Bolanca Tomislav, Cerjan-Stefanović Stefica, Regelja Melita, Regelja Hrvoje, Loncarić Sven
Laboratory of Analytical Chemistry, Faculty of Chemical Engineering and Technology, University of Zagreb, Zagreb, Croatia.
J Sep Sci. 2005 Aug;28(13):1427-33. doi: 10.1002/jssc.200400056.
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.
离子色谱（IC）中的梯度洗脱具有多个优点：可显著缩短总分析时间、提高混合物的整体分离度、改善峰形（拖尾减少）并提高有效灵敏度（因为峰形变化很小）。更重要的是，它能提供每时间单位的最大分离度。本工作的目的是开发一种合适的人工神经网络（ANN）梯度洗脱保留模型，该模型可用于多种应用，以进行离子色谱中无机阴离子的方法开发和保留建模。使用多层感知器人工神经网络来模拟氟化物、氯化物、亚硝酸盐、硫酸盐、溴化物、硝酸盐和磷酸盐的保留行为与梯度洗脱起始时间以及线性梯度洗脱曲线斜率的关系。所开发模型的优点是应用了优化的两相训练算法，使研究人员能够在一个训练过程中利用一阶和二阶训练算法的优点。这导致更好的预测能力，且计算所需时间更少。就获得精确准确的保留模型而言，对隐藏层神经元数量和用于训练集的实验数据点进行了优化，以尽量减少不必要的实验和计算过程所需的时间。本研究表明，所开发的人工神经网络是离子色谱中无机阴离子保留建模的首选方法。
