Madden J E, Avdalovic N, Haddad P R, Havel J
Department of Analytical Chemistry, Faculty of Science, Masaryk University, Kotlarska, Brno, Czech Republic.
J Chromatogr A. 2001 Feb 23;910(1):173-9. doi: 10.1016/s0021-9673(00)01185-7.
The feasibility of using an artificial neural network (ANN) to predict the retention times of anions when eluted from a Dionex AS11 column with linear hydroxide gradients of varying slope was investigated. The purpose of this study was to determine whether an ANN could be used as the basis of a computer-assisted optimisation method for the selection of optimal gradient conditions for anion separations. Using an ANN with a (1, 10, 19) architecture and a training set comprising retention data obtained with three gradient slopes (1.67, 2.50 and 4.00 mM/min) between starting and finishing conditions of 0.5 and 40.0 mM hydroxide, respectively, retention times for 19 analyte anions were predicted for four different gradient slopes. Predicted and experimental retention times for 133 data points agreed to within 0.08 min and percentage normalised differences between the predicted and experimental data averaged 0.29% with a standard deviation of 0.29%. ANNs appear to be a rapid and accurate method for predicting retention times in ion chromatography using linear hydroxide gradients.
研究了使用人工神经网络（ANN）预测在不同斜率的线性氢氧化物梯度下从Dionex AS11柱洗脱时阴离子保留时间的可行性。本研究的目的是确定ANN是否可用作计算机辅助优化方法的基础，以选择阴离子分离的最佳梯度条件。使用具有（1, 10, 19）结构的ANN和一个训练集，该训练集包含在分别为0.5和40.0 mM氢氧化物的起始和结束条件之间用三个梯度斜率（1.67、2.50和4.00 mM/min）获得的保留数据，针对四个不同的梯度斜率预测了19种分析物阴离子的保留时间。133个数据点的预测保留时间和实验保留时间在0.08分钟内一致，预测数据和实验数据之间的归一化差异百分比平均为0.29%，标准偏差为0.29%。人工神经网络似乎是一种快速准确的方法，用于预测使用线性氢氧化物梯度的离子色谱中的保留时间。
