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Mohammadhosseini, M. (2018). Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR). Journal of Chemical Health Risks, 4(1), -. doi: 10.22034/jchr.2018.544059
Majid Mohammadhosseini. "Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR)". Journal of Chemical Health Risks, 4, 1, 2018, -. doi: 10.22034/jchr.2018.544059
Mohammadhosseini, M. (2018). 'Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR)', Journal of Chemical Health Risks, 4(1), pp. -. doi: 10.22034/jchr.2018.544059
Mohammadhosseini, M. Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR). Journal of Chemical Health Risks, 2018; 4(1): -. doi: 10.22034/jchr.2018.544059

Prediction of the GC-MS Retention Indices for a Diverse Set of Terpenes as Constituent Components of Camu-camu (Myrciaria dubia (HBK) Mc Vaugh) Volatile Oil, Using Particle Swarm Optimization-Multiple Linear Regression (PSO-MLR)

Article 9, Volume 4, Issue 1, Spring 2014  XML PDF (872.26 K)
DOI: 10.22034/jchr.2018.544059
Author
Majid Mohammadhosseini email
Islamic Azad University, Shahrood Branch, Shahrood, Iran
Receive Date: 18 May 2014,  Revise Date: 15 October 2019,  Accept Date: 29 October 2018 
Abstract
A reliable quantitative structure retention relationship (QSRR) study has been evaluated to predict the retention indices (RIs) of a broad spectrum of compounds, namely 118 non-linear, cyclic and heterocyclic terpenoids (both saturated and unsaturated), on an HP-5MS fused silica column. A principal component analysis showed that seven compounds lay outside of the main cluster. After elimination of the outliers, the data set was divided into training and test sets involving 80 and 28 compounds. The method was tested by application of the particle swarm optimization (PSO) method to find the most effective molecular descriptors, followed by multiple linear regressions (MLR). The PSO-MLR model was further confirmed through “leave one out cross validation” (LOO-CV) and “leave group out cross validation” (LGO-CV), as well as external validations. The promising statistical figures of merit associated with the proposed model (R2train=0.936, Q2LOO=0.928, Q2LGO=0.921, F=376.4) confirm its high ability to predict RIs with negligible relative errors of predictions (REP train=4.8%, REP test=6.0%).
Keywords
Particle swarm Optimization-multiple linear regression (PSO-MLR); Quantitative structure-retention relationship (QSRR); Retention indices (RIs); Prediction Essential oil; Cross validation; External validation GC GC-MS
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