毛细管电泳径向基神经网络校正法定量分析核苷

采用径向基神经网络算法对一组已知样品的核苷及内标物浓度与毛细管电泳峰面积数据进行回归计算,建立峰面积与核苷浓度之间的关系模型,对未知样品中待测核苷浓度作出预测,形成了毛细管电泳定量分析新方法．将其用于鸟嘌呤核苷含量测定,所建模型预测结果平均相对误差为0．86％,明显低于线性回归及BP神经网络模型的2．60％和1．07％．研究结果表明,本方法简便易用,能有效提高毛细管电泳定量分析的准确度,优于线性回归及BP神经网络法．     

基于荧光猝灭原理的光纤化学传感器定量分析模 型的建立与应用

根据光纤化学传感器的结构与信号传输特征,推导并建立了适合于定量描述基于荧光猝灭原理的光纤化学传感器对待测物响应的非线性数学模型。以此为基础,为更加简捷、直观地反映响应信号与待测物浓度的函数关系,建立可线性化的回归方程,准确预报待测物的浓度,提出了多模型回归技术建立此类光纤化学传感器定量分析模型的方法。为难定量分析模型的适用性,将该法应用于芘丁酸光纤化学传感器对甲硝唑、呋喃妥因和氧氟沙星等待测物响应的最佳数学模型的选择之中。结果表明,所选的数学模型对各待测物在一定范围内的浓度都能进行准确的预报。     

基于磁性纳米材料的固相萃取技术研究新进展

<正>由于实际样品中待测物的含量往往较低,且基质复杂,所以在进行定量分析时往往需要对样品进行前处理,以达到减小干扰组分、浓缩富集待测组分以适于特定检测分析目的的需要,因此样品前处理技术是整个分析过程中最关键的一环。传统的样品前处理方法如液液萃取、索氏抽提、振荡提取、固相萃取等存在样品需要量较大、萃取时间长、使用大量     

正交回归多组分标准加入光度法同时测定污水中的铜、镍、镉和锌

1引言多组分同时测定的光度法都依据比耳定律的线性加和原理，所以对实测样品中存在非线性干扰的多组分体系就很难简单处理。利用正交回归多波长光度法，可以引入反映组分间相互影响的交互作用项，并依据多组分标准加入法的原理，给出各波长处吸光度A（λ）与各待测组分浓度Xi（规范变量）间的关系为：均为回归系数。这里及di分别为组分i的浓度，基准浓度及变化间距。采用标准加入法，就是将含有多个待测组分的待测样中，按正交设计加入一定量的标准溶液，测定各试样溶液在一定显色体系中的吸光度。经回归分析求得回归方程中的回归系数。若…     

近红外光谱法对头孢氨苄粉末药品的非破坏定量分析

采用近红外漫反射光谱法对头孢氨苄粉末药品中主要成分头孢氨苄进行快速、无损定量分析.采用偏最小二乘法建立近红外光谱信息与待测组分含量间的最佳数学校正模型.对3种光谱(SNV光谱、一阶导数、二阶导光谱)的预测结果进行了比较,讨论了光谱的预处理方法和主成分数对偏最小二乘法定量预测能力的影响,并对预测集样品进行预测.     

分枝定界算法用于有机混合物的同时定性定量分析

分枝定界法用于多组分同时定性定量分析，只需解析一份试样测得的数据，即可同时得到待测样品中所含组分的种类、数目及含量，具有简单、快速、准确等优点。此文对分枝定界法在判据应用方面进行了改进，应用四个判据，解决了最佳子集难判断的问题。建立了精蒽中不经分离同时测定蒽、菲和咔唑的方法。     

X射线衍射定量相分析新方法

在基体清洗法的基础上提出一种新的X射线衍射定量分析方法，该方法是利用混合样品X射线衍射谱图中的全部衍射峰数据，把清洗剂同样品中所包含的所有待测物相的纯相按比例混合，在同一水平下同时测定各物相与清洗剂对应的几个K值，结合JCPDS卡中的各相标准谱峰的相对强度分布数据进行最小二乘方程组的抛弃平均法回归分析，求得混合物样品中各相间的全谱峰匹配强度比，用于混合物样品的X射线衍射定量相分析，有利于提高定量相分析的精度，通过对四组分混合物样品的分析，实验结果和理论完全一致。     

高效液相色谱用于生物样品中待测组分的定性方法

高效液相色谱(HPLC)近年来在生化领域迅速发展,用HPLC对生物样品的定性定量分析显示出极大优越性。但生物样品中待测组分含量极微,稳定性差,不便以剧烈手段除去干扰组分。进行HPLC分离时谱峰多而杂,因此仅靠常规的保留值定性是不够的,必须配合其它定性方     

近红外光谱相似性评估结合局部回归方法无损检测苹果糖度

基于Bayesian相似性评估方法结合偏最小二乘局部回归,对苹果近红外数据库进行数据挖掘。通过相似性计算方法搜索出与预测样品相近的近红外光谱,形成校正子集后采用局部回归方法获得待测样品的相关信息。该方法所建立局部模型的平均检验标准偏差(SEV)约为0.57,分析30个预测样品的预测标准偏差(SEP)约为0.61;基于马氏距离的传统方法建立的偏最小二乘局部模型的平均SEV为0.59,分析30个待测样品的预测SEP为0.64;而采用整个数据库建立的全局偏最小二乘模型的SEV约为0.65,分析30个预测样品SEP约为0.70。基于Bayesian相似性评估的局部回归方法在苹果糖度的近红外无损定量分析中获得较好的应用结果,在实际应用中该方法比全局回归方法具有更强的适用性,为近红外光谱分析提供了新的分析工具。     

影响离子色谱测量准确性的因素

介绍了离子色谱测量过程中色谱条件的变化、样品前处理方法和操作过程等因素对测量结果准确性的影响,提出了相应的解决措施和校准方法.以实际样品测定为例介绍了样品前处理过程中待测组分浓度和形态的变化,对定量分析结果造成的误差及修正方法.     

Computational chemical analysis of the retention of acidic drugs on a pentyl-bonded silica gel in reversed-phase liquid chromatography

A fast method to obtain a quantitative structure-retention relationship is required in chromatography for the rapid optimization of chromatographic separation conditions. Chromatographic data of acidic drugs are analyzed by a computational chemical method to simulate chromatographic simulation. The direct interaction between a model phase and a drug is calculated as an energy value using the molecular mechanics calculation of CAChe. Computational chemistry using a model adsorbent is a new method for quantitative analysis of retention in reversed-phase liquid chromatography. The correlation coefficient is 0.878 (n = 19) between the retention factors of acidic drugs and interaction energy values of the final structure (DeltaFS) between an acidic drug and model pentyl-bonded phase.     

木薯品质分析的近红外光谱模型建立及其应用研究

应用近红外光谱法(NIRS)建立木薯中淀粉、水分定量分析的近红外光谱数学模型,探讨了修正偏最小二乘法(MPLS)、偏最小二乘法(PLS)以及主成分回归法(PCR)等优化处理对定标模型的影响,确定了修正偏最小二乘法(MPLS)是建立模型最适合的数学方法。并对模型预测结果的准确性进行了评价。结果表明:验证集样品的化学值和近红外预测值拟合存在较好的线性关系,相关性显著。淀粉模型预测标准偏差(Sep)为0.850,系统偏差(Bias)为-0.095,相关系数(r)为0.971。水分模型预测标准偏差(Sep)为0.075,系统偏差(Bias)为0.007,相关系数(r)为0.980。淀粉、水分定量分析的NIRS数学模型具有较高的预测准确性,可应用于木薯批量收购中的品质等分析。     

核磁共振内标法定量分析肝素钠中多硫酸软骨素

1引言肝素钠是一种由葡萄糖胺,L-艾杜糖醛苷、N-乙酰葡萄糖胺和D-葡萄糖醛酸交替组成的黏多糖硫酸脂钠盐,是临床大量应用的抗凝血药。研究证明,肝素钠还具有抗炎、抗过敏、抗病毒、抗癌等多种生物学功能。2008年初,欧美各国发生了肝素钠注射液事件,致使81人死亡。随后,美国FDA应用核磁共振方法在肝素钠原料药中定性查出杂质多硫酸软     

A strategy for enhancing the quantitative determination ability of the diffuse reflectance near-infrared spectroscopy

Near-infrared diffuse reflectance spectroscopy (NIRDRS) has been proved to be a convenient and fast quantitative method for complex samples. The high detection limit or the low sensitivity of the method, however, is a big problem obstructing its application in the analysis of low concentration samples. A strategy for quantitative determination of low concentration samples was developed by using NIRDRS. The method takes an adsorbent as a substrate for gathering the analytes from a solution, and uses the multivariate calibration technique for quantitative calculation. So, the detection limit can be improved and the interferences can be eliminated when complex samples are analyzed. Taking benzoic and sorbic acids as the analyzing targets and the alumina as the adsorbent, partial least squares (PLS) model is built from the NIRDRS of the adsorbates. The results show that the concentrations that can be quantitatively detected are as low as 0.011 and 0.013 mg mL(-1) for benzoic and sorbic acids, respectively, and the co-adsorbates do not interfere each other.     

序贯数论优化法用于“灰色”体系多组分同时定量分析

将序贯为九论优化法,结合采用约束背影双线性比模型用于多组分“灰色”体系同时测定,且对ＳＮＴＯ的收缩比和收敛判据进行了改进。结果表明,ＳＮＴＯ法是一种实用的全局优化方法,收缩比和收敛判据的改进可大大缩短计算时间。     

分析化学知识内容的模块化功能划分

将分析化学的理论教学与实验教学相统一,对其知识内容进行模块化构建。因为定量分析化学的核心任务是"量"的分析,围绕这个核心将其分为条件控制与定量计算两大板块。条件控制是定量分析的前提保证,可划分为基础理论研究和实际操作技术两部分。定量计算获得结果则是分析化学的最终目的,其划分为结果计算和结果评价两部分。经过模块化功能划分,分析化学知识结构构架清晰,具有纵向层次性、横向类比性,有助于培养学生归纳、总结知识和分析、解决问题的能力,使学生不仅能够较好地掌握知识内容,而且能够提升自主学习能力,最终实现终身学习。     

Quantitative fingerprinting based on the limited‐ratio quantified fingerprint method for an overall quality consistency assessment and antioxidant activity determination of Lianqiao Baidu pills using HPLC with a diode array detector combined with chemometric methods

Lianqiao Baidu pills are widely used herbal medicinal preparation that were analyzed to develop a quality consistency technique. The characteristic fingerprints of 28 batches of Lianqiao Baidu pill samples were established at five wavelengths and simultaneously assessed by using a limited‐ratio quantified fingerprint method using 15 marker compounds. The principal component analysis and fingerprinting results were compared, and the qualitative classification of the samples by principal component analysis agreed with their quantitative evaluation by the limited‐ratio quantified fingerprint method. Furthermore, the antioxidant activities of the samples were surveyed and determined using a 2,2‐diphenyl‐1‐picrylhydrazyl radical‐scavenging approach. A relationship between the common peaks in the fingerprints and the antioxidant activities was established using a partial least squares model. The relationship can be used both to determine the antioxidant activities of the Lianqiao Baidu pill preparations in vitro and as a reference for the selection of active constituents for sample quality classification. The classification results for the samples based on principal component analysis agreed with the quantitative evaluation by the limited‐ratio quantified fingerprint method, which demonstrated that the method can be applied to the holistic quality control of traditional Chinese medicine and herbal preparations.     

Assessment and acceleration of binding energy calculations for protein–ligand complexes by the fragment molecular orbital method

In the field of drug discovery, it is important to accurately predict the binding affinities between target proteins and drug applicant molecules. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics‐based force fields, although they cannot fully describe protein–ligand interactions. A noteworthy computational method in development involves large‐scale electronic structure calculations. Fragment molecular orbital (FMO) method, which is one of such large‐scale calculation techniques, is applied in this study for calculating the binding energies between proteins and ligands. By testing the effects of specific FMO calculation conditions (including fragmentation size, basis sets, electron correlation, exchange‐correlation functionals, and solvation effects) on the binding energies of the FK506‐binding protein and 10 ligand complex molecule, we have found that the standard FMO calculation condition, FMO2‐MP2/6‐31G(d), is suitable for evaluating the protein–ligand interactions. The correlation coefficient between the binding energies calculated with this FMO calculation condition and experimental values is determined to be R = 0.77. Based on these results, we also propose a practical scheme for predicting binding affinities by combining the FMO method with the quantitative structure–activity relationship (QSAR) model. The results of this combined method can be directly compared with experimental binding affinities. The FMO and QSAR combined scheme shows a higher correlation with experimental data (R = 0.91). Furthermore, we propose an acceleration scheme for the binding energy calculations using a multilayer FMO method focusing on the protein–ligand interaction distance. Our acceleration scheme, which uses FMO2‐HF/STO‐3G:MP2/6‐31G(d) at R_int = 7.0 Å, reduces computational costs, while maintaining accuracy in the evaluation of binding energy. © 2015 Wiley Periodicals, Inc.     

Chromatography in silico, quantitative analysis of retention of aromatic acid derivatives

A quantitative analysis of the retention of aromatic acid derivatives in reversed-phase liquid chromatography (RPLC) is conducted using a molecular mechanics calculation in the CAChe program. The molecular interaction energy value is calculated by subtracting the energy value of the complex from the sum of energy values of a model phase and an analyte. Several model phases are constructed, and the feasibility of applying the method to a variety of compounds is examined based on improving the contact surface area and the capability of computer software and hardware. Interaction energy values are calculated for both molecular and ionic forms. The predicted retention factors of partially ionized acids obtained using a combination of dissociation constants correlated well with the values measured by RPLC with pH-controlled eluents.     

Multivariate data analysis for x-ray fluorescence spectrometry

A multivariate data analysis procedure that uses singular value decomposition and the Ho-Kashyap algorithm is proposed to obtain calibration constants for x-ray fluorescence spectrometry. These calibration constants can be used to obtain results from experimental data by means of a simple dot product calculation. The method was tested on experimental data from the literature. Comparison of results showed that the method performs at least as well or better than the Rasberry-Heinrich method or its modifications. The method can be used to express calibration results obtained with a theoretically based program in such a way that they can be used conveniently in routine applications.