Quantitative Structure-retention Relationship Study of Volatile Components from Rosa Banksiae Ait

In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.

Quantitative Structure-retention Relationship Study of Polychlorinated Dibenzothiophenes by Molecular Electronegativity Distance Vector(MEDV)

Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.

Quantitative Structure-chromatographic Retention Relationship for Polychlorinated Dibenzothiophenes and Their Corresponding Sulfones

Polychlorinated dibenzothiophenes（PCDTs）and their corresponding sulfone（PCDTO2）compounds are a group of important persistent organic pollutants.In the present study,geometrical optimization and subsequent calculations of electrostatic potentials（ESPs）on molecular surface have been performed for all 135 PCDTs and 135 PCDTO2 congeners at the HF/6-31G*level of theory.A number of statistically-based parameters have been extracted.Linear relationship between gas-chromatographic retention index（RI）and the structural descriptors have been established by multiple linear regression.The result shows that two descriptors derived from positive electrostatic potential on molecular surface, ■ and π,together with the molecular volume(Vmc)and the energy of the lowest unoccupied molecular orbital(ELUMO)can be well used to express the quantitative structure-retention relationship（QSRR）of PCDTs and PCDTO2.Predictive capability of the two models has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient(RCV)of 0.996 and 0.997,respectively.Furthermore,the predictive power of the models is further examined for the external test set.Correlation coefficients（R）between the observed and predicted RI values for the external test set are 0.997 and0.998,respectively,validating the robustness and good prediction of our model.The QSRR model established may provide again a powerful method for predicting chromatographic properties of aromatic organosulfur compounds.

价键原子指数用于酱香型习酒香气成分保留性质研究

酱香型习酒是一种风味极其独特的酱香型白酒,具有风味醇厚、幽雅细腻和回味悠长的香气,香气成分繁多而复杂.为探究酱香型白酒的主要挥发性物质的组成及香气风味成分的差异,研究建构酱香型习酒香气成分保留指数的定量结构-保留相关性模型,根据酱香型习酒香气成分分子中原子的空间与电性结构,基于拓扑理论,提出了一种新的结构参数-价键原子指数~mA.另计算了酱香型习酒香气成分的电拓扑状态指数(Em),优化筛选了指数中的~0A、E_(1)、E_(2)、E_(14),将4种结构参数作为神经网络三层结构的输入节点数,色谱保留指数作为三层结构的输出节点数,网络结构采用4-12-1方式.建立的预测香气成分保留指数模型的相关系数达到0.9965,计算得到的酱香型习酒香气成分保留指数RI预测值的平均相对误差为1.41%.结果表明,酱香型习酒挥发性香气成分的保留指数,与价键原子指数和电拓扑状态指数之间具有很好的非线性关系,非氢原子结构及—CH_(3)、>CH_(2)、—O—等基团,是影响酱香型习酒香气成分保留指数的主要因素.

定位键指数用于含氧多环芳烃对斑马鱼胚胎的急性毒性预测

含氧多环芳烃化合物是一种在多环芳烃苯环上,含有一个或数个羰基氧原子的持久性污染物,它具有比亲代多环芳烃更强的毒性、致癌、致畸和致突变性,因此探究高效的研究方法来建立毒性数据库非常重要.为研究含氧多环芳烃对斑马鱼胚胎的急性毒性与其分子结构之间的定量结构-毒性关系,根据含氧多环芳烃分子中原子空间拓扑结构,提出了一种新的结构指数–定位键指数L,并分别计算了32个含氧多环芳烃化合物分子的电性拓扑态指数Em、电性距离矢量M_(n),分析优化筛选了电性拓扑态指数其中的E16、电性距离矢量其中的M_(14)、M_(21)和M_(32)作为结构描述符,将它们与定位键指数L有机结合,与含氧多环芳烃化合物对斑马鱼胚胎的生物毒性进行回归分析,以5种结构参数为输入变量点价值,神经网络结构采用5-3-1,建构了一种预测含氧多环芳烃急性毒性lgEC_(50)的神经网络模型,该预测模型的总相关系数R_(T)值达到较高的0.9826,对毒性的预测值与实验值两者之间的平均误差仅为0.112;结果表明,含氧多环芳烃的急性毒性与定位键指数、电性拓扑态指数、电性距离矢量等结构参数有良好的非线性关系.研究可为含氧多环芳烃的环境污染及生态风险评估提供理论指导.

基于氢谱修饰指数的润滑油添加剂抗磨损性能的预测研究

润滑油添加剂对保障机械运行的稳定性和安全性、延长设备的使用寿命、节约能耗具有重要作用,由于通过实验获得数据费时费力,故用定量结构摩擦性能关系研究方法,来获取抗磨损性能数据成为一种简便而高效的方法.为研究酰肼类及磷酸类润滑油添加剂抗磨损性能与结构的关系,定义并建构了新的氢谱修饰指数^(0)H,并计算36个酰肼类及磷酸类分子的电性距离矢量,筛选了M_(2)、M_(3)、M_(21)和M_(59),将这5个结构描述符作为BP神经网络的输入变量,磨损体积量度V s作为神经网络输出变量,用5-3-1的神经网络结构,构建了神经网络法预测模型,模型相关系数0.944,磨损体积量度V s的预测值与文献实验值的平均相对误差为2.61%,优于文献方法结果.结果表明:酰肼类及磷酸类润滑油添加剂抗磨损性能与氢谱修饰指数有良好的非线性关系,影响抗磨损性能的主要因素是分子中原子特性、基团片段的—CH_(3)、—CH_(2)—、>CH—、—O、—N—和—P—等.该研究对指导合成新型抗磨损性能好的化合物分子有理论意义.

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships （QSRR） models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties （SGBP）, involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance （ACC）. ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonuclcotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.

Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.

Quantitative structure-retention relationship for polychlorinated dibenzofurans based on molecular interaction field analysis

A new quantitative structure-retention relationship (QSRR) model is developed for polychlorinated dibenzofurans (PCDFs) based on molecular interaction field (MIF) analysis. The MIF of all 135 PCDFs is calculated using DRY, C1= and C3 probe, characterizing the hydrophobic and steric interaction between PCDFs and different groups of stationary phase. Then QSRR model is constructed by multiblock partial least squares (MBPLS), and the significance of each block is evaluated by the block importance in the prediction (BIP) method. The model used for prediction is statistically significant, with calibration and cross-validation correlation coefficients 0.9990 and 0.9980 respectively, and relative error less than 1.0%. The results of MBPLS and BIP show that the steric properties have dominant influence on the retention behavior of PCDFs, and then the hydrophobic effects.

植物精油成分气相色谱保留指数的全息定量构效关系

气相色谱保留指数(RI)是色谱分析中的重要参数,但通过实验获取RI值的过程较为繁琐,需要建立一种简便、高效、准确的模型来预测RI值。本文搜集了60种植物精油成分的RI实验值,构建了精油成分化合物的结构性质与RI值之间的全息定量构效关系(HQSAR)模型。当碎片大小(fragment size)、碎片特征(fragment distinction)和全息长度(hologram length)模型参数分别设置为“1~4”、“C,Ch”和199时,可以建立最优HQSAR模型。利用外部测试集验证和留一交叉验证对模型进行检验,经外部测试集验证的预测均方根误差(RMSEP)、预测决定系数(Q_(F3)^(2))、一致性相关系数(CCC)和平均相对误差(MRE)分别为40.45、0.984、0.968和2.20%;经留一交叉验证的交叉验证均方根误差(RMSECV)和MRE分别为72.56和4.17%。此外,HQSAR模型的分子贡献图表明,芳香族化合物的烷基链在连接了羟基基团后,其RI值会增大;脂肪族化合物中存在的长链烷基也会导致RI值增大。研究结果表明,所建立的HQSAR模型能够用于预测植物精油成分的RI值,并为其他精油成分RI值的预测提供可靠依据。

硅烷偶联剂折光率的全息定量构效关系研究

建立了85个硅烷偶联剂折光率的全息定量构效关系(HQSAR)模型。当设置参数fragment distinction为“A、B、C、H、DA”、fragment size为“4~7”、holograms length为“151”时可建立最优HQSAR模型。采用外部测试集验证和留一交叉验证两种方法评估了模型的预测能力和稳健性。外部测试集验证的RMSRE、RMSEP、Q2F3、CCC分别为0.916、0.013、0.924、0.936,留一交叉验证的RMSRE、RMSECV、q2cv和R2分别为1.364、0.020、0.659和0.829。验证结果表明建立的HQSAR模型稳健可靠,可用于预测硅烷偶联剂的折光率。此外,通过所得HQSAR模型的分子贡献图可知,硅烷偶联剂中苯基和氧原子上长链烷基的存在会提高折光率。

Three-Dimensional Quantitative Structure-Activity Relationships of flavonoids and estrogen receptors based on docking

Flavonoids are endocrine disrupting compounds that occur ubiquitously in foods of plant origin.The Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) model based on ligand-receptor docking is established between 20 flavonoids and estrogen receptor alpha (ERα),which may provide further theoretical basis for research on the relationship between flavones and estrogen.Comparative molecular field analysis (CoMFA) was employed and the best results of cross-validation and non cross validation were 0.845 and 0.988,respectively.Correspondingly,molecular similarity index analysis (CoMSIA) was employed and the results of cross-validation and non cross validation were 0.670 and 0.990,respectively.The CoMFA/CoMSIA and docking results reveal the structural features for estrogen activity and key amino acid residues in binding pocket,and provide an insight into the interaction between the ligands and these amino acid residues.

小麦叶面积指数与冠层反射光谱的定量关系

在分析不同氮素水平下小麦叶面积指数（I棚）和冠层光谱反射率随生育期变化模式的基础上，确立了LAI与冠层光谱反射率及光谱参数的相关关系，提出了小麦LAI的敏感光谱参数及预测方程．结果表明，小麦LAI和近红外短波段（760—1220nm）反射率都随施氮量的增加呈上升趋势，可见光波段反射率则相反；从拔节期到成熟期，LAI和近红外短波段反射率均表现为先上升后下降的趋势，而可见光波段（460—710nm）反射率随生育期的推进先降低后升高，以孕穗期反射率最低，近红外长波段区域（1480-1650nm）反射率的变化与可见光部分相同．LAI与可见光波段反射率呈负相关，与近红外短波段反射率呈极显著正相关，其中以810nm相关性最好．可以选择RVI（810，510）和DVI（810，560）作为反演小麦LAI的光谱参数．另外，在证明垂直植被指数PVI和转换型土壤调整指数TSAVI对LAI预测能力的同时，发现利用RVI（810，510）、DVI（810，560）和PVI3个植被指数共同推算小麦LAI的准确度更高．

一个新的分子拓扑指数

通过将键参数和量子数引入原子点价,重新定义原子点价δYi,认为分子中某一原子的δYi与该原子的杨氏电负性力标、价电子数、成键电子数及最外层主量子数有关.由此提出新的分子拓扑指数mXY.并用一级指数1XY与饱和烷烃的沸点、液体热容、气体热容、蒸发热、临界温度、临界压力,卤代苯的辛醇/水分配系数,烷氧氯硅烷的气相色谱保留指数、含氮杂环化合物的毒性,碱金属卤化物的晶格焓与F心能带,卤化锡的119SnMossbauer等性质/活性进行相关关系的研究.结果表明,1XY与有机物和无机物的性质/活性间具有广泛良好的相关性.

小麦叶片氮积累量与冠层反射光谱指数的定量关系

利用不同小麦品种在不同施氮水平下的3年田间试验数据,研究了小麦叶片氮积累量与冠层反射光谱间的定量关系。结果显示,不同试验中拔节后叶片氮积累量均随施氮水平呈上升趋势,同时冠层光谱反射率在不同施氮水平下存在明显差异。对于低、中、高蛋白质含量的品种类型,近红外区域若干相邻波段和可见光波段组成的比值植被指数与单位土地面积上叶片氮素积累量的相关关系均表现较好,因此可用760、810、870、950和1 100 nm反射率的平均值与660nm组成的比值植被指数对不同蛋白质类型小麦品种的叶片氮素积累量进行定量监测,但回归方程的斜率在不同类型品种之间存在显著差异。本研究确立的小麦叶片氮积累量与冠层反射光谱的定量关系可用于不同的小麦品种、生育时期和施氮水平,为小麦氮素营养的监测诊断与精确施肥等提供理论依据和技术途径。

小麦叶片氮含量与冠层反射光谱指数的定量关系

本文以3种蛋白质类型的小麦品种在不同施氮水平下的3年田间试验为基础,研究了小麦叶片氮含量与冠层反射光谱的定量关系。结果显示,不同试验中拔节后叶片氮含量均随施氮水平呈上升趋势,同时冠层光谱反射率在不同施氮水平下存在明显差异。对于低、中、高籽粒蛋白质含量的品种,叶片氮含量与冠层反射光谱的归一化植被指数NDVI(1 220,710)和红边位置均有密切的定量关系,决定系数在0.80左右。对于不同品质类型小麦品种,均可利用统一的回归方程描述其叶片氮含量随反射光谱参数的变化,对于低蛋白类型品种,采用单独的回归系数即可提高叶片氮含量估测的准确性。本研究确立的小麦叶片氮含量与冠层反射光谱的定量关系可用于不同的小麦品种、生育时期和施氮水平,为小麦氮素营养的监测诊断与精确施肥等提供理论依据和技术途径。

单硫醚在不同极性固定相上的气相色谱保留指数与结构的相关性研究

在计算出 61个单硫醚的各种量化参数之后 ,用逐步回归方法 ,预测了单硫醚在 4种极性固定相上的气相色谱保留值 ,建立了相应的定量模型。结果表明 ,该模型具有较强的预测能力 ,回归方程的均方根误差不超过 4 %。

棉花冠层高光谱指数与叶片氮积累量的定量关系

利用冠层高光谱反射率及演变的多种高光谱植被指数(VI),分析了不同施氮水平下不同棉花品种叶片氮积累量与冠层反射光谱的定量关系,建立了棉花叶片氮积累量的敏感光谱参数及预测方程。结果显示,棉花叶片氮积累量和冠层高光谱反射率均随不同施氮水平显著变化;棉花叶片氮积累量的敏感光谱波段为600-700 nm的红谷波段和750-900 nm的近红外波段,叶片氮积累量与光谱指数NVD672有密切的定量关系,且不同品种可以用统一的方程来描述,从而为棉花氮素营养的监测诊断与精确施肥提供了技术支持。

棉花冠层高光谱参数与叶片氮含量的定量关系

建立棉花（Gossypium hirsutum）氮素状况的光谱监测技术对于棉花营养诊断和长势估测具有重要意义。该研究利用冠层高光谱反射率及演变的多种高光谱参数，分析了不同施氮水平下不同棉花品种叶片氮含量与冠层反射光谱的定量关系，建立了棉花叶片氮含量的敏感光谱参数及预测方程。结果显示，棉花叶片氮含量和冠层高光谱反射率随不同施氮水平呈显著变化。棉花叶片氮含量的敏感光谱波段为600—700nm的可见光波段和750—900nm的近红外波段，且叶片氮含量与比值植被指数RV／[average（760—850），700]有密切的定量关系，4个品种的平均决定系数在0．70左右。进一步分析表明，可以用统一的回归方程来描述不同品种、不同生育时期和不同氮素水平下棉花叶片氮含量随反射光谱参数的变化模式，从而为棉花氮素营养的监测诊断与精确施肥提供技术依据。

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。