In The Name of God

The Compationate, The M

erciful

Chemistry Department of Urmia University

Sponsors:

nd2 Iranian Biennial Seminar of

C H E M O M E T R I C S

دانشگاه صنعتی ارومیه

Urmia University Of

Technology

شرکت ملی صنایع

پتروشیمی ایران

پارك علم و فن آوری

استان آذربایجان

غربی

استانداری استان آذربایجان غربی

شرکت آب و فاضالب

استان آذربایجان غربی

وزارت کشور

دزی را زن ه چی گر ره نداب دیآ مز ا

IV

Praise and thank God who led mankind into thought, wisdom and recognition of enormity of

creation, and made knowledge the ray and cause of salvation and placed scholars and learned

men as the bright lights in the path of seekers of eminent center of humanity which are insight

into the perceiving of the nature of entity.

For the advancement of chemometrics as well as other sciences, Iranian professors of

chemometrics applied all efforts to present new discoveries in the field of chemometrics that

were affected through these discoveries and their significance considering economic and

efficiency issues. Therefore comprehensive attempts should be carried out in ways relating to

analytical chemistry. It is hoped that the seminar will open the path to take further steps ndtowards new approaches under Divine Kindnesses. It is our great pleasure to host the 2

biennial seminar of chemometrics in a true collaboration with Iranian Society of chemistry and

participation of most experienced and outstanding Iranian colleagues in this beautiful and

historic city of Urmia. Undoubtedly, with increasing attention to economic issues during the

recent years, it is required that exchange of knowledge and increase of the national

collaborations to improve human efforts in the field of CHEMOMETRICS be carried out.

We would like to express our appreciation to respectable Vice Chancellors of Urmia University,

board of directors of Iranian Society of Chemistry, all members of scientific and organizing

committees and also my colleagues for their dedicated efforts to present and manage this

seminar.

We wish you all a pleasant stay in Urmia and hope that you will take advantage of this

opportunity.

Morteza Bahram-Ph.DScientific Secretary of IBSC 2009

In The Name of God

V

Dear Colleagues

Thank God who created the universes and put the responsibility and burden of discovering

the facts and knowledge to mankind. We are pleased to welcome everybody present in the

seminar. We hope that your 3-day stay in Urmia will be pleasant.

We wish to express our gratitude for your presence and congratulate the coincidence of the

birthday of Imam Reza and 2nd Iranian Biennial Seminar of Chemometrics.

We hope that the seminar will meet your expectations.

Hasan Sedghi-Ph.D

President of Urmia University

VI

Scientific Committee

of Seminar

Dr. H. Abdollahi

Dr. G. AzimiArak University

Dr. M. Bahram Urmia University

Dr. M. FatemiMazandaran University

Dr. J. Ghasemi

Dr. B. HematinejadShiraz University

Dr. M. Jalali-HeraviSharif University of Technoloy

Dr. T. Khayamian Isfahan University of Technology

Dr. M. Kompany

Institute for Advanced Studies in Basic Sciences, Zanjan

K.N. Toosi University of Technology

Institute for Advanced Studies in Basic Sciences, Zanjan

Dr. A. NaseriTabriz University

Dr. ZeinaliIslamic Azad University, Arak Branch

Executive Committee

of Seminar

Dr. M. Bahram Urmia University

Dr. Kh. FarhadiUrmia University

Dr. H. RezaeeUrmia University

Dr. R. SabziUrmia University

Dr. N. SamadiUrmia University

M.Sc. F. Hajilari

M.Sc. F. Khalilzade

M.Sc. Y. Shamchi

M.Sc. S. Talebi

West Azerbaijan Water and Wastewater Company

West Azerbaijan Water and Wastewater Company

West Azerbaijan Water and Wastewater Company

West Azerbaijan Water and Wastewater Company

XI

Organizer Committee

of Seminar

Dr. H. SedghiPresident of Urmia University

Dr. N. SamadiFinancial Vice-President of Urmia Unicersity

Dr. M. MahamResearch and Technology Vice-President of Urmia University

Dr. H. GhahramanloHead of Faculty of Science of Urmia University

Dr. H. AbdollahiChairman of Chemometrics Committee of Iranian Society of Chemistry

Referee Committee

of Seminar

Dr. H. Abdollahi

Dr. K. AsadpurTabriz University

Dr. G. AzimiArak University

Dr. M. BahramUrmia University

Dr. M. FatemiMazandaran University

Dr. J. Ghasemi

Dr. B. HemmateenejadShiraz University

Dr. M. Jalali-HeraviSharif University of Technoloy

Dr. G. JouybanTabriz University of Medical Science

Institute for Advanced Studies

in Basic Sciences, Zanjan

K.N. Toosi University of Technology

Dr. T. KhayamianI s f a h a n U n i v e r s i t y o f Technology

Dr. M. Kompany

Dr. M. Mousavi

Dr. A. Naseri

Tabriz University

Dr. A. Niazi

Islamic Azad University, Arak Branch

Dr. R. Tabaraki

Ilam University

Institute for Advanced Studies in Basic Sciences, Zanjan

hahid Bahonar University of Kerman

Content

The Story of Chemometrics

What is the Meaning of Feasible Band Boundaries in Self-Modeling/Multivariate Curve Resolution?

Orthogonalization in Variable Reduction and Selection

Orthogonal Signal Correction in Spectrophotometric and Voltammetric Data

Chemometrics Methods for Determination of Kinetic Parameters of Different Enzymatic Reactions

On the Effect of Mean Centering of Ratio Spectra as a Preprocessing Method Prior to Soft Modeling Approach: An

Introduction

The Use of Chemometrics Methods in Electroanalytical Chemistry

Applications of Chemometrics in Water and Wastewater Analysis; Iranian Water and Wastewater industries needs

Resolving Factor Analysis Using Chaotic Particle Swarm Optimization

Uncertainties and error propagation in kinetic and equilibrium hard-modelling of spectroscopic and pH-metric data

Application of Multivariate Curve Resolution based on Alternative Least Square assisted with Trilinearity Constraint (TC-

MCR-ALS) for Resolution of Multi-Way Rank Deficient Systems

Classification of Drugs by Means of Their Milk/Plasma Concentration Ratio Using Supervised Chemometric Procedures

Application of Successive Projections Algorithm (SPA) as a Variable Selection in a QSPR Study to Predict of the

Octanol/Water Partition Coefficients (Kow) of Some Halogenated Organic Compounds

Second-Order Advantage From Micelle Concentration Gradual Change–Visible Spectra Data

Mehdi Jalali-Heravi

Hamid Abdollahi

Mohsen Kompany-Zareh

Ali Niazi, Jahanbakhsh Ghasemi

A. Naseri

Morteza Bahram

Karim Asadpour-Zeynali

Fatemeh Hajilari, Sohrab Talebi

Hamid Abdollahi, Samira Beyramy soltan

Hamid Abodollahi, Parvin Darabi

Mohsen Kompany-Zareh, Fatemeh Ghasemi-Moghadam

M.H Fatemi, M. Ghorbanzad'e, E. Baher

Mohammad Goodarzi, Nasser Goudarzi

Hamid Abdollahi, Mahmoud Chamsaz, Tahereh Heidari

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XI

Partial Swarm Optimization Approach for Training of an Artificial Neural Network Applied in Thermal Investigation of

Nanocomposites

Application of Standardization Methods in Simple Kinetic and Equilibrium Studies

Random Forests, a Novel Approach for Prediction of the Acute Toxicity of Substituted Benzenes to Tetrahymena

Pyriformis

Application of Bayesian Adaptive Regression Splines for QSAR Modeling of Glutamate Inhibitors

Simultaneous Spectrophotometric Determination of 2-Furaldehyde and 5-Hydroxymethyl-2-Furaldehyde by Using Ant

Colony Algorithm-Based Wavelength Selection-Partial Least Squares Regression

Theoretical Study of Inhibition Effect of Some Imidazole Derivatives on Mild Steel

Mean Field Independent Component Analysis (MF-ICA) as a Self-Modeling Curve Resolution (SMCR) Technique

Application of Multiple Regression Systems in Mixture Analysis Using Non-Selective Spectral Data

New QSPR Model for Aqueous Solubility Prediction of Drugs

Prediction of Some Thermodynamic Properties forBinary Mixtures of Water and Ionic Liquids of Pyridinium-Based

Quantitative Structure-Inhibition Relationship Studies of Trifluoromethylimidazoles and Phenylpyrazoles for Xanthine

Oxidase by MLR and WNN

Use of Self-Training Artificial Neural Networks in Modeling of SPME–GC–MS Relative Retention Times of the

Constituents of Saffron Aroma

Mohammadreza Khanmohammadi, Nafiseh Khoddami, Mohammad Hossein Ahmadi Azghandi, A m i r

Bagheri Garmarudi, Masumeh Foroutan, Mahdieh Ansaryan

Mohsen Kompany-Zareh, Maryam Khoshkam

Anahita Kyani

Mehdi Jalali-Heravi, Ahmad Mani-Varnosfaderani

M. Shamsipur, A.A. Miran Beigi, V. Zare-Shahabadi, M. Teymouri, S. Ghahremani

Mehdi Mousavi, Mohammad Mohammadalizadeh

Mehdi Jalali-Heravi, Hadi Parastar

Hamid. Abdollahi, Akram. Rostami

Ali Shayanfar, Abolghasem Jouyban

A. Naseri, M. H. Soleimanian

Shahin Salimpour, Reza Tabaraki

Karim Asadpour-Zeynali, Naser Jalili-Jahani, Javad Vallipour

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XII

Quantitative Analysis of Ternary Organic Mixture by Multivariate Curve Resolution

Mean Centering of the Ratio Spectra for Preprocessing of Spectrophotometric Complexometric Data to

Determine the Stability Constants

An Investigation on the Macroscopic and Microscopic Acidity Constants of Benzene Tricarboxylic Acids by NMR

Spectroscopy Method; a Model Based Analysis

Hard-Modeling Thermodynamic Characterization of Methylene Blue Dimerization and Complexation with

Some Cyclodextrins

Thermodynamic Characterization of Benzoylacetone Tautomerization Equilbrium in the Presence of b-

Cyclodextrin

QSAR Studies on Benzodiazepine Classes as a Selective GABA a5 Inverse Agonist Using Homology Modeling, AMolecular Dynamic Simulation, Docking and Support Vector Machine

Combining Hard and Soft Modelling Parallel Factor Analysis to Solve Equilibrium Process

QSAR Analysis of Diaryl COX-2 Inhibitors: Comparison of Feature Selection Methods

+2Using of Box Behnken Design Method to Optimize Effective Parameters for Removal of Ni from Aqueous

Solution by ZSM-5 Zeolite

Theoretical Determination of the Number of Branches in the PAMAM Dendrimers

Spectrophotometric Simultaneous Determination Cobalt and Nickel Using 5-Br-PADAB in Alloys by Partial Least

Squares

Karim Asadpour-Zeynali, Javad Vallipour

Morteza Bahram, Setareh Gorji, Mehdi Mabhooti, Abdolhosein Naseri, Nader Norouzi-

Pesian

Azimi Gholamhassan, Azadi Marzieh, Zolgharnein Javad, Sangi Mohammad Reza

H. Abdollahi, F. Rabbani

H. Abdollahi, A. Safavi, S. Zeinali

S. Gharaghani, T. Khayamian, F. Keshavarz

H. Abdollahi, S.M. Sajjadi

Hoda Abolhasani, Somaieh Soltani, Abolghasem Jouyban

M. Abrishamkar, S. N. Azizi, H. Kazemian

A.H. Massoudi, J. Lari, O. Louie, S.Sajjadifar, A. Agah

Z. Aghajani, M. Bordbar, M. M. Ahari-Mostafavi, M. Rezai-Bina

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XIII

Using Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to the Qquantitative Analysis of

Retinoic Acid Isomers (Tretinoin, Isotretinoin and Alitretinoin) in Lotion Formulations

QSRR Study of Benzenoid, Aldehyde, Ketone, Cycloalka/Enes and Heterocyclic Aromates Derivatives Using

Linear and Nonlinear Chemometrics Methods

Prediction of Retention Times of Benzenoid, Aldehyd, Ketone, Cycloalka/Enesand Heterocyclic Aromates

Derivatives Using Different Chemometrics Methods

Molecular Recognition of Arginine and Lysine Complexes Toward CalixCrown-Biolinker: FT-IR Vibration Analysis

Comparison of Artificial Neural Network With Multivariate Linear Models for Prediction of Retention Times of

Chlorinated Pesticides, Herbicides, and Organohalides

Prediction of Retention Times of Phenols Based on Quantitative Structure-Retention Relationships

Application of Response Surface Methodology (RSM) for Optimization of Thallium (I) Removal by Modified

Ulmus Carpinifolia Tree Leaves

The Hydrogen Perturbation in Molecular Connectivity Indices and Their Application to QSPR Study

Prediction Drug Aqueous Solubility by Support Vector Machine from Their Theoretical Molecular Descriptors

Simultaneous Spectrophotometric Determination of Atenolol and Propranolol in Combined Tablet Preparation

by Partial Least Square Regression Method

Development and Validation of a Method for Fast Chromatographic Determination of Aflatoxins in Iranian

Pistachio Nuts from Complex HPLC-DAD Signals

Quantitative Structure Property Relationships Study of Air to Liver Partition Coefficients for Volatile Organic

Compounds Using Partial Least Squares and Artificial Neural Network

M. Bordbar, A. Yeganeh faal, M. M. Ahari- Mostafavi

Zahra Garkani-Nejad, Behzad Ahmadi-Roudi

Zahra Garkani-Nejad, behzad Ahmadi-Roudi

Afsaneh Amiri, Mehri Abdollahi fard, mona damavandi

Jahanbakhsh Ghasemi, Mahmood Chamsaz, Saeid Asadpour, Mehdi Alizadeh

Jahanbakhsh Ghasemi, Mahmood Chamsaz, Saeid Asadpour, Mehdi Alizadeh

Javad Zolgharnein, Neda Asanjarani, Tahere Shariatmanesh

M. Atabati, K. Zarei, R. Emamalizadeh

M.H. Fatemi, E. Baher, M. Ghorbanzade

Amir H.M .Sarrafi, Masoumeh Bakhtiari

Maryam Vosough, Mahin Bayat

Zahra Dashtbozorgi, Hassan Golmohammadi

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XIV

Response Surface Method for Simultaneous Optimization of VariousExperimental Parameters in Cloud Point

Extraction and Determination of Cd(II),Cr(III), Fe(II) and Ni(II) in Water Samples by Flame Atomic Absorption

Spectrometry

Experimental Design for the Optimization of Cloud Point Extraction andDetermination of Co(II), Cu(II) and Ag(I)

by Flame Atomic Absorption Spectrophotometry

Optimization of Dispersive Liquid-Liquid Microextraction Followed by Flame Atomic Absorption Determination

of Cu(II), Zn(II) and Cd(II) Based on the Complexation Reaction With 2,3,3-Trimethyl-3H-Pyrrolo (3,2-h)

Quinoline by Experimental Design

Central Composite Design and Response Surface Methodaology for the Optimization of Dispersive Liquid-

Liquid Microextraction and Analysis of Organophosphorus Pesticides by High-Performance Liquid

Chromatography

Quantitative Structure-Activity Relationship Study of HIV-1 Integrase Inhibitors Using Particle Swarm

Optimization

Utilization of Central Composite Design Methodin the Optimization of a Chemiluminescence Reaction

Parameters of Penicillin G Potassium Determination in Real Samples

The Effect of Surfactant Micelles on Acidity Constant of Bromothymol Blue-Sodium Salt

Application of ACA-PLS and GA-PLS for Simultaneous Spectrophotometic Determination of Thiophene, 2-

Methyl Thiophene and 3-Methyl Thiophene

Multiwavelength Spectrophotometric Determination of Acidity Constant of 5-Nitro-2-(2-Nitro-Phenyleazo)-

Phenol,(4-e) in Water, Water SDS and Water-Triton X-100 Micellar Media Solutions

Determination of Acidity Constant of 2-(2H-Benzo[d] [1, 2, 3] Triazol-2-yl) Phenol in Water and Micellar Media

Solutions

N. Samadi, M.R. Vardast, B. Mehrara, M. Bahram

Naser Samadi, Mohammad Reza Vardast, Amir Chehrehgani, Morteza Bahram

N. Samadi, M.R. Vardast, B. Mehrara, M.A. Farajzadeh

M. Jalali-Heravi, H. Ebrahimi-Najafabadi

M.H. Sorouraddin, M. Fadakar-Sardroud, M. Iranifam, A. Imani-Nabiyyi

Amir H. M. Sarrafi, Samane Famili

N.Farzin-Nejad, E.Shams Solari1, M.K.Amini, A.A.Miran Beigi, V. Zare-Shahabadi

Mohammad Ghalei, Amir Hosein Moohsen Sarafi

Amir H. M. Sarrafi, Negin Ghorashi

M. A. Farajzadeh, M. R. Vardast

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Spectrophotometric Determination of Acidity Constant of Bromocresol Purple in Water, Water-Brij-35 and

Water-SDS

Monitoring of Some Pesticides in Water Samples With SPE- HPLC Method Including an Uncertainty Estimation

of the Analytical Results

Prediction of Anti HIV-1 Activity of Non-Nucleoside Inhibitors by QSAR Approaches

Optimization Of Theoretical Plate Heights in Chromatography

QSPR Modeling of Optical Rotation for Biodegradable Polymers Using an Artificial Neural Network

Prediction of Inherent Viscosity for Optically Active Polymers from the Theoretical Derived Molecular Descriptors

Prediction of Water-to-Polydimethylsiloxane Partition Coefficient for Some Organic Compounds Using QSPR

Approaches

Quantitative Structure-Property Relationship Study of Electrophoretic Mobilities of Some Organic and Inorganic

Compounds Using SVM

Simultaneous Spectrophotometric Determination of Uranium and Zirconium Using Cloud Point Extraction and

Multivariate Methods

Simultaneous Determination of Paracetamol, Phenylephrine Hydrochloride and Chlorpheniramine Maleate

Using Partial Least Squares-1 (PLS-1) Regression

In Silico Prediction of Aqueous Solubility of Some Organic Compounds

Artificial Neural Networks and Least-Square Support Vector Machine Applied for Simultaneous Analysis of

Mixtures of Nitrophenols by Conductometric Acid-Base Titration

Amir H. M. Sarrafi, Negin Ghorashi, Mahboobeh Nimroozi

A. Ghorbani, F. Aflaki, M. Aghaei

Mohammad Hossein Fatemi, Zahra Ghorbannezhad

Kiumars Ghowsi, Hossein Ghowsi

Hassan Golmohammadi, Zahra Hassanzadeh

M. A. Farajzadeh, M. R. Vardast, Hassan Golmohammadib

Hassan Golmohammadi, Zahra Dashtbozorgi

Nasser Goudarzi, Mohammad Goodarzi, M. H. Fatemi

Jahanbakhsh Ghasemi, Beshare Hashemi

Abdolraouf Samadi–Maybodi, Seyed Karim Hassani Nejad–Darzi

Mohammad Hossein Fatemi, Afsane Heidari

Gholamhossein Rounaghi, Roya Mohammad Zadeh, Tahereh Heidari

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XVI

Spectrophotometric Determination of Trace Amounts of Beryllium in Natural Water Using Mean Centering of

Ratio Spectra Method and Orthogonal Signal Correction-Partial Least Squares Regression

QSAR Study of Some Anti Fungous Benzofurans Using Artificial Neural Networks

H-point Standard Addition Method Applied to Simultaneous Kinetic Determination of Antimony(III) and

Antimony(V) by Adsorptive Linear Sweep Voltammetry

Simultaneous Spectrophotometric Determination of Lead, Copper and Nickel Using Xylenol Orange by Partial

Least Squares Calibration Method

Simultaneous Kinetic Spectrophotometic Determination of Levodopa and Benserazide Based on the Surface

Plasmon Resonance Band of Silver Nanoparticle and Artificial Neural Network

Application of Artificial Neural Network in Infrared Spectrometric Quality Control of Dairy Products

Speciation and Determination of Inorganic Selenium Species by a Simple and Rapid Technique Using Selective

Separation on Mercury Coated Electrode Coupled With Electrothermal Atomic Absorption Spectroscopy (ED-

ETAAS) in Water Samples

Simultaneous Extractive Spectrophotometric Determination of Fe(II) and Fe(III) Using PAR and HDPB by Partial

Least Squares Method

Prediction of the Peptides' Affinities for Carbon Nanotubes Using Linear Interaction Energy Model

-1Prediction of Log (IGC ) for Benzene Derivatives to Ciliate Tetrahymena Pyriformis from Their Molecular 50Descriptors.

Simultaneous Spectrophotometric Determination of Ascorbic Acid and Epinephrine by Kinetic H-Point Standard

Addition Method

Zeinab Rohbakhsh, Akram Hajinia, Tahereh Heidari

Zakieh Izakian

K. Zarei, M. Atabati, M. Karami

Jahan Bakhsh. Ghasemi, Samira. Kariminia

Mohammadreza Khanmohammadi, Amir Bagheri Garmarudi, Keyvan Ghasemi

J. Ghasemi, S. H. Kiaee

Anahita Kyani, Bahram Goliaei

Mohammad H. Fatemi, Hanieh Malekzadeh

Alireza Mohadesi, Hamideh Mirzaabdollahi

M.Reza Hormozi Nezhad, J.Tashkhourian, J. Khodaveisi

Nahid Mashkouri Najafi, Shahram Seidi, Alireza Ghasempour, Reza Alizadeh,

Hamed Tavakoli, Ensieh Ghasemi

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Determination of PABA Concentration in B-Complex Tablets by MCR-ALS Method

Study of Synthesis of Biologically Active Pyrimido [2,1-b]Benzothiazoles from Propiolic Acid and Benzotiazol-

2Amino by Chemometrics

Application of Soft-Modeling Approaches to Resolution of Electron Donor- Acceptor Complex Formation of

Morpholine and 2,4,6-Trimorpholino-1,3,5-Triazin With Iodine in Different Solutions

Ab initio Calculation of Absolute pK Value in Aqueous Solution for Nicotineb

Studies on the Quantitative Relationship Between the Retention Indices of Essential Oils and Their Molecular

Structures

Application of Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) Technique for Quantitative

Determination of Acetaminophen in Pharmaceutical Tablets

Simultaneous Spectrophotometric Determination of Lead and Mercury in Waste Water by Least-Squares

Support Vector Machine and Partial Least Squares Methods

Prediction of Binding Affinity of Pharmaceutical Compounds Using Different Chemometrics Methods

Spectrophotometric and Thermodynamic Study of Praseodymium with 4-(2-Pyridylazo) Resorcinol Complex

using Chemometrics Methods

A Comparative Study Between PLS, GA-PLS, OSC-PLS and GA-OSC-PLS in the Simultaneous Voltammetric

Determination of Antimony and Bismuth: Effect of Variable Selection

QSAR/QSPR Study of Toxicity of Nitrobenzene Derivatives and Alcohols by Mechanic Quantum and Structure

Descriptor by Chemometrics Methods

Mohammad Mirzaei, Mehdi Khayyati

Mohammad Mohammadalizadeh, Mehdi Mousavi, Hassan Sheibani

Tayyebeh Madrakian, Masoumeh Mohammadnejad, Faezeh Hojati

Moradi Robati Gh R., Moradi Sh., Asni Ashari M B

Mehdi Nekoei, Majid Mohammadhosseini, Farzad Sadeghi

1Mohammadreza Khanmohammadi, Hamid Abdollahi, Hossein Nemati

Ali Niazi, Ateesa Yazdanipour, Zahra Ahmari

Sasan Sharifi, Ali Niazi, Amir Ezatpanah

Ali Niazi, Bahareh Yasar, Mehrana Motiee

Ali Niazi, Faezeh Jaberi, Samira Sadeghi, Riccardo Leardi

Sasan Sharifi, Ali Niazi, Fahimeh Rezaei

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Quantitative Structure-Activity Relationships (QSAR) Study of Phenol Heterogenic by Orthogonal Descriptor

Correction-Partial Least Squares Method

Simultaneous Spectrophotometric Determination of Cobalt, Copper and NickelUsing 4-(2-thiazolylazo)-

resorcinol by Partial Least Squares and Parallel Factor Analysis

Cloud Point Extraction for Pre-concentration and Simultaneous Spectrophotometric Determination of Trace

Amounts of Bismuth and Copper by PLS and OSC-PLS

Orthogonal Signal Correction- Partial Least Squares Method for Simultaneous Spectrophotometric

Determination of Cobalt, Copper and Nickel

A Novel Quantitative Structure-Property Relationship Model for Prediction of Depletion Percentage of Skin

Allergic of Glutathione Compounds: A Combined Data Splitting-Feature Selection Strategy

Successive Projection Algorithm-Based Wavelength Selection in Multi-component Spectrophotometric

Determination by PLS: Application on Copper, Nickel and Zinc Mixture

Quantitative Structure Retention Relationship Study of Linear Alkanes and Alkenes using Different

Chemometrics Methods

Quantitative Structure Retention Relationship Study of Linear Alkanes and Alkenes using Different

Chemometrics Methods

Principal Component-Wavelet-Neural Network as Multivariate Calibration Method for Simultaneous

Spectrophotometric Determination of Folic Acid, Thiamine, Riboflavin and Pyridoxal

Extraction and Simultaneous Spectrophotometric Determination of Copper and Cobalt by TAN With Partial

Least Squares

Sasan Sharifi, Ali Niazi, Farnaz Samnejad

Ali Niazi, Giti Yamini

Ali Niazi, Kobra Karimi

Ali Niazi, Marjan Mehran, Masomeh Asgari

Ali Niazi, Maryam Ghiasi, Mina Montazeri, Shamsi Rafatpanah

Ali Niazi, Masomeh Asgari, Marjan Mehran

Mehrana Motiee, Ali Niazi

Mehrana Motiee, Ali Niazi

Ali Niazi, Pegah Saligheh Fard, Jahanbakhsh Ghasem

Ali Niazi, Reza Moradi

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XIX

A Comparative Study Between Least-Squares SupportVector Machine and Partial Least Squares in Simultaneous

Spectrophotometric Determination of Cobalt, Cadmium and Nickel

Spectrophotometric and Thermodynamic Determination of Acidity Constants of Hydroxy Naphthol Blue in

Different Solvents by DATAN

Nondestructive Quantitative Analysis of Tomato Fruit Using Raman Spectroscopy and Chemometrics

Identification of Binding Mode and Determination of Binding Constant Between DNA and Quinones by

Chemimetrics Programs

2+ 3+ 2+ 2+ 2+Sepctrophotometric Studies of Complexationof Co , Cr , Ni , Pb and Zn With Para-Tert-Butyl Calix[n]arene

Prediction of the Retention Time GC-MS of Organic Compounds Based on Molecular Structural Descriptors

Using MLR and Wavelet-Neural Network Methods

Comparison of ANN and WT-ANN in Calculatingof Half-Wave Potential of Some Organic Compounds

Simultaneous Spectrophotometric Determination of Silicate and Phosphate in Boiler Water of Power Plant

andSewage Sample by Partial Least Squares and Simplex Design Methods

Design of a New Thallium(I)-Selective Electrode Based on Calix[6]arene using Experimental Design

The Components of the Iranian Rosemary Essential Oil Characterized and Identified Using (GC-MS) Combined

With the Curve Resolution Techniques

Prediction of Retention Factor of Organic Compounds in Different Mobile Phase Compositions in RP-LC by LFER

Parameters

Ali Niazi, Samira Sadeghi, Faezeh Jaberi

Ali Niazi, Simin Moradi, Sadaf Mahmoudzadeh

A.M. Nikbakht, R. Malekfar, T. Tavakoli Hashtjin, B. Gobadian, N. Mohammadi

Hossein Peyman, Mohammad Bagher Gholivand, Soheila Kashanian, Hamideh

Roshanfekr

Amir H. M. Sarafi, Afsaneh Amiri, Fatemeh Pirouzi

Z. Garkani-Nejad, H. Rashidi-Nodeh

Z. Garkani-Nejad, H. Rashidi-Nodeh

M. Rohani, S. Dadfarnia, M. A. Haji Shabani, Jahan B. Ghasemi

Sayed Yahya Kazemi, Akram Sadat Hamidi

Mehdi Jalali – Heravi, Rudabeh–Sadat Moazeni, Hassan Sereshti

Seyedeh Maryam Sadeghi, Mohammad Hossein Fatemi

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XX

Development and Validation of a Reversed-Phase HPLC Method for Simultaneous Estimation of Carbamazepine

and Phenytoin Using an Experimental Design

A Comparison of Partial Least Squares Regression and Artificial Neural Networks for Kinetic Spectrophotometric

Determination of Selenium and Tellurium Mixture in Alloy Samples

Modeling of Methylene Blue Electroactive Label Signal in Pencil Graphite Based DNA Biosensors

Simultaneous Determination of Thorium(IV) and Zirconium(IV) Ions Using Partial Least Squares Method

Prediction of IAM-LC Retention of Some Drugs From Their Molecular Structure Descriptors and LFER Parameters

A Simple Variable Selection Method Based on the Partial Least Squares Loadings: Application to Quantitative

Structure-Activity Relationships Data

Investigation of Optimum Extraction Conditions for Determination of Quercetin in Sea Parsnip (Echinophora

Spinosa L.) by Using Experimental Design and HPLC.

QSAR Study of Substituted Pteridin-4[3H]-One and Dihydroxypyrazolo [1, 5 -α] Pyrimidine Derivatives, Two

Novel Classes of Xanthine Oxidase Inhibitors

Application of Orthogonal Array Design for the Optimization of Sample Preparation for Determination of

Chromium, Copper, Lead, Iron, Manganese, Molybdenum, Nickel and Zinc in Human Hair by Flame and

Electrothermal Atomic Absorption Spectrometry

Measurement Uncertainty of Co, Cr, Mo, and Zn Determination in Human Hair by Electrothermal Atomic

Absorption Spectrometry

Statistical Process Control of Edible Salt Production to Improve Salt Quality at National Standard Level

E. Konoz, M.H. Fatemi, H. Baghri sadeghi, Sh. Lashgari

Nahid Sarlak, Abbas Afkhami, Ali Reza Zarei

M.S. Hejazi, R.E. Sabzi , F. Golabi, B. Sehatnia

Behnaz Shafiee, Hamid Reza Pouretedal

Hoda Shamseddin, Mohammad Hossein Fatemi

Masoumeh Hasani, Masoud Shariati-Rad

Mohammadreza Hadjmohammadi, Vahid Sharifi

Shahin Salimpour, Reza Tabarak

Fariba Tadayon, Mohammad Saber Tehrani, Mahmod. R. Sohrabi, Shiva Motahar

F. Tadayon, N. Mashkouri Najafi, M. Saber-Tehrani, A. Ghorbani

Gholamreza Vatankhah, Nahid Tavakkoli, efat Asghari

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Determination of Some Volatile Organic Compound in Honey Samples Using Hollow Fiber- Ultrasound Assisted

Emulsification Microextraction (HF-USAEME) Comparative With Conventional Headspace Single Drop

Microextractio With the Aid of Response Surface Methodology and Experimental Design

Classification of Iranian Bottled Waters as Indicated by Manufacturer’s Labellings

Development and Validation of Chemometrics-Assisted Spectrophotometry for Determination of Water

Soluble Vitamins in B-Complex Tablets

A Comparison Between LS-SVM and BP-ANN for Simultaneous Spectrophotometric Determination of Some

Ingredients in Detergent Powder

Application of Artificial Neural Network and Near IR Diffuse Reflectance Spectroscopy for Estimation the Range

of Particle Size of Nano-TiO2

Simulation of Precipitation Titration for Some Cations Using pH Glass Electrode

Simultaneous Determination of 2-Nitrophenol and 4-Nitrophenol by Bismuth Modified Pencil Lead Electrode

With Net Analyte Signal Standard Addition Method

Simultaneous Polarographic Determination of Antazoline and Naphazoline by Differential Pulse Polarograhy

Method and Support Vector Regression

Multivariate Curve Resolution of Overlapping Polarograms to the Quantitative Analysis of Metals Mixture

Application of Parallel Factor Analysis and Multivariate Curve Resolution-Alternating Least Square for

Resolution of Kinetic Data of L-ascorbic Acid Oxidation in Multivitamin Tablets by UV Spectrophotometry

Yadollah Yamini, Shahram Seidi, Abolfazl saleh, Mahnaz Ghambarian

K. Yekdeli Kermanshahi, R. Tabaraki

Fereshteh Zandkarimi, Maryam Shekarchi, Ali Akbar Tajali

Mohammadreza Khanmohammadi, Mohammadhossein Ahmadi Azghandi, Nafiseh

Khoddami, Amir Bagheri Garmarudi

Mohammadreza Khanmohammadi, Nafiseh Khoddami, Amir Bagheri Garmarudi

A. Nezhadali; B. Ahmadi

Karim Asadpour-Zeynali, Parvaneh Najafi

Karim Asadpour-Zeynali, Payam Soheyli-Azaz

Karim Asadpour-Zeynali, Javad Vallipour

Mohammadreza khanmohammadi. Mohammad Babaei Roochi. Nafise khoddami.

Zahra Amani

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Application of Experimental Design Methodology to the Optimization of Catalytic Kinetic Determination of

Osmium by Janus Green-Hydrogen Peroxide System

A New Spectrophotometric Study on the Simultaneous Determination of Benzodiazepines in Plasma employing

Multivariate Calibration Methods Combined with Genetic Algorithm on Ordinary and Derivative Spectra

Rapid Chemometric Method for Simultaneous Determination of Imipramine and Clomipramine in Serum and

Validation by HPLC

Simultaneous Spectrophotometric Determination of Co(II) and Ni(II) Based on the Complexation Reaction With

Phenylfluorone Using Partial Least Squares Regression

Application of Experimental Design Methodology in Optimization and Determination of Trace Amount of

Nitrite Using Dispersive Liquid-Liquid Microextraction Followed by Spectrophotometric Detection

Prediction of Receptor Binding Constant of 6-Methoxy Benzamides, Using ANN and MLR

Optimization of Quercetin Nanoparticle Emulsion Preparation Using Experimental Design and Multiple Linear

Regression

Comparing Different Subset Selection Methods for Nonlinear Modeling the Acidity Constants of Some Organic

Compound in DMSO

QSPR Studies of Refractive Indices of Polymers by GA-MLR and ANN

Prediction of Aqueous Solubility of Drug-Like Compounds Based on Multilayer Regression and Neural Network

Modeling

Application of Topological Index in Description of Chemical Properties

Hasan Bagheri, Parviz Shahbazikhah, Masoud Reza Shishehbore, Mehdi Nekoei

Siavash Riahi, Kowsar Bagherzadeh, Mohammad Reza Ganjali, Parviz Norouzi

Siavash Riahi, Kowsar Bagherzadeh, Behrouz Akbari-Adergani, Mohammad Reza

Ganjali, Parviz Norouzi

Mohammad Alizadeh, Hamid Daryani, Morteza Bahram, Reza E. Sabzi

M. Bahram, M.R Vardast, F. Eshghian, M.A Farajzadeh

Mohammad Hossein Fatemi, Fereshteh Dorostkar

Pouneh Ebrahimi, Fereshteh Pourmorad, Soheila honary, Bahar Ebrahim magham

Gholamhasan Azimi, Sara Ebrahimi, Mohsen Kompany-Zareh, Yousef Akhlaghi

M.Ali Ferdowsi, H. Nikoofard, N. Goudarzi and Z. Kalantar

M. Ali Ferdowsi , H. Nikoofard and N. Goudarzi and Z. Kalantar

M.Ali Ferdowsi, H. Nikoofard , N. Goudarzi and Z. Kalantar

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2-Dimensional Quantitative Structure-Property Relationship Modeling Study of Some Organic Compounds

Henry's Law Constant Based on GA-MLR and MLR

Application of Response Ssurface Methodology and Central Composite Design for Modeling and Optimization

of Hollow Fiber Liquid Phase Microxtraction for Selenium and Tellurium Speciation

Prediction of Retention Indices of Some Essential Oils Using Linear and Nonlinear QSPR Methods

A New Method for Simultaneous Spectrophotometric Determination of Psuedoephedrine and Guaifenesin in

Pharmacuticals Products: Chemometrics and Derivative Spectroscopy

Application of Box-Behnken Design in the Optimization of Catalytic Behavior of a New Mixed Chelate of Copper

(II) Complex in Chemiluminescence Reaction of Luminol

Optimization of Dispersive Liquid Microextraction Based on Ionic Liquid for Preconcentration and

Determination of Copper in Water Samples Using Response Surface Methodology and Experimental Design

Classification of Iranian Bottled Mineral Waters Using Chemometrics Methods

Development of Comprehensive Descriptors for Multiple Linear Regression and Artificial Neural Network

Modeling of Drug Bioavailability

Application of Response Surface Methodology (RSM) for Optimization of Carrier Mediated Hollow Fiber Liquid

Phase Microextraction Combined With HPLC–UV for Preconcentration and Determination of Dexamethasone

in Biological Samples

Application of Response Surface Method for Determination and Preconcentration of Lead Using Dispersive

Liquid-Liquid Microextraction Based on Ionic Liquid and Flame Atomic Absorbtion

Prediction of Voltametric Oxidation of Catecol Derivatives Using DFT Calculation and Linear Regression (LR)

M.Ali Ferdowsi, H. Nikoofard, N. Goudarzi, Z. Kalantar

Nahid Mashkouri Najafi, Ensieh Ghasemi, Farhad Raofie, Alireza Ghassempour

Nasser Goudarzi, H. Salimi and M. Arab Chamjangali

Farshad hadiloo, Siavash riahi, Mohamad reza milani

Tahereh Khajvand, OmLeila Nazari, Mohammad Javad Chaichi, Hamid Golchoubian

Roohollah khani, Farzaneh Shemirani, Behrooz majidi

Mohammad Reza Khoshayand, Hamid Abdollahi, Seyed Mohammad Shariatpanahi, and

Hasan Akbari

E. Konoz, M.H. Fatemi, Sh. Lashgari

Katayoun Mahdavi Ara, Homeyra Ebrahimzadeh, Shahram Seidi

Behrooz majidi, Farzaneh Shemirani, Roohollah khani

Mansouri Ailin, Hokmi Akram, Nematollahi Davood, Jamehbozorghi Saeed

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Structure-Property Modelling of Complex Formation of Potassium With Diverse 18-Crown-6 Ethers in Methanol

+2Comparative Studies of Univariate and Multivariate Optimizations for Determination of Drugs by Ru(phen) -3Ce(IV) Chemiluminescence System

Simultaneous Spectrophotometric Determination of Copper (II) and Nickel(II) Using Partial Least-Squares

Calibration Method

Simultaneous Determination of Cobalt (II) and Zinc (II) by Partial Least-Squares Calibration Method

Simultaneous Spectrophotometric Determination of A.C Red 27 and Methyl Red Using Multivariate Calibration

Methods

Application of Rank Annihilation Factor Analysis (RAFA) to the Quantitative Analysis of Pharmaceutical Samples

Simple and Fast QSAR Method for Prediction of HIV-1 PR Inhibitory of Novel Fullerene (C60) Analogues

Application of Genetic Algorithm-Support Vector Machine (GA-SVM) for Prediction of BK Channels Activity

Quantum Chemical Calculations to Reveal the Relationship Between the Chemical Structure and the

Fluorescence Characteristics of Phenylquinolinylethynes and Phenylisoquinolinylethynes Derivatives, and to

Predict their Relative Fluorescence Intensity

Improving a Drawback in QSPR Study; QSPR Study of Fluorescence Characteristic of Six 4, 7-Disubstituted

Benzofurazan Compounds in 20 Different Solvents

A Novel Technique by Using a CCD Camera for Kinetic Determination of Iron(III)

The Use of CCD Camera and RGB Model for Kinetic Determination of Vanadium (V)

Shahin Ahmadi, Zohreh Mehri

A. Mokhtari, B. Rezaei

Shahla Mozaffari, Maryam Mohammadzadeh

Shahla Mozaffari, Zahra Dini Khezri

A. Naseri, H. Ayadi, A. Parchehbaf Jadid

H. Abdollahi, F. Norooz Yeganeh, M. R. Khoshayand

Eslam Pourbasheer, Mohammad Reza Ganjali, Siavash Riahi, Parviz Norouzi

Eslam Pourbasheer, Mohammad Reza Ganjali, Siavash Riahia, Parviz Norouzi

Abolghasem Beheshti, Siavash Riahi, Mohammad Reza Ganjali, Parviz Norouzi

Abolghasem Beheshti, Siavash Riahi, Mohammad Reza Ganjali, Parviz Norouzi

M Kompany-Zareh, H Tavallali, N Shakernasab

H. Tavalli, M. Kompany Zare, S.E Shamsdin

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Applied Artificial Neural Networks Modeling to Quantitative Structure-Properties Relationship Study of

Lipophilicity Activity of Some Long Hydrocarbon Chain Keto-Diols and Their Phosphates Esters and Acids

Derivation

Voltammetry Determination of Stability Constants of Cadmium Complexes with Diallyl Disulfide by

Electroanalytical Technique: Hard and Soft-Modeling Approaches

Modeling and Optimization of Dispersive Liquid-Liquid Microextraction for Speciationof Tellurium with the Aid

of Response Surface Methodology and Experimental Design

Response Surface Methodology (RSM) Based on BoxBehnken Design as a Chemometric Tool for Optimization of

Dispersive-Solidificative Solvent Microextraction for Speciation of Selenium

Prediction of Retention of LC-MS Pesticides in Water Using QSRR Approach

Factorial Analysis and Response Surface Optimization of a Peroxyoxalate Chemiluminescence of Trazinyl

Derivative in the Presence and Absence of Some Surfactants

Super Modified Simplex Optimization Chemiluminescence from Reaction of Peroxyoxalate Ester (TCPO),

Hydrogen Peroxide and tetraazapentacyclo Derivative as Fluorescer and Study Quenching Effect of Some

Cations and Amino Acids on Optimized Chemiluminescence System.

Determination of Dissociasion Constant of Preotonated Form a Triazin Derivative Dye by Spectrophotometric

and Spectroflourimetric Method: A Study Chemometrics approach

Determination of Dissociasion Constant of Preotonated Form a Triazin Derivative Dye by Spectrophotometric and

Spectroflourimetric Method: A Study Chemometrics approach Determination of Main Factors in Silane Grafting of

Linear Low Density Polyethylene Using Experimental Design

Prediction of Inhibitor Activity of 1,3,4-Thiadiazole-2-Thion Derivative to Carbonic Anhydrase by QSAR

Methodology Using Genetic Algorithm-Artificial Neural Network Technique

M.R.Sohrabi, Nasser Goudarzi, F.Hamidi,

M.A. Kamyabi, F. Soleymani Bonuti

Nahid Mashkouri Najafi, Hamed Tavakoli, Reza alizade, Shahram seidi

Nahid Mashkouri Najafi, Hamed Tavakoli, Reza alizadeh

Amir H. M. Sarrafi, Fateme Yaghoobi

A. Yeganeh-faal, T. H. Shayeste , J. Ghasemi, M. Bordbar

A. Yeganeh-faal, B. Jamalian, J. Ghasemi, M. Salavati

A. Yeganeh-faal, G. Dabaghian, M. Haggo, M. Bordbar

E.Konoz, M.H.Fatemi, E.Zamani Farahani

Mehdi Mousavi, Solmaz Ahmadgolami

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Prediction of Inhibitor Activity of Amino-Caprolactam Derivatives to Y-secretase by QSAR Methodology Using

MLR and Artificial Neural Network

An Improved HPLC Method for Rapid Quntitation of Atorvastatin Using an Experimental Design

Artificial Neural Network Modeling of the Blood-Brain Penetration Coefficient of Drugs

Predictive Ability of Multivariate Calibration Methods for Simultaneous Quantification of Tebaine and +2Noscapine Using Chemiluminescence System of Ru(phen) and Acidic Ce(IV)3

Hard-Modeling Approach for the Thermodynamic and Spectroscopic Studies of Cu(II), Ni(II), Co(II) and Zn(II)

Complexes With Two Newly Synthesized Ligands in Acetonitrile Solution

Modeling of Decolorization of Allura Red solutions Using Response Surface Methodology

Modeling and Optimization of Simultaneous Decolorization of A.C Red 27 (AR 27) and Methyl Red (MR) Dyes

Simultaneous Determination of Trimetoprim and Phthalazine Using HPLC and Multivariate Calibration Methods

Application of Artificial Neural Network and Wavelet Neural Network in Simultaneous Determination of Iodine

Species by Kinetic Spectrophotometry

Applied Artificial Neural Networks Modeling to Uantitative Structure-Properties Relationship Study of

Lipophilicity Activity of Some Long Hydrocarbon Chain Keto-Diols and Their Phosphates Esters and Acides

Derivatives

Taguchi's Experimental Design for Optimization of Effective Parameters on Diazinon by Cloud Point Extraction

A Simple and Cheap Double-Beam Photocolorimeter Fabricated for Simultaneous Determination of Binary and

Ternary Mixtures

Mehdi Mousavi, Solmaz Ahmadgolami

E. Konoz, M.H. Fatemi, S. Ardalani

E. Konoz, M.H. Fatemi, S. Ardalani

A. Mokhtari, B. Rezaei

Nasser Samadi, Mina Salamati, Morteza Bahram, Ali Soldouzi

E. Ghorbani–Kalhor, A. Naseri, Soheila Mohammadian

H. Ayadi, A. Naseri

A. Naseri, S. Asadi, M. R. Rashidi

A. Benvidi, F. Heidari

M.R.Sohrabi, Nasser Goudarzi, F.Hamidi

Sarah Jamshidi, Mahmud Reza Sohrabi, Vahid Kiarostami

Mohammad-Hossein, Sorouraddin, Masoud Saadati

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InvitedLectures

InvitedLectures

InvitedLectures

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The Story of Chemometrics

Mehdi Jalali-Heravi

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

For the very first time, in 1971 the term “Chemomterics” was coined by a young scientist, Svante Wold, from the Umea University

in Sweden. After visiting Bruce Kowalski at University of Washington at Seattle in 1974, Svante and Bruce together with their

graduate students founded the International Chemometrics Society. Svente Wold has by now authored and co-authored more

than 400 scientific papers and has received a number of scientific medals and other honors. He is retired, but Swedish chemical

society awarded another young scientist, Johan Trygg, from the same university with the most prestigious international award for

researches in Chemometrics. The prize is honoring scientists for major achievements in the field of chemometry, a research field in

chemistry, which focuses on optimal measurement procedures by applying and developing statistical and mathematical methods.

Although only well-known and legendary professors have been awarded with the medal in pure gold, Johan Trygg as a young

associate professor in chemistry was rewarded for his efforts in research field of multivariate analysis. The method Johan

developed is called OPLS (orthogonal projections to latent structures). It is already in use by more than 150 Swedish companies, 50

international institutions and the ten largest pharmaceutical companies in the world. It has also become standard in the rapidly

growing field of metabolomics, the quantitative study of small molecules involved in the metabolism. This confirms that Umeå

University remains a leader in the area and succeeded with new recruitments after the retirement of Professor Svante Wold.

Being a first generation chemometrician in Iran, I am very impressed by the growth of Chemometrics in this country, but the

question is that do we have similar universities or scientific organizations in Iran? What can we do to develop Chemometrics in Iran

in a healthy way? Chemometrics is more relevant and needed than ever and all of us hope that it continues to develop to stay

relevant and improve its usability.

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What is the Meaning of Feasible Band

Boundaries in Self-Modeling/Multivariate Curve Resolution?

Hamid Abdollahi

Faculty of Chemistry, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran

Multivariate curve resolution (MCR) methods are based on a soft bilinear model that attempts the decomposition of an

experimental data matrix into the product of two simpler matrices with physical meaning, one related to the rows of the original

data matrix and the other related to the columns of the original data matrix. For instance, the matrix D can be decomposed using a

bilinear model into the product of a concentration matrix C and of a spectral matrix S (D = C S ).. There is no unique set of matrices

C and S. In fact, there is an infinite number of possible and mathematically equivalent solutions of C and S (feasible solutions),

which multiplied with each other, give the same result. This decomposition is ambiguous if no additional information is available,

or in other words, there is rotational and scale freedom in this decomposition. This problem is often called in the literature as the

factor analysis ambiguity problem.

Related to rotational ambiguity in MCR solutions, there are several questions such as: How the feasible solutions can be

calculated? How the rotational ambiguities can be quantitatively calculated? What are the boundaries of feasible solutions? How

the feasible band boundaries can be calculated? and … There are several studies in literature related to these questions [1-3] and

the attempts for finding the proper answers to such problems are in progress. In this presentation, some of these problems will

basically consider.

References:

1) Comprehensive Chemometrics Chemical and Biochemical Data Analysis Four-Volume Set, Elsevier, chapter 2-20, 2009.

2) H. Abdollahi, M. MAeder and R. Tauler, Anal. Chem., 81, 2115-2122, 2009.

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Orthogonalization in Variable Reduction and Selection

Mohsen Kompany-Zareh

Institute for Advanced Studies in Basic sciences, GavaZang, Zanjan, Iran.

Orthogonalization is a simple linear algebraic procedure applicable to a series of vectors. In chemistry, vectors are rows or columns

of a data table from a considered chemical system. Data table (matrix) could be the spectral data from a series of samples,

descriptor values from a QSAR study, electrochemical data from a number of samples, or a series of chromatograms. Usually the

number of variables in the chemical data matrices from instruments and descriptor from different softwares are very large.

Selection of a limited number of informative variables or reduction of the number of variables using other proper approach,

reduces the calculation and simplifies interpretation of results. In this way, employment of variable reduction and selection

procedures are important.

A simple application of orthogonalization in variable reduction is the recent procedure of fast principal component analysis, based

on Gram-Schmidt Orthogonalization [1]. Successive projection algorithm (SPA) is another orthogonalization based method

applicable to variable selection and reduction [2,3].

Ridge regression and similar sparse regression methods are among the recent variable selection methods [4]. This presentation is

on the effect of orthogonalization on results from these variable selection/reduction methods. The proper cross-validation is

applied to both the model selection and verification steps.

References:

1) A. Sharma, K.K. Paliwa, Pattern Recogn lett 28 (2007) 1151-1155.

2) M. Kompany-Zareh, Y. Akhlaghi, J. Chemometr. 21 (2007)239-250.

3) Y. Akhlaghi, M. Kompany-Zareh, J. Chemometr. 20 (2006) 1-12.

4) J. J. Kraker, D.M. Hawkins, S. C. Basak, R. Natarajan, D. Mills, Chemom Intell Lab Syst 87 (2007) 33-42.

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Orthogonal Signal Correction in Spectrophotometric and Voltammetric Data

1 2Ali Niazi , Jahanbakhsh Ghasemi

1- Department of Chemistry, Faculty of Science, Islamic Azad University, Arak Branch, Arak, Iran

2- Department of Chemistry, Faculty of Science, K.N. Toosi University of Technology, Tehran, Iran

The application of quantitative chemometrics methods, particularly partial least squares (PLS) to multivariate chemical data is

becoming more widespread owing to the availability of digitized spectroscopic and electrochemical data and commercial

software for laboratory computers. Each method needs a calibration step, where the relationship between the spectra and the

component concentration is deduced from a set of reference samples, followed by a prediction step in which the results of the

calibration are used to determine the component concentrations from the sample signal. Orthogonal signal correction (OSC) was

introduced by Wold et al. to remove systematic variation from the response matrix X that is unrelated, or orthogonal, to the

property matrix Y. Therefore, one can be certain that important information regarding the analyte is retained. Since then, several

groups have published various OSC algorithms in an attempt to reduce model complexity by removing orthogonal components

from the signal. This paper describes a review to application of OSC as preprocessing method for simultaneous determination

using spectrophotometric and electrochemical data by a multivariate calibration technique (partial least squares).

References:

1) A. Niazi, A. Yazdanipour, J. Hazard. Mat., 146 (2007) 421.

2) A. Niazi, A. Azizi, M. Ramezani, Spectrochim. Acta Part A, 71 (2008) 1172.

3) A. Niazi, J. Zolgharenin, M.R. Davoodabadi, Ann. Chim., 97 (2007) 1181.

4) A. Niazi, J. Braz. Chem. Soc., 17 (2006) 1020.

5) A. Niazi, M. Goodarzi, Spectrochim Acta Part A, 69 (2008) 1165.

6) J. Ghasemi, A. Niazi, Talanta, 65 (2005) 1168.

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Chemometrics Methods for Determination of

Kinetic Parameters of Different Enzymatic Reactions

A. Naseri

Department of Applied Chemistry, Islamic Azad University, Tabriz Branch, Tabriz, Iran

Kinetic studies on enzymes are among the most important tools for understanding biological interactions at the molecular level

and obtaining information about the kinetic parameters[1]. Details of the kinetics are important because they provide essential

information about how an enzyme will behave or respond in given situations. Simple enzyme kinetics is generally described by

Michaelis-Menten equation or rearranges form of it, known as a Lineweaver-Burk plot.

The spectrophotometric determination of enzyme activity through Lineweaver-Burk is carried out by monitoring the consumption

of substrate or the production of the product compound at selective wavelength. In other words, it is necessary to find a

wavelength, where only one of substrate or product has absorbance. For many systems, particularly those with similar

components, this is not the case, and these have been difficult to analyze. Therefore, to overcome this problem we have to employ

multiwavelength spectra and different chemometrics methods for analyzing of them [2, 3]. In such cases, much more information

can be extracted if multivariate (Multiwavelength) spectrophotometric data are analyzed by means of an appropriate multivariate

data analysis method. Model-based (Hard modeling) methods include traditional least-squares curve fitting approaches, based on

a previous postulation of a chemical model, i.e. the postulation of a set of species defined by their kinetic constants, which are

then refined by least-squares minimization.

By using multiwavelength model based method, kinetic parameters for first-order enzymatic reactions can be easily calculated,

regardless of any spectral overlap. This technique can be used to measure the Km and Vmax values for different enzymatic

reactions. In addition kinetic parameters for each reaction were calculated using traditional Lineweaver-Burk method and the

results obtained from two methods were compared. There was no significant difference between results obtained by two

methods. Simplicity, low cost, ease to use and also being a fast approach makes the proposed chemometric method a strong tool

for kinetic studies of different first-order enzymatic reactions.

References:

1) J. M. Amigo, A. de Juan, J. Coello, S. Maspoch, Anal. Chim. Acta 567 (2006) 245–254

2) M. H. Sorouraddin, E. Fooladi, A. Naseri, M.R. Rashidi, J. Biochem. Biophys. Methods 70 (2008) 999–1005

3) M. H. Sorouraddin, E. Fooladi, A. Naseri, M.R. Rashidi, Iranian Journal of Pharmaceutical Research (2009), 8 (3): 169-17

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On the Effect of Mean Centering of Ratio Spectra as a

Preprocessing Method Prior to Soft Modeling Approach: An Introduction

Morteza Bahram

Department of Chemistry, Faculty of Science, Urmia University, Urmia, Iran

PARAFAC, MCR etc. are, by definition, model-free or soft-modeling methods that focuses on describing the evolution of the

experimental multi-component measurements through their pure component contributions. So, only the tensor D or matrix D of

measurements is needed to perform the analysis. Nevertheless, if the analyst has information about the data, it can orient the

resolution process and improve significantly the final results obtained. These improvements have been particularly interesting in

complex systems, where perturbations in the natural form of profiles can affect convergence more significantly and in the

application of equality constraints (i.e., constraints that incorporate information on a partially or totally known profile shape), due

to the unavoidable [1-2].

Processes monitored by difference spectroscopy always have the spectrum of the initial stage subtracted from each spectrum in

the data matrix [2-3]. Usually this preprocessing technique eliminates the number of spectrally active components in the data set.

Also, in particular, mean centering of ratio spectra can be used to remove the contribution of an absorbing reagent from data

matrix exactly and therefore the absorbance of the known reagent(s) is exactly eliminated [4]. This is achieved by using a known

profile and is an alternative for equality constraint in soft-modeling approaches. In this work an introduction on the effect of mean

centering of ratio spectra as a preprocessing method prior to soft modeling analysis is presented. This is obvious that when the

number of components decreased by one or two better estimation(s) and rapid convergence can be obtained for the

concentration profile(s). On the other hand by using mean centering of ratio spectra or difference spectra, based on the nature of

data handling (and because the negative region(s) is appeared in data) the analyst can not use the non-negativity constraint at

least in one mode. The effect of these pre-processing on the robustness, correctness and convergence of MCR results is

introduced in this work.

References:

1) A. de Juan, R. Tauler, Critical Rev. Anal Chem. 36(3-4) 2000, 163-176.

2) L. Blanchet, C. Ruckebusch, J. P. Huvenne, A. de Juan, Chemometrics and Intelligent Laboratory Systems 89 (2007) 26.

3) C. Zscherp, A. Barth, Biochemistry 40 (2001) 1875–1883.

4) M.Bahram, M. Mabhooti, Analytica Chimica Acta 639 (2009) 19–28.

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The Use of Chemometrics Methods in Electroanalytical Chemistry

Karim Asadpour-Zeynali

Department of Analytical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran

Electroanalytical techniques are powerful tools in analytical chemistry that have not been combined with chemometrics as

expected, especially in comparison with use of chemometrics in spectroscopy. Paradoxically, this is a consequence of the intimate

link between electroanalytical chemistry and mathematics. However, recently, the electroanalytical techniques have been

improved with the use of chemometrics methods for simultaneous determination of analytes or resolving overlapping signals and

the most used methods are principal component regression (PCR), partial least squares, (PLS), artificial neural networks (ANNs),

and multiple curve resolution methods (MCR-ALS, N-PLS and PARAFAC). Experimental design is one of the chemometrics

branches and is used for optimization of experimental conditions and effective parameters in order to reach the most satisfactory

results that is another application of chemometrics in the electroanalytical techniques. Electroanalytical data were also used for

classification and pattern recognition purposes. In this paper, an overview on the used of chemometrics methods to

electroanalyical data is presented.

References:

1) M. Esteban, C. Arino, and J. M. Dıaz-Cruz, Crit. Rev. Anal. Chem 2006, 36, 295.

2) M. Esteban, C. Arino, and J. M. Dıaz-Cruz, Trends Anal. Chem. 2006, 25, 86.

3) Y. Ni, S. Kokotc, Anal. Chim. Acta. 2008, 626, 130.

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Applications of Chemometrics in Water and

Wastewater Analysis; Iranian Water and Wastewater industries needs

Fatemeh Hajilari, Sohrab Talebi

West Azerbaijan Water and Wastewater Company, Urmia, Iran

The use of novel sciences in industries causes their increasing development. Water and wastewater industry is one of the basic

industries in each country that need recent sciences and modern technologies. Chemometrics is a new science that has found

wide range of various applications in industries.

Recently the chemometrics science has been frequently applied for water and wastewater analysis including water quality

assessment of rivers, wells, aquifers etc, modeling and prediction of trihalomethane formation in the water works plants,

modeling and process monitoring of water treatment plants, drinking water classification, evaluation of the changes of rivers

seasonal quality parameters, classification and assessment of monitoring locations and Evaluation of performance in wastewater

treatment plants, supervisory control of wastewater treatment plants, estimation of wastewater composition and data analysis of

pollutants in effluents [1-4]. This is a review of chemometrics applications in the different parts of water and wastewater industry

to show the potential of chemometrics science in water and wastewater analysis which can be generalized in Iranian industries.

References:

1) Kunwar P. Singha, Analytica chimica acta 6 3 0 ( 2008 ) 10–18

2) Feng Zhou, Huaicheng Guo, Yong Liu, Yumei Jiang, Marine Pollution Bulletin, Volume 54, 2007, Pages 745-756.

3) E.M. Smeti, N.C. Thanasoulias, E.S. Lytras, P.C. Tzoumerkas, S.K. Golfinopoulos, Water Research, In Press, 2009.

4) R. R. Velinova, B. K. Koumanova, Water Research, Volume 29, 1995, Pages 2541-2547.

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OralPresentations

OralPresentations

Resolving Factor Analysis Using Chaotic Particle Swarm Optimization

Hamid Abdollahi, Samira Beyramy soltan

Institute for Advanced Studies in Basic Science (IASBS)

Resolving factor analysis is one of the soft modeling methods that its task defined as finding the one set T for which the products -1 tC=UT and A=T SV are physically correct. C is concentration profile and A is the spectral profile which satisfy the D=CA. In RFA,

rotated PCA solutions are modified iteratively to fulfill the constraints and the perturbed solutions are then used to calculate the

residuals of the least squares function to be minimized by a non-linear optimization procedure; Non-linear optimization was

performed by Newton–Gauss-Levenberg/Marquardt algorithm [1]. Chaotic particle swarm optimization method is optimization

approach based on the proposed particle swarm optimization (PSO) with adaptive inertia weight factor (AIWF) and chaotic local

search (CPSO) where the parallel population-based evolutionary searching ability of PSO and chaotic searching behavior are

reasonably combined [2].

In the present work, chaotic particle swarm optimization (CPSO) combined with RFA is introduced as self-modeling curve

resolution (SMCR) method, and also is recommended as the method for avoiding divergence problems in RFA. In RFA, if there is

not unique solution, the nonlinear least square does not converge even in two component systems. The proposed method enables

to solve this problem due to advantage of CPSO. To investigate the performance of the method, chromatograms of varying noise

level, and overlap were generated and subsequently analysed, and to demonstrate its potential, this method applied to three and

four component real datasets.

The results show that RFA using CPSO is robust under conditions that traditional RFA fails and converges without difficulty.

Furthermore unlike traditional SMCR, convergence is achieved even with random initial estimates; this method enables to resolve

datasets with lesser of five components. To the best of our knowledge, it is the first report of applying CPSO to optimize

transformation matrix T.

References:

1) Mason CJ, Maeder M, Whtson A. Resolving Factor Analysis. Anal. Chem. 2001; 73; 1587-1594.

2) Liu Bo, Wang Ling, Jin Yi Hui, Tang Fang, Huang De Xian. Improved Particle Swarm Optimization Combined With Chaos. Chaos, Solitons

Fractals 2005; 25(5); 1261-1271.

3) Eberhart Russel C, Kennedy James. Particle Swarm Optimization. IEEE Int Conf Neural Networks 1995; 4; 1942-1947.

4) Shinzawa H, Jiang J-H, Iwahashi M, Noda I, Ozaki Y. Self-modeling Curve Resolution (SMCR) by Particle Swarm Optimization(PSO). Analytica

Chimica Acta 2007; 595; 275-281.

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Uncertainties and Error Propagation in Kinetic and

Equilibrium Hard-Modelling of Spectroscopic and pH-Metric Data

Hamid Abodollahi, Parvin Darabi

Institute for Advanced Studies in Basic Science (IASBS)

Quantitative studies play a dominant role in analytical chemistry. Thereby, the errors that occur in such studies are of supreme

importance. Thus, a key principle will be that, no quantitative results are of any value unless they are accompanied by some

estimate of the errors inherent in them. This principle naturally applies not only to analytical chemistry but to any field of study in

which numerical experimental results are obtained [1]. Yet with modern and highly reliable probes, certain more conventional

sources of error such as sampling or instrumental noise are much less serious than problems with estimation of initial

concentrations. In a real laboratory practice, because of problems due to weighing, dissolution, imperfect mixing and so on, there

is some uncertainty as to the true concentrations of reactants at the beginning of a reaction. Thus, chemists often do not

accurately know these [2].

In the present work, the impact of uncertainties in the initial concentrations on the error of fitted equilibrium and rate constants,

for spectroscopic and pH-metric studies of acid-base and complexation equilibria and also spectroscopic study of coupled kinetic-

equilibrium systems were investigated, for the first time. For this, a rigorous approach based on classical error propagation was

used. The performance of the method has been evaluated by using synthetic data sets. Multivariate data were analysed by model-

based fitting using the Newton-Gauss-Levenberg/Marquardt optimization algorithm. Then, for each of simulated systems, the

effects of different initial concentrations and different equilibrium constants on output of algorithm (error of fitted parameters)

were investigated by variation of them in the reasonable ranges. Furthermore, spectroscopic and pH-metric methods for studying

complex formation and acid-base equilibria were compared in the same conditions. The results of pH-metric method were more

precise than spectroscopic method.

The important consequence of this study is that, our findings have an immediate application in the optimum experimental design

of these processes. This method of error propagation is flexible and straightforwardly extended to propagate other sources of

error.

References:

1) J. N. Miller, J. C. Miller, "Statistical and Chemometrics for Analytical Chemistry", Fourth Edition, Prentice Hall, 2000.

2) A. R. Carvalho, R. G. Brereton, T. J. Thurston, R. E. A. Escott, Chemom. Int. Lab. Syst. 71 (2004) 47.

3) J. Billeter, Y. M. Neuhold, L. Simon, G. Puxty, K. Hungerbühler, Chemom. Int. Lab. Syst. 93 (2008) 120.

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Application of Multivariate Curve Resolution based on

Alternative Least Square assisted with Trilinearity Constraint

(TC-MCR-ALS) for Resolution of Multi-Way Rank Deficient Systems

Mohsen Kompany-Zareh, Fatemeh Ghasemi-Moghadam

Institute for Advanced Studies in Basic Sciences (IASBS), GavaZang, Zanjan Iran

Multivariate curve resolution based on alternative least square assisted with trilinearity constraint (TC-MCR-ALS) has the ability to

resolve the full rank trilinear data, with results similar to PARAFAC [1]. PARAFAC is not a proper resolution method when dealing

with rank deficient data. A proper alternative resiolution method in the presence of rank deficiency is Tucker3. In this study, the

ability of TC-MCR-ALS for resolution of three-way rank deficient data was investigated.

Unfolded data, to maximum rank, was resolved by TC-MCR-ALS to matrices Z and C. Z matrix contained the information in two

modes (matrices A and B) of data. With application of trilinearity constraint not only rotation ambiguity was decreased but also

matrices A and B were extracted from matrix Z [2, 3]. This method was successfully applied on any kind of simulted data with rank

deficiency in one or two modes.

To study the merit of TC-MCR-ALS in resolution of the data with rank deficiency in all three modes, both simulated and

experimental data were examined. Three-way excitation-emission spectrofluorimetric data from solutions containing different

concentrations of analytes; catechol, hydroquinone, indole and tryptophane was considered emperical data. Chemical rank of

this data was estimated using two mode comparison subspace algorithm [4]. Maximum estimated rank of data in all three modes,

was three, although four components were present in the system. In the three-way data with rank deficiency in all three modes, a

number of columns in matrix Z were not trilinear, theoricaly, but TC-MCR-ALS performed well. It was due to possibility of rotation

of Z to a trilinear combination of Z columns.

Therefore TC-MCR-ALS performs as well as Tucker3 for many kinds of rank deficient data. The method resolves a data with ranks

4, 3, 2 in three modes into four cubes with rank 1, but Tucker3 resolves it to less than 24 (4x3x2=24) arrays with rank 1. Then the

solution and interpretation of TC-MCR-ALS is simpler than Tucker3.

References:

1) E. Pere-Trepat, A. Ginebreda, R. Tauler, Chem. Int. Lab. Syst., 88 (2007) 69-83.

2) R. Tauler, I. Marques, E. Casassas, J. Chemom, 12 (1998) 55-75.

3) E. Bezemer, S.C. Rutan, Chemom. Int. Lab. Syst., 81 (2006) 82-93.

4) H.P. Xie, J.H. Jiang, N. Long, G.L. Shen, H.L. Wu, R.Q. Yu, Chem. Int. Lab. Syst., 66 (2003) 101-115.

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Classification of Drugs by Means of Their Milk/Plasma

Concentration Ratio Using Supervised Chemometric Procedures

M.H Fatemi*, M. Ghorbanzad'e, E. Baher

Faculty of Chemistry, Mazandaran University, Babolsar, Iran

Development of reliable computational models to classify drugs based on their milk to plasma (M/P) concentration ratio is a

challenging object. Support vector machine (SVM) and counter propagation artificial neural network (CPANN) were applied to

distinguish the potential risk of drugs in this work. The features of each drug were encoded by five LFER descriptors including: the

solute excess molar refractivity (E), the solute dipolarity/polarizability (S), the McGowan volume (V) and overall hydrogen bond

acidity (A) and basicity (B). These descriptors were used as inputs of SVM and CPANN to classify drugs as high risk (with M/P > 0.1)

and low risk (with M/P < 0.1) drugs for lactating women. The classification accuracy of training set, internal and external test sets

for SVM was 91.12%, 90.00% and 80.00%, respectively. Also, the classification accuracy of training, internal and external test

sets for CPANN was 100.00%, 100.00% and 90.00%, respectively. The total accuracy for SVM and CPANN models in

classification of drugs was 90.25% and 99.35%, respectively. Comparison of the two methods shows that the performance of

CPANN was better than that of SVM, which implies that the CPANN method is more precise tool in evaluating the risk of drugs. It

was concluded that these models can be used for in silico prediction of new, not yet investigated drug risk for lactating woman.

References:

1) Todeschini R and Consonni V (2000) Handbook of molecular descriptors, Wiley-VCH.

2) Zupan J, Novic M and Ruisanchez I, Chemom. Intell. Lab. Sys. 38, 1-23 (1997)

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Application of Successive Projections Algorithm (SPA) as a

Variable Selection in a QSPR Study to Predict of the Octanol/Water

Partition Coefficients (Kow) of Some Halogenated Organic Compounds

1,3 2Mohammad Goodarzi , Nasser Goudarzi

1- Department of Chemistry, Faculty of Sciences, Azad University, Arak, Iran,

2- Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran,

3- Young Researchers Club, Azad University, Arak, Iran

The successive projections algorithm (SPA) is a variable selection method that has been compared with genetic algorithm (GA) due

to its ability in solving the descriptor selection problems in QSPR model development. For model development, the popular linear

algorithm Partial Least Squares (PLS) was employed to build the model. These methods were used for the prediction of

octanol/water partition coefficients Kow of 10 kinds of selected halogen benzoic acids. The root means square error of prediction

(RMSEP) for training and prediction sets by GA-PLS and SPA-PLS models were 0.26, 0.28, 0.13 and 0.16, respectively. Also, the

relative standard error of prediction (RSEP) for training and prediction sets by GA-PLS and SPA-PLS models were 8.02, 3.92, 8.68

and 4.98 respectively. The resultant data showed that SPA-PLS produced better results than GA-PLS in these class compounds.

Keywords: QSPR, Octanol-water partition coefficients, SPA-PLS, GA-PLS

References:

1) Nasser Goudarzi, Mohammad Goodarzi; Mario. C. U. Araujo, R. K. H. GALVA ; J. Agric. Food Chem. 2009, 57, 7153–7158

2) Nasser Goudarzi, Mohammad Goodarzi; Molecular Physics; 2008, 106, 2525–2535

3) Nasser Goudarzi, Mohammad Goodarzi; Molecular Physics; 2009, 107, 1615–1620

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Second-Order Advantage From Micelle

Concentration Gradual Change–Visible Spectra Data

1 2 2Hamid Abdollahi* , Mahmoud Chamsaz , Tahereh Heidari

1- Department of Chemistry, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran

2- Department of Chemistry, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

Second-order calibration is used for second-order data. Such data is produced by instruments that give a matrix of responses for a

single measured standard or unknown sample. This allows for determination of analyte of interest in the presence of uncalibrated

sample constituents, a property known as the second-order advantage [1]. Malachite green has found extensive use all over the

world in the fish farming industry as a fungicide, ectoparasiticide and disinfectant [2].This dye has also been used extensively for

dyeing silk, wool, jute, leather and cotton [3].A similar situation is valid for crystal violet, which is used to control fungi and

intestinal parasites in humans, as an antimicrobial agent on burn victims, to treat umbilical cords of infants, for the treatment of

long-term vaginal candidosis, for various purposes in veterinary medicine, etc.[4]. It has been shown recently that some members

of this group of compounds are linked to an increased risk of cancer and also act as liver tumor-enhancing agent. It was discovered

that a second order spectra data matrix of malachite green and crystal violet produced from the micelle (of triton X-100 surfactant)

concentration gradual change–visible absorption spectra can be expressed as the combination of two bilinear data matrices.

Based on this discovery, a new method for the determination of malachite green and crystal violet in black systems using second

order calibration algorithms has been developed. The second order calibration algorithms were based on the rank annihilation

factor analysis (RAFA), un folded partial least-squares/residual bilinearisation (U-PLS/RBL)[5] and bilinear least squares/residual

bilinearisation (BLLS/RBL)[6]. In the method described here, the concentration of the surfactant (sufficiently beyond the critical

micelle concentration) was changed gradually and the absorption spectra of samples were recorded. Thus, the concentration of

malachite green and crystal violet in black system could be determined from the spectra matrices using second order calibration

algorithms. This method is simple, convenient and dependable. The method has been used to determine malachite green and

crystal violet in simulated textile dye effluent, goldfish farming water and waste of nutrient broth-grown cell with satisfactory

results.

References:

1) Smilde AK, Tauler R, J and Bro R Anal Chim Acta 1999:398: 237–251.

2) Alderman DJ. Malachite green: a review. J Fish Dis 1985;8:289–98.

3) Culp SJ, Beland FA. Malachite green: a toxicological review. J Am College Toxicol 1996;15:219–38.

4) Rushing LG, Bowman MC. J Chromatogr Sci 1980;18:224–32.

5) Olivieri AC. J Chemometrics 2005: 19:253-265.

6) Linder M, Sundberg, R Chemom. Intell Lab Syst 1998: 42: 159-165.

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Partial Swarm Optimization Approach for Training of an Artificial

Neural Network Applied in Thermal Investigation of Nanocomposites

1 1 1Mohammadreza Khanmohammadi* , Nafiseh Khoddami , Mohammad Hossein Ahmadi Azghandi , Amir Bagheri 1,2 2 2Garmarudi , Masumeh Foroutan , Mahdieh Ansaryan

1- Chemistry Department, Faculty of Science, IKIU, Qazvin, Iran

2- School of Chemistry, University College of Science, University of Tehran, Tehran, Iran

Artificial neural Network (ANN) has become most common for modern data processing. It is able to solve numerous complex

problems and has well known advantages like possibility of learning from examples, generalization ability, parallel computation,

nonlinear mapping nature, etc [1]. Most applications use feed forward ANNs which use the standard back-propagation (BP)

learning algorithm or some improved BPs [2] but some intrinsic problems do frequently exist in application of this algorithm, such

as very slow convergence speed in training, get stuck easily in a local minimum especially in problem domains with high

dimensionality and also it needs to predetermine some important learning parameters such as learning rate, momentum and

structure [3,4]. Accordingly, a new ANN model based on partial swarm optimization algorithm has been introduced which has

these defects less than BP-ANN and also gives more accurate (in terms of sum square error) and faster (in terms of number of

iterations and simulation time) results than BP-ANN [1]. PSO is a population based stochastic optimization technique, inspired by

social behavior of bird flocking or fish schooling. It has been proved to be a competitor to GA when it comes to optimization of

problems. PSO algorithm was used to train a multi-layer feed forward ANN for investigation of the kinetic parameters in thermal

degradation of nanocomposite samples based on polyimide and silica nano particles, using thermogravimetry analysis (TGA).

Different heating rates in TGA were applied. The adoption of a PSO model to train the perceptrons in prediction of kinetic

parameters is presented. The obtained results illustrated that the successful prediction can be achieved by PSO trained ANN.

Moreover, it is capable of producing faster and more accurate results than its counterparts of a benchmarking back-propagation

ANN.

References:

1) M. Geethanjali, S. Mary Raja Slochanal, R. Bhavani, Neurocomp. 71 (2008) 904–918

2) Yu Jianbo, Xi Lifeng, Wang Shijin, Neural. Process. Lett. 26 (2007) 217–231.

3) K.W. Chau, C.T. Cheng, Lect. Not. Artif. Intell. 2557 (2002) 715–715.

4) R. Govindaraju, A. Rao, Artificial Neural Networks in Hydrology, Kluwer Academic Publishers, Dordrecht, 2000.

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Application of Standardization Methods in Simple Kinetic and Equilibrium Studies

Mohsen Kompany-Zareh, Maryam Khoshkam

Institute for Advanced Studies in Basic Sciences (IASBS),GavaZang, Zanjan, Iran.

Hard model based and soft resolution approaches are useful tools for estimation of concentration and spectral profiles in kinetic or

equilibrium systems [1]. Both resolution methods can be applied to the analysis of an individual and the augmented data matrices

[2]. Simultaneous analysis of multiple process runs under linearly independent conditions is proposed to break rank deficiency in

the data. Presence of more information in the augmented data results in less rotational and intensity ambiguities in the resolved

profiles [2, 3]. Assumption in dealing with augmented data matrices is that the pure spectra of absorbing species in column-wise

augmentation are the same in all data matrices [4]. In many conditions spectral profiles between the augmented data matrices are

not the same and the resulting profiles and parameters from the augmented data would not be reliable [4, 5].

Standardization is a popular technique to solve such problems in multivariate calibration systems, by standardization of calibration

and test data sets into same space [6]. The most feasible approach for the problem is judged to be methods developed under the

premises of having measured the same samples on either instruments or conditions [6, 7].

In this study, we apply the standardization methods for first order kinetic and simple equilibrium systems. To our knowledge this is

the first application of standardization method in kinetic and equilibrium studies. The method is tested in simulated and

experimental data and the obtained results showed that in presence of spectral variation in different conditions, by applying

standardization methods, better fit and more reliable parameters can be obtained. By standardizing of data, the obtained

parameters were improved for both hard and soft methods.

References:

1) M. Maeder, Y. M. Neuhold, "Practical Data Analysis in Chemistry", Newcastle, Australia, September, 2006.

2) J. Saurina, S. Herna´ Ndez-Cassou, R. Tauler, A. IZquierdo-Ridorsa, J. Chemometrics, 12, 183–203 (1998)

3) R. Tauler, A. Smilde and B. R. Kowalski, J. Chemometrics, 9, 31–58 (1995).

4) D. B. Gil, A. M. Pen, A. A. Juan, G. M. Escandar, A. C. Olivieri, Anal. Chem., 78, 8051-8058 (2006).

5) S.D. Brown, "Comprehensive Chemometris", Chap. 3.08, 345-378 (2009)

6) "Notes on calibration of instruments ", June 2002.

7) R. N. Feudale, N. A. Woody, H. Tan, A. J. Myles, S. D. Brown, J. Ferre, Chemom. Intell. Lab. Syst., 64, 181– 192 (2002).

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Random Forests, a Novel Approach for Prediction of the

Acute Toxicity of Substituted Benzenes to Tetrahymena Pyriformis

Anahita Kyani

Department of Chemistry, Tarbiat Modares University, Tehran, Iran

Random forests (RF) is an ensemble of unpruned classification trees created by using bootstrap samples of the training data and

random subsets of variables to define the best split at each node [1]. Prediction is made by the average of the individual tree

predictions. RF offers some unique features that make it suitable for QSAR tasks. These features include estimation of prediction

accuracy, measures of descriptor importance, and a measure of similarity between molecules. This method is extremely accurate in

a variety of applications [2].

In the present work, random forests (RF) was employed as a novel approach for the prediction of toxicity of a diverse data set

consisted of 264 substituated benzene compounds such as phenols, nitrobenzenes, benzonitriles, carboxyl acids, amides, amines

and aldehydes toward Tetrahymena pyriformis [3]. The most important variables were determined by the decrease in a node's

impurity every time the variable is used for splitting. Among a large number of simple zero-, one- and two-dimensional

descriptors, parameters concern with hydrophobicity and electronic interactions were revealed as the important ones. 2Satisfactory results (Error = 0.125 and R = 0.865) indicate that the RF is able to model pIC of a diverse chemical class of OOB 50

compounds with more than one mechanism of toxicity using simple and interpretable descriptors. Random forests exhibited

interesting features not only in terms of prediction accuracy but also by providing meaningful probabilities for the predictions.

References:

1) Zhang, Q.U.; Aires-de-Sousa, J. O.; Random forest prediction of mutagenicity from empirical physicochemical descriptors. J. Chem. Inf. Model.

2007, 47, 1.

2) Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P.; Random forest: A classification and regression tool for compound

classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2003, 43, 1947.

3) Burden, F. R.; Winkler, D. A.; A quantitative structure-activity relationship model for the acute toxicity of substituated benzens to Tetrahymena

Pyriformis using Bayesian-regularized neural networks. Chem, Res, Toxicol. 2000, 13, 430.

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Application of Bayesian Adaptive Regression

Splines for QSAR Modeling of Glutamate Inhibitors

Mehdi Jalali-Heravi*, Ahmad Mani-Varnosfaderani

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

The present work deals with application of Bayesian adaptive regression splines (BARS) for quantitative structure-activity

relationship (QSAR) study of 85 drug-like glutamate antagonists [1-3]. The BARS method is a powerful nonparametric regression

technique and uses a reversible jump Markov-Chain-Monte-Carlo (MCMC) engine to perform spline-based non-parametric

regressions. In order to compare BARS and other linear and non-linear modeling techniques, the modeling was also performed by

using Bayesian regularized genetic neural networks (BRGNNs), genetic algorithms partial least squares (GA-PLS) and genetic

algorithms multiple linear regression (GA-MLR). The obtained results for RMSEtest revealed that BARS is better than GA-PLS and

GA-MLR for the modeling but the results of BRGNNs were superior to BARS. Although BRGNNs