高效离心分配色谱仪对异丙基膦酸单(1-己基-4-乙基)-辛酯-稀土萃取体系的研究

首次用高效离心分配色谱仪（ＨＰＣＰＣ）研究用异丙基膦酸单（１－己基－４－已基）辛酯（ＰＴ－２）分离三价重稀土元素，以分离 Ｙｂ（Ⅲ）， Ｅｒ（Ⅲ）及 Ｌｕ（Ⅲ）为例，考察了流动相酸度、流速、固定相浓度、ＨＰＣＰＣ的转速等因素对ＨＰＣＰＣ分离稀土效果的影响，并用ＨＰＣＰＣ法和液－液萃取法研究了ＰＴ－２萃取重稀土的机理。     

人工神经网络-伏安分析法同时测定邻、间、对二硝基苯

将反向传播算法的前馈神经网络用于导数脉冲伏安分析法同时测定邻、间、对二硝基苯。实验在盐酸－氯化钾－乙醇介质中进行，悬汞电极作为工作电极。通过对网络结构和参数的优化，加快了训练速度，提高了预测的准确度。用该法对邻、间、对二硝基苯混合物进行定量分析，预测的相对标准误差（ＳＥＰ）分别为４２６％，４９９％和４８６％。对人工神经网络（ＡＮＮ）和偏最小二乘法（ＰＬＳ）的结果进行的比较表明，ＡＮＮ法优于ＰＬＳ法。     

高效离心分配色谱仪对异丙基膦酸单（1—己基—4—乙基）—辛酯稀土萃取体系的研

首次用高效离心分配色谱仪研究用异丙基膦酸单辛酯分离三阶重稀土元素，以分离Ｙｂ（Ⅲ），Ｅｒ（Ⅲ）及Ｌｕ（Ⅲ）为例，考察了流动相酸度，流速，固定相浓度，ＨＰＣＰＣ的转速等因素对ＨＰＣＰＣ分离稀土效果的影响。     

Author Index Vol. 7, 1996

ＡｕｔｈｏｒＩｎｄｅｘＶｏｌ．７，１９９６￥／／ＡｌＸ．Ｃ．（７１）ＣＨＥＮＮ．Ｙ．（７８１）ＤＥＮＧＣ．Ｈ．（５６９）ＦＵＸ．Ｙ．（６３１）ＡＮＬ．Ｄ．（７３７）ＣＨＥＮＰ．（３６１）ＤＥＮＧＤ．Ｌ．（８４５）ＦＵＹ．Ｈ．（８５３）ＢＡＩＤ．Ｌ．（...     

5—氨基水杨酸锌及相关物质的高效毛细管电泳分离与测定

１引言５－氨基水杨酸是治疗胃溃疡和Ｃｒｏｈｎ疾病的药物。为提高稳定性和药物疗效，最近合成了新药物５－氨基水杨酸锌。我们对５－氨基水杨酸锌与主要杂质水杨酸，以及可能降解产物５－氢基水杨酸与对氨基苯酚等物质的高效毛细管电泳（ＨＰＣＥ）的分离与测定进行了研究，结果表明，本法分离效果好，测定准确，具有较好的应用价值。２实验部分１２２９ＨＰＣＥＡＮＡＬＹＳＥＲ（北京新技术应用研究所），石英毛细管（６５．５ｃｍ×５０μｍｉ．ｄ，有效长度５７．３ｃｍ，河北永年光纤厂）。５－氨基水杨酸锌（ＺｎＡＳＡ，９９％），…     

用超分子化学观点研究毛细管电泳法拆分十八甲基炔诺酮

以超分子化学观点作ＨＰＣＥ手性选择剂的理论基础，研究ＨＰＣＥ法分离十八甲基炔诺酮对映体，讨论了操作条件对分离的影响，使十八甲基炔诺酮对映异构体达基线分离。     

A NEW TRITERPENOID SAPONIN, ASTERYUNNANOSIDE H, FROM ASTER YUNNANENSIS

ＡＮＥＷＴＲＩＴＥＲＰＥＮＯＩＤＳＡＰＯＮＩＮ，ＡＳＴＥＲＹＵＮＮＡＮＯＳＩＤＥＨ，ＦＲＯＭＡＳＴＥＲＹＵＮＮＡＮＥＮＳＩＳ￥ＹｕＳＨＡＯａｎｄＢｉｎｇＮａｎＺＨＯＵ；ＫａｎＭＡａｎｄＨｏｕＭｉｎｇＷＵ（ＳｈａｎｇｈａｉＩｎｓｔｉｔｕｔｅｏｆＭａｔｅ...     

高效毛细管电泳分离中的化学平衡

在高效毛细管电泳（ＨＰＣＥ）中缓冲对分离有重要影响，缓冲中的化学平衡决定了待测组分的存在状态或形态，从而决定了组分的电泳尚度，淌度的差异是ＨＰＣＥ分离之基础，因此，溶液化学平衡在ＨＰＣＥ分离中起着重要作用，本文综述了电泳淌度，ＨＰＣＥ分离与化学平衡的关系，并要介绍了电泳淌度与酸碱平衡，络合平衡的关系，从化学平衡的角度，讨论了酸碱性化合物，金属离子，对映异构体等几大类化合物的ＨＰＣＥ分离。     

ASTERYUNNANOSIDE A AND B，TWO NEW TRITERPENOID SAPONINS FROM ASTER YUNNANENSIS FRANCH

ＡＳＴＥＲＹＵＮＮＡＮＯＳＩＤＥＡＡＮＤＢ，ＴＷＯＮＥＷＴＲＩＴＥＲＰＥＮＯＩＤＳＡＰＯＮＩＮＳＦＲＯＭＡＳＴＥＲＹＵＮＮＡＮＥＮＳＩＳＦＲＡＮＣＨ￥ＹｕＳＨＡＯ；ＪｉｎＨａｉＧＡＯａｎｄＢｉｎｇＮａｎＺＨＯＵ（ＳｔａｔｅＫｅｙＬａｂｏｒａｔｏｒｙｏ...     

第六届亚太地区化学工程会议（APCCHE＇93）介绍

第六届亚太地区化学工程会议（ＡＰＣＣＨＥ＇９３）介绍王立新（中国科学院化工冶金研究所生化工程国家重点实验室北京１０００８０）Ｔｈｅ６ｔｈＡｓｉａｎ－ｐａｃｉｆｉｃＣｏｎｆｅｒｅｎｃｅｏｎＣｈｅｍｉｃａｌＥｎｇｉｎｅｅｒｉｎｇ（ＡＰＣＣＨＥ＇９３）ｈｅ...     

Application of Artificial Neural Networks in Multifactor Optimization of Selectivity in Capillary Electrophoresis

ABSTRACT

A methodology based on the coupling of experimental design and artificial neural networks (ANNs) was proposed in the optimization of selectivity in capillary electrophoresis. The effect of the buffer composition, concentration, SDS concentration, ethanol percentage and the applied voltage on the separation of six choice solutes was examined by using orthogonal design. Feedforward-type neural networks with faster back propagation (BP) algorithm were applied to model the separation process, and then optimization of the experimental conditions was carried out in the modeled neural network with 5-7-1 structure, which had been confirmed to be able to provide the maximum performance. It was demonstrated that by combining ANN modeling with experimental design, the number of experiments necessary to search and find optimal separation conditions can be reduced significantly. Because of its general validity, the new proposed approach can also be applied in other separation conditions.     

Optimization of the micellar electrokinetic capillary chromatographic determination of dauricine and daurisoline in Rhizoma Menispermi and its herbal medicine using experimental design and radial basis function neural network

Orthogonal design has been applied to the optimization of separation and determination of dauricine and daurisoline in Rhizoma Menispermi and its herbal medicine by micellar electrokinetic capillary chromatography. Operational variables, such as the voltage, micelle concentration, buffer concentration and pH were optimized. Their different effects on peak resolution were studied by the experimental design method. Optimized separation conditions were obtained and successfully applied to the separation and determination of dauricine and daurisoline in real samples. The proposed method allows alkaloids in real samples to be determined within 15 min using a buffer system composed of 25 mM HAc, 25 mM NaAc and 2% polyoxyethylene sorbitan monolaurate (Tween-20) (pH 4.5). In addition, a radial basis function neural network with a “4-18-1” structure was developed based on the experimental results of orthogonal design and uniform design, and applied to the prediction of peak resolution of dauricine and daurisoline under the optimum separation conditions given by orthogonal design. The predicted results were in good agreement with the experimental values, indicating that radial basis function neural network may be a potential method for the selection of separation conditions in capillary electrophoresis.     

Application of experimental design and radial basis function neural network to the separation and determination of active components in traditional Chinese medicines by capillary electrophoresis

Orthogonal design has been used to the optimization of separation and determination of two active components in traditional Chinese medicines by capillary electrophoresis. The concentration of phosphate, applied voltage, organic modifier content and buffer pH were selected as variable parameters. Their different effects on peak resolution were studied by the experimental design method. Optimized separation conditions were obtained and successfully applied to the separation and determination of aconitine and hypaconitine in Aconitum medicinal herbs. Good separation was achieved within 7 min using a buffer system composed of 20 mmol L⁻¹ phosphate and 35% acetonitrile at pH 9.5. The applied voltage was 14 kV and the detection was set at 235 nm. In addition, a radial basis function neural network with a “4-18-1” structure was developed based on the experimental results of orthogonal design and uniform design, and was applied to the prediction of peak resolution of the two active components under the optimum separation conditions given by orthogonal design. The predicted results were in good agreement with the experimental values, indicating that radial basis function neural network is a potential way for the selection of separation conditions in capillary electrophoresis.     

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP‐HPLC using feed‐forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP‐HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two‐dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An “early stopping” strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.     

模拟退火神经网络用于药物液相色谱梯度分离条件的优化

模拟退火神经网络用于药物液相色谱梯度分离条件的优化。使用均匀设计法以乙腈在线性梯度展开时的初始浓度和线性梯度的斜率为优化参数,对六种药物混合体系进行优化。采用退火神经网络方法建立了有效的分离条件预测模型。对神经网络模型所预测的最佳分离条件进行试验,分离结果满意。模拟退火神经网络可有效地用于药物液相色谱分离条件的优化。     

小波神经网络-遗传算法用于2-(9-咔唑)乙基氯甲酸酯衍生化氨基酸的胶束电动力学色谱分离优化

将小波神经网络和遗传算法应用到2-(9-咔唑)-乙基氯甲酸酯衍生化氨基酸的胶束电动力学色谱分离优化。小波神经网络结合正交试验设计用于分离过程的多因素模型建立。以训练好的小波神经网络模型为目标函数,采用实数编码的遗传算法搜寻确定最佳分离条件,在此条件下分离得到的归一化分离度积与正交试验设计中最佳条件相比,提高了12．5％。     

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.     

Studies on the applicability of artificial neural network (ANN) in emulsion liquid membranes

An artificial neural network (ANN) model of emulsion liquid membrane (ELM) process is proposed in the present study which is able to predict solute concentration in feed during extraction operation and ultimate % extraction at different initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time. Because of the complexity in generalization of the phenomenon of ELM process by any mathematical model, the neural network proves to be a very promising method for the purpose of process simulation. The network uses the back-propagation algorithm (BPA) for evaluating the connection strengths representing the correlations between inputs (initial solute concentration in feed phase, internal reagent concentration, treat ratio, volume fraction of internal aqueous phase in emulsion and time) and outputs (solute concentration in feed during extraction operation and % extraction). The network employed in the present study uses five input nodes corresponding to the operating variables and two output nodes corresponding to the measurement of the performance of the network (solute concentration in feed during extraction and % extraction). Batch experiments are performed for separation of nickel(II) from aqueous sulphate solution of initial concentration in the 200–100 mg/l ranges. The network employed in the present study uses two hidden layers of optimum number of nodes being thirty and twenty. A leaning rate of 0.3 and momentum factor of 0.4 is used. The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be well within ±10%.     

预测毛细管区带电泳有效淌度的支持向量回归建模方法

提出预测毛细管电泳迁移行为的支持向量回归建模方法。以核苷为实际研究对象,利用正交试验获得的数据,结合二标记物技术,用支持向量回归算法建立毛细管区带电泳的柱温、电压、缓冲液浓度和pH值与3种核苷的有效淌度之间的相关模型。将其与偏最小二乘回归和人工神经网络方法相比较,结果表明所建模型的预测准确性优于后两者,适宜用于毛细管电泳迁移行为的预测。