Calculation of Retention Times of Anthocyanins with Orthogonalized Topological Indices

Abstract

The HPLC retention times (RT) of 12 anthocyanidin malonylglucosides, which appear in flowers of Hibiscus syriacus, were calculated using several structure-property models based on three different types of orthogonalized topological indices, that is, the path numbers, the connectivity indices, and the Harary indices.The best agreement between the experimental and calculated values is achieved with a model with path numbers. This model is recommended to experimental researchers which are interested in quick assessment of the retention times of their newly found anthocyanin derivatives.

New Developments in QSPR/QSAR Modeling Based on Topological Indices

“…Randic [36,37] and Amic et al [48] have also observed that the MLR models with orthogonal descriptors are more stable but not better than MLR models with nonorthogonal descriptors, that is, both types of models possess the same values of the correlation coefficient ( R ) , the standard error (S) and the F-test (the ratio of regression and residual variances) [32,49,50].…”

Spectral-Structure Activity Relationship (Spectral-SAR) Assessment of Ionic Liquids’ in Silico Ecotoxicity

“…The result is that the QSAR/Spectral-SAR equation is now directly delivered by the determinant (11) and not through matrices products, as in the statistical Pearson approach, while directly providing the Spectral-SAR correlation equation and not only the parameters of multi-variate correlation [77][78][79][80][81][82][83]. Furthermore, the Spectral-SAR algorithm is also invariant to the order of descriptors that are chosen in orthogonalization procedure, which provides equivalent determinants only with rearranged lines; this is a matter that was not previously achieved by other orthogonalization techniques [84][85][86][87].…”

Modeling based on subspace orthogonal projections for QSAR and QSPR research

“…A regression model using orthogonal variables reduces the influence of the collinearity between the variables by orthogonalization. Furthermore, it has also some interesting features, such as possessing the same correlation coefficient R, the standard error S and the F-test value as the regression model using nonorthogonal variables [25][26][27][28][29]. Unfortunately, this procedure is strongly dependent on the selection of the first regression variable, since the following orthogonalization process is based on this [29].…”