保留值的方法,并用文献数据给予验证。     

液相色谱专家系统中除莠剂类样品分析方法的推荐及作用指数定性

我们在建立液相色谱专家系统中,为了验证从理论上总结出来的柱系统推荐规则的正确性,第一次建立了液相色谱谱图库和采用反相液相色谱作用指数进行定性的方法,本文又以除莠剂类样品的分析方法及其定性进行验证。 除莠剂类样品分析方法的推荐 除莠剂分为苯氧羧酸类、苯脲类、三嗪类、醚类和酚类等,由于大部分除莠剂类化合物极性大、蒸气压低,所以通常都采用液相色谱来分析,而反相液相色谱技术作为目前最常用的分离和分析水样、谷物、土壤中除莠剂的方法,既方便又先进。对于易解离的苯氧羧酸类除莠剂,还可采用酸抑制反相色谱技术,使流动相的pH值小于组分的pKa值,以抑制     

反相液相色谱中保留值变化规律的研究: 同系物和柱温对保留值的影响

根据反相液相色谱保留模型和Martin方程, 采用热力学方法导出了一组包括各种色谱参数(如柱温、流动相中有机改性剂浓度以及溶质、溶剂和键合相表面烷基配体中的碳数)的线性保留方程。利用这组方程能够解释反相液相色谱中各种线性保留规律和实验现象, 并预测和实验验证了多种文献未报道的线性规律。     

非水反相高效液相色谱-气相色谱法分析苏子油甘油三酯的组成

苏子油是目前已知α－亚麻酸含量最高的植物物种。首次对苏子油甘油三酯主要组分的组成结构进行了研究。非水反相高效液相色谱和气相色谱的结合为油脂中甘油三酯的分离分析提供了一个简便准确的方法。     

茶多酚的色谱分析法

 对茶多酚的主要成分――儿茶素的色谱分析法进行了综述。重点介绍了应用最多的反相高 效液相色谱法,同时简述了平板色谱法、气相色谱法以及目前出现的新的色谱分析方法。     

Approach for the calculation of high‐order connectivity indices of polymers and its application

A method for the calculation of connectivity indices of polymers was developed that can be used to calculate any order connectivity indices of polymers in a consistent and unambiguous manner without any truncation errors. The proposed method makes it possible to correlate the physical properties of polymers in terms of connectivity indices of higher than first order, which may not only improve the correlation and prediction accuracy but also possibly eliminate the normally required atomic and group correction terms to develop more general correlations with reliable predictive capability. As an example, two correlations with connectivity indices up to the third order are proposed to correlate the thermal conductivities at room temperature for 20 amorphous polymers. The two proposed correlations give comparable or even better accuracy than the existing one, which requires connectivity indices up to the first order and an atomic correction term. Because only connectivity indices were used in the newly proposed correlations, they are general models with better predictive capability. © 2002 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 40: 401–407, 2002; DOI 10.1002/polb.10104     

Molecular Distance‐Edge Vector (μ) and Chromatographic Retention Index of Alkanes

A new method of quantitative structure‐retention relationship (QSRR) is proposed for estimating and predicting gas chromatographic retention indices of alkanes by using a novel molecular distance‐edge vector, called μ vector, containing 10 elements. The QSRR model (Ml), between the μ vector and chromatographic retention indices of 64 alkanes, was developed by using multiple linear regression (MLR) with the correlation coefficient being R = 0.9992 and the root mean square (RMS) error between the estimated and measured retention indices being RMS = 5.938. In order to explain the equation stability and prediction abilities of the M1 model, it is essential to perform a cross‐validation (CV) procedure. Satisfactory CV results have been obtained by using one external predicted sample every time with the average correlation coefficient being R = 0.9988 and average RMS = 7.128. If 21 compounds, about one third drawn from all 64 alkanes, construct an external prediction set and the 43 remaining construct an internal calibration set, the second QSRR model (M2) can be created by using calibration set data with statistics being R = 0.9993 and RMS = 5.796. The chromatographic retention indices of 21 compounds in the external testing set can be predicted by the M2 model and good prediction results are obtained with R = 0.9988 and RMS = 6.508.     

Discrimination between linear and non-linear models describing retention data of alkylbenzenes in gas-chromatography

Summary Detailed statistical analysis is presented to describe the retention indices of alkylbenzenes as a function of their physical (boiling point, modrefraction) and topological (connectivity and complexity indices) properties. With the help of several statistical characteristics (examination of residuals, F test, partial F test, termination criteria, correlation indices) a discrimination is made among different models. A nonlinear equation was chosen which describes the retention data on slightly polar phases with the practically attainable precision. A comparison with literature sources shows that this equation provides the smallest residual error and, hence, it can be applied for prediction purposes. A correlation was found between the preexponential factor in the simple exponential model and the polarity of stationary phases on the Tarján scale.     

氯代苯、醇、酯气相色谱保留指数与其正辛醇-水分配系数相关性

报道了氯代苯、醇、酯类化合物气相色谱保留指数与其正辛醇－水分配系数的相关性，研究了固定相极性对相关性影响，得出弱极性柱上测得的化合物保留指数能更准确地预测其分配系数，从而为极性化合物氯代苯、醇、酯分配系数的测定和预测提供了一种简便易行的新方法。     

基于三维分子描述符的埃坡霉素类衍生物抗癌活性预测

为预测埃坡霉素类衍生物的抗癌活性, 定义了一套表征分子形状的描述符, 即K阶形状参数, 并计算了67个表征分子的电子、拓扑和几何结构的分子描述符. 描述符经遗传算法筛选, 用于建立基于支持向量学习机(SVM)的抗癌活性分类模型; 用留一法和5重交叉验证法对SVM模型参数进行了优化. 结果表明模型具有较高的预测性且两种方法得到相近结果, 交叉验证的预测正确率达80.6%; 经筛选后的描述符有30个, 其中含有5个K阶形状参数, 这些描述符对埃坡霉素类衍生物的抗癌活性的模型建立具有比较重要的作用.     

饱和醇定量结构-保留相关研究中人工神经网络的应用

以拓扑指数（分子连接性指数）为结构描述符,用人工神经网络技术建立了醇类化合物的结构与色谱保留值的相关性模型。研究了网络构造对模型稳定性的影响,考察了模型在单一固定相上及固定相上的适应性。与多元线性回归法相比较,人工神经网络模型具有更好的预测结果,但外推能力较弱。     

基于分子结构预测气相色谱程序升温保留指数

从拓扑指数出发,研究了分子结构与气相色谱程序升温保留指数之间的关系。对所选择的部分分子结构,利用主成分回归(PCR)的相关系数R=0.9998,标准偏差S=9.987,交互检验(leave one out cross-valida-tion)所得标准偏差S=11.17。同时,对同一柱型不同升温速率条件下的保留指数之间的关系、同一升温速率不同柱型条件下保留指数之间的关系进行了初步探讨,建立的模型线性关系明显。     

The study of the relationship between the new topological index A(m) and the gas chromatographic retention indices of hydrocarbons by artificial neural networks

The newly developed topological indices A_m1-A_m3 and the molecular connectivity indices ^mX were applied to multivariate analysis in structure-property correlation studies. The topological indices calculated from the chemical structures of some hydrocarbons were used to represent the molecular structures. The prediction of the retention indices of the hydrocarbons on three different kinds of stationary phase in gas chromatography can be achieved applying artificial neural networks and multiple linear regression models. The results from the artificial neural networks approach were compared with those of multiple linear regression models. It is shown that the predictive ability of artificial neural networks is superior to that of multiple linear regression method under the experimental conditions in this paper. Both the topological indices ²X and A_m1 can improve the predicted results of the retention indices of the hydrocarbons on the stationary phase studied.     

脂肪醇气相色谱保留指数与结构的相关性研究

在分子图的邻接矩阵基础上,构建了一个化合物均价连接性指数mL,mL=∑(Ai·Aj·Ak…)-0.5,其中一阶指数1L及定位基参数β与25种脂肪醇在6种固定相(SE-30,OV-3,OV-7,OV-11,OV-17和OV-25)上的气相色谱保留指数I显著相关,相关系数均大于0.98。所建定量结 构-保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了脂肪醇在不同固定相上气相色谱保留指数的变化规律。