含氧有机物脂水分配系数与结构参数的关系

应用半经验量子化学AM1方法得到了108种含氧有机分子的优势构象,利用AM1方法和分子图形学技术获得了它们的电子结构和几何结构参数,并将这些参数与脂水分配系数相关联,采用逐步回归分析方法,成功地建立了拟合度高、物理意义明确、预测能力强的含氧有机分子脂水分配系数-结构参数定量关系方程,找出了影响含氧有机分子脂水分配系数的主要结构因素.     

单酮类有机物气相色谱保留值与结构参数的相关性

应用半经验量子化学AM1方法和分子图形学技术获得了42种单酮类有机物分子的电子结构和几何结构参数，并将这些参数与它们在非极性固定相(SE-30柱)上的气相色谱保留值相关联，采用逐步回归分析方法，成功地建立了拟合度高、物理意义明确、预测能力强的气相色谱保留值一结构参数定量关系方程，找出了影响气相色谱保留值的主要结构因素．     

烷基苯酚类化合物的气相色谱保留值与其结构参数的定量关系

计算了44个烷基苯酚类化合物的组成、拓扑、几何、静电和量子化学等结构参数,运用启发式方法对这些结构参数进行筛选,得到了含3个变量的化合物的定量结构与色谱保留值的线性关系模型,同时以这3个变量作为支持向量机模型的输入变量建立非线性回归模型。两种方法的相关系数(R2 )分别为0.98和0.92,相应的均方根误差分别是0.99和2.77。通过对两种模型的稳定性和预测能力的比较,发现线性模型能够更好地反映烷基苯酚的气相色谱保留值与其结构参数之间的定量关系。在已知烷基苯酚类化合物结构参数的情况下,线性回归模型更有助于它们的色谱分析。     

咪唑啉衍生物缓蚀剂的定量构效关系及分子设计

采用量子化学密度泛函理论(DFT)及线性回归分析方法, 对十一烷基咪唑啉衍生物缓蚀剂抗H2S、CO2腐蚀性能进行了定量构效关系(QSAR)研究. 通过回归分析, 筛选出了影响缓蚀剂缓蚀性能的主要因素, 建立了QSAR模型, 并使用留一法交叉验证对模型的稳定性及预测能力进行了分析. 结果表明, 电子转移参数△N、咪唑环上非氢原子静电荷之和∑Qring及分子极化率α对咪唑啉类缓蚀剂的缓蚀性能有很大的贡献, 所得模型的拟合相关系数(R2)和交叉验证相关系数(q2)分别为0.924 和0.917, 模型对此类缓蚀剂抗H2S、CO2腐蚀性能具有较好的预测效果. 应用QSAR研究结果进行了分子设计, 在理论上提出了一些具有较高抗H2S、CO2腐蚀性能的新型咪唑啉衍生物, 为实验工作者合成新型缓蚀剂提供理论参考.     

基于启发式方法和支持向量机方法预测有机物的热导率

构建147个有机物分子结构与其热导率值之间的定量结构-性质关系(QSPR)模型, 探讨影响有机物热导率的结构因素. 以147个化合物作为样本集, 随机选择118个作为训练集, 29个作为测试集. 应用CODESSA软件计算了组成、拓扑、几何、静电和量子化学等描述符, 通过启发式方法(HM)筛选得到5个结构参数并建立线性回归模型; 用所选5个结构参数作为支持向量机(SVM)的输入, 建立非线性的支持向量机回归模型. 预测结果表明: 支持向量机回归模型的性能(复相关系数R²=0.9240)虽略低于启发式回归模型的性能(R²=0.9267), 但是支持向量机方法预测性能(R²=0.9682)高于启发式方法的预测性能(R²=0.9574), 对于QSPR模型来说, 预测性能更重要. 因此, 总体来说支持向量机方法优于启发式方法. 支持向量机方法和启发式方法的提出为工程上提供了一种根据分子结构预测有机物热导率的新方法.     

Characterization of Gasoline by 1H Nuclear Magnetic Resonance and Chemometrics

Automotive fuel adulteration is an old and significant problem. One common type of fuel adulteration is the addition of diesel to gasoline. Unsupervised models were developed through hierarchical cluster and principal component analysis models. Supervised models through partial least square discriminant analysis using ¹H nuclear magnetic resonance spectra as the input were used to classify samples as adulterated or unadulterated. Quantitative models were developed using partial least squares to determine the gasoline and diesel concentrations in the samples. This set contained samples composed of pure gasoline and anhydrous ethanol reproducing commercial gasoline and other samples treated with diesel. Hierarchical cluster and principal component analysis did not distinguish between adulterated and unadulterated samples except for the most adulterated materials. However, partial least square discriminant analysis classified 100% of the samples correctly. The partial least square algorithm provided excellent regression models for the gasoline and diesel content. The determination coefficient was 0.9920 for both models, whereas the root mean square error of cross-validation and root mean square error of prediction for the diesel model were 2.32 and 1.42%, respectively, and 2.40 and 1.38% for the gasoline model.     

分子三维投影法在苯酚类化合物构效关系研究中的应用

对苯酚类化合物进行三维投影得到了5个形状参数,将其与3个Am指数及8个量子化学参数相结合.由最佳变量子集回归法对变量进行了压缩与选择,运用多元回归分析和人工神经网络法分别构造了预测数学模型,得到了满意的结果.     

Application of FT-IR spectroscopy to the characterisation and classification of wines, brandies and other distilled drinks

FT-IR spectra were employed for the differentiation and classification of wines and brandies during their ageing process, as well as for the characterisation and differentiation of distilled drinks from several producing countries. The FT-IR spectra have enabled the differentiation of the six scales of ageing of sherry wine. A good linear regression fit (regression coefficient=0.995) has been obtained between ageing scale and the data of the FT-IR spectrum. In the case of Brandy of Jerez, it has been possible to differentiate the three degrees of ageing and to devise a system of classification by means of linear discriminant analysis, with a reliability of 83%. The application of the regression by partial least squares has allowed us to obtain a regression between the degree of ageing and the data of the FT-IR spectrum (correlation coefficient=0.986). Lastly, Spanish, French and South African brandies, as well as cognacs and armagnacs have been characterised, and a complete differentiation of the latter two types from the rest of the samples of distilled drinks has been obtained.     

Discrimination and molecular design of new theoretical hypolipaemic agents using the molecular connectivity functions

The molecular topology model and discriminant analysis have been applied to the prediction and QSAR interpretation of some pharmacological properties of hypolipaemic drugs using multivariable regression equations with their statistical parameters. Regression analysis showed that the molecular topology model predicts these properties. The corresponding stability (cross-validation) studies done on the selected prediction models confirmed the goodness of the fits. The method used for hypolipaemic activity selection was a linear discriminant analysis (LDA). We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and design of new hypolipaemic agents.     

Quantum-chemical foundations of the topological substructural molecular design

The topological substructural molecular design (TOPS-MODE) approach is formulated as a tight-binding quantum-chemical method. The approach is based on certain postulates that permit to express any molecular property as a function of the spectral moments of certain types of molecular and environment-dependent energies. We use several empirical potentials to account for these intrinsic and external molecular energies. We prove that any molecular property expressed in terms of a quantitative structure-property and structure-activity relationships (QSPR/QSAR) model developed by using the TOPS-MODE method can be expressed as a bond additivity function. In addition, such a property can also be expressed as a substructural cluster expansion function. The conditions for such bond contributions being transferable are also analyzed here. Several new statistical-mechanical electronic functions are introduced as well as a bond-bond thermal Green's function or a propagator accounting for the electronic hopping between pairs of bonds. All these new concepts are applied to the development and application of a new QSAR model for describing the toxicity of polyhalogenated-dibenzo-1,4-dioxins. The QSAR model obtained displays a significant robustness and predictability. It permits an easy structural interpretation of the structure-activity relationship in terms of bond additivity functions, which display some resemblances with other theoretical parameters obtained from first principle quantum-chemical methods.     

Application of Density Functional Theoretic Descriptors to Quantitative Structure-Activity Relationships with Temperature Constrained Cascade Correlation Network Models of Nitrobenzene Derivatives

IntroductionNitrobenzene derivatives have been used as organ-ic chemical materials and intermediates for many yearsin the chemical industry. Many of those chemicals arereleased into environment and especially into aquaticsystems, and thus have a high pote…     

Monitoring storage time and quality attribute of egg based on electronic nose

The objective of this study was to investigate the potential of an electronic nose (E-nose) technique for monitoring egg storage time and quality attributes. An electronic nose was used to distinguish eggs under cool and room-temperature storage by means of principal component analysis (PCA), linear discriminant analysis (LDA), BP neural network (BPNN) and the combination of a genetic algorithm and BP neural network (GANN). Results showed that the E-nose could distinguish eggs of different storage time under cool and room-temperature storage by LDA, PCA, BPNN and GANN; better prediction values were obtained by GANN than by BPNN. Relationships were established between the E-nose signal and egg quality indices (Haugh unit and yolk factor) by quadratic polynomial step regression (QPSR). The prediction models for Haugh unit and yolk factor indicated a good prediction performance. The Haugh unit model had a standard error of prediction of 3.74 and correlation coefficient 0.91; the yolk factor model had a 0.02 SEP and 0.93 correlation coefficient between predicted and measured values respectively.     

A chemometric approach to characterization of ionic liquids for gas chromatography

A chemometric study was carried out to characterize three ionic liquid types (ILs) with hexacationic imidazolium, polymeric imidazolium, and phosphonium cationic cores, using a range of contra-anions such as halogens, thiocyanate, boron anions, triflate, and bistriflimide. The solvation parameter model developed by Abraham et al., unsupervised techniques as cluster analysis (CA), and supervised techniques as linear discriminant analysis (LDA), step-LDA, quadratic discriminant analysis (QDA), and multivariate regression techniques as discriminant partial least squares (D-PLS), or multiple linear regression (MLR) were used to characterize the functionalized ILs above. CA established two main groups of phases, those with an acidic H-bond and those with basic ones. Once detected, the two natural groups, a linear and quadratic delimiters with good classification (>96 %) and prediction (>92 %) capacities were computed. The use of step-LDA technique allowed us to establish that a, b, and s solvation parameters were the most discriminant variables. These variables were used for modeling purposes, and a D-PLS and MLR models were constructed using a binary response. The explained variance of categorical variable by the model validated by cross-validation was 65 %, and 94.5 % of ILs were correctly predicted. IL characterization carried out would allow the appropriate selection of phases for gas chromatography (GC).