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Factorial-Based Analysis and QSXR Studies of Components of Essential Oils and Environmental Pollutants

Ebrahimi Najafabadi, Heshmatollah | 2011

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 42193 (03)
  4. University: Sharif University of Technology
  5. Department: chemistry
  6. Advisor(s): Jalali Heravi, Mahdi
  7. Abstract:
  8. This PhD dissertation presents the chemometric techniques as a tool to obtain maximum information from the chemical and biological systems. In this regards, two objectives have been followed. The first objective was to apply the design of experiment methods for approaching the most reliable data. In this section, the main concepts and applications of experimental design approaches to optimize the common analytical chemistry techniques are reviewed. The critical steps and tools for screening including Plackett-Burman, full factorial- and fractional factorial designs and response surface methodology such as central composite, Box-Behnken and Doehlert designs are discussed. Descriptions of applications of these techniques on extraction, chromatographic separation, capillary electrophoresis, spectroscopy and electroanalytical methods are presented. Furthermore, Design of experiments approaches were carried out to optimize the effective factors of ultrasonic solvent extraction as a tool for extracting the volatile components of Iranian saffron. The variables affecting the extraction procedure were screened by using a 25−1 fractional factorial design and among them; sample amount, solvent volume, solvent ratio and extraction time were optimized by applying a rotatable central composite design (CCD). Some new compounds were identified for the first time in saffron by using the proposed procedure. The second objective was the use of the quantitative structure activity/property-relationship and multivariate calibration techniques for uncovering the relation between variables in the data. The following is a brief description of different subsections of this objective. i. The gas chromatography retention indices of 168 pesticides were used to construct a robust quantitative structure-retention relationship (QSRR) model. After outlier detection by Cook’s influence measurement, the remaining compounds were subjected to two different modeling strategies. The first one was stepwise multiple linear regression (stepwise-MLR). The other strategy was kernel orthogonal projection to latent structure (KOPLS). Monte Carlo corss-validation and y-randomization techniques were applied on the data for evaluating the models. ii. The gas chromatography retention indices of 100 different components of essential oils, on three columns with stationary phases of different polarities, were used to develop robust quantitative structure–retention relationship (QSRR) models. Two linear models with only one variable, i.e. solvation entropy, were developed, which explain 95 and 94% of variances of the test set for dimethyl silicone and dimethyl silicone with 5% phenyl group columns, respectively. These models are extremely simple and easy to interpret, but they show higher errors compared with more robust models such as partial least square (PLS) and ridge regressions. For the third column (polyethylene glycol (PEG)), 24 hydrogen bonding descriptors were calculated and were used for modeling. Kernel orthogonal projection to latent structure (KOPLS), as a non-linear technique, was applied for the modeling of the retention indices of the compounds on the PEG column. R2 values for the test set established by Monte Carlo cross-validation and SPXY (sample set partitioning based on joint x–y distances) of the KOPLS model were 0.92 and 0.94, respectively. y-Randomization indicated that chance plays no role in constructing the KOPLS model. iii. The ladder particle swarm optimization (LPSO) was used for modeling of oxadiazole- and triazole-substituted naphthyridines as human immunodeficiency virus-1 integrase inhibitors. Artificial neural network (ANN) and Monte Carlo cross-validation techniques were combined with LPSO to develop a quantitative structure–activity relationship model. The techniques of LPSO, ANN and sample set partitioning based on joint x–y distances were applied as feature selection, mapping and model evaluation, respectively. The variables selected by LPSO were used as inputs of Bayesian regularization ANN. The statistical parameters of correlation of determination, R2, and root-mean-square error for the test set were 0.88 and 0.23, respectively. Robustness of the model was confirmed by Y-randomization method. Comparison of the LPSO–ANN results with those of stepwise multiple linear regression (MLR), LPSO–MLR and LPSO–MLR–ANN showed the superiority of LPSO–ANN. Inspection of the selected variables indicated that atomic mass, molecular size and electronic structure of the molecules play a significant role in inhibitory behavior of oxadiazole- and triazole-substituted naphthyridines. iv. The near infrared spectroscopy was applied for the identification and quantification of the fraudulent addition of barley in roasted and ground coffee samples. Nine different types of coffee including pure Arabica, Robusta and mixture of them at different roasting degrees were blended with four types of barley. The blending degrees were between 2 and 20 weight percent of barley. D-optimal design has been applied to select 100 and 30 experiments to use as calibration and test sets, respectively. Partial lest squares regression (PLS) has been employed to construct the models aimed at predicting the amounts of barley in coffee samples. In order to take into account only informative regions of the spectral profiles, a genetic algorithm (GA) has been applied on the data. A completely independent external set has also been used to test the model performances. The models showed excellent predictive ability with a root mean square error (RMSE) for the test and external set equal to 1.2% w/w and 1.1% w/w, respectively. Y-randomization test indicated that chance correlation play no role in such models.
  9. Keywords:
  10. Experimental Design ; Modeling ; Quantitative Structure-Activity Relationship (QSAR)Model ; Particles Swarm Optimization (PSO) ; Near Infrared Spectrometry

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