饱和醇结构-保留定量相关的人工神经网络模型

以拓扑指数为结构描述符，用基于Levenberg-Marquardt优化的BP神经网络建立了醇类化合物的结构与色谱保留值的相关性模型，用于未知醇类化合物在SE－30和OV－3两根色谱柱上保留指数的同时预测，其学习速率优于文献中普通BP神经网络法，预测准确度与普通BP神经网络法接近，但优于多元线性回归法，因而是一种较好的预测有机化合物气相色谱保留指数的方法。     

静态GM（0,6）灰色模型在预测气相色谱保留指数上的应用

本文从麦克雷诺（ＭｃＲｅｙｎｏｌｄｓ）相常数法出发推导了五种标准物质与任意溶质ｉ的色谱保留指数之间的关系符合灰色ＧＭ（０，６）模型，对它们进行了灰色建模。获得了很高的模型精度，并根据模型式预测了涂有不同固定液色谱柱中的溶质ｉ的保留指数。通过预测值与文献值的比较，得到了良好的预测精度。     

萜类化合物的QSRR研究及其在结构鉴定中的应用

采用顶空固相微萃取(SPME)-气相色谱质谱(GC-MS)联用技术分析石香薷和腊梅鲜花中的萜类化合物, 通过保留指数与质谱解析相结合, 分别对化合物进行结构鉴定, 共鉴定出17种单萜化合物, 30种倍半萜化合物. 采用遗传算法(GFA)分别对单萜及倍半萜化合物建立定量结构-色谱保留关系(QSRR)预测模型, 并对该模型进行显著性及预测能力的检测. 同时, 利用计算所得到的模型分别对随机选取的几个萜类化合物进行保留指数预测. 结果表明: 计算保留指数与预测保留指数接近, 模型预测性能较好. 该研究为各种单萜化合物及倍半萜化合物保留指数的预测提供了一种有效手段, 同时, 为建立有效的GC-MS定性方法提供了一定的依据.     

应用新定义的拓扑指数预测烷氧氯硅烷、单硫醚的气相色谱保留指数

根据分子中成键原子i的结构特征和所处的化学环境，新定义了原子i的价点价δi^Y，以价连接矩阵为基础构建了1个新的结构信息价连接性指数^mY。利用线性回归技术分别建立了22个烷氧氯硅烷、61个单硫醚化合物的^mY与这些物质的气相色谱保留指数RI的定量结构／保留相关关系模型(QSRR)。新模型物理意义明确，计算简便，对不同类型化合物在不同极性固定相上的气相色谱保留指数RI具有良好的稳定性和预测能力，新的结构信息价连接性指数能很好地反映化合物的结构特征。     

甲基烷烃结构与色谱保留指数相关性的拓扑指数法研究

计算了207个甲基烷烃的127个拓扑指数变量,把变量选择方法GAPLS方法引入到定量结构与气相色谱保留关系研究中,对127个拓扑指数变量进行选择,得到了含7个变量的化合物的定量结构与色谱保留指数关系(QSRR)模型,其复相关系数的平方为0.99998,标准偏差为2.88。交互验证的复相关系数为0.99997,交互验证的预测标准偏差为2.95,表明该模型具有良好的稳定性和可靠性。对获得的7个变量进行了合理的结构解释,表明甲基烷烃色谱保留指数完全能用拓扑指数来精确表征。     

新的定位基指数及连接性指数的应用

在邻接矩阵基础上提出了一个新的连接性指数X和定位基指数T，并计算了15个酚分子的X和T值，发现X和T不仅对这些化合物具有较好的结构选择性，而且与其气相色谱保留指数I具有较好的相关性，并以此建立了X、T和I的回归方程，所建模型预测结果和文献数据十分接近，预测能力 优于文献。     

烷基苯类化合物气相色谱保留指数与分子结构的相关性研究

利用分子力学和量子化学方法计算出烷基苯类化合物的分子结构描述参数,用逐步回归法建立烷基苯类化合物在不同极性色谱柱上的QSRR模型。烷基苯类化合物在不同极性色谱柱上的气相色谱保留指数与其分子结构描述参数之间具有较好的线性关系。建立了在不同极性色谱柱上的烷基苯类化合物的色谱保留QSRR模型,并预测烷基苯类化合物的色谱保留值,结果具有较好的稳定性和准确性。     

醇类化合物气相色谱保留指数的分子拓扑研究

分子中原子i的特征值(ti)定义为tj＝1 ∑hi。并计算了醇类化合物的氢连接性指数，藉助多元线性回归技术分别建立了25个醇类化合物的指数与这些物质的气相色谱保留指数的定量结构／性质相关关系模型。模型具有良好的稳定性和预测能力，氢连接性指数能较好地反映化合物的结构特征。     

碳氢化合物、醛、酮和硫醇的定量结构-色谱保留指数相关性研究

采用平衡电负性和相对化学键长对传统距离矩阵进行修正,构建新拓扑指数Nt。结合路径数,建立碳氢化合物、醛、酮和硫醇等化合物在24种极性和非极性色谱柱上的定量结构-色谱保留指数关系(QSRR)模型,23种模型的相关系数大于0.99。模型经留n法交叉检验,显示出良好稳健性和预测能力。模型物理意义明确,表明色谱保留指数可用分子的大小、平衡电负性、支化度和形状等内在结构信息进行有效表征。模型经Needham公式分析,结果显示新指数Nt对保留指数影响最大。借助Hyperchem软件进行对比研究,结果表明拓扑化学法优于量子化学AM1法。     

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。     

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.     

Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms

This paper describes development of artificial neural network (ANN) retention model, which can be used for method development in variety of ion chromatographic applications. By using developed retention model it is possible both to improve performance characteristic of developed method and to speed up new method development by reducing unnecessary experimentation. Multilayered feed forward neural network has been used to model retention behaviour of void peak, lithium, sodium, ammonium, potassium, magnesium, calcium, strontium and barium in relation with the eluent flow rate and concentration of methasulphonic acid (MSA) in eluent. The probability of finding the global minimum and fast convergence at the same time were enhanced by applying a two-phase training procedure. The developed two-phase training procedure consists of both first and second order training. Several training algorithms were applied and compared, namely: back propagation (BP), delta-bar-delta, quick propagation, conjugate gradient, quasi Newton and Levenberg-Marquardt. It is shown that the optimized two-phase training procedure enables fast convergence and avoids problems arisen from the fact that every new weight initialization can be regarded as a new starting position and yield irreproducible neural network if only second order training is applied. Activation function, number of hidden layer neurons and number of experimental data points used for training set were optimized in order to insure good predictive ability with respect to speeding up retention modelling procedure by reducing unnecessary experimental work. The predictive ability of optimized neural networks retention model was tested by using several statistical tests. This study shows that developed artificial neural network are very accurate and fast retention modelling tool applied to model varied inherent non-linear relationship of retention behaviour with respect to mobile phase parameters.     

Optimization of Artificial Neural Networks for Modeling of Atorvastatin and Its Impurities Retention in Micellar Liquid Chromatography

Artificial Neural Networks (ANNs) present a powerful tool for the modeling of chromatographic retention. In this paper, the main objective was to use ANNs as a tool in modeling of atorvastatin and its impurities?? retention in a micellar liquid chromatography (MLC) protocol. Factors referred to MLC were evaluated through 30 experiments defined by the Central Composite Design. In this manner, 5?Cx?C3 topology as a starting point for ANNs?? optimization was defined too. In the next step, in order to set the network with the best performance, network optimization was done. In the first part, the number of nodes in the hidden layer and the number of experimental data points in training set were simultaneously varied, and their importance was estimated with suitable statistical parameters. Furthermore, a series of training algorithms was applied to the current network. The Back Propagation, Conjugate Gradient-descent, Quick Propagation, Quasi-Newton, and Delta-bar-Delta algorithms were used to obtain the optimal network. Finally, the predictive ability of the optimized neural network was confirmed through several statistical tests. The obtained network showed high ability to predict chromatographic retention of atorvastatin and its impurities in MLC.     

Determination of Inorganic Ions in Fertilizer Industry Wastewater by Ion Chromatography

The aim of this work is development of methodology for analysis of inorganic cations (sodium, ammonium, potassium, magnesium and calcium) in fertilizer industry wastewater. Method development includes optimization of eluent flow rate and concentration of eluent competing ion in order to obtain optimal separation within reasonable analysis time. For that purpose artificial neural network retention model was developed and used in combination with normalized resolution product criteria function. Developed artificial neural network retention model shows good predictive ability R² ≥ 0.9983. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing validation procedure and number of statistical tests it is shown that developed ion chromatographic method has superior performance characteristic: linearity R² ≥ 0.9984, recovery = 99.81% − 99.44%, repeatability RSD ≤ 0.52%. That result proves that proposed method can be used for routine monitoring analysis in fertilizer industry.     

神经元网络用于PCDD定量构效关系的研究

研究了不同PCDD（全名Polychlorinateddioxin）同系物分子结构的表达及特征参数的选择,应用神经元网络方法对其分子结构与色谱保留值进行了关联。对49种PCDD同系物在DWS往上不同温度下保留时间进行了预测,结果95％以上的数据点相对误差小于10％,而80％以上的数据点相对误差小于5％。     

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。     

Fractal fingerprinting of chromatographic profiles based on wavelet analysis and its application to characterize the quality grade of medicinal herbs

Extracting chemical fingerprints is an important step for representing and interpreting chromatographic data. In this paper, the chromatographic profile is decomposed into components at different resolution levels using wavelet analysis, then the fractal dimensions of these components are computed as the chemical fingerprints. The chromatographic fingerprint is characterized by the vector composed of these chemical fingerprints, which can represent the chemical patterns of different categories of complex samples. Computer simulations reveal that the fractal fingerprints are more stable than the original chromatographic profile data with respect to variations of peak retention time. To demonstrate the validity of this method, the evaluation of the quality of the medicinal herb Angelica sinensis (Oliv.) diels is investigated. Principal component analysis of the fractal fingerprints indicates that samples belonging to the same quality grade are clustered together, while those belonging to different quality grades are separated. Using these fractal fingerprints taken from the chromatographic scans as inputs for an artificial neural network (ANN). The quality grades of two sets of the herbs were verified by cross-validation, indicating that 96.7% of the herbs are correctly identified with respect to their quality grades evaluated by experienced experts, and 100.0% of the herbs are correctly identified with respect to their quality grades determined by pharmacodynamical evaluation.