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    Simultaneous determination of protocatechuic aldehyde and protocatechuic acid using the localized surface plasmon resonance peak of silver nanoparticles and chemometric methods

    , Article Quimica Nova ; Volume 38, Issue 7 , 2015 , Pages 896-901 ; 01004042 (ISSN) Khodaveisi, J ; Shabani, A. M. H ; Dadfarnia, S ; Moghadam, M. R ; Hormozi-Nezhad, M. R ; Sharif University of Technology
    Sociedade Brasileira de Quimica  2015
    A simple and sensitive spectrophotometric method is proposed for the simultaneous determination of protocatechuic acid and protocatechuic aldehyde. The method is based on the difference in the kinetic rates of the reactions of analytes with [Ag(NH3)2]+ in the presence of polyvinylpyrrolidone to produce silver nanoparticles. The data obtained were processed by chemometric methods using principal component analysis artificial neural network and partial least squares. Excellent linearity was obtained in the concentration ranges of 1.23-58.56 ?g mL-1 and 0.08-30.39 ?g mL-1 for PAC and PAH, respectively. The limits of detection for PAC and PAH were 0.039 and 0.025 ?g mL-1, respectively  

    Integration of handheld NIR and machine learning to “Measure & Monitor” chicken meat authenticity

    , Article Food Control ; Volume 112 , 2020 Parastar, H ; Van Kollenburg, G ; Weesepoel, Y ; Van den Doel, A ; Buydens, L ; Jansen, J ; Sharif University of Technology
    Elsevier Ltd  2020
    By combining portable, handheld near-infrared (NIR) spectroscopy with state-of-the-art classification algorithms, we developed a powerful method to test chicken meat authenticity. The research presented shows that it is both possible to discriminate fresh from thawed meat, based on NIR spectra, as well as to correctly classify chicken fillets according to the growth conditions of the chickens with good accuracy. In all cases, the random subspace discriminant ensemble (RSDE) method significantly outperformed other common classification methods such as partial least squares-discriminant analysis (PLS-DA), artificial neural network (ANN) and support vector machine (SVM) with classification... 

    Pattern recognition analysis of gas chromatographic and infrared spectroscopic fingerprints of crude oil for source identification

    , Article Microchemical Journal ; Volume 153 , 2020 Hashemi Nasab, F. S ; Parastar, H ; Sharif University of Technology
    Elsevier Inc  2020
    In this study, a chemometric strategy was developed for analysis of gas chromatographic (GC) and infrared spectroscopic (FT-IR) fingerprints of nine crude oil samples from the main oil wells of Iran to classify them and to find their origins. In this regard, a fractionation method based on saturated, aromatic, resin, and asphaltene (SARA) test was used. Then, these fractions were analyzed by GC-FID and GC–MS. Also, nine crude oil samples were analyzed by FT-IR. The obtained GC fingerprints were aligned using correlation optimized warping (COW) and auto-scaled, and then analyzed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Evaluation of PCA scores plot... 

    Characterization and determination of fatty acids in fish oil using gas chromatography-mass spectrometry coupled with chemometric resolution techniques

    , Article Journal of Chromatography A ; Volume 1024, Issue 1-2 , 2004 , Pages 165-176 ; 00219673 (ISSN) Jalali Heravi, M ; Vosough, M ; Sharif University of Technology
    Elsevier  2004
    Characterization and determination of a complex mixture of fatty acid methyl esters was performed for commercial fish oil using two-dimensional GC-MS data coupled with resolution techniques. Various principle component analysis methods such as significant factor analysis and fixed size moving window evolving factor analysis were used for the number of factors, zero concentration and selective regions. Then, the convoluted chromatograms were resolved into pure chromatograms and mass spectra using heuristic evolving latent projections (HELP) method. Fatty acids of C16:1ω7, C18:4ω3, C18:1ω11, C18:1ω9, C18:0, C20:2ω6, C20:1ω9, C 22:1ω11, C22:1ω9 and C24:1ω9 were resolved and identified by using... 

    RMet: An automated R based software for analyzing GC-MS and GC×GC-MS untargeted metabolomic data

    , Article Chemometrics and Intelligent Laboratory Systems ; Volume 194 , 2019 ; 01697439 (ISSN) Moayedpour, S ; Parastar, H ; Sharif University of Technology
    Elsevier B.V  2019
    Gas chromatography-mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) are powerful techniques for measurement of all metabolites in complex metabolic samples. However, analyzing GC-MS and especially GC×GC-MS metabolomic data is a major challenge to the researchers in the field of metabolomics mainly due to the complexity and large data size. In this regard, an automated R based software entitled RMet has been developed to overcome the challenges in the metabolomic analysis workflow of GC-MS and GC×GC-MS data sets. Additionally, it is able to facilitate the complex process of extracting reliable and useful biological information from... 

    Linear and Non-Linear Modeling of Electrophoretic Mobility of Peptides

    , M.Sc. Thesis Sharif University of Technology Darvizeh, Fatemeh (Author) ; Jalali Heravi, Mehdi (Supervisor)
    Regarding the importance of biological systems in daily life and the complexity of these systems, this project is concerned with this problem and especially with applications of chemometrics in proteomics. In this respect, specific importance of peptides has been taken into account in the process of construction of especial and necessary proteins for human body. Due to the risks involved in some experimental investigations, it is quite preferable to utilize modeling approaches using different sets of data. Achieving a number of specific descriptors, a powerful can be established. This model could be quiet comprehensive for the prediction of the electrophoretic mobility of peptides. This... 

    Development of a Model for Prediction of Inhibitors of HIV1 Virus

    , M.Sc. Thesis Sharif University of Technology Hakimi, Fatemeh (Author) ; Jalali Heravi, Mehdi (Supervisor)
    The main aim of this study is developing a robust QSAR model for describing and predicting the inhibitory activities of O-(2-phthalimidoethyl)-N-substituted thiocarbamates derivatives as novel HIV-1 non-nucleoside reverse transcriptase (HIV-1 NNRTIs) inhibitors. These drugs change the active site of the reverse transcriptase enzyme, and finally halter the HIV reproduction cycle. As the first step of this study, a multiple linear regression (MLR) model was built but it has no satisfied prediction ability. As a next step, the nonlinear correlation of the molecular descriptors and activities has been investigated by using artificial neural networks (ANN). In this section the effects of variable... 

    N-way partial least squares with variable importance in projection combined to GC × GC-TOFMS as a reliable tool for toxicity identification of fresh and weathered crude oils

    , Article Analytical and Bioanalytical Chemistry ; Volume 407, Issue 1 , 2014 , pp 285-295 ; ISSN: 16182650 Mostafapour, S ; Parastar, H ; Sharif University of Technology
    In this study, N-way partial least squares (NPLS) is proposed to correlate comprehensive two-dimensional gas chromatography-time of flight mass spectrometry (GC × GC-TOFMS) data of different aromatic oil fractions (fresh and weathered) to their toxicity values. Before NPLS modeling, since drift and wander of baseline interfere with information of sought analytes in GC × GC-TOFMS data, a novel method called two-dimensional asymmetric least squares is thus developed for comprehensive correction of the baseline contributions in both chromatographic dimensions. The algorithm is termed comprehensive because it functions to correct the entire chromatogram in both dimensions and it preserves the... 

    Detecting intermediate particles in the growth of colloidal zinc oxide nanoparticles in different chemical routes using MCR-ALS

    , Article Journal of Chemometrics ; Volume 27, Issue 10 , 2013 , Pages 353-358 ; 08869383 (ISSN) Hormozi Nezhad, M. R ; Jalali Heravi, M ; Kafrashi, F ; Sharif University of Technology
    Zinc oxide (ZnO) nanoparticles have been used in a wide-ranging of applications such as transparent UV protection, light emitting diodes, sensors and photovoltaic cells. Many applications of ZnO nanoparticles could be improved by changing the particle size, shape and morphology. Therefore, studying the mechanism of shape evolution is crucial for size and shape controlled synthesis of nanocrystaline ZnO particles. As an alternative to sophisticated techniques such as transmission electron microscopy (TEM) and X-ray powder diffraction (XRD), commonly used for studying nanoparticles evolution, we herein employed UV/Vis spectroscopy for monitoring the evolution process of the ZnO nanocrystals... 

    Chemometrics-assisted gas chromatographic-mass spectrometric analysis of volatile components of olive leaf oil

    , Article Journal of the Iranian Chemical Society ; Volume 10, Issue 1 , 2013 , Pages 169-179 ; 1735207X (ISSN) Konoz, E ; Abbasi, A ; Moazeni, R. S ; Parastar, H ; Jalali Heravi, M ; Sharif University of Technology
    Iranian olive leaf essential oil components were extracted by microwave-assisted hydrodistillation and analyzed using gas chromatography-mass spectrometry. Ninety-seven components were identified by direct similarity searches for olive leaf essential oil. Chemometrics was used to find more components with the help of multivariate curve resolution methods. Eigenvalues-based methods and Malinowski functions were used for chemical rank determination of GC-MS data. Multivariate curve resolution-alternative least squares as an iterative method was used for resolving the overlapped and embedded peaks. With the use of this method the number of 97 components was extended to 127 components. Major... 

    Multivariate Curve Resolution Methods for Qualitative and Quantitative Analysis in Analytical Chemistry

    , Article Data Handling in Science and Technology ; Volume 29 , 2015 , Pages 393-345 ; 09223487 (ISSN) ; 9780444635273 (ISBN) Parastar, H ; Sharif University of Technology
    Owing to the fact that obtaining useful information from complex analytical systems is one of the keys to make correct decisions about the systems under study, in this chapter, the capabilities and versatility of multivariate curve resolution (MCR) methods are discussed in light of the nowadays issues in qualitative and quantitative analysis in analytical chemistry. In this regard, classification of MCR methods, data structures, data arrangement, bilinear model, and pros and cons of MCR methods is presented. Then, MCR scenarios for qualitative and quantitative purposes in analytical chemistry are discussed in the course of some of the recent applications of MCR methods. Furthermore,... 

    Chemometrical modeling of electrophoretic mobilities in capillary electrophoresis

    , Article Chemometric Methods in Capillary Electrophoresis ; 2009 , Pages 323-343 ; 9780470393291 (ISBN) Jalali Heravi, M ; Sharif University of Technology
    John Wiley & Sons, Inc  2009

    Dataset of the application of handheld NIR and machine learning for chicken fillet authenticity study

    , Article Data in Brief ; Volume 29 , 2020 Van Kollenburg, G ; Weesepoel, Y ; Parastar, H ; van den Doel, A ; Buydens, L ; Jansen, J ; Sharif University of Technology
    Elsevier Inc  2020
    Diffuse reflectance near-infrared (NIR) data (908–1676 nm) of chicken breast fillets was recorded in a non-destructive way using a portable miniaturised NIR spectrometer. The NIR data was used to discriminate between fresh and thawed breast fillets and to determine the birds’ growth conditions. NIR data was recorded of 153 commercial supermarket chicken fillet samples by applying the NIR device equipped with the standard issue collar on the samples in three different ways: (i) directly on the meat (ii) through the top foil of the package (i.e. with an air pocket between the foil and the breast fillet), and (iii) through the top foil with the packaging turned bottom up (i.e. no air pocket... 

    Recent trends in application of chemometric methods for GC-MS and GC×GC-MS-based metabolomic studies

    , Article TrAC - Trends in Analytical Chemistry ; Volume 138 , 2021 ; 01659936 (ISSN) Feizi, N ; Hashemi Nasab, F. S ; Golpelichi, F ; Sabouruh, N ; Parastar, H ; Sharif University of Technology
    Elsevier B.V  2021
    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... 

    Evaluation of the effect of organic pollutants exposure on the antioxidant activity, total phenolic and total flavonoid content of lettuce (Lactuca sativa L.) using UV–Vis spectrophotometry and chemometrics

    , Article Microchemical Journal ; Volume 170 , 2021 ; 0026265X (ISSN) Nikzad, N ; Parastar, H ; Sharif University of Technology
    Elsevier Inc  2021
    In the present contribution, the effects of different contaminants of emerging concerns (CECs), including parabens, drugs, and polycyclic aromatic hydrocarbons (PAHs) on antioxidant activity of Lactuca sativa L. in different concentration levels (10–500 µg L−1) were evaluated using ultraviolet–visible (UV–Vis) spectrophotometry combined with chemometric techniques. The extracts of lettuce samples were investigated for the antioxidant activity (AA), total phenolic content (TPC), and total flavonoid content (TFC) after 39 days of planting and 14 days of exposure. Then, the spectroscopic data were arranged in two different data matrices, including (i) the control lettuce samples and PAHs... 

    Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

    , Article Journal of Chromatography A ; Volume 927, Issue 1-2 , 2001 , Pages 211-218 ; 00219673 (ISSN) Jalali Heravi, M ; Garkani Nejad, Z ; Sharif University of Technology
    Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models. © 2001... 

    Analytical figures of merit for multisensor arrays

    , Article ACS Sensors ; Volume 5, Issue 2 , 2020 , Pages 580-587 Parastar, H ; Kirsanov, D ; Sharif University of Technology
    American Chemical Society  2020
    Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for... 

    Assessment of the Potential of Chemometric Methods in the Analysis of Mass Spectrometry Images Towards Obtaining Pure Spectral and Spatial information

    , M.Sc. Thesis Sharif University of Technology Mohammadi Tanouraghaj, Saeedeh (Author) ; Parastar Shahri, Hadi (Supervisor)
    Recently, the use of mass spectrometry imaging (MSI) has been introduced as a powerful tool to study the compounds in biological and environmental samples. The lack of need to sample preparation have made it possible to quantify and qualify these compounds in complex matrix such as biological matrices. This technique will also allow analyzing the compounds in a very wide mass range and precise structural information this method gives leads to employ this method widely for quantification and qualification of wide variety compounds. Accuracy and precision of results obtained by this method beside unique information it gives about system lead to wide use of this method in scientific... 

    An innovative chemometric approach for simultaneous determination of polycyclic aromatic hydrocarbons in oil-contaminated waters based on dispersive micro-solid phase extraction followed by gas chromatography

    , Article Microchemical Journal ; Volume 159 , 2020 Saburouh, N ; Jabbari, A ; Parastar, H ; Sharif University of Technology
    Elsevier Inc  2020
    In the present study, an analytical strategy was developed using reduced graphene oxide (rGO) as an effective sorbent in dispersive micro-solid phase extraction (DMSPE) for simultaneous determination of seven polycyclic aromatic hydrocarbons (PAHs) combined with gas chromatography (GC-FID). rGO was synthesized using modified Hummer's method and characterized using scanning electron microscope (SEM), atomic force microscope (AFM) and Raman spectroscopy. A rotatable central composite design (CCD) combined with multiple linear regression (MLR) and analysis of variance (ANOVA) was used for designing, modelling and optimization of the extraction procedure. In this regard, principal component... 

    Prediction of thermal conductivity detection response factors using an artificial neural network

    , Article Journal of Chromatography A ; Volume 897, Issue 1-2 , 2000 , Pages 227-235 ; 00219673 (ISSN) Jalali Heravi, M ; Fatemi, M. H ; Sharif University of Technology
    The main aim of the present work was the development of a quantitative structure-activity relationship method using an artificial neural network (ANN) for predicting the thermal conductivity detector response factor. As a first step a multiple linear regression (MLR) model was developed and the descriptors appearing in this model were considered as inputs for the ANN. The descriptors of molecular mass, number of vibrational modes of the molecule, molecular surface area and Balaban index appeared in the MLR model. In agreement with the molecular diameter approach, molecular mass and molecular surface area play a major role in estimating the thermal conductivity detector response factor...