| Novel computer-assisted approach to quick prediction and optimization of gradient separation for online enrichment-reversed phase liquid chromatography tandem system |
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| 作者姓名： | Shuying Han Yilin Song Xinyi Jiang Junqin Qiao An Kang Haishan Deng Dong Zhu Rui Liu Hongzhen Lian |
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| 作者单位： | 1. College of Pharmacy, Nanjing University of Chinese Medicine;2. Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine;3. State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry & Chemical Engineering and Center of Materials Analysis, Nanjing University |
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| 基金项目： | supported by National Natural Science Foundation of China (Nos. 82174090, 22176085, 21874065, 21577057)；;Natural Science Foundation for Colleges of Jiangsu (No. 21KJA360007)；;Jiangsu Qinglan Project；;Jiangsu “333” Project； |
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| 摘 要： | An algorithm capable of predicting and optimizing the gradient separation of LC × LC system was developed in this paper. Two groups of structural analogues, five ginsenosides as well as eight bisphenols,which were difficult to discriminate in routine analysis, were used to verify the effectiveness of the proposed algorithm in fast separation optimization. Average errors of retention times below 1% were found in the retention prediction for all types of gradient programs, implying that the theory...
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| 收稿时间： | 18 September 2022 |
Novel computer-assisted approach to quick prediction and optimization of gradient separation for online enrichment-reversed phase liquid chromatography tandem system |
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| Shuying Han,Yilin Song,Xinyi Jiang,Junqin Qiao,An Kang,Haishan Deng,Dong Zhu,Rui Liu,Hongzhen Lian.Novel computer-assisted approach to quick prediction and optimization of gradient separation for online enrichment-reversed phase liquid chromatography tandem system[J].Chinese Chemical Letters,2023,34(9):108139-301. |
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| Institution: | 1. College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China;2. State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry & Chemical Engineering and Center of Materials Analysis, Nanjing University, Nanjing 210023, China;3. Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, National and Local Collaborative Engineering Center of Chinese Medicinal Resources Industrialization and Formulae Innovative Medicine, Nanjing 210023, China;1. PCFM, LIFM Lab, School of Chemistry, Sun Yat-sen University, Guangzhou 510275, China;2. Guangdong Provincial Key Laboratory of Optical Chemicals, Xinhuayue Group, Maoming 525000, China;3. Shenzhen Grubbs Institute, Department of Chemistry, Southern University of Science and Technology, Shenzhen 518055, China;4. Pohang Accelerator Laboratory, Postech, Pohang, Gyeongbuk, Korea;1. Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Chemical Engineering, Nanjing Forestry University, Nanjing 210037, China;2. Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Department of Chemistry, Fudan University, Shanghai 200433, China;3. State Key Laboratory of Organometallic Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China;4. Green Catalysis Center and College of Chemistry, Zhengzhou University, Zhengzhou 450001, China;5. Circa Renewable Chemistry Institute, Green Chemistry Centre of Excellence, University of York, York YO105DD, United Kingdom;1. College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China;2. State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;1. College of Pharmaceutical Sciences, Key Laboratory of Public Health Safety of Hebei Province, Key Laboratory of Medicinal Chemistry and Molecular Diagnosis, Ministry of Education, Hebei University, Baoding 071002, China;2. College of Basic Medical Science, Key Laboratory for Proteomics of Liaoning Province, Dalian Medical University, Dalian 116044, China;1. Key Laboratory of Light Conversion Materials and Technology of Shandong Academy of Sciences, Advanced Materials Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China;2. College of Chemistry and Materials Science, Northwest University, Xi''an 710069, China;3. College of Chemistry, State Key Laboratory of Elemento-Organic Chemistry, Nankai University, Tianjin 300071, China;4. Institute of Materia Medica, Chinese Academy of Medical Sciences, Beijing 100050, China;5. Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Department of Chemistry, Fudan University, Shanghai 200438, China;6. Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei 230026, China;7. School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China;8. Key Laboratory for Advanced Materials, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Chemistry and Molecular Engineering, Institute of Fine Chemicals, East China University of Science and Technology, Shanghai 200237, China;9. College of Chemistry, Sichuan University, Chengdu 610065, China;10. Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China |
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| Abstract: | An algorithm capable of predicting and optimizing the gradient separation of LC × LC system was developed in this paper. Two groups of structural analogues, five ginsenosides as well as eight bisphenols, which were difficult to discriminate in routine analysis, were used to verify the effectiveness of the proposed algorithm in fast separation optimization. Average errors of retention times below 1% were found in the retention prediction for all types of gradient programs, implying that the theory could lead to high quality in prediction of the retention times under gradients elution. Meanwhile, 84% of relative average deviations (RADs) between the predicted peak width and the measured ones were less than 20%. The larger deviation occurred at the time when the peak appeared while the gradient of the mobile phase changed, which led the deviations increased to 20%–42%. In all, method development and optimization for LC × LC tandem system was realized by the homemade user-friendly software. The present protocol may turn on great opportunities for the convenient method development in analysis of trace structural analogues in environmental, food and biological samples. |
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