Publications by authors named "Mehdi Jalali-Heravi"

39 Publications

Use of chemometrics to optimize a glucose assay on a paper microfluidic platform.

Anal Bioanal Chem 2017 Apr 1;409(10):2697-2703. Epub 2017 Feb 1.

Department of Chemistry and Biochemistry, California State University, 5151 State University Drive, Los Angeles, CA, 90032-8202, USA.

We describe the use of a chemometrics-based computational platform to optimize a glucose assay on a microfluidic paper-based analytical device (μPAD). Glucose is colorimetrically detected in the presence of glucose oxidase (GOx), horseradish peroxidase (HRP), and potassium iodide (KI). Using a Y-shaped paper microfluidic chip, the concentration of glucose, volume of reagents, and the length and width of the microfluidic channel were examined. The responses of the microfluidic chips were analyzed at the halfway point of the channel length. Variables affecting the response were screened by using a 2 factorial design, and among them, volume and concentration of the glucose were optimized by applying a rotatable central composite design (CCD). The optimum and experimental responses are 151.58 and 149.80, respectively, with an absolute error of 1.2%.
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http://dx.doi.org/10.1007/s00216-017-0214-0DOI Listing
April 2017

A simple graphical approach to predict local residue conformation using NMR chemical shifts and density functional theory.

J Comput Chem 2016 May 4;37(14):1296-305. Epub 2016 Mar 4.

Department of Chemistry, Sharif University of Technology, Tehran, Iran.

The dependency of amino acid chemical shifts on φ and ψ torsion angle is, independently, studied using a five-residue fragment of ubiquitin and ONIOM(DFT:HF) approach. The variation of absolute deviation of (13) C(α) chemical shifts relative to φ dihedral angle is specifically dependent on secondary structure of protein not on amino acid type and fragment sequence. This dependency is observed neither on any of (13) C(β) , and (1) H(α) chemical shifts nor on the variation of absolute deviation of (13) C(α) chemical shifts relative to ψ dihedral angle. The (13) C(α) absolute deviation chemical shifts (ADCC) plots are found as a suitable and simple tool to predict secondary structure of protein with no requirement of highly accurate calculations, priori knowledge of protein structure and structural refinement. Comparison of Full-DFT and ONIOM(DFT:HF) approaches illustrates that the trend of (13) C(α) ADCC plots are independent of computational method but not of basis set valence shell type.
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http://dx.doi.org/10.1002/jcc.24323DOI Listing
May 2016

An improved alkaline direct formate paper microfluidic fuel cell.

Electrophoresis 2016 Feb 15;37(3):504-10. Epub 2015 Dec 15.

Department of Chemistry and Biochemistry, California State University, Los Angeles, CA, USA.

Paper-based microfluidic fuel cells (MFCs) are a potential replacement for traditional FCs and batteries due to their low cost, portability, and simplicity to operate. In MFCs, separate solutions of fuel and oxidant migrate through paper due to capillary action and laminar flow and, upon contact with each other and catalyst, produce electricity. In the present work, we describe an improved microfluidic paper-based direct formate FC (DFFC) employing formate and hydrogen peroxide as the anode fuel and cathode oxidant, respectively. The dimensions of the lateral column, current collectors, and cathode were optimized. A maximum power density of 2.53 mW/cm(2) was achieved with a DFFC of surface area 3.0 cm(2) , steel mesh as current collector, 5% carbon to paint mass ratio for cathode electrode and, 30% hydrogen peroxide. The longevity of the MFC's detailed herein is greater than eight hours with continuous flow of streams. In a series configuration, the MFCs generate sufficient energy to power light-emitting diodes and a handheld calculator.
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http://dx.doi.org/10.1002/elps.201500360DOI Listing
February 2016

CS-MINER: A Tool for Association Mining in Binding-Database.

Mol Inform 2015 04 10;34(4):185-96. Epub 2015 Mar 10.

Department of Chemistry and Biochemistry, California State University, Los Angeles, CA 90032-8202, USA.

This paper introduces the algorithms, implementation strategies, features, and applications of CS-MINER, a tool for visualization and analysis of drug-like chemical space. The CS-MINER is the abstract abbreviation for Chemical Space Miner and correlates the medicinal target space and chemical space, in a systematic way. The database in this software consists of a large collection of drug-like molecules. To prepare this database, a large number of molecules for 110 important biological targets were collected from Binding-DB. A total of 1497 physicochemical properties were calculated for each molecule. The CS-MINER uses the discriminant analysis techniques for tracing the collected data and finally separates the molecules based on their therapeutic targets and activities. The developed multivariate classifiers can be used for ligand-based virtual screening of more than 0.5 million random molecules of PubChem and ZINC databases. In order to validate the models, selected subspaces in CS-MINER were compared with DrugBank molecules. At the end of the analysis, the software provides an interactive environment for visualization of the selected chemical subspaces in the form of 2- and 3-dimensional plots. In general, CS-MINER is a tool for comparing the relative position of active biosimilar molecules in chemical space and is freely available at www.csminer.com.
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http://dx.doi.org/10.1002/minf.201400142DOI Listing
April 2015

How can chemometrics improve microfluidic research?

Anal Chem 2015 Apr 17;87(7):3544-55. Epub 2015 Feb 17.

Department of Chemistry and Biochemistry, California State University, Los Angeles, 5151 State University Drive, Los Angeles, California 90032-8202, United States.

Chemometrics has the potential to embolden microfluidics to become that enabling technology for so long sought after. In this Feature article, we describe a historical perspective on microfluidics and its current challenges, a perspective on chemometric methods including response surface methodology (RSM), and how a combination of artificial neural network with experimental design (ANN-ED) have demonstrated promise in addressing basic microfluidic problems.
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http://dx.doi.org/10.1021/ac504863yDOI Listing
April 2015

Elimination of chromatographic and mass spectrometric problems in GC-MS analysis of Lavender essential oil by multivariate curve resolution techniques: Improving the peak purity assessment by variable size moving window-evolving factor analysis.

J Chromatogr B Analyt Technol Biomed Life Sci 2015 Mar 13;983-984:83-9. Epub 2015 Jan 13.

Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran.

In analysis of complex natural matrices by gas chromatography-mass spectrometry (GC-MS), many disturbing factors such as baseline drift, spectral background, homoscedastic and heteroscedastic noise, peak shape deformation (non-Gaussian peaks), low S/N ratio and co-elution (overlapped and/or embedded peaks) lead the researchers to handle them to serve time, money and experimental efforts. This study aimed to improve the GC-MS analysis of complex natural matrices utilizing multivariate curve resolution (MCR) methods. In addition, to assess the peak purity of the two-dimensional data, a method called variable size moving window-evolving factor analysis (VSMW-EFA) is introduced and examined. The proposed methodology was applied to the GC-MS analysis of Iranian Lavender essential oil, which resulted in extending the number of identified constituents from 56 to 143 components. It was found that the most abundant constituents of the Iranian Lavender essential oil are α-pinene (16.51%), camphor (10.20%), 1,8-cineole (9.50%), bornyl acetate (8.11%) and camphene (6.50%). This indicates that the Iranian type Lavender contains a relatively high percentage of α-pinene. Comparison of different types of Lavender essential oils showed the composition similarity between Iranian and Italian (Sardinia Island) Lavenders.
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http://dx.doi.org/10.1016/j.jchromb.2015.01.005DOI Listing
March 2015

Experimental design in analytical chemistry--part II: applications.

J AOAC Int 2014 Jan-Feb;97(1):12-8

This paper reviews the applications of experimental design to optimize some analytical chemistry techniques such as extraction, chromatography separation, capillary electrophoresis, spectroscopy, and electroanalytical methods.
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http://dx.doi.org/10.5740/jaoacint.sgeebrahimi2DOI Listing
May 2014

Experimental design in analytical chemistry--part I: theory.

J AOAC Int 2014 Jan-Feb;97(1):3-11

This paper reviews the main concepts of experimental design applicable to the optimization of analytical chemistry techniques. The critical steps and tools for screening, including Plackett-Burman, factorial and fractional factorial designs, and response surface methodology such as central composite, Box-Behnken, and Doehlert designs, are discussed. Some useful routines are also presented for performing the procedures.
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http://dx.doi.org/10.5740/jaoacint.sgeebrahimi1DOI Listing
May 2014

The use of multivariate curve resolution methods to improve the analysis of muramic acid as bacterial marker using gas chromatography-mass spectrometry: an alternative method to gas chromatography-tandem mass spectrometry.

J Chromatogr B Analyt Technol Biomed Life Sci 2014 Feb 2;949-950:1-6. Epub 2014 Jan 2.

Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, Iran. Electronic address:

In analysis of muramic acid (MA) as bacterial marker, two dominant disturbing factors lead the researchers to use gas chromatography-tandem mass spectrometry (GC-MS/MS) technique instead of gas chromatography-mass spectrometry (GC-MS). These factors are the trace concentration of MA and fundamental disturbance of base line mass channels in GC-MS technique. This study aimed to utilize multivariate curve resolution (MCR) methods combined with GC-MS to improve the analysis of MA. First, the background and noise in GC-MS analysis were corrected and reduced using MCR methods. In addition, the MA overlapped peaks were resolved to its pure chromatographic and mass spectral profiles. Then the two-way response of each component was reconstructed by the outer product of the pure chromatographic and mass spectral profiles. The overall volume integration (OVI) method was used for quantitative determination. The MA peak area was decreased dramatically after the background correction and noise reduction. The findings severely ratify the appropriateness of using MCR techniques combined with GC-MS analysis as a simple, fast and inexpensive method for the analysis of MA in complex mixtures. The proposed method may be considered as an alternative method to GC-MS/MS for thorough analysis of the bacterial marker.
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http://dx.doi.org/10.1016/j.jchromb.2013.12.032DOI Listing
February 2014

Integrated One-Against-One Classifiers as Tools for Virtual Screening of Compound Databases: A Case Study with CNS Inhibitors.

Mol Inform 2013 Aug 19;32(8):742-53. Epub 2013 Jul 19.

Department of Analytical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, P. O. Box 14335-186, Tehran, Iran.

A total of 21 833 inhibitors of the central nervous system (CNS) were collected from Binding-database and analyzed using discriminant analysis (DA) techniques. A combination of genetic algorithm and quadratic discriminant analysis (GA-QDA) was proposed as a tool for the classification of molecules based on their therapeutic targets and activities. The results indicated that the one-against-one (OAO) QDA classifiers correctly separate the molecules based on their therapeutic targets and are comparable with support vector machines. These classifiers help in charting the chemical space of the CNS inhibitors and finding specific subspaces occupied by particular classes of molecules. As a next step, the classification models were used as virtual filters for screening of random subsets of PUBCHEM and ZINC databases. The calculated enrichment factors together with the area under curve values of receiver operating characteristic curves showed that these classifiers are good candidates to speed up the early stages of drug discovery projects. The "relative distances" of the center of active classes of biosimilar molecules calculated by OAO classifiers were used as indices for sorting the compound databases. The results revealed that, the multiclass classification models in this work circumvent the definition inactive sets for virtual screening and are useful for compound retrieval analysis in Chemoinformatics.
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http://dx.doi.org/10.1002/minf.201200126DOI Listing
August 2013

Thorough tuning of the aspect ratio of gold nanorods using response surface methodology.

Anal Chim Acta 2013 May 2;779:14-21. Epub 2013 Apr 2.

Department of Chemistry, Sharif University of Technology, Tehran, Iran.

In the present work a central composite design based on response surface methodology (RSM) is employed for fine tuning of the aspect ratios of seed-mediated synthesized gold nanorods (GNRs). The relations between the affecting parameters, including ratio of l-ascorbic acid to Au(3+) ions, concentrations of silver nitrate, CTAB, and CTAB-capped gold seeds, were explored using a RSM model. It is observed that the effect of each parameter on the aspect ratio of developing nanorods highly depends on the value of the other parameters. The concentrations of silver ions, ascorbic acid and seeds are found to have a high contribution in controlling the aspect ratios of NRs. The optimized parameters led to a high yield synthesis of gold nanorods with an ideal aspect ratio ranging from 1 (spherical particle) to 4.9. In addition, corresponding tunable surface Plasmon absorption band has been extended to 880 nm. The resulted nanorods were characterized by UV-visible spectrometry and transmission electron microscopy.
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http://dx.doi.org/10.1016/j.aca.2013.03.056DOI Listing
May 2013

Multivariate curve resolution-particle swarm optimization: a high-throughput approach to exploit pure information from multi-component hyphenated chromatographic signals.

Anal Chim Acta 2013 Apr 6;772:16-25. Epub 2013 Mar 6.

Department of Chemistry, University of Isfahan, Isfahan, Iran.

Multivariate curve resolution-particle swarm optimization (MCR-PSO) algorithm is proposed to exploit pure chromatographic and spectroscopic information from multi-component hyphenated chromatographic signals. This new MCR method is based on rotation of mathematically unique PCA solutions into the chemically meaningful MCR solutions. To obtain a proper rotation matrix, an objective function based on non-fulfillment of constraints is defined and is optimized using particle swarm optimization (PSO) algorithm. Initial values of rotation matrix are calculated using local rank analysis and heuristic evolving latent projection (HELP) method. The ability of MCR-PSO in resolving the chromatographic data is evaluated using simulated gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography-diode array detection (HPLC-DAD) data. To present a comprehensive study, different number of components and various levels of noise under proper constraints of non-negativity, unimodality and spectral normalization are considered. Calculation of the extent of rotational ambiguity in MCR solutions for different chromatographic systems using MCR-BANDS method showed that MCR-PSO solutions are always in the range of feasible solutions like true solutions. In addition, the performance of MCR-PSO is compared with other popular MCR methods of multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least squares (MCR-ALS). The results showed that MCR-PSO solutions are rather similar or better (in some cases) than other MCR methods in terms of statistical parameters. Finally MCR-PSO is successfully applied in the resolution of real GC-MS data. It should be pointed out that in addition to multivariate resolution of hyphenated chromatographic signals, MCR-PSO algorithm can be straightforwardly applied to other types of separation, spectroscopic and electrochemical data.
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http://dx.doi.org/10.1016/j.aca.2013.02.042DOI Listing
April 2013

Determination of volatile components of green, black, oolong and white tea by optimized ultrasound-assisted extraction-dispersive liquid-liquid microextraction coupled with gas chromatography.

J Chromatogr A 2013 Mar 16;1280:1-8. Epub 2013 Jan 16.

Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran.

Ultrasound assisted extraction (UAE) followed by dispersive liquid-liquid microextraction (DLLME) was used for extraction and preconcentration of volatile constituents of six tea plants. The preconcentrated compounds were analyzed by gas chromatography-mass spectrometry (GC-MS). Totally, 42 compounds were identified and caffeine was quantitatively determined. The main parameters (factors) of the extraction process were optimized by using a central composite design (CCD). Methanol and chloroform were selected as the extraction solvent and preconcentration solvent, respectively .The optimal conditions were obtained as 21 in for sonication time; 32°C for temperature; 27 L for volume of extraction solvent and 7.4% for salt concentration (NaCl/H(2)O). The determination coefficient (R(2)) was 0.9988. The relative standard deviation (RSD %) was 4.8 (n=5), and the enhancement factors (EFs) were 4.0-42.6.
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http://dx.doi.org/10.1016/j.chroma.2013.01.029DOI Listing
March 2013

Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques.

Talanta 2012 Sep 25;99:175-9. Epub 2012 May 25.

Department of Pharmacy, University of Genova, Via Brigata Salerno 13, I-16147 Genova, Italy.

The current study presents an application of near infrared spectroscopy for 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 mixtures of them at different roasting degrees were blended with four types of barley. The blending degrees were between 2 and 20 wt% of barley. D-optimal design was applied to select 100 and 30 experiments to be used as calibration and test set, respectively. Partial least squares regression (PLS) was employed to build the models aimed at predicting the amounts of barley in coffee samples. In order to obtain simplified models, taking into account only informative regions of the spectral profiles, a genetic algorithm (GA) was applied. A completely independent external set was also used to test the model performances. The models showed excellent predictive ability with root mean square errors (RMSE) for the test and external set equal to 1.4% w/w and 0.8% w/w, respectively.
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http://dx.doi.org/10.1016/j.talanta.2012.05.036DOI Listing
September 2012

Chromatographic fingerprint analysis of secondary metabolites in citrus fruits peels using gas chromatography-mass spectrometry combined with advanced chemometric methods.

J Chromatogr A 2012 Aug 15;1251:176-187. Epub 2012 Jun 15.

Department of Chemistry, Sharif University of Technology, P.O. Box 11155-3516, Tehran, Iran.

Multivariate curve resolution (MCR) and multivariate clustering methods along with other chemometric methods are proposed to improve the analysis of gas chromatography-mass spectrometry (GC-MS) fingerprints of secondary metabolites in citrus fruits peels. In this way, chromatographic problems such as baseline/background contribution, low S/N peaks, asymmetric peaks, retention time shifts, and co-elution (overlapped and embedded peaks) occurred during GC-MS analysis of chromatographic fingerprints are solved using the proposed strategy. In this study, first, informative GC-MS fingerprints of citrus secondary metabolites are generated and then, whole data sets are segmented to some chromatographic regions. Each chromatographic segment for eighteen samples is column-wise augmented with m/z values as common mode to preserve bilinear model assumption needed for MCR analysis. Extended multivariate curve resolution alternating least squares (MCR-ALS) is used to obtain pure elution and mass spectral profiles for the components present in each chromatographic segment as well as their relative concentrations. After finding the best MCR-ALS model, the relative concentrations for resolved components are examined using principal component analysis (PCA) and k-nearest neighbor (KNN) clustering methods to explore similarities and dissimilarities among different citrus samples according to their secondary metabolites. In general, four clear-cut clusters are determined and the chemical markers (chemotypes) responsible to this differentiation are characterized by subsequent discriminate analysis using counter-propagation artificial neural network (CPANN) method. It is concluded that the use of proposed strategy is a more reliable and faster way for the analysis of large data sets like chromatographic fingerprints of natural products compared to conventional methods.
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http://dx.doi.org/10.1016/j.chroma.2012.06.011DOI Listing
August 2012

Navigating Drug-Like Chemical Space of Anticancer Molecules Using Genetic Algorithms and Counterpropagation Artificial Neural Networks.

Mol Inform 2012 Jan 13;31(1):63-74. Epub 2012 Jan 13.

Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, Iran tel: +98-21-66165315; fax: +98-21-66012983.

A total of 6289 drug-like anticancer molecules were collected from Binding database and were analyzed by using the classification techniques. The collected molecules were encoded to a diverse set of descriptors, spanning different physical and chemical properties of the molecules. A combination of genetic algorithms and counterpropagation artificial neural networks was used for navigating the generated drug-like chemical space and selecting the most relevant molecular descriptors. The proposed method was used for the classification of the molecules according to their therapeutic targets and activities. The selected molecular descriptors in this work define discrete areas in chemical space, which are mainly occupied by particular classes of anticancer molecules. The obtained structure-activity relationship (SAR) patterns and classification rules contain valuable information, which help to screen the large databases of compounds, more precisely. Such rules and patterns can be considered as virtual filters for mining the large databases of compounds and are useful in finding new anticancer candidates.
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http://dx.doi.org/10.1002/minf.201100098DOI Listing
January 2012

Resolution and quantification of complex mixtures of polycyclic aromatic hydrocarbons in heavy fuel oil sample by means of GC × GC-TOFMS combined to multivariate curve resolution.

Anal Chem 2011 Dec 23;83(24):9289-97. Epub 2011 Nov 23.

Department of Chemistry, Sharif University of Technology, Tehran, Iran.

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) combined to multivariate curve resolution-alternating least-squares (MCR-ALS) is proposed for the resolution and quantification of very complex mixtures of compounds such as polycyclic aromatic hydrocarbons (PAHs) in heavy fuel oil (HFO). Different GC × GC-TOFMS data slices acquired during the analysis of HFO samples and PAH standards were simultaneously analyzed using the MCR-ALS method to resolve the pure component elution profiles in the two chromatographic dimensions as well as their pure mass spectra. Outstandingly, retention time shifts within and between GC × GC runs were not affecting the results obtained using the proposed strategy and proper resolution of strongly coeluted compounds, baseline and background contributions was achieved. Calibration curves built up with standard samples of PAHs allowed the quantification of ten of them in HFO aromatic fractions. Relative errors in their estimated concentrations were in all cases below 6%. The obtained results were compared to those obtained by commercial software provided with GC × GC-TOFMS instruments and to Parallel Factor Analysis (PARAFAC). Inspection of these results showed improvement in terms of data fitting, elution process description, concentration relative errors and relative standard deviations.
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http://dx.doi.org/10.1021/ac201799rDOI Listing
December 2011

Recent trends in application of multivariate curve resolution approaches for improving gas chromatography-mass spectrometry analysis of essential oils.

Talanta 2011 Aug 27;85(2):835-49. Epub 2011 May 27.

Department of Chemistry, Sharif University of Technology, P.O. Box 11155-3516, Tehran, Iran.

Essential oils (EOs) are valuable natural products that are popular nowadays in the world due to their effects on the health conditions of human beings and their role in preventing and curing diseases. In addition, EOs have a broad range of applications in foods, perfumes, cosmetics and human nutrition. Among different techniques for analysis of EOs, gas chromatography-mass spectrometry (GC-MS) is the most important one in recent years. However, there are some fundamental problems in GC-MS analysis including baseline drift, spectral background, noise, low S/N (signal to noise) ratio, changes in the peak shapes and co-elution. Multivariate curve resolution (MCR) approaches cope with ongoing challenges and are able to handle these problems. This review focuses on the application of MCR techniques for improving GC-MS analysis of EOs published between January 2000 and December 2010. In the first part, the importance of EOs in human life and their relevance in analytical chemistry is discussed. In the second part, an insight into some basics needed to understand prospects and limitations of the MCR techniques are given. In the third part, the significance of the combination of the MCR approaches with GC-MS analysis of EOs is highlighted. Furthermore, the commonly used algorithms for preprocessing, chemical rank determination, local rank analysis and multivariate resolution in the field of EOs analysis are reviewed.
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http://dx.doi.org/10.1016/j.talanta.2011.05.045DOI Listing
August 2011

Modeling of retention behaviors of most frequent components of essential oils in polar and non-polar stationary phases.

J Sep Sci 2011 Jul 27;34(13):1538-46. Epub 2011 May 27.

Department of Chemistry, Sharif University of Technology, Tehran, Iran.

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. R(2) values for the test set established by Monte Carlo cross-validation and SPXY (sample set partitioning based on joint x-y distances) test set of the KOPLS were 0.92 and 0.94, respectively. y-Randomization indicated that chance plays no role in constructing the KOPLS model.
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http://dx.doi.org/10.1002/jssc.201100042DOI Listing
July 2011

Analysis of Iranian rosemary essential oil: application of gas chromatography-mass spectrometry combined with chemometrics.

J Chromatogr A 2011 May 21;1218(18):2569-76. Epub 2011 Mar 21.

Department of Chemistry, Sharif University of Technology, Tehran, Iran.

This paper focuses on characterization of the components of Iranian rosemary essential oil using gas chromatography-mass spectrometry (GC-MS). Multivariate curve resolution (MCR) approach was used to overcome the problem of background, baseline offset and overlapping/embedded peaks in GC-MS. The analysis of GC-MS data revealed that sixty eight components exist in the rosemary essential oil. However, with the help of MCR this number was extended to ninety nine components with concentrations higher than 0.01%, which accounts for 98.23% of the total relative content of the rosemary essential oil. The most important constituents of the Iranian rosemary are 1,8-cineole (23.47%), α-pinene (21.74%), berbonone (7.57%), camphor (7.21%) and eucalyptol (4.49%).
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http://dx.doi.org/10.1016/j.chroma.2011.02.048DOI Listing
May 2011

Optimisation of a microwave-assisted method for extracting withaferin A from Withania somnifera Dunal. using central composite design.

Phytochem Anal 2010 Nov-Dec;21(6):544-9

Department of Phytochemistry, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, Iran.

Introduction: Recently, there have been growing attention on the modification and optimisation of new extraction and quantification methods, caused by the lack of environmentally friendly methodologies for the extraction of phytochemicals from complex matrices. In the case of pharmaceutical compounds, not only the extraction procedure but also the analysis method should be efficient, precise, fast and easy.

Objectives: The essential pharmaceutical characteristics and trace concentration of withanolides led us to modify and optimise the previously reported extraction and quantification procedure for withaferin A (WA) as a candidate for withanolides.

Material And Methods: The WA from the air-dried aerial part of Withania somnifera Dunal. was extracted using a microwave-assisted extraction (MAE) technique. Four variables affecting the extraction procedure were optimised using the central composite design approach. The method of high-performance thin-layer chromatography assay was validated and applied for the quantification of each experiment.

Results: The optimum values of factors were: extraction time (150 s), extraction temperature (68°C) and 17 mL of methanol : water in the ratio 25 : 75 as extracting solvent. The solvent system consisted of ethyl acetate : toluene : formic acid : 2-propanol (7.0 : 2.0 : 0.5 : 0.5, v/v/v/v), and densitometric scanning at 220 nm was applied for the analysis. The dynamic linear range, LOD, LOQ and recovery with the inter-day, and intra-day RSDs of the developed method indicated the validity of the method.

Conclusion: A pressurised MAE method for extracting WA from the plant's aerial part was optimised using factorial-based design. The net effect of time, temperature, solvent volume and its ratio suggests that the yield of WA increases until each factor reaches its optimum value, and decreases with further increase in temperature or solvent ratio.
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http://dx.doi.org/10.1002/pca.1230DOI Listing
February 2011

Towards obtaining more information from gas chromatography-mass spectrometric data of essential oils: an overview of mean field independent component analysis.

J Chromatogr A 2010 Jul 24;1217(29):4850-61. Epub 2010 May 24.

Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, Iran.

Mean field independent component analysis (MF-ICA) along with other chemometric techniques was proposed for obtaining more information from multi-component gas chromatographic-mass spectrometric (GC-MS) signals of essential oils (mandarin and lemon as examples). Using these techniques, some fundamental problems during the GC-MS analysis of essential oils such as varying baseline, presence of different types of noise and co-elution have been solved. The parameters affecting MF-ICA algorithm were screened using a 2(5) factorial design. The optimum conditions for MF-ICA algorithm were followed by deconvolution of complex GC-MS peak clusters. The number of independent components (ICs) (chemical constituents) in each peak cluster was estimated using morphological score method. Eigenvalue profiles of evolving factor analysis (EFA) and pure variables from orthogonal projection approach (OPA) were used as initial mixing matrix (chromatograms) in iterative process. The resolved mass spectra were satisfactorily identified using NIST mass spectral search system. Finally, the results of optimized MF-ICA were compared with those obtained using multivariate curve resolution-alternating least square (MCR-ALS), multivariate curve resolution-objective function minimization (MCR-FMIN) and heuristic evolving latent projection (HELP) methods. It is demonstrated that MF-ICA can be used as an alternative method for a quick and accurate analysis of real multi-component problematic systems such as essential oils.
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http://dx.doi.org/10.1016/j.chroma.2010.05.026DOI Listing
July 2010

Self-modeling curve resolution techniques applied to comparative analysis of volatile components of Iranian saffron from different regions.

Anal Chim Acta 2010 Mar 18;662(2):143-54. Epub 2010 Jan 18.

Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, Iran.

Volatile components of saffron from different regions of Iran were extracted by ultrasonic-assisted solvent extraction (USE) and were analyzed by gas chromatography-mass spectrometry (GC-MS). Self-modeling curve resolution (SMCR) was proposed for resolving the co-eluted GC-MS peak clusters into pure chromatograms and mass spectra. Multivariate curve resolution-objective function minimization (MCR-FMIN) and multivariate curve resolution-alternating least square (MCR-ALS) were successfully used for this purpose. The accuracy of the qualitative and quantitative results was improved considerably using SMCR techniques. Comparison of the results of saffron from different regions of Iran showed that their volatile components are different from chemical components and relative percentages points of view. Safranal is the main component of all samples. In addition, 4-hydroxy-2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde (HTCC), 2(5H)-furanone, 2,4,4-trimethyl-3-carboxaldehyde-5-hydroxy-2,5-cyclohexadien-1-one and 2(3H)-furanone, dihydro-4-hydroxy were common in all samples with high percentages. The results proved that combining of SMCR techniques with USE-GC-MS produces a powerful tool for the analysis of the complex samples.
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http://dx.doi.org/10.1016/j.aca.2010.01.013DOI Listing
March 2010

Characterization of volatile components of Iranian saffron using factorial-based response surface modeling of ultrasonic extraction combined with gas chromatography-mass spectrometry analysis.

J Chromatogr A 2009 Aug 30;1216(33):6088-97. Epub 2009 Jun 30.

Chemistry Department, Sharif University of Technology, Tehran, Iran.

The volatile components of Iranian saffron were extracted using ultrasonic solvent extraction (USE) technique and then were separated and detected by gas chromatography-mass spectrometry (GC-MS). Variables affecting the extraction procedure were screened by using a 2(5-1) 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). The optimum values of factors were: 2.38g sample, 29.04mL solvent, 69.23% MeOH solvent ratio and 71.8min for the extraction time. Forty constituents were identified for Iranian saffron by GC-MS representing 90% of the total peak area. The major components were 2,6,6-trimethyl-1,3-cyclohexadiene-1-carboxaldehyde, namely safranal (26.29%), bicyclo[3,2,0]hept-2-ene-,4-ethoxy-,endo (5.69%), linoleic acid (4.77%), 4-hydroxy-2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, namely HTCC (4.44%), and nonadecanol (3.32%). Some new compounds were identified for the first time in saffron. In addition, the results of this study were compared with those of Greek saffron.
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http://dx.doi.org/10.1016/j.chroma.2009.06.067DOI Listing
August 2009

Multivariate optimization of hydrodistillation-headspace solvent microextraction of thymol and carvacrol from Thymus transcaspicus.

Talanta 2009 Aug 9;79(3):695-9. Epub 2009 May 9.

Department of Phytochemistry, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, G. C., Evin, Tehran, Iran.

In this paper multivariate response surface methodology (RSM) has been used for the optimization of hydrodistillation-headspace solvent microextraction (HD-HSME) of thymol and carvacrol in Thymus transcaspicus. Quantitative determination of compounds of interest was performed simultaneously using gas chromatography coupled with flame ionization detector (GC-FID). Parameters affecting the extraction efficiency were assessed and the optimized values were 5 min, 2 microL and 3 min for the extraction time, micro-drop volume and cooling time after extraction, respectively. The amounts of analyte extracted increased with plant weight. The calibration curves were linear in the ranges of 6.25-81.25 and 1.25-87.50 mg L(-1) for thymol and carvacrol, respectively. Limit of detection (LOD) for thymol and carvacrol was 1.87 and 0.23 mg L(-1), respectively. Within-day and between-day precisions for both analytes were calculated in three different concentrations and recoveries obtained were in the range of 89-101% and 95-116% for thymol and carvacrol, respectively.
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http://dx.doi.org/10.1016/j.talanta.2009.04.068DOI Listing
August 2009

Neural networks in analytical chemistry.

Methods Mol Biol 2008 ;458:81-121

Department of Chemistry, Sharif University of Technology, Tehran, Iran.

This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-layer artificial neural network for modeling the anti-HIV activity of the HETP derivatives and activity parameters (pIC50) of heparanase inhibitors. The use of a genetic algorithm-kernel partial least squares algorithm combined with an artificial neural network (GA-KPLS-ANN) is described for predicting the activities of a series of aromatic sulfonamides. The retention behavior of terpenes and volatile organic compounds and predicting the response surface of different detection systems are presented as typical applications of ANNs in chromatographic area. The use of ANNs is explored in electrophoresis with emphasizes on its application on peptide mapping. Simulation of the electropherogram of glucagons and horse cytochrome C is described as peptide models. This chapter also focuses on discussing the role of ANNs in the simulation of mass and 13C-NMR spectra for noncyclic alkenes and alkanes and lignin and xanthones, respectively.
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http://dx.doi.org/10.1007/978-1-60327-101-1_6DOI Listing
January 2009

Development of a method for analysis of Iranian damask rose oil: combination of gas chromatography-mass spectrometry with Chemometric techniques.

Anal Chim Acta 2008 Aug 12;623(1):11-21. Epub 2008 Jun 12.

Department of Chemistry, Sharif University of Technology, P.O. Box, 11155-9516 Tehran, Iran.

Gas chromatography-mass spectrometry (GC-MS) combined with Chemometric resolution techniques were proposed as a method for the analysis of volatile components of Iranian damask rose oil. The essential oil of damask rose was extracted using hydrodistillation method and analyzed with GC-MS in optimized conditions. A total of 70 components were identified using similarity searches between mass spectra and MS database. This number was extended to 95 components with concentrations higher than 0.01% accounting for 94.75% of the total relative content using Chemometric techniques. For the first time in this work, an approach based upon subspace comparison is used for determination of the chemical rank of GC-MS data. The peak clusters were resolved using heuristic evolving latent projection (HELP) and multivariate curve resolution-alternating least square (MCR-ALS) by applying proper constraints, and the combination of both methods for some cases. It is concluded that a thorough analysis of the complex mixtures such as Iranian damask rose requires sophisticated GC-MS coupled with the Chemometric techniques.
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http://dx.doi.org/10.1016/j.aca.2008.05.078DOI Listing
August 2008

Comparative structure-toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shuffling-adaptive neuro fuzzy inference system and artificial neural networks.

Chemosphere 2008 Jun 21;72(5):733-40. Epub 2008 May 21.

Department of Chemistry, Sharif University of Technology, Tehran, Iran.

The purpose of this study was to develop the structure-toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis. The results of the proposed model were compared with the model of linear-free energy response surface and also the principal component analysis Bayesian-regularized neural network (PCA-BRANN) trained using the same data. The presented model shows a better statistical parameter in comparison with the previous models. The results of the model are promising and descriptive. Five descriptors of octanol-water partition coefficient (logP), bond information content (BIC0), number of R-CX-R (C-026), eigenvalue sum from Z weighted distance matrix (SEigZ) and fragment based polar surface area (PSA) selected by Shuffling-ANFIS reveal the role of hydrophobicity, electronic and steric interactions in the mechanism of toxic action. Sequential zeroing of weights (SZW) as a sensitivity analysis method revealed that the hydrophobicity and electronic interactions play a major role in toxicity of these compounds. Satisfactory results (q(2)=0.828 and RMSE=0.348) in comparison with the previous works indicate that the Shuffling-ANFIS-ANN technique is able to model a diverse chemical class with more than one mechanism of toxicity by using simple and interpretable descriptors. Shuffling-ANFIS can be used as powerful feature selection technique, because its application in prediction of toxicity potency results in good statistical and interpretable physiochemical parameters.
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http://dx.doi.org/10.1016/j.chemosphere.2008.03.026DOI Listing
June 2008

Quantitative structure-mobility relationship study of a diverse set of organic acids using classification and regression trees and adaptive neuro-fuzzy inference systems.

Electrophoresis 2008 Jan;29(2):363-74

Department of Chemistry, Sharif University of Technology, Tehran, Iran.

A quantitative structure-mobility relationship was developed to accurately predict the electrophoretic mobility of organic acids. The absolute electrophoretic mobilities (mu(0)) of a diverse dataset consisting of 115 carboxylic and sulfonic acids were investigated. A set of 1195 zero- to three-dimensional descriptors representing various structural characteristics was calculated for each molecule in the dataset. Classification and regression trees were successfully used as a descriptor selection method. Four descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system. The root mean square errors for the calibration and prediction sets are 1.61 and 2.27, respectively, compared with 3.60 and 3.93, obtained from a previous mechanistic model.
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http://dx.doi.org/10.1002/elps.200700136DOI Listing
January 2008

Optimization of peroxidase-catalyzed oxidative coupling process for phenol removal from wastewater using response surface methodology.

Environ Sci Technol 2007 Oct;41(20):7073-9

Department of Biotechnology, University College of Science, University of Tehran, Tehran, Iran.

Hydroxylated aromatic compounds (HACs) are considered to be primary pollutants in a wide variety of industrial wastewaters. Horseradish peroxidase (HRP) is suitable for the removal of these toxic substances. However, development of a mathematical model and optimization of the HRP-based treatment considering the economical issues by novel methods is a necessity. In the present study, optimization of phenol removal from wastewater by horseradish peroxidase (HRP) was carried out using response surface methodology (RSM) and central composite design (CCD). As the initial experimental design, 2(4-1) half-fraction factorial design (H-FFD) is accomplished in triplicate at two levels to select the most significant factors and interactions in the phenol removal procedure. Temperature (degrees C), pH, concentration of enzyme (unit mL(-1)), and H202 (mM) were determined as the most effective independent variables. Finally, a fourfactor-five coded level CCD, 30 runs, was performed in order to fit a second-order polynomial function to the results and calculate the economically optimum conditions of the reaction. The goodness of the model was checked by different criteria including the coefficient of determination (R2 = 0.93), the corresponding analysis of variance ((Pmodel > F) < 0.0001) and parity plot (r = 0.96). These analyses indicated that the fitted model is appropriate for this enzymatic system. With the assumption that the minimum enzyme concentration was 0.26 unit mL(-1), the analysis of the response surface contour and surface plots defined the optimum conditions as follows: pH = 7.12, hydrogen peroxide concentration 1.72 mM, and 10 degrees C. This work improves phenol removal operation economically by applying minimum enzyme concentration and highest removal in comparison with previous studies.
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http://dx.doi.org/10.1021/es070626qDOI Listing
October 2007
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