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QSRR Study of Linear Retention Indices for Volatile Compounds using Statistical Methods

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1TitleTitle of DocumentQSRR Study of Linear Retention Indices for Volatile Compounds using Statistical Methods
2CreatorAuthor's name, affiliation, country ASSIA BELHASSAN1,2, SAMIR CHTITA1, TAHAR LAKHLIFI1 and MOHAMMED BOUACHRINE2*
1MCNS Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco
2Materials, Environment and Modeling Laboratory, (ESTM) High School of Technology, Moulay Ismail University, Meknes, Morocco
3SubjectDicipline(s) Chemical Science
3SubjectKeywords Volatile compounds, Linear retention indices, Quantitative structure retention relationship, Multiple linear regression, Artificial neural network
4DescriptionAbstract ACD/ChemSketch, MarvinSketch and ChemOffice programs were used to calculate several molecular descriptors of 138 volatile compounds (32 hydrocarbons, 29 ketones, 28 aldehydes, 23 alcohols, 7 carboxylic acids, 6 halogenated compounds, 4 furans, 2 pyrazines, 1 ester, 1 sulphur compounds, 1 pyridine, 1 amine and three other compounds). The best descriptors were selected to establish the quantitative structure retention relationship (QSRR) of linear retention indices of volatile compounds using multiple linear regressions (MLR), multiple non-linear regressions (MNLR) and artificial neural network (ANN) methods. We propose quantitative models according to these analyses. The models were used to predict the linear retention indices of the test set compounds and agreement between the experimental and predicted values was verified. The descriptors showed by QSRR study were used for study and designing of new compounds. The statistical results indicate that the predicted values are in good agreement with the experimental results. To validate the predictive power of the resulting models, external validation multiple correlation coefficient was calculated and has both in addition to a performance prediction power, a favorable estimation of stability.
5PublishersOrganizing agency, location WWW Publications, India
6Contributor Sponsor(s) -
7DateDate (YYYY-MM-DD) -
8TypeStatus & genre Peer-reviewed Article
8TypeType
9FormateFile Formate PDF
10IdentifierUniform Resource Identifier Click Here
10IdentifierDigital Object Identifier DOI:10.7598/cst2018.1501
11SourceJournal/conference title; vol., no. (Year)Chemical Science Transactions, Volume  7 , Number  (4), (2018)
12LanuguageEnglish=en en
13RelationSupp.files
14Coverage -
15CopyrightCopyright and permissions
Chemical Science Transactions | Chem Sci Trans | CST | Online Chemistry Journal | Open Access
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