Национальный цифровой ресурс Руконт - межотраслевая электронная библиотека (ЭБС) на базе технологии Контекстум (всего произведений: 634938)
Контекстум
Руконтекст антиплагиат система
Журнал структурной химии  / №8 2016

STRUCTURAL CHARACTERIZATION AND PREDICTION OF KOVATS RETENTION INDICES (RI) FOR ALKYLBENZENE COMPOUNDS (300,00 руб.)

0   0
Первый авторLiao
АвторыLi J.F., Lei G.D.
Страниц8
ID544453
АннотацияA new molecular structural characterization (MSC) method called the molecular vertex eigenvalue correlative index (MVECI) is constructed and used to describe the structures of 122 al-kylbcnzcnc compounds. Through multiple linear regression (MLR) and stepwise multiple regression (SMR), a quantitative structure-retention relationship (QSRR) model with correlation coefficient {R) of 0.995 is obtained. Through partial least-square regression (PLS), another QSRR model with correlation coefficient (R) of 0.991 is obtained. The estimation stability and prediction ability of the two models are strictly analyzed by both internal and external validations. For the internal validation, the cross-validation (CV) correlation coefficients (Rev) of the two models are 0.993 and 0.988. For the external validation, the correlation coefficients (/?tesl) of the two models are 0.996 and 0.995, respectively. The results show that the stability and predictability of the models arc good, and the molecular vertex eigenvalue correlative index can successfully describe the structures of alkylbcnzcnc compounds
УДК541.6:548.737
Liao, L.M. STRUCTURAL CHARACTERIZATION AND PREDICTION OF KOVATS RETENTION INDICES (RI) FOR ALKYLBENZENE COMPOUNDS / L.M. Liao, J.F. Li, G.D. Lei // Журнал структурной химии .— 2016 .— №8 .— С. 49-56 .— URL: https://rucont.ru/efd/544453 (дата обращения: 02.05.2024)

Предпросмотр (выдержки из произведения)

2016.  57,  8 UDC 541.6:548.737 STRUCTURAL CHARACTERIZATION AND PREDICTION OF KOVATS RETENTION INDICES (RI) FOR ALKYLBENZENE COMPOUNDS L.-M. Liao1,2, J.-F. Li1,2, G.-D. Lei1 2College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, P. R. China Received June, 10, 2015 1College of Chemistry and Chemical Engineering, Neijiang Normal University, Neijiang, Sichuan, P. R. China E-mail: leigdnjtc@126.com Revised — July, 09, 2015 A new molecular structural characterization (MSC) method called the molecular vertex eigenvalue correlative index (MVECI) is constructed and used to describe the structures of 122 alkylbenzene compounds. <...> Through multiple linear regression (MLR) and stepwise multiple regression (SMR), a quantitative structure-retention relationship (QSRR) model with correlation coefficient (R) of 0.995 is obtained. <...> Through partial least-square regression (PLS), another QSRR model with correlation coefficient (R) of 0.991 is obtained. <...> The estimation stability and prediction ability of the two models are strictly analyzed by both internal and external validations. <...> For the internal validation, the cross-validation (CV) correlation coefficients (RCV) of the two models are 0.993 and 0.988. <...> For the external validation, the correlation coefficients (Rtest) of the two models are 0.996 and 0.995, respectively. <...> The results show that the stability and predictability of the models are good, and the molecular vertex eigenvalue correlative index can successfully describe the structures of alkylbenzene compounds. <...> INTRODUCTION Alkylbenzene compounds are widely applied in the chemical industry. <...> The main qualitative index in the chromatographic analysis is the retention value. <...> However, some data cannot be obtained through experiments, and the prediction of some retention data for organic compounds has become a simple and effective way of the qualitative analysis [ 1 ]. <...> In addition, there is a certain correlation between the GC retention value and the octanol/water partition coefficient (lgKow) of compounds [ 2 ]. <...> The octanol/water partition coefficient (lgKow) of compounds is a vital index in environmental chemistry. <...> The prediction of retention values can also provide useful references for the researches of octanol/water partition coefficients (lgKow) of organic compounds. <...> The structures of compounds firstly need to be parameterized to construct the corresponding structural descriptors for building up a quantitative structure — retention <...>