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)

Abstract

A reliable quantitative structure retention relationship (QSRR) study has beenevaluated to predict the retention indices (RIs) of a broad spectrum of compounds, namely 118non-linear, cyclic and heterocyclic terpenoids (both saturated and unsaturated), on an HP-5MSfused silica column. A principal component analysis showed that seven compounds lay outside ofthe main cluster. After elimination of the outliers, the data set was divided into training and testsets involving 80 and 28 compounds. The method was tested by application of the particle swarmoptimization (PSO) method to find the most effective molecular descriptors, followed by multiplelinear regressions (MLR). The PSO-MLR model was further confirmed through “leave one outcross validation” (LOO-CV) and “leave group out cross validation” (LGO-CV), as well as externalvalidations. 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 withnegligible relative errors of predictions (REP train=4.8%, REP test=6.0%).