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Application of an artificial neural network in chromatography - retention behavior prediction and pattern recognition
Zhao, RH; Yue, BF; Ni, JY; Zhou, HF; Zhang, YK
关键词Artificial Neural Network Error Back-propagation Algorithm Retention Behavior Reversed-phase High Performance Liquid Chromatography (Rp-hplc) High Resolution Gas Chromatography Pattern Recognition
刊名CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
1999-01-18
45期:1-2页:163-170
收录类别ISTP ; SCI
文章类型Article
WOS标题词Science & Technology ; Technology ; Physical Sciences
类目[WOS]Automation & Control Systems ; Chemistry, Analytical ; Computer Science, Artificial Intelligence ; Instruments & Instrumentation ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
研究领域[WOS]Automation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentation ; Mathematics
关键词[WOS]LIQUID-CHROMATOGRAPHY ; BILE-ACIDS ; CHEMOMETRICS ; CLASSIFICATION
英文摘要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-tetrathydrofuran-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. (C) 1999 Elsevier Science B.V. All rights reserved.
语种英语
WOS记录号WOS:000078497200018
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被引频次:23[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://cas-ir.dicp.ac.cn/handle/321008/138525
专题中国科学院大连化学物理研究所
作者单位Chinese Acad Sci, Dalian Inst Chem Phys, Natl Chromatog R & A Ctr, Dalian 116011, Peoples R China
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GB/T 7714
Zhao, RH,Yue, BF,Ni, JY,et al. Application of an artificial neural network in chromatography - retention behavior prediction and pattern recognition[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,1999,45(1-2):163-170.
APA Zhao, RH,Yue, BF,Ni, JY,Zhou, HF,&Zhang, YK.(1999).Application of an artificial neural network in chromatography - retention behavior prediction and pattern recognition.CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,45(1-2),163-170.
MLA Zhao, RH,et al."Application of an artificial neural network in chromatography - retention behavior prediction and pattern recognition".CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 45.1-2(1999):163-170.
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