Comparison of multiple linear regression, partial least squares and artificial neural network for quantitative structure retention relationships of some polycyclic aromatic hydrocarbons

Linear Regression ◽

Least Squares ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Polycyclic Aromatic ◽

Structure Retention

Quantitative structure-property relationship (QSPR) study to predict retention time of polycyclic aromatic hydrocarbons using the random forest and artificial neural network methods

Structural Chemistry ◽

10.1007/s11224-019-01476-w ◽

2020 ◽

Vol 31 (4) ◽

pp. 1281-1288

Author(s):

Moona Emrarian ◽

Mahmoud Reza Sohrabi ◽

Nasser Goudarzi ◽

Fariba Tadayon

Keyword(s):

Neural Network ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Structure Property ◽

Network Methods ◽

Structure Property Relationship ◽

Polycyclic Aromatic ◽

Qspr Study

Quantitative structure–property relationship models to predict thermodynamic properties of some mono and polycyclic aromatic hydrocarbons using genetic algorithm‐multiple linear regression

Journal of the Chinese Chemical Society ◽

10.1002/jccs.201900319 ◽

2019 ◽

Vol 67 (6) ◽

pp. 969-982

Author(s):

Fatemeh Dialamehpour ◽

Fatemeh Shafiei

Keyword(s):

Genetic Algorithm ◽

Linear Regression ◽

Thermodynamic Properties ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Structure Property ◽

Structure Property Relationship ◽

Polycyclic Aromatic

Ranking and similarity for quantitative structure–retention relationship models in predicting Lee retention indices of polycyclic aromatic hydrocarbons

Analytica Chimica Acta ◽

10.1016/j.aca.2011.11.061 ◽

2012 ◽

Vol 716 ◽

pp. 92-100 ◽

Cited By ~ 30

Author(s):

Károly Héberger ◽

Biljana Škrbić

Keyword(s):

Quantitative Structure Retention Relationship

Aromatic Hydrocarbons ◽

Retention Indices ◽

Quantitative Structure ◽

Polycyclic Aromatic ◽

Structure Retention ◽

Gas chromatographic quantitative structure–retention relationships of trimethylsilylated anabolic androgenic steroids by multiple linear regression and partial least squares

Journal of Chromatography A ◽

10.1016/j.chroma.2009.09.066 ◽

2009 ◽

Vol 1216 (47) ◽

pp. 8404-8420 ◽

Cited By ~ 26

Author(s):

A.G. Fragkaki ◽

A. Tsantili-Kakoulidou ◽

Y.S. Angelis ◽

M. Koupparis ◽

C. Georgakopoulos

Keyword(s):

Linear Regression ◽

Quantitative Structure Retention Relationships

Least Squares ◽

Partial Least Squares ◽

Anabolic Androgenic Steroids ◽

Quantitative Structure ◽

Structure Retention ◽

Androgenic Steroids ◽

Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons

Talanta ◽

10.1016/j.talanta.2008.02.004 ◽

2008 ◽

Vol 76 (1) ◽

pp. 66-71 ◽

Cited By ~ 10

Author(s):

Snežana Sremac ◽

Aleksandar Popović ◽

Žaklina Todorović ◽

Đuro Čokeša ◽

Antonije Onjia

Keyword(s):

Neural Network ◽

Aromatic Hydrocarbons ◽

Chromatographic Separation ◽

Network Modeling ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Polycyclic Aromatic ◽

Gas Chromatographic Separation

Quantitative structure–affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2014.03.001 ◽

2014 ◽

Vol 134 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Xiaojun Wang ◽

Yongyuan Sun ◽

Lei Wu ◽

Shaojin Gu ◽

Ruina Liu ◽

...

Keyword(s):

Neural Network ◽

Linear Regression ◽

Azo Dyes ◽

Cellulose Fibers ◽

Quantitative Structure ◽

Relationship Study ◽

Artificial Neural

Quantitative structure-property relationship modeling of water-to-wet butyl acetate partition coefficient of 76 organic solutes using multiple linear regression and artificial neural network

Journal of Separation Science ◽

10.1002/jssc.201000448 ◽

2010 ◽

Vol 33 (23-24) ◽

pp. 3800-3810 ◽

Cited By ~ 11

Author(s):

Zahra Dashtbozorgi ◽

Hassan Golmohammadi

Keyword(s):

Neural Network ◽

Linear Regression ◽

Partition Coefficient ◽

Butyl Acetate ◽

Organic Solutes ◽

Quantitative Structure ◽

Structure Property ◽

Structure Property Relationship

Predicting blood β-hydroxybutyrate using milk Fourier transform infrared spectrum, milk composition, and producer-reported variables with multiple linear regression, partial least squares regression, and artificial neural network

Journal of Dairy Science ◽

10.3168/jds.2017-14076 ◽

2018 ◽

Vol 101 (5) ◽

pp. 4378-4387 ◽

Cited By ~ 18

Author(s):

R.S. Pralle ◽

K.W. Weigel ◽

H.M. White

Keyword(s):

Neural Network ◽

Fourier Transform ◽

Linear Regression ◽

Infrared Spectrum ◽

Least Squares ◽

Partial Least Squares Regression ◽

Milk Composition ◽

Least Squares Regression

A comparison of quantitative structure-activity relationships for the effect of benzoic and cinnamic acids on Listeria monocytogenes using multiple linear regression, artificial neural network and fuzzy systems

Journal of Applied Microbiology ◽

10.1111/j.1365-2672.1997.tb02847.x ◽

1997 ◽

Vol 82 (2) ◽

pp. 168-176 ◽

Cited By ~ 26

Author(s):

M.E. Ramos-Nino ◽

C.A. Ramirez-Rodriguez ◽

M.N. Clifford ◽

M.R. Adams

Keyword(s):

Neural Network ◽

Listeria Monocytogenes ◽

Linear Regression ◽

Quantitative Structure Activity Relationships

Fuzzy Systems ◽

Quantitative Structure ◽

Cinnamic Acids ◽

Structure Activity ◽

Prediction of Acute in vivo Toxicity of Some Amine and Amide Drugs to Rats by Multiple Linear Regression, Partial Least Squares and an Artificial Neural Network

Analytical Sciences ◽

10.2116/analsci.23.1091 ◽

2007 ◽

Vol 23 (9) ◽

pp. 1091-1095 ◽

Cited By ~ 3

Author(s):

Mohamad Khayatzadeh MAHANI ◽

Marzieh CHALOOSI ◽

Mohamad Ghanadi MARAGHEH ◽

Ali Reza KHANCHI ◽

Daryoush AFZALI

Keyword(s):

Neural Network ◽

Linear Regression ◽

Least Squares ◽

Partial Least Squares ◽

In Vivo Toxicity ◽

Artificial Neural