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.     

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.     

Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms

This paper describes development of artificial neural network (ANN) retention model, which can be used for method development in variety of ion chromatographic applications. By using developed retention model it is possible both to improve performance characteristic of developed method and to speed up new method development by reducing unnecessary experimentation. Multilayered feed forward neural network has been used to model retention behaviour of void peak, lithium, sodium, ammonium, potassium, magnesium, calcium, strontium and barium in relation with the eluent flow rate and concentration of methasulphonic acid (MSA) in eluent. The probability of finding the global minimum and fast convergence at the same time were enhanced by applying a two-phase training procedure. The developed two-phase training procedure consists of both first and second order training. Several training algorithms were applied and compared, namely: back propagation (BP), delta-bar-delta, quick propagation, conjugate gradient, quasi Newton and Levenberg-Marquardt. It is shown that the optimized two-phase training procedure enables fast convergence and avoids problems arisen from the fact that every new weight initialization can be regarded as a new starting position and yield irreproducible neural network if only second order training is applied. Activation function, number of hidden layer neurons and number of experimental data points used for training set were optimized in order to insure good predictive ability with respect to speeding up retention modelling procedure by reducing unnecessary experimental work. The predictive ability of optimized neural networks retention model was tested by using several statistical tests. This study shows that developed artificial neural network are very accurate and fast retention modelling tool applied to model varied inherent non-linear relationship of retention behaviour with respect to mobile phase parameters.     

应用人工神经网络鉴定高效液相色谱峰纯度

首次应用人工神经网络（ａｒｔｉｆｉｃｉａｌｎｅｕｒａｌｎｅｔｗｏｒｋ,简称ＡＮＮ）鉴定色谱峰纯度,根据判定指标,采用改良反向传播算法对系统进行描述和预报,结果正确,收敛较快,具有一定的理论和实用价值。     

Sample preparation followed by HPLC under harmless 100% aqueous conditions for determination of oxytetracycline in milk and eggs

A simple and hazardous chemical-free method for the high-performance liquid chromatographic determination of oxytetracycline (OTC) residues in milk and eggs has been developed. Sample preparation consists in homogenization with an aqueous solution by means of a handheld ultrasonic homogenizer followed by centrifugal ultrafiltration. HPLC is performed with an isocratic aqueous mobile phase and a photodiode array detector. Average recoveries of OTC (0.05, 0.1, and 0.2 microg mL(-1) for milk; 0.1, 0.2, and 0.4 microg mL(-1) for eggs) were > or =84% with relative standard deviations of < or =2.3%. The total time required for the analysis of one sample and LOQs were <30 min and <0.1 microg mL(-1), respectively. In all the processes, no organic solvents or hazardous reagents were used.     

Prediction of the chromatographic signal in gradient elution ion chromatography

This study describes the development of a signal prediction model in gradient elution ion chromatography. The proposed model is based on a retention model and generalized logistic peak shape function which guarantees simplicity of the model and its easy implementation in method development process. Extensive analysis of the model predictive ability has been performed for ion chromatographic determination of bromate, nitrite, bromide, iodide, and perchlorate, using KOH solutions as eluent. The developed model shows good predictive ability (average relative error of gradient predictions 1.94%). The developed model offers short calculation times as well as low experimental effort (only nine isocratic runs are used for modeling).     

离子色谱法同时分析啤酒中的有机酸和无机阴离子

建立了以NaOH-甲醇混合淋洗液,离子交换色谱柱,电导检测器检测,分离啤酒中有机酸和无机阴离子的离子色谱法.结果表明：在20 min内分离出啤酒中的11种主要有机酸与无机阴离子,方法回收率和相对标准偏差分别为96.30%～104.8%和0.02%～4.0%.     

Evaluation of select variables in the ion chromatographic determination of F-, Cl-, Br-, NO(-)3, SO(-2)4, and PO(-3)4 in serum samples

A full experimental design at two levels is applied for the estimation of the significance of select factors that may influence the ion chromatography (IC) determination of F-, Cl-, Br-, NO(-)3, SO(-2)4, and PO(-3)4 in serum samples. The factors studied are various sample deproteinization procedures, eluent composition, and flow rates. Deproteinization using either acetonitrile-NaOH or ultrafiltration can be used in order to obtain a significant protein removal before IC analysis; however, the former is recommended because it is less time-consuming and cheaper. Better resolution is obtained when a sodium hydroxide solution is used as the eluent. There is no influence of the sample's deproteinization procedures on the chromatographic resolution.     

人工神经网络方法预测气相色谱保留值

本文运用一典型的人工神经网络模型－“反向传播“模型的改进形式，研究了诱导效应指数Ｉ，摩尔折射度Ｒｏ，疏水亲脂参数ＩｇＰ，以及分子联通性指数与气象色谱保留行为的关系，实现了对色谱保留植的预测。神经网络预测模型的最大相对误差不超过８．７％。结果表明，该方法性能良好，可望成为色谱保留值预测的有效手段。     

Ligand-exchange ion chromatographic determination of malic acid enantiomers in apple juice with photometric detection.

An ion chromatographic separation with photometric detection using a chiral copper(II) complex as the eluent has been developed for the resolution of enantiomers of malic acid in commercially available apple juices. The results obtained by this method were in good agreement with those by an enzymatic method with separation by high-performance liquid chromatography.     

饱和醇结构-保留定量相关的人工神经网络模型

以拓扑指数为结构描述符，用基于Levenberg-Marquardt优化的BP神经网络建立了醇类化合物的结构与色谱保留值的相关性模型，用于未知醇类化合物在SE－30和OV－3两根色谱柱上保留指数的同时预测，其学习速率优于文献中普通BP神经网络法，预测准确度与普通BP神经网络法接近，但优于多元线性回归法，因而是一种较好的预测有机化合物气相色谱保留指数的方法。     

离子色谱-串联质谱法检测茶叶中的高氯酸盐

建立了离子色谱-串联质谱检测茶叶中高氯酸盐的分析方法。选用高容量、强亲水性的IonPac AS20阴离子交换柱（2 mm）进行分离,以淋洗液自动发生器在线产生的70 mmol/L氢氧化钾为淋洗液等浓度淋洗,TSQ Quantiva三重四极杆质谱仪作检测器,采用电喷雾电离源负离子（ESI^-）模式、多反应监测（MRM）模式进行分析,内标法定量。结果表明,高氯酸盐在0.02~10.0 μg/L范围内线性关系良好,相关系数（r）为0.9991,定量限为2 μg/kg。运用该方法测定5种茶叶中的高氯酸盐,加标回收率为87.3%~112.2%。该方法具有操作简单、专属性强、灵敏度高等优点,可满足茶叶中高氯酸盐的检测要求。     

Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions

In this work, three different methods for modeling of gradient retention were combined with several optimization objective functions in order to find the most appropriate combination to be applied in ion chromatography method development. The system studied was a set of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) with a KOH eluent. The retention modeling methods tested were multilayer perceptron artificial neural network (MLP-ANN), radial-basis function artificial neural network (RBF-ANN), and retention model based on transfer of data from isocratic to gradient elution mode. It was shown that MLP retention model in combination with the objective function based on normalized retention difference product was the most adequate tool for optimization purposes.     

Application of Artificial Neural Network and Multiple Linear Regression Retention Models for Optimization of Separation in Ion Chromatography by Using Several Criteria Functions

This work focuses on problems regarding empirical retention modelling and optimization of separation in ion chromatography. Influences of eluent flow rate and concentration of eluent competing ion (OH^–) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulphate, bromide, nitrate, and phosphate) were investigated. Artificial neural networks and multiple linear regression retention models in combination with several criteria functions were used and compared in global optimization process. It can be seen that general recommendations for optimization of separation in ion chromatography is application of chromatography exponential function criterion in combination with artificial neural networks retention model.     

Method development for separating organic carbonates by ion‐moderated high‐performance liquid chromatography

An ion‐moderated partition high‐performance liquid chromatography method was developed for the separation and identification of common organic carbonates. The separation of organic carbonates was achieved on an ion exclusion column with an exchangeable hydrogen ion. An isocratic, aqueous mobile phase was used for elution and detection was performed with a refractive index detector. The developed method was validated for specificity, linearity, limits of detection and quantification, precision and accuracy. All calibration curves showed excellent linear regression (R² > 0.9990) within the testing range. The limits of detection were 3.8–30.8 ppm for the analyzed carbonates. Improvements in the peak resolution of the chromatograms were achieved by decreasing the column temperature. Addition of the organic modifier, acetonitrile, to the eluent was found to have insignificant effects on the peak resolution. The developed method was demonstrated for analyzing organic carbonate components in the electrolyte system of a commercial lithium ion battery.     

Optimization via experimental design of an SPE-HPLC-UV-fluorescence method for the determination of valsartan and its metabolite in human plasma samples

A chemometric approach was applied for the optimization of the extraction and separation of the antihypertensive drug valsartan and its metabolite valeryl-4-hydroxy-valsartan from human plasma samples. Due to the high number of experimental and response variables to be studied, fractional factorial design (FFD) and central composite design (CCD) were used to optimize the HPLC-UV-fluorescence method. First, the significant variables were chosen with the help of FFD; then, a CCD was run to obtain the optimal values for the significant variables. The measured responses were the corrected areas of the two analytes and the resolution between the chromatographic peaks. Separation of valsartan, its metabolite valeryl-4-hydroxy-valsartan and candesartan M1, used as internal standard, was made using an Atlantis dC18 100 mm x 3.9 mm id, 100 angstroms, 3 microm chromatographic column. The mobile phase was run in gradient elution mode and consisted of ACN with 0.025% TFA and a 5 mM phosphate buffer with 0.025% TFA at pH 2.5. The initial percentage of ACN was 32% with a stepness of 4.5%/min to reach the 50%. A flow rate of 1.30 mL/min was applied throughout the chromatographic run, and the column temperature was kept to 40+/-0.2 degrees C. In the SPE procedure, experimental design was also used in order at achieve a maximum recovery percentage and extracts free from plasma interferences. The extraction procedure for spiked human plasma samples was carried out using C8 cartridges, phosphate buffer (pH 2, 60 mM) as conditioning agent, a washing step with methanol-phosphate buffer (40:60 v/v), a drying step of 8 min, and diethyl ether as eluent. The SPE-HPLC-UV-fluorescence method developed allowed the separation and quantitation of valsartan and its metabolite from human plasma samples with an adequate resolution and a total analysis time of 1 h.     

Quantitative determination of bovine caseinoglycomacropeptide in infant formulas by ultra-high-performance liquid chromatography-electrospray-ionization mass spectrometry

An ultra-high-performance liquid chromatography-electrospray ionization coupled to mass spectrometry method has been developed for determining caseinoglycomacropeptide (CGMP) in infant formulas by selected ion reaction and area monitoring modes. The present study focused on the optimization of sample pretreatment, chromatographic resolution and mass spectrometry parameters. After a simple sample pretreatment, the two genetic variants of caseinoglycomacropeptide, CGMP(A) and CGMP(B), were separated using a BEH300 C(18) column by gradient elution. The established method was extensively validated by determining the linearity (R(2)>0.999), average recovery (95.8-118.4%), inter-day precision (relative standard deviation ≤7.81%) and intra-day precision (relative standard deviation ≤6.99%) based on two scan modes. To further verify the applicability of the method, 21 brands of commercial available infant formulas were analyzed. The results showed that the present method is selective, sensitive and reliable for separating and quantifying two genetic variants (CGMP(A) and CGMP(B)) of caseinoglycomacropeptide in infant formulas with complex matrix.