Application of an artificial neural network in chromatography—retention behavior prediction and pattern recognition

Layered feed-forward neural networks are powerful tools particularly suitable for the analysis of nonlinear multivariate data. In this paper, an artificial neural network using improved error back-propagation algorithm has been applied to solve problems in the field of chromatography. In this paper, an artificial neural network has been used in the following two applications: (1) To model retention behavior of 32 solutes in a methanol–tetrahydrofuran–water system and 49 solutes in methanol–acetonitrile–water system as a function of mobile phase compositions in high performance liquid chromatography. The correlation coefficients between the calculated and the experimental capacity factors were all larger than 0.98 for each solute in both the training set and the predicting set. The average deviation for all data points was 8.74% for the tetrahydrofuran-containing system and 7.33% for the acetonitrile-containing system. 2). To classify and predict two groups of different liver and bile diseases using bile acid data analyzed by reversed-phase high performance liquid chromatography (RP-HPLC). The first group includes three classes: healthy persons, choledocholithiasis patients and cholecystolithiasis patients; the total consistent rate of classification was 87%. The second group includes six classes: healthy persons, pancreas cancer patients, hepatoportal high pressure patients, cholelithiasis patients, cholangietic jaundice patients and hepatonecrosis patients; the total consistent rate of classification was 83%. It was shown that artificial neural network possesses considerable potential for retention prediction and pattern recognition based on chromatographic data.