裂解色谱法研究渣油中硫化物的结构及组成特征

采用裂解气相色谱(PY-GC)方法研究重油中的大分子硫化物。实验考察了裂解时间和裂解温度对裂解反应的影响,在此基础上确定了裂解色谱条件,并分析了渣油裂解产物的组成。通过对大港、俄罗斯、前郭、孤岛及俄罗斯-大庆混合减压渣油等5种渣油的裂解实验,发现不同来源的渣油裂解产物中硫化物的组成和含量存在较大差异。实验中所得到的裂解产物中硫化物的组成为渣油中硫化物的组成提供了重要信息。     

一种香精的气相色谱—裂解气相色谱联用分析方法

讨论了裂解二次反应对气相色谱—裂解气相色谱联用定性分析起的作用,改进了气相色谱—裂解气相色谱联用装置,对7组分的香精作定性分析,研究了多种实验条件与定性参数的关系,提出可用一组裂解色谱参数作为实用的主要定性依据,另一组参数作为辅助依据。     

气相色谱-裂解气相色谱联用定性参数的研究

〕研究了实验条件对气相色谱-裂解气相色谱联用的定性参数的影响,由于组分的各裂解产物的相对保留值在多数实验条件改变时重现性良好,可作为实际定性的主要依据,而裂解产物的相对峰面积可作为定性的辅助依据。     

裂解气相色谱-质谱法分析裂解气压缩机垢样的组成

采用裂解气相色谱-质谱联用技术对大庆石化公司裂解气压缩机EC301不同压缩段的垢样进行分析。实验使用了RJ-1型管炉式裂解器,在500℃下对垢样进行裂解,色谱柱为60 m DB-1型毛细管色谱柱,质谱采用电子轰击电离源,电离能70 eV。研究表明,垢样的形成与环戊二烯密切相关。裂解气相色谱-质谱法可以作为分析该垢样成因的有效手段。     

裂解气相色谱—质谱法对聚乙烯醋酸乙烯酯裂解机理的探讨

本文采用裂解气相色谱和裂解气相色谱—质谱法对聚乙烯醋酸乙烯酯(简称EVA)在不同实验条件下的裂解行为进行了探讨,得出了EVA组成与裂片产量的线性关系式,从而不仅证实了E.M.Barrall等人提出的EVA裂解生成醋酸的机理,而且提出了EVA裂解生成甲烷、二氧化     

居里点裂解毛细管色谱与傅里叶变换红外光谱联用鉴定聚丁二烯裂解产物

裂解色谱法(PY—GC)分析聚合物已得到广泛的应用。为了更好地鉴定研究聚合物的裂解产物,近年来开始使用裂解色谱与质谱联用(PY—GC/MS)。然而,裂解毛细管色谱与傅里叶红外光谱联用(PY—GC/FT—IR)鉴定裂解产物却很少见报道。本文采用PY—GC/FT—IR系统,鉴定了聚丁二烯(PBD)的裂解产物。实验表明:该方     

简易热丝裂解色谱用于高聚物的定量分析方法

简易热丝裂解气相色谱是最先发展的一种裂解色谱方法。其裂解器的主要优点是装置简单、操作简便、温度连续可调等;而其缺点一般认为二次反应较多、裂解的重复性较差。早期国外除了用作定性鉴定外,也用于定量分析上,但报导少而不详。本工作采用改进的简易镍铬热丝裂解色谱法,通过较严格地控制实验条件,主要包括样品用量、样品厚度、操作过程,以及裂解和色谱条件等,对多种高聚物进行了定量分析研究,获得了良好的结果。     

低分子有机物的裂解色谱定性

裂解色谱多用于不挥发的合成、天然和生物高分子的分析和鉴别,裂解色谱-质谱联用也有用于气相分子裂解反应的研究,裂解色谱作为低分子有机物的定性方法则尚未见报道。 本文用气相色谱将混合物分离,再用裂解色谱将分离的组分逐个得出裂解反应产物的色谱图,与标准物裂解色谱指纹图比较,可以得到混合物各组分的定性结果。本法设     

气相色谱一四温裂解气相色谱联用仪

本文用四个并联的蒸气相裂解器在气相色谱—裂解气相色谱联用装置,四个裂解器的温度为406.554,658及768℃,解决了实用的气相色谱—裂解气相色谱联用分析要求有普遍适用的裂解温度的问题,同时得到较多较大的裂解色谱峰,使定性更加准确。     

氯甲基化聚苯乙烯含氯量分析

本文介绍了一种利用裂解色谱测定氯甲基化聚苯乙烯含氯量的方法。该法快速、方便、结果可靠。实验表明,氯甲基化聚苯乙烯裂解产物中乙烯基甲苯峰高百分数的对数与样品含氯量呈线性关系,籍以可测定样品含氯量。     

PCA-LVQ法及其在RS-FTIR大气环境监测数据处理中的应用

将主成分分析(PCA)用于遥感傅里叶变换红外光谱(Remote Sensing Fourier Transform Infrared:RS-FTIR)的特征提取,结合学习矢量量化(LVQ)神经网络,实现了PCA-LVQ对大气中的8组分混合体系进行快速定性分析的建模方法。并与单纯的LVQ神经网络、反向传播人工神经网络(BP-ANN)得到的结果进行了比较。PCA-LVQ显示出较好的处理数据的能力,它不仅提高了运算速度,而且提高了模型的预测准确度,分类精度达到91.7%。PCA-LVQ的这一预测精度及运算速度,足以满足遥感傅里叶变换红外光谱对大气中有毒气体的实时、在线监测的需要。     

Simultaneous determination of iron and aluminium by differential kinetic spectrophotometric method and chemometrics

A differential kinetic spectrophotometric method was researched and developed for the simultaneous determination of iron and aluminium in food samples. It was based on the direct reaction kinetics and spectrophotometry of these two metal ions with Chrome Azurol S (CAS) in ethylenediamine-hydrochloric acid buffer (pH 6.3). The results were interpreted with the use of chemometrics. The kinetic runs and the visible spectra of the complex formation reaction were studied between 540 and 750 nm every 30 s over a total period of 285 s. A set of synthetic metal mixture samples was used to build calibrations models. These were based on the spectral and kinetic two-way data matrices, which were processed separately by the radial basis function-artificial neural network (global RBF-ANN) method. The prediction performance of these models was poorer than that from the combined kinetic-spectral three-way array, which was similarly processed by the same method (% relative prediction error (RPE_T) = 5.6). These results demonstrate that improved predictions can be obtained from the data array, which has more information, and that appropriate chemometrics methods can enhance analytical performance of simple techniques such as spectrophotometry.Other chemometrics models were then applied: N-way partial least squares (NPLS), parallel factor analysis (PARAFAC), back propagation-artificial neural network (BP-ANN), single radial basis function-artificial neural network (RBF-ANN), and principal component neural network (PC-RBF-ANN). There was no substantial difference between the methods with the overall %RPE_T range being 5.0-5.8. These two values corresponded to the NPLS and BP-ANN models, respectively. The proposed method was applied for the determination of iron and aluminium in some commercial food samples with satisfactory results.     

人工神经网络在示波计时电位法中的应用

研究了误差反传神经网络和小波变换在示波计时电位测定中应用的可能性。对Ｐｂ２ ＋ 等无机离子测定的实验结果表明： 人工神经网络可较好地用于高次微分和经小波变换处理后的示波计时电位测定。 与经典的示波测定方法相比较, 该法基本上消除了人为误差的影响, 提高了分析速度和分析结果的可靠性。     

主成分分析法-BP神经网络算法用于电位滴定法测定有机酸

采用电位滴定法测定混合液中3种以上的多元弱酸,主成分分析法处理了电位滴定的曲线数据,用BP神经网络进行有机酸预测计算。建立了滴定时滴定剂和混合液中有机酸浓度之间关系的模型,实现了不经分离直接测定溶液中几种小分子多元弱酸。结果表明:该神经网络计算出有机酸的预测值与实际值相对误差不大于5.0%。     

Simultaneous kinetic-spectrophotometric determination of carbidopa, levodopa and methyldopa in the presence of citrate with the aid of multivariate calibration and artificial neural networks

The kinetic methodology based on the difference of reaction rates, is based on the reaction between a common oxidizing agents such as tris(1,10-phenanthroline) and iron(III) complex (ferriin, [Fe (phen)₃]³⁺) in the presence of citrate and spectrophotometrically, monitoring the changes of absorbance at the maximum wavelength of 511 nm. Experimental conditions such as pH, reagents and citrate concentrations were optimized, and the data obtained from the experiments were processed by several chemometric approaches, such as artificial neural network (ANN) and partial least squares (PLS). A set of synthetic mixtures of carbidopa (CD), levodopa (LD) and methyldopa (MD) was evaluated and the results obtained by the applications of these chemometric approaches were discussed and compared. It was found that the back propagation artificial neural network (BP-ANN) method afforded better precision relatively than those of radial basis function artificial neural networks (RBF-ANN) and PLS. The proposed method was also applied satisfactorily to the determination of carbidopa, levodopa and methyldopa in real samples.     

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．     

Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis

Recently, we have developed an artificial neural network model, which was able to predict accurately the electrophoretic mobilities of relatively small peptides. To examine the robustness of this methodology, a 3-3-1 back-propagation artificial neural network (BP-ANN) model was developed using the same inputs as the previous model, which were the Offord's charge over mass term (Q/M(2/3)), corrected steric substituent constant (E(s,c)) and molar refractivity (MR). The data set consisted of 102 peptides with a larger range of size than that of our earlier report - up to 42 amino acid residues as compared to 13 amino acids in the initial study - that also included highly charged and hydrophobic peptides. The entire data set was obtained from the published result by Janini and co-workers. The results of this model are compared with those obtained using multiple linear regressions (MLR) model developed in this work and the multi-variable model released by Janini et al. Better predictive ability of the BP-ANN model over the MLR indicates the non-linear characteristics of the electrophoretic mobility of peptides. The present model exhibits better robustness than the MLR models in predicting CZE mobilities of a diverse data set at different experimental conditions. To explore the utility of the ANN model in simulation of the CZE peptide maps, the profiles for the endoproteinase digests of melittin, glucagon and horse cytochrome C is studied in the present work.     

基于独立分量和神经网络的近红外多组分分析方法

采用小波变换对光谱数据进行压缩,用独立分量分析(ICA)方法提取近红外光谱数据矩阵的独立成分和相应的混合矩阵,再用BP神经网络对混合矩阵和实测浓度矩阵进行建模,提出了基于独立分量分析-神经网络回归(ICA-NNR)的近红外分析建模方法。进一步研究了独立分量数和网络中间隐层的神经元数对模型性能的影响,经优化后的ICA-NNR模型在相关系数与均方根误差两个指标上均优于直接用光谱矩阵作为输入所建立的模型。本方法用于玉米中水分、淀粉、蛋白质3种主要成分含量的同时测定,检验样品集的化学检测值与近红外预测值的相关系数分别达到:淀粉r=0.971,蛋白质r=0.976,水分r=0.975。     

偏最小二乘-速差动力学分光光度法同时测定杀虫剂残杀威和异丙威两组分

采用速差动力学分光光度法对氨基甲酸脂类杀虫剂残杀威和异丙威进行同时测定，这两种化 的能在碱性条件下水解生成酚盐，进而与对氨基苯酚及高碘酸钾的反应产物醌亚胺发生偶联反应，生成蓝色化合物。反应的速率适中，可用于动力学分析。实验采集了多个时间点下538－700nm间的动力学－吸光度数据，构成量测矩阵，并采用偏最小二乘（PLS）法对量测数据进行解析。本文还对合成样品和环境水样中的残杀威和异丙威含量进行测定，获较好的结果。     

A novel technique based on diffuse reflectance near-infrared spectrometry and back-propagation artificial neural network for estimation of particle size in TiO2 nano particle samples

Determination of particle size is one of the critical parameters in nanotechnology. The relationship between particle size and diffuse reflectance (DR) spectra in near-infrared region has been applied to introduce a method for estimation of particle size. Back-propagation artificial neural network (BP-ANN) as a nonlinear model was applied to estimate average particle size based on near-infrared diffuse reflectance spectra. Thirty five different nano TiO₂ samples with different particle size were analyzed by DR-FTNIR spectrometry and the obtained data were processed by BP-ANN. The network was trained by 30 samples and was evaluated by the remaining 5 samples. In order to establish whether the new method is applicable for estimation of particle size of nano structured samples, the optimized model was applied to analyze 44 nano TiO₂ samples. It was observed that ANN using the back-propagation algorithm is capable of generalization and could correctly predict the average particle size of nano-sized particles.