Molecular Classification of Pesticides Including Persistent Organic Pollutants, Phenylurea and Sulphonylurea Herbicides
Pesticide residues in wine were analyzed by liquid chromatography-tandem mass spectrometry. Retentions are modelled by structure-property relationships. Bioplastic evolution is an evolutionary perspective conjugating effect of acquired characters and evolutionary indeterminacy-morphological determination-natural selection principles; its application to design co-ordination index barely improves correlations. Fractal dimensions and partition coefficient differentiate pesticides. Classification
... s. Classification algorithms are based on information entropy and its production. Pesticides allow a structural classification by nonplanarity, and number of O, S, N and Cl atoms and cycles; different behaviours depend on number of cycles. The novelty of the approach is that the structural parameters are related to retentions. Classification algorithms are based on information entropy. When applying procedures to moderate-sized sets, excessive results appear compatible with data suffering a combinatorial explosion. However, equipartition conjecture selects criterion resulting from classification between hierarchical trees. Information entropy permits classifying compounds agreeing with principal component analyses. Periodic classification shows that pesticides in the same group present similar properties; those also in equal period, maximum resemblance. The advantage of the classification is to predict the retentions for molecules not included in the categorization. Classification extends to phenyl/sulphonylureas and the application will be to predict their retentions. the Ebro River Basin was described [19, 20] . Several researchers have reported the quantitative structure-activity/property relationships (QSAR/QSPR) of pesticides. The Benfenati group modelled the QSPR of the octanol/water partition coefficient of organometallic substances by optimal SMILESbased descriptors  , QSAR of the toxicity of organic substances to Daphnia magna via freeware CORAL  , and optimal descriptor as a translator of eclectic data into endpoint prediction and mutagenicity of fullerene as a mathematical function of conditions  . The Roy group modelled predictive chemometrics and three-dimensional toxicophore mapping of diverse organic chemicals causing bioluminescent repression of the bacterium genus Pseudomonas  and QSAR for toxicity of ionic liquids to D. magna analyzing aromaticity vs. lipophilicity  . The chromatographic retention time was correlated to the stationary and mobile phases of the system. In earlier publications the free energy of solvation and partition coefficients in methanol-water binary mixtures were analized  . Stationary phase was modelled in size-exclusion chromatography with binary eluents as a strategy in size-exclusion chromatography  . Stationary-mobile phase distribution coefficient for polystyrene standards was represented  . A new chemical index inspired by plastic evolution was presented  and applied to valence-isoelectronic series of aromatics  . QSPR of retention times of phenylureas [31,32] and pesticides  was described by plastic evolution. A simple computerized algorithm was proposed to be useful for establishing relationships between chemical structures and biosignificance [34, 35] . Starting point is to use information entropy for pattern recognition. Entropy is formulated on basis of similarity matrix between two biochemical species. As entropy is weakly discriminating for classification, the more powerful concepts of entropy production and equipartition conjecture were introduced  . The aim of the present report is to find properties that distinguish pesticide structures according to retention times. The study applies a chemical index to pesticides. The goal is index usefulness validation via the capability to distinguish between pesticides, and interest as a predictive index for retention as compared with fractal dimensions and partition coefficients. Section 2 illustrates and discusses the results. Section 3 presents the computational method, including classification algorithm, information entropy, equipartition conjecture of entropy production and learning procedure. Finally, the last section summarizes our conclusions.