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.     

Active Thermochemical Tables: accurate enthalpy of formation of hydroperoxyl radical, HO2

Through the use of the Active Thermochemical Tables approach, the best currently available enthalpy of formation of HO2 has been obtained as delta(f)H(o)298 (HO2) = 2.94 +/- 0.06 kcal mol(-1) (3.64 +/- 0.06 kcal mol(-1) at 0 K). The related enthalpy of formation of the positive ion, HO2+, within the stationary electron convention is delta(f)H(o)298 (HO2+) = 264.71 +/- 0.14 kcal mol(-1) (265.41 +/- 0.14 kcal mol(-1) at 0 K), while that for the negative ion, HO2- (within the same convention), is delta(f)H(o)298 (HO2-) = -21.86 +/- 0.11 kcal mol(-1) (-21.22 +/- 0.11 kcal mol(-1) at 0 K). The related proton affinity of molecular oxygen is PA298(O2) = 100.98 +/- 0.14 kcal mol(-1) (99.81 +/- 0.14 kcal mol(-1) at 0 K), while the gas-phase acidity of H2O2 is delta(acid)G(o)298 (H2O2) = 369.08 +/- 0.11 kcal mol(-1), with the corresponding enthalpy of deprotonation of H2O2 of delta(acid)H(o)298 (H2O2) = 376.27 +/- 0.11 kcal mol(-1) (375.02 +/- 0.11 kcal mol(-1) at 0 K). In addition, a further improved enthalpy of formation of OH is briefly outlined, delta(f)H(o)298 (OH) = 8.93 +/- 0.03 kcal mol(-1) (8.87 +/- 0.03 kcal mol(-1) at 0 K), together with new and more accurate enthalpies of formation of NO, delta(f)H(o)298 (NO) = 21.76 +/- 0.02 kcal mol(-1) (21.64 +/- 0.02 kcal mol(-1) at 0 K) and NO2, delta(f)H(o)298 (NO2) = 8.12 +/- 0.02 kcal mol(-1) (8.79 +/- 0.02 kcal mol(-1) at 0 K), as well as H(2)O(2) in the gas phase, delta(f)H(o)298 (H2O2) = -32.45 +/- 0.04 kcal mol(-1) (-31.01 +/- 0.04 kcal mol(-1) at 0 K). The new thermochemistry of HO2, together with other arguments given in the present work, suggests that the previous equilibrium constant for NO + HO2 --> OH + NO2 was underestimated by a factor of approximately 2, implicating that the OH + NO2 rate was overestimated by the same factor. This point is experimentally explored in the companion paper of Srinivasan et al. (next paper in this issue).     

Lanthanide Contraction within a Series of Asymmetric Dinuclear [Ln2] Complexes

A complete isostructural series of dinuclear asymmetric lanthanide complexes has been synthesized by using the ligand 6‐[3‐oxo‐3‐(2‐hydroxyphenyl)propionyl]pyridine‐2‐carboxylic acid (H₃ L ). All complexes have the formula [Ln₂(H L )₂(H₂ L )(NO₃)(py)(H₂O)] (Ln=La ( 1 ), Ce ( 2 ), Pr ( 3 ), Nd ( 4 ), Sm ( 5 ), Eu ( 6 ), Gd ( 7 ), Tb ( 8 ), Dy ( 9 ), Ho ( 10 ), Er ( 11 ), Tm ( 12 ), Yb ( 13 ), Lu ( 14 ), Y ( 15 ); py=pyridine). Complexes of La to Yb and Y have been crystallographically characterized to reveal that the two metal ions are encapsulated within two distinct coordination environments of differing size. Whereas one site maintains the coordination number (nine) through the whole series, the other one increases from nine to ten owing to a change in the coordination mode of an NO₃^? ligand. This series offers a unique opportunity to study in detail the lanthanide contraction within complexes of more than one metal. This analysis shows that various representative parameters proportional to this contraction follow a quadratic decay as a function of the number n of f electrons. Slater’s model for the atomic radii has been used to extract, from these decays, the shielding constant of 4f electrons. The average of O???O distances within the coordination polyhedra shared by both metals and of the Ln???Ln separations follow also a quadratic decay, therefore showing that such dependence holds also for parameters that receive the contribution of two lanthanide ions simultaneously. The magnetic behavior has been studied for all nondiamagnetic complexes. It reveals the effect of the spin–orbit coupling and a weak antiferromagnetic interaction between both metals. Photoluminescent studies of all the complexes in the series reveal a single broad emission band in the visible region, which is related to the coordinated ligand. On the other hand, the Nd, Er, and Yb complexes show features in the near‐IR region due to metal‐based transitions.     

Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography

The reliability of predicted separations in ion chromatography depends mainly on the accuracy of retention predictions. Any model able to improve this accuracy will yield predicted optimal separations closer to the reality. In this work artificial neural networks were used for retention modeling of void peak, fluoride, chlorite, chloride, chlorate, nitrate and sulfate. In order to increase performance characteristics of the developed model, different training methodologies were applied and discussed. Furthermore, the number of neurons in hidden layer, activation function and number of experimental data used for building the model were optimized in terms of decreasing the experimental effort without disruption of performance characteristics. This resulted in the superior predictive ability of developed retention model (average of relative error is 0.4533%). Copyright © 2008 John Wiley & Sons, Ltd.     

From Isocratic Data to a Gradient Elution Retention Model in IC: An Artificial Neural Network Approach

Gradient elution is used in ion chromatography to achieve rapid analysis with reasonable separation. Optimization and prediction of the gradient is clearly a multidimensional problem, however. One approach to prediction of gradient retention behavior is based on isocratic experimentation. In this work, a gradient model for simultaneous prediction of the retention behavior of fluoride, chlorite, chloride, chlorate, nitrate, and sulfate ions, on the basis of isocratic experimental data, is proposed. An artificial neural network was used to predict isocratic results; the network was optimized with regard to the number of data in the training set (25) and number of neurons in the hidden layer (6). A slight systematic error was observed in the isocratic prediction, but this did not effect gradient prediction. Good predictions were achieved for all the anions investigated (average error 1.79%). Deviations were somewhat higher for prediction of sulfate retention than for the other anions, probably because of the higher charge and larger size of sulfate in comparison with the other ions examined.     

HF molecules and poly(hydrogen fluoride) anions as ligands to metal centers

The paper is dealing with the two sets of the coordination compounds:

• (a) 

  the coordination compounds in which anhydrous HF is acting as a ligand to the metal ions

• (b) 

  the compounds in which poly(hydrogen–fluoride) anions of the type HnFn+1− (n = 1, 2, 3) are coordinated to the metal centers and connecting them in the 3D structures.

The main purpose of this work is the review of the above mentioned compounds which were recently isolated in our laboratory. The coordination of the metal centers and their connection in the three-dimensional network are illustrated with their crystal structures. A very brief review of the achievements of some other groups is also mentioned where it is appropriate.     

Ion chromatographic method development for monitoring of seawater quality used in over-the-counter pharmaceutical industry

A methodology is developed for the analysis of inorganic anions (fluoride, chloride, bromide, sulphate) in seawater used for over-the-counter (OTC) nasal spray production. The eluent flow rate and concentration of eluent competing ions are optimised by using an artificial neural network resolution model in combination with normalised resolution product criterion function. The developed artificial neural network resolution model shows good predictive ability R2 > or = 0.9973. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing a validation procedure and a number of statistical tests, it is shown that the developed ion chromatographic method has superior performance characteristics: linearity R2 > or = 0.9993, recovery = 99.77-100.65%, repeatability RSD < or = 1.85%. This result proves that the proposed method can be used for routine quality assurance analysis in OTC pharmaceutical industry.     

Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Gradient elution in ion chromatography (IC) offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved (less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per time unit. The aim of this work was the development of a suitable artificial neural network (ANN) gradient elution retention model that can be used in a variety of applications for method development and retention modelling of inorganic anions in IC. Multilayer perceptron ANNs were used to model the retention behaviour of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to the starting time of gradient elution and the slope of the linear gradient elution curve. The advantage of the developed model is the application of an optimized two-phase training algorithm that enables the researcher to make use of the advantages of first- and second-order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This study shows that developed, ANNs are the method of first choice for retention modelling of inorganic anions in IC.