Isolation of new straight chain compounds from acacia nilotica - Chaubal, Rohini; Tambe, Amruta; Biswas, Sujata; Rojatkar, Supada; Deshpande, Vishnu; Deshpande, Nirinala (Indian Journal of Chemistry Section B-Organic Chemistry including Medicinal Chemistry, 2006)
A novel diester, pentacosane dioic acid dihexadecyl ester 1 has been isolated for the first time from n-hexane soxhlet extract of Acacia nilotica. Saponification of the diester reveals the presence of an alcohol, hexadecanol and pentacosane dioic acid. The structures of both the compounds are confirmed by GCMS analysis. Saponification of pet-ether extract of Acacia nilotica has been carried out. Unsaponiflable matter is further purified by column chromatography followed by repeated mixed solvent crystallization to afford heptacosan-1,2,3-triol 2.
Keywords: Acacia nilotica; diester; pentacosane dioic acid; saponification

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices - Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S. (Journal of Chromatography A, 2015)
The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R-2) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science. (C) 2015 Elsevier B.V. All rights reserved.
Keywords: Artificial intelligence; Gas chromatography; genetic programming; Kovats retention index; Molecular descriptors; Quantitative structure-retention relationships