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
1
2
3
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5
6
7
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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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18
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25
26
27
28
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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39
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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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48
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50
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54
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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XV
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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XVII
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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XXII
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
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