QSPR study of the retention/release property of odorant molecules in pectin gels using statistical methods

Abstract

The ACD/ChemSketch, MarvinSketch, and ChemOffice programmes were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). The best descriptors were selected to establish the Quantitative Structure-Property Relationship (QSPR) of the retention/release property of odorant molecules in pectin gels using Principal Components Analysis (PCA), Multiple Linear Regression (MLR), Multiple Non-linear Regression (MNLR) and Artificial Neural Network (ANN) methods We propose a quantitative model based on these analyses. PCA has been used to select descriptors that exhibit high correlation with the retention/release property. The MLR method yielded correlation coefficients of 0.960 and 0.958 for PG-0.4 (pectin concentration: 0.4% w/w) and PG-0.8 (pectin concentration: 0.8% w/w) media, respectively. Internal and external validations were used to determine the statistical quality of the QSPR of the two MLR models. The MNLR method, considering the relevant descriptors obtained from the MLR, yielded correlation coefficients of 0.978 and 0.975 for PG-0.4 and PG-0.8 media, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outside compounds. The effects of different descriptors on the retention/release property are described, and these descriptors were used to study and design new compounds with higher and lower values of the property than the existing ones. Keywords: Odorant Molecules, Retention/Release, Pectin Gels, Quantitative Structure Property Relationship, Multiple Linear Regression, Artificial Neural Network