Novel PSO-MLR Algorithm to Predict the Chromatographic Retention Behaviors of Natural Compounds

• Published in: Analytical chemistry letters (Online) Vol. 3; no. 4; pp. 226 - 248
• Main Author: Mohammadhosseini, Majid
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 04.07.2013
• Subjects: Cross validation; Particle swarm optimization-multiple linear regression (PSO-MLR); Quantitative structure-retention relationship (QSRR); Retention indices (RIs)
• ISSN: 2229-7928; 2230-7532
• DOI: 10.1080/22297928.2013.861164

The present report deals with a novel quantitative structure retention relationship (QSRR) study to predict the retention indices (RIs) of a large set of natural compounds involving 95 saturated or unsaturated linear, non-linear, cyclic and heterocyclic terpenoids. A principal component analysis revealed that there was no outlier in the main cluster. After splitting the data set into training and test sets involving 76 and 19 compounds, particle swarm optimization (PSO) algorithm was used to find the most effective molecular descriptors, followed by multiple linear regression (MLR). Four molecular descriptors were adopted for construction of the model belonging to constitutional and topological groups. The method was validated by cross validation and external validation and its robustness was confirmed by Y-randomization test over ten iterations. The promising statistical figures of merit associated with the proposed model (R 2 train =0.956, R 2 test =0.968, Q 2 LGO =0.950, Q 2 LGO =0.947) represent its high capability to predict RIs with low relative errors of predictions.