Sharif Digital Repository

Multivariate curve resolution based on the minimization of an objective function (MCR-FMIN) defined directly from the non-fulfillment of constraints was applied for the first time as a deconvolution method to separate co-eluted gas chromatographic-mass spectrometric (GC-MS) signals. Simulated and real (standard real mixture and limon oil) GC-MS data were used to evaluate the feasibility of this method. The MCR-FMIN solutions have been obtained based on the rotation of principal component analysis (PCA) solutions using the non-linear optimization algorithms. Calculation of the initial values of R rotation matrix using model free analysis methods such as fixed-size moving window-evolving... 

This paper describes an attempt to shed some light on road safety in Asia Pacific region by characterizing and assessing its road accidents. The relevant national road accident data were extracted from centralized data sources of international agencies. Due to data incompleteness and missing values, 21 Asia Pacific countries, presenting more than half of the world's population, were selected for detailed analysis. The study database consisted of 7 variables, covering years 1980 and 1995. The univariate and multivariate statistical analyses for the selected countries showed interesting results and relations for the selected variables. Accident rates, reflecting road accident risk and... 

Chromatographic fingerprinting is an effective methodology for authentication and quality control of herbal products. In the presented study, a chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and multivariate pattern recognition methods was used to establish a gas chromatography-mass spectrometry (GC-MS) fingerprint of saffron. For this purpose, the volatile metabolites of 17 Iranian saffron samples, collected from different geographical regions, were determined using the combined method of ultrasound-assisted solvent extraction (UASE) and dispersive liquid-liquid microextraction (DLLME), coupled with GC-MS. The resolved elution profiles and... 

In this work, the inhibitory activity of pyridine N-oxide derivatives against human severe acute respiratory syndrome (SARS) is predicted in terms of quantitative structure-activity relationship (QSAR) models. These models were developed with the aid of multivariate adaptive regression spline (MARS) and adaptive neuro-fuzzy inference system (ANFIS) combined with shuffling cross-validation technique. A shuffling MARS algorithm is utilized to select the most important variables in QSAR modeling and then these variables were used as inputs of ANFIS to predict SARS inhibitory activities of pyridine N-oxide derivatives. A data set of 119 drug-like compounds was coded with over hundred calculated... 

The performances of gas chromatography with mass spectrometry and of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry are examined through the comparison of Daphnia magna metabolic profiles. Gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with mass spectrometry were used to compare the concentration changes of metabolites under saline conditions. In this regard, a chemometric strategy based on wavelet compression and multivariate curve resolution–alternating least squares is used to compare the performances of gas chromatography with mass spectrometry and comprehensive two-dimensional gas chromatography with... 

Objective(s): This study aims to evaluate combined proton nuclear magnetic resonance (1H NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) metabolic profiling approaches, for discriminating between mustard airway diseases (MADs) and healthy controls and for providing biochemical information on this disease. Materials and Methods: In the present study, analysis of serum samples collected from 17 MAD subjects and 12 healthy controls was performed using NMR. Of these subjects, 14 (8 patients and 6 controls) were analyzed by GC-MS. Then, their spectral profiles were subjected to principal component analysis (PCA) and orthogonal partial least squares regression discriminant... 

Signal direction-of-arrival (DOA) estimation using an array of sensors has been the subject of intensive research and development during the last two decades. Efforts have been directed to both, better solutions for the general data model and to develop more realistic models. So far, many authors have assumed the data to be independent and identically distributed (i.i.d.) samples of a multivariate statistical model. Although this assumption reduces the complexity of the model, it may not be true in certain situations where signals show temporal correlation. Some results are available on the temporally correlated signal model in the literature. The temporally correlated stochastic Cramér-Rao... 

This study deals with developing a quantitative structure-activity relationship (QSAR) model for describing and predicting the inhibition activity of 1-(3,3-diphenylpropyl)-piperidinyl derivatives as CCR5 modulators. Applying the multiple linear regressions (MLR) and its inability in predicting the inhibition behavior showed that the interaction has no linear characteristics. To assess the nonlinear characteristics of the inhibition activity artificial neural networks (ANN) was used for data modeling. In order to select the variables needed for developing ANNs, three variable selection algorithms were used: Stepwise-MLR, genetic algorithm-partial least squares (GA-PLS), and Bayesian... 

Although multivariate statistical process control has been receiving a well-deserved attention in the literature, little work has been done to deal with multi-attribute processes. While by the NORTA algorithm one can generate an arbitrary multi-dimensional random vector by transforming a multi-dimensional standard normal vector, in this article, using inverse transformation method, we initially transform a multi-attribute random vector so that the marginal probability distributions associated with the transformed random variables are approximately normal. Then, we estimate the covariance matrix of the transformed vector via simulation. Finally, we apply the well-known T2 control chart to the... 

In this paper multivariate response surface methodology (RSM) has been used for the optimization of hydrodistillation-headspace solvent microextraction (HD-HSME) of thymol and carvacrol in Thymus transcaspicus. Quantitative determination of compounds of interest was performed simultaneously using gas chromatography coupled with flame ionization detector (GC-FID). Parameters affecting the extraction efficiency were assessed and the optimized values were 5 min, 2 μL and 3 min for the extraction time, micro-drop volume and cooling time after extraction, respectively. The amounts of analyte extracted increased with plant weight. The calibration curves were linear in the ranges of 6.25-81.25 and... 

In this paper, a replacement-repair model is developed to study a warranty servicing policy for a class of multi-state deteriorating and repairable products, based on a computer simulation analysis. In each working state there is a determined probability for transition to each of the subsequent states, given that it has made a transition out of that state. There are two parameters that determine the manufacturer's decision to repair or replace a failed item, assuming that the buyer's claim is valid; the deterioration degree of the item and the length of the residual warranty period. Beside these two parameters, other inputs to the model are: the number of working and failure states, the... 

A new methodology is proposed in this paper to both monitor an overall mean shift and classify the states of a multivariate quality control system. Based on the Bayesian rule (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005), the belief that each quality characteristic is in an out-of-control state is first updated in an iterative approach and the proof of its convergence is given. Next, the decision-making process of the detection and classification the process mean shift is modeled. Numerical examples by simulation are provided in order to understand the proposed methodology and to evaluate its performance. Moreover, the in-control and... 

S. lanata has been traditionally used as a medicinal plant due to its various biological activities such as antioxidant activity. Therefore, identification and quality control studies of this plant are of great importance. To this end, gas chromatography (GC) combined with chemometrics was proposed for fingerprint analysis of S. lanata samples. This study sought to classify GC fingerprints of twenty-eight S. lanata samples from eight different regions of Iran and more importantly, to correlate fingerprints to the antioxidant activity to select S. lanata volatile antioxidant markers. S. lanata samples were classified into five and three classes using partial least squares-discriminant... 

In this work, high-performance thin-layer chromatography (HPTLC) coupled with multivariate image analysis (MIA) is proposed as a fast and reliable tool for authentication and adulteration detection of Iranian saffron samples based on their HPTLC fingerprints. At first, the secondary metabolites of saffron were extracted using ultrasonic-assisted solvent extraction (UASE) which was optimized using central composite design (CCD). Next, the RGB coordinates of HPTLC images were used for estimation of saffron origin based on principal component analysis (PCA). The PCA scores plot showed that saffron samples were clustered into two clear-cut groups which was 92% matched with the geographical... 

Teratozoospermia is one of conditions that can cause male infertility. The mechanism of teratozoospermia remains unclear. The knowledge of the metabolites in human seminal plasma (HSP) is meaningful for the pathological study of teratozoospermia. Analysis of changed metabolites in HSP can help understand the cellular mechanism, find the novel biomarkers and subsequently design a diagnosis test. In this study, the analysis of samples performed by proton nuclear magnetic resonance spectroscopy (1H NMR spectroscopy) to identify the various metabolites, with the aim of finding metabolic profiles and biomarkers related to male infertility. Eighteen de-regulated metabolites were identified in... 

A robust multivariable strategy for pitch and torque control design of variable-speed variable-pitch wind turbines in the full load region is introduced in this paper. The pitch and torque control loops that share the tracking and active damping of drivetrain torsional mode objectives are designed simultaneously using a novel decomposition scheme. This permits the systematic design of robust multivariable controllers for wind turbines in a manner that facilitates industrial application. We achieve this by making the process of weighting function design fast, intuitive, and simple and by giving the designer a clear insight on the compromising aspects of the various control system objectives.... 

The interaction of n-dodecyl trimethylammonium bromide (DTAB) with oxyhemoglobin A and oxyhemoglobin S is investigated using UV-visible absorption spectra and chemometric resolution techniques. Oxyhemoglobins (A and S) induced to partial oxidized form (ferrihemoglobin) by DTAB and finally transform to fully oxidized hemichrome. Hemichrome mole fractions of HbS are more than HbA because of more hydrophobic interaction of DTAB-HbS in second set of binding site relative to DTAB-HbA. The visible spectra between 500 and 650 nm are used for identifying the present components in solution because each species of hemoglobin has a specific spectrum in this region. The number of components and mole... 

In a production process, when the quality of a product depends on more than one characteristic, multivariate quality control techniques are used. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In this paper, a new methodology has been developed to monitor multi-attribute processes, in which the defect counts are important and different types of defect are dependent random variables. In order to do this, based on the symmetric square root transformation concept, first, multi-attribute data is transformed, such that the correlation between variables either vanishes or... 

Since the product quality of many industrial processes depends upon more than one dependent variable or attribute, they are either multivariate or multi-attribute in nature. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In this article, we develop a new methodology to monitor multi-attribute processes. To do this, first we transform multi-attribute data in a way that their marginal probability distributions have almost zero skewness. Then, we estimate the transformed covariance matrix and apply the well-known T2 control chart. In order to illustrate the proposed method... 

Introduction: This study was conducted to investigate the relationship of glomerular and tubular dysfunctions with glycaemic control, lipid, lipoprotein, apolipoproteins and antioxidant status in 72 patients with type 2 diabetes mellitus. Methods: Urine albumin concentration was measured by immunoturbidimetric and urine N-acetyl-beta-D-glucosaminidase (NAG) and alanine aminopeptidase (AAP) activities with colorimetric methods. Glycated haemoglobin was measured using affinity chromatography. Erythrocyte glu ta thione reductase and glutathione peroxidase activities and serum levels of malondialdehyde, lipids, lipoproteins and apolipoproteins were determined in patients with type 2 diabetes...