Browsing by Author "Freitas, Mirlaine R."

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    Aug-MIA-SPR/PLS-DA Classification of Carbonyl Herbicides according to Levels of Soil Sorption
    (Geoderma, 2016) Freitas, Mirlaine R.; Barigye, Stephen J.; Daré, Joyce K.; Freitas, Matheus P.
    A major challenge in the design of new herbicides lies in the development of highly active, environmentally friendly compounds. Soil sorption is an ecotoxicological parameter used to probe the prospective environmental fate of persistent organic pollutants, such as some herbicides. This parameter, described in terms of logKOC (the logarithm of the soil/water partition coefficient normalized to organic carbon), is usually estimated using the octanol/water partition coefficient (logP, easily calculated or determined experimentally). However, estimations obtained with the logP are not always accurate. Thus, this work reports the use of molecular descriptors derived from multivariate image analysis of carbonyl herbicides to achieve a predictive classification model based on the partial least squares-discriminant analysis (PLS-DA) method. This model yields 80% accuracy in calibration, 75% in leave-one-out cross-validation and 100% in external validation. In addition, the Y-randomization test reveals that the obtained model is stable from fortuitous correlation, since the accuracy in calibration after shuffling the classes block is only 0.5%. Chemical interpretation in terms of the structural features that affect soil sorption is performed, based on the weights of the selected variables in the classification model. Finally, novel herbicides are rationally designed, based on the inferences arrived at in the structural interpretation experiment and predictions of their qualitative and quantitative soil sorption profiles performed, using the built aug-MIA-SPR and Wang's models, respectively.
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    Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis
    (Chemosphere, 2016) Freitas, Mirlaine R.; Barigye, Stephen J.; Dare, Joyce K.; Freitas, Matheus P.
    The bioconcentration factor (BCF) is an important parameter used to estimate the propensity of chemicals to accumulate in aquatic organisms from the ambient environment. While simple regressions for estimating the BCF of chemical compounds from water solubility or the n-octanol/water partition coefficient have been proposed in the literature, these models do not always yield good correlations and more descriptive variables are required for better modeling of BCF data for a given series of organic pollutants, such as some herbicides. Thus, the logBCF values for a set of carbonyl herbicides comprising amide, urea, carbamate and thiocarbamate groups were quantitatively modeled using multivariate image analysis (MIA) descriptors, derived from colored image representations for chemical structures. The logBCF model was calibrated and vigorously validated (r2 = 0.79, q2 = 0.70 and rtest2 = 0.81), providing a comprehensive three-parameter linear equation after variable selection (logBCF = 5.682 − 0.00233 × X9774 − 0.00070 × X813 − 0.00273 × X5144); the variables represent pixel coordinates in the multivariate image. Finally, chemical interpretation of the obtained models in terms of the structural characteristics responsible for the enhanced or reduced logBCF values was performed, providing key leads in the prospective development of more eco-friendly synthetic herbicides.