Cross-column retention prediction in reversed-phase high-performance liquid chromatography by artificial neural network modelling

Analytica Chimica Acta ◽

10.1016/j.aca.2011.12.047 ◽

2012 ◽

Vol 717 ◽

pp. 52-60 ◽

Cited By ~ 19

Author(s):

Angelo Antonio D’Archivio ◽

Andrea Giannitto ◽

Maria Anna Maggi ◽

Fabrizio Ruggieri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

High Performance Liquid Chromatography ◽

Liquid Chromatography ◽

High Performance ◽

Reversed Phase ◽

Retention Prediction ◽

Network Modelling ◽

Neural Network Modelling ◽

Artificial Neural Network Modelling

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Multi-variable retention modelling in reversed-phase high-performance liquid chromatography based on the solvation method: A comparison between curvilinear and artificial neural network regression

Analytica Chimica Acta ◽

10.1016/j.aca.2011.01.056 ◽

2011 ◽

Vol 690 (1) ◽

pp. 35-46 ◽

Cited By ~ 12

Author(s):

Angelo Antonio D’Archivio ◽

Maria Anna Maggi ◽

Fabrizio Ruggieri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

High Performance Liquid Chromatography ◽

Liquid Chromatography ◽

High Performance ◽

Reversed Phase ◽

Variable Retention ◽

Artificial Neural

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Artificial neural network modelling of retention of pesticides in various octadecylsiloxane-bonded reversed-phase columns and water–acetonitrile mobile phase

Analytica Chimica Acta ◽

10.1016/j.aca.2009.05.019 ◽

2009 ◽

Vol 646 (1-2) ◽

pp. 47-61 ◽

Cited By ~ 10

Author(s):

Angelo Antonio D’Archivio ◽

Maria Anna Maggi ◽

Pietro Mazzeo ◽

Fabrizio Ruggieri

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mobile Phase ◽

Reversed Phase ◽

Network Modelling ◽

Neural Network Modelling ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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Artificial neural network modelling for biodecolorization of Basic Violet 03 from aqueous solution by biochar derived from agro-bio waste of groundnut hull: Kinetics and thermodynamics

Chemosphere ◽

10.1016/j.chemosphere.2021.130191 ◽

2021 ◽

Vol 276 ◽

pp. 130191

Author(s):

Saravanan Praveen ◽

Josephraj Jegan ◽

Thillainayagam Bhagavathi Pushpa ◽

Ravindiran Gokulan

Keyword(s):

Neural Network ◽

Aqueous Solution ◽

Artificial Neural Network ◽

Network Modelling ◽

Kinetics And Thermodynamics ◽

Neural Network Modelling ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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Support Vector Machine and Artificial Neural Network Modelling of Orogenic Gold Prospectivity Mapping in the Swayze greenstone belt, Ontario, Canada

Ore Geology Reviews ◽

10.1016/j.oregeorev.2020.103968 ◽

2020 ◽

pp. 103968

Author(s):

Francisca Maepa ◽

Richard S. Smith ◽

Abera Tessema

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Greenstone Belt ◽

Orogenic Gold ◽

Support Vector ◽

Network Modelling ◽

Neural Network Modelling ◽

Artificial Neural Network Modelling ◽

Prospectivity Mapping

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Artificial Neural Network Modelling for Short Term Load Forecasting

10.1109/ict-pep53949.2021.9601105 ◽

2021 ◽

Author(s):

Mahshooq Abdul Majeed ◽

Soumya Mudgal ◽

Lalit Tak ◽

Janavi Popat ◽

Harsh Kakadiya ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Load Forecasting ◽

Short Term ◽

Network Modelling ◽

Neural Network Modelling ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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Artificial neural network modelling of Cr(VI) surface adsorption with NiO nanoparticles using the results obtained from optimization of response surface methodology

Neural Computing and Applications ◽

10.1007/s00521-017-3172-8 ◽

2017 ◽

Vol 29 (10) ◽

pp. 969-979 ◽

Cited By ~ 3

Author(s):

Saber Khodaei Ashan ◽

Nasim Ziaeifar ◽

Rana Khalilnezhad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Surface Adsorption ◽

Nio Nanoparticles ◽

Network Modelling ◽

Neural Network Modelling ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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Estimations of Stress Concentration Factors (Cw/Kts) For Helical Circular/Square Cross Sectional Tension-Compression Springs And Artificial Neural Network Modelling

Journal of Polytechnic ◽

10.2339/politeknik.718550 ◽

2020 ◽

Author(s):

M. Tolga ÖZKAN ◽

İhsan TOKTAŞ ◽

Kaan DOĞANAY

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Stress Concentration ◽

Network Modelling ◽

Cross Sectional ◽

Neural Network Modelling ◽

Concentration Factors ◽

Compression Springs ◽

Stress Concentration Factors ◽

Artificial Neural Network Modelling

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A retention prediction system in reversed-phase high-performance liquid chromatography based on the hydrophobic parameter for alkylbenzene derivatives

Analytica Chimica Acta ◽

10.1016/s0003-2670(00)84892-8 ◽

1983 ◽

Vol 152 ◽

pp. 25-34 ◽

Cited By ~ 27

Author(s):

Kiyokatsu Jinno ◽

Kazuya Kawasaki

Keyword(s):

High Performance Liquid Chromatography ◽

Liquid Chromatography ◽

High Performance ◽

Reversed Phase ◽

Retention Prediction ◽

Prediction System

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Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model

Journal of Chromatography A ◽

10.1016/j.chroma.2018.01.053 ◽

2018 ◽

Vol 1541 ◽

pp. 1-11 ◽

Cited By ~ 23

Author(s):

Yabin Wen ◽

Mohammad Talebi ◽

Ruth I.J. Amos ◽

Roman Szucs ◽

John W. Dolan ◽

...

Keyword(s):

High Performance Liquid Chromatography ◽

Liquid Chromatography ◽

High Performance ◽

Reversed Phase ◽

Retention Prediction ◽

Quantitative Structure ◽

Structure Retention ◽

Quantitative Structure Retention Relationships

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Minimum fluidization velocities of binary Solid mixture – Empirical correlation and Genetic Algorithm‐Artificial Neural Network Modelling

Chemical Engineering & Technology ◽

10.1002/ceat.202100170 ◽

2021 ◽

Author(s):

Sudipta Let ◽

Nirjhar Bar ◽

Ranjan Kumar Basu ◽

Sudip Kumar Das

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Empirical Correlation ◽

Network Modelling ◽

Solid Mixture ◽

Neural Network Modelling ◽

Minimum Fluidization ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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