Prediction of the chromatographic retention of saturated alcohols on stationary phases of different polarity applying the novel semi-empirical topological index

Biological Control of Asparagus Pests Using Synthetic Herbivore-Induced Volatiles

Natural enemies can be important regulators of pests in agroecosystems, and they often rely on volatile chemical cues to find hosts. Herbivore-induced plant volatiles (HIPVs) have been a focal point in many studies that seek to increase the efficacy of biological control programs by increasing recruitment and retention of natural enemies. Our research sought to explore the role of HIPVs in asparagus agroecosystems to answer the following questions: 1) What is the composition of HIPV produced by asparagus ferns following feeding by a chewing herbivore? 2) Do field deployed lures baited with synthetic asparagus HIPVs attract natural enemies? and 3) Can HIPV lures increase biological control of asparagus pests? Volatiles were field collected from the headspace of healthy asparagus ferns, mechanically damaged ferns, and ferns fed upon by asparagus beetle larvae (Crioceris asparagi L.) (Coleoptera: Chrysomelidae). We found that asparagus beetle damaged asparagus had significantly higher concentrations of (E)-β-ocimene, (E,E)-α-farnesene, and (1)-tetradecanol than healthy or mechanically damaged ferns. Field experiments demonstrated that lures baited with isolates of ocimene and farnesene attracted parasitoids without attracting pests, but had no impact on predator recruitment. Finally, we determined that overall parasitism rates were not increased by synthetic HIPV lures but found evidence that lures may increase parasitism of asparagus miner (Ophiomyia simplex Loew) (Diptera: Agromyzidae) by pteromalids.

A Quantitative Structure-Property Relationship (QSPR) Study of aliphatic alcohols by the method of dividing the molecular structure into substructure

A quantitative structure–property relationship (QSPR) analysis of aliphatic alcohols is presented. Four physicochemical properties were studied: boiling point (BP), n-octanol–water partition coefficient (lg P_OW), water solubility (lg W) and the chromatographic retention indices (RI) on different polar stationary phases. In order to investigate the quantitative structure–property relationship of aliphatic alcohols, the molecular structure ROH is divided into two parts, R and OH to generate structural parameter. It was proposed that the property is affected by three main factors for aliphatic alcohols, alkyl group R, substituted group OH, and interaction between R and OH. On the basis of the polarizability effect index (PEI), previously developed by Cao, the novel molecular polarizability effect index (MPEI) combined with odd-even index (OEI), the sum eigenvalues of bond-connecting matrix (SX_1CH) previously developed in our team, were used to predict the property of aliphatic alcohols. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. QSPR models were generated using only calculated descriptors and multiple linear regression techniques. These QSPR models showed high values of multiple correlation coefficient (R > 0.99) and Fisher-ratio statistics. The leave-one-out cross-validation demonstrated the final models to be statistically significant and reliable.

QSRR Models for Kováts' Retention Indices of a Variety of Volatile Organic Compounds on Polar and Apolar GC Stationary Phases Using Molecular Connectivity Indexes

Quantitative structure-retention relationship (QSRR) approaches, based on molecular connectivity indices are useful to predict the gas chromatography of Kováts relative retention indices (GC-RRIs) of 132 volatile organic compounds (VOCs) on different 12 (4 apolar and 8 polar) stationary phases (C₆₇, C₁₀₃, C₇₈, C_∞, POH, TTF, MTF, PCL, PBR, TMO, PSH and PCN) at 130 °C. Full geometry optimization based on Austin model 1 semi-empirical molecular orbital method was carried out. The sets of 30 molecular descriptors were derived directly from the topological structures of the compounds from DRAGON program. By means of the final variable selection method, which is elimination selection stepwise regression algorithms, three optimal descriptors were selected to develop a QSRR model to predict the RRI of organic compounds on each stationary phase with a correlation coefficient between 0.9378 and 0.9673 and a leave-one-out cross-validation correlation coefficient between 0.9325 and 0.9653. The root mean squares errors over different 12 phases were within the range of 0.0333–0.0458. Furthermore, the accuracy of all developed models was confirmed using procedures of Y-randomization, external validation through an odd–even number and division of the entire dataset into training and test sets. A successful interpretation of the complex relationship between GC RRIs of VOCs and the chemical structures was achieved by QSRR. The three connectivity indexes in the models are also rationally interpreted, which indicated that all organic compounds’ RRI was precisely represented by molecular connectivity indexes.

Theoretical prediction of the Kovat's retention index for oxygen-containing organic compounds using novel topological indices

For the retention index of polar compounds, polar groups in molecules would participate in polar interactions between eluents and stationary phases and thus would be expected to make large and separate contributions to the total retention index (RI). The characterization of the structural feature will help to elucidate the quantitative structure-retention relationship (QSRR). In this paper, on the basis of the PEI index previously developed by Cao, two novel molecular polarizability effect index, modified molecular polarizability index (MPEI(m)) and modified inner molecular polarizability index (IMPEI(m)) were proposed to predict the GC retention of a variety of oxygen-containing organic compounds with diverse chemical structures on OV-1 and SE-54 stationary phases. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. Simple linear regression equations between the RI and the topological indices were established for each stationary phase separately (R>0.99). Statistical analysis showed that the QSRR models have high internal stability and good predictive ability for external groups. The molecular properties known to be relevant for GC retention data, such as molecular size, branching and polar functional groups were well covered by the generated descriptors. The models with topological indices were compared with those based on quantum-chemical descriptors. It is observed that topological indices produce better correlations with Kovat's retention index. The results indicate the efficiency of presented indices in the structure-retention index correlations of complex compounds with polar multi-functional groups.

Quantitative structure-(chromatographic) retention relationships

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Theoretical analysis of the retention behavior of alcohols in gas chromatography

Quantitative structure-retention relationship (QSRR) models for the chromatographic (GC) retention times of alcohols on Superox 20M-diglycerol polarity stationary phase have been developed. Semi-empirical quantum chemical method (AM1) in MOPAC and Hartree-Fock (HF) method in Gaussian 98 implemented were employed to calculate a set of molecular descriptors of alcohols and ethyl acetate. Using multiple linear regression (MLR), we obtained the empirical functions with high correlation coefficient between retention times and quantum-chemical descriptors. The retention mechanism of alcohols of separation operating in the gas chromatogram was discussed. The results indicated that the QSRR models proposed were satisfactory.

Semi-empirical topological index: a tool for QSPR/QSAR studies

The semi-empirical topological index (I(ET)), developed to predict the chromatographic retention of a series of organic compounds, is extended to predict other properties and biological activities of aliphatic alcohols. This topological index takes into account the contribution of each individual atom type to the property considered and is able to encode information about structural features of the molecules. The efficiency of this index is verified by high quality Structure - Property and structure - Activity Relationships (QSPR/QSAR) models obtained for several representative physicochemical properties, biological activities and toxicities of aliphatic alcohols. Most of the properties investigated are well modeled (r > 0.98) employing the I(ET). Cross-validation using the more general leave-one-out method demonstrates that these models are highly statistically reliable. The proposed I(ET) index promises to be a useful descriptor in the QSPR/QSAR studies.

Prediction of gas chromatographic retention indices of a diverse set of toxicologically relevant compounds

For a set of 846 organic compounds, relevant in forensic analytical chemistry, with highly diverse chemical structures, the gas chromatographic Kovats retention indices have been quantitatively modeled by using a large set of molecular descriptors generated by software Dragon. Best and very similar performances for prediction have been obtained by a partial least squares regression (PLS) model using all considered 529 descriptors, and a multiple linear regression (MLR) model using only 15 descriptors obtained by a stepwise feature selection. The standard deviations of the prediction errors (SEP), were estimated in four experiments with differently distributed training and prediction sets. For the best models SEP is about 80 retention index units, corresponding to 2.1-7.2% of the covered retention index interval of 1110-3870. The molecular properties known to be relevant for GC retention data, such as molecular size, branching and polar functional groups are well covered by the selected 15 descriptors. The developed models support the identification of substances in forensic analytical work by GC-MS in cases the retention data for candidate structures are not available.

Martin’s rule revisited

The linear relation ln k' = Bn + ln A between the retention factor k' in liquid adsorption chromatography (LAC) and the number of repeat units n within a homologous series of oligomers is called Martin's rule. This empirical relation was supported by the retention behavior of the homologous series of different classes of oligomers but had no theoretical justification. In this paper, it is demonstrated that Martin's rule is a consequence of the general theory of liquid chromatography and the molecular sense of coefficients B and A is clarified: B is the Gibbs energy of the repeat unit of the long polymer chain adsorbed at the wall surface, and A is a combination different parameters which characterize the column and the adsorption correlation length H. The theory predicts the deviations from the linear dependence under conditions of weak adsorption between repeat units and stationary phase when H is close to radius of gyration Rg. Experimental data for retention volumes and selectivity of poly(ethylene glycol)s are given for normal and reversed-phase LAC on different columns in acetone-water and methanol-water as mobile phases. These data show excellent agreement between the theory and experiments. It is shown that Martin's rule holds under special conditions, which are theoretically defined by the relation H > Rg/1.5.