Sharif Digital Repository

This article presents experimental results on the electrophoretic deposition (EPD) of bioresorbable chitosan/45S5 Bioglass® composite coatings for orthopaedic implants based on the Taguchi design of experiments (DOE) approach. The influence of EPD parameters including Bioglass® concentration, electric voltage and deposition time on deposition yield was studied by an orthogonal Taguchi array of L18 type. Multivariate analysis of variance (MANOVA) and regression analysis based on the partial least-square method were used to identify the significant factors affecting the deposition yield and its stability during constant-voltage EPD. The coatings were characterised by high resolution scanning... 

This work scrutinized the adulteration of pistachio butter with three potential edible oils using Fourier transform infrared spectroscopy (FT-IR) and multivariate classification methods. Each of the classes, including non-adulterated samples and adulterated samples consisting of pistachio butter mixed with various concentrations of peanut oil, corn oil and sunflower oil, were classified. For this purpose, multivariate methods, including soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA), were applied to classify the FTIR data. After evaluating the model on unknown samples, the results indicated that PLS-DA was better than the SIMCA... 

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... 

Synchronization behavior of electroencephalographic (EEG) signals is important for decoding information processing in the human brain. Modern multichannel EEG allows a transition from traditional measurements of synchronization in pairs of EEG signals to whole-brain synchronization maps. The latter can be based on bivariate measures (BM) via averaging over pair-wise values or, alternatively, on multivariate measures (MM), which directly ascribe a single value to the synchronization in a group. In order to compare BM versus MM, we applied nine different estimators to simulated multivariate time series with known parameters and to real EEGs. We found widespread correlations between BM and MM,... 

The main aim of this work was to assess the ability of Bayesian multivariate adaptive regression splines (BMARS) and Bayesian radial basis function (BRBF) techniques for modelling the gas chromatographic retention indices of volatile components of Artemisia species. A diverse set of molecular descriptors was calculated and used as descriptor pool for modelling the retention indices. The ability of BMARS and BRBF techniques was explored for the selection of the most relevant descriptors and proper basis functions for modelling. The results revealed that BRBF technique is more reproducible than BMARS for modelling the retention indices and can be used as a method for variable selection and... 

In multi-attribute process monitoring, when a control chart signals an out-of-control condition indicating the existence of a special cause, knowing when the process has really changed (the change point) accelerates the identification of the source of the special cause and makes the corrective measures to be employed sooner. This, of course, results in a considerable amount of savings in time and money. Since many real world multi-attribute processes are Poisson and most process changes are step-change, a new method is proposed, in this paper, to derive the maximum likelihood estimator of the time of a step-change in the mean vector of multivariate Poisson processes. In this method, two... 

Essential oils (EOs) are valuable natural products that are popular nowadays in the world due to their effects on the health conditions of human beings and their role in preventing and curing diseases. In addition, EOs have a broad range of applications in foods, perfumes, cosmetics and human nutrition. Among different techniques for analysis of EOs, gas chromatography-mass spectrometry (GC-MS) is the most important one in recent years. However, there are some fundamental problems in GC-MS analysis including baseline drift, spectral background, noise, low S/N (signal to noise) ratio, changes in the peak shapes and co-elution. Multivariate curve resolution (MCR) approaches cope with ongoing... 

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... 

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... 

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 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... 

Equivariant adaptive separation via independence (EASI) is one of the most successful algorithms for blind source separation (BSS). However, the user has to choose non-linearities, and usually simple (but non-optimal) cubic polynomials are applied. In this paper, the optimal choice of these non-linearities is addressed. We show that this optimal non-linearity is the output score function difference (SFD). Contrary to simple non-linearities usually used in EASI (such as cubic polynomials), the optimal choice is neither component-wise nor fixed: it is a multivariate function which depends on the output distributions. Finally, we derive three adaptive algorithms for estimating the SFD and... 

Metabolomics is the science of studying small molecules (metabolites) in biological systems with the aim of getting insight into cells, biofluids and organisms. Chemometric methods are powerful tools to address data problems generated in metabolomic studies and to extract valuable information. This review focuses mainly on a range of chemometric methods used for processing of metabolomics data generated from gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). Herein, essential skills used for preprocessing of raw data, multivariate resolution, pattern recognition, variable selection and identification of... 

Recently, we have developed an artificial neural network model, which was able to predict accurately the electrophoretic mobilities of relatively small peptides. To examine the robustness of this methodology, a 3-3-1 back-propagation artificial neural network (BP-ANN) model was developed using the same inputs as the previous model, which were the Offord's charge over mass term (Q/M2/3), corrected steric substituent constant (E s,c) and molar refractivity (MR). The data set consisted of 102 peptides with a larger range of size than that of our earlier report - up to 42 amino acid residues as compared to 13 amino acids in the initial study - that also included highly charged and hydrophobic... 

Recently, remote sensing considered as important tool in studies of water quality issues. The Aras River flows across a transboundary basin in northern Iran. In this study, the aim is to model the water quality parameters (WQPs) using remote sensing and an artificial neural network (ANN), which is a new method proposed to find WQPs based on multivariate regression approaches. The relationship between WQPs and digital data from the Sentinel-2 satellite was determined to estimate and map the WQPs in this river. Using the field data and digital image data, the obtained results show that multivariate regression approaches and high-resolution remote sensing could monitor and predict the... 

In the present contribution, a new approach based on mutual information (MI) is proposed for exploring the independence of feasible solutions in two component systems. Investigating how independent are different feasible solutions can be a way to bridge the gap between independent component analysis (ICA) and multivariate curve resolution (MCR) approaches and, to the best of our knowledge, has not been investigated before. For this purpose, different chromatographic and hyperspectral imaging (HSI) datasets were simulated, considering different noise levels and different degrees of overlap for two-component systems. Feasible solutions were then calculated by both grid search (GS) and... 

A fast high-performance liquid chromatography-diode array detection (HPLC-DAD) approach combined to solid phase extraction (SPE) as a pre-concentration step is developed for simultaneous determination of five selected pharmaceuticals (carbamazepine, naproxen, diclofenac, gemfibrozil and mefenamic acid) in water samples. The effective factors on the efficiency of SPE procedure are optimized using faced-centered central composite design (FCCD). In addition, multi-response optimization by using Derringer's desirability function is used to find the optimum experimental conditions for extraction of analytes from well and river waters. Due to the complexity of water matrices and the presence of...