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MultiDA: Chemometric software for multivariate data analysis based on Matlab
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Qianxu Yang, Liangxiao Zhang, Longxing Wang, Hongbin Xiao
Multivariate data analysis (MultiDA), a user-friendly interface chemometric software, is developed for the routine metabolomics/metabonomics data analysis. There are mainly two advantages for MultiDA. First, it could simultaneously provide multiply methods for data preprocessing and multivariate analysis. The main chemometric methods in MultiDA contains k-means cluster analysis, k-medoid cluster analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA), robust principal component analysis (ROPCA), non-linear PCA (NLPCA), non-linear iterative partial least squares (NIPALS), SIMPLS, discriminate analysis (DA), canonical discriminate analysis (CDA), stepwise discriminate analysis (SDA), uncorrelated linear discriminate analysis (ULDA) and some data preprocessing methods, such as standardization, outlier detection, genetic algorithm for feature selection (GAFS), orthogonal signal correction (OSC), weight analysis (Weight) etc. Second, multi-model comparison could be conducted to obtain the best outcome. Moreover, this software is available for free.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Qianxu Yang, Liangxiao Zhang, Longxing Wang, Hongbin Xiao
Multivariate data analysis (MultiDA), a user-friendly interface chemometric software, is developed for the routine metabolomics/metabonomics data analysis. There are mainly two advantages for MultiDA. First, it could simultaneously provide multiply methods for data preprocessing and multivariate analysis. The main chemometric methods in MultiDA contains k-means cluster analysis, k-medoid cluster analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA), robust principal component analysis (ROPCA), non-linear PCA (NLPCA), non-linear iterative partial least squares (NIPALS), SIMPLS, discriminate analysis (DA), canonical discriminate analysis (CDA), stepwise discriminate analysis (SDA), uncorrelated linear discriminate analysis (ULDA) and some data preprocessing methods, such as standardization, outlier detection, genetic algorithm for feature selection (GAFS), orthogonal signal correction (OSC), weight analysis (Weight) etc. Second, multi-model comparison could be conducted to obtain the best outcome. Moreover, this software is available for free.
Highlights
► User-friendly chemometric software is developed for multivariate data analysis. ► Multi-model comparison is available for obtaining robust and comprehensive result. ► This software is available for free.MVC3: A MATLAB graphical interface toolbox for third-order multivariate calibration
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Alejandro C. Olivieri, Hai-Long Wu, Ru-Qin Yu
A new MATLAB graphical interface toolbox for implementing third-order multivariate calibration methodologies is discussed. Multivariate calibration 3 (MVC3) is a sequel of the already described first-order (MVC1) and second-order (MVC2) toolboxes. MVC3 accepts a variety of ASCII data for input, depending on whether the third-order data are vectorized or matricized. If required, data for sample sets are arranged into four-way arrays for processing with several quadrilinear and non-quadrilinear algorithms. Quadrilinear decomposition techniques and latent structured models based on partial least-squares regression and residual trilinearization are included in the software. Appropriate working sensor regions in the three data dimensions can be selected. Model development and its subsequent application to unknown samples are straightforward from the interface. Prediction results are provided along with analytical figures of merit and standard concentration errors, as calculated by modern concepts of uncertainty propagation.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Alejandro C. Olivieri, Hai-Long Wu, Ru-Qin Yu
A new MATLAB graphical interface toolbox for implementing third-order multivariate calibration methodologies is discussed. Multivariate calibration 3 (MVC3) is a sequel of the already described first-order (MVC1) and second-order (MVC2) toolboxes. MVC3 accepts a variety of ASCII data for input, depending on whether the third-order data are vectorized or matricized. If required, data for sample sets are arranged into four-way arrays for processing with several quadrilinear and non-quadrilinear algorithms. Quadrilinear decomposition techniques and latent structured models based on partial least-squares regression and residual trilinearization are included in the software. Appropriate working sensor regions in the three data dimensions can be selected. Model development and its subsequent application to unknown samples are straightforward from the interface. Prediction results are provided along with analytical figures of merit and standard concentration errors, as calculated by modern concepts of uncertainty propagation.
Highlights
► We developed a MATLAB graphical interface. ► It implements several third-order calibration algorithms. ► Calibration, prediction, statistics and figures of merit are provided. ► Useful profile and calibration plots are produced.FSDA: A MATLAB toolbox for robust analysis and interactive data exploration
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Marco Riani, Domenico Perrotta, Francesca Torti
We present the FSDA (Forward Search for Data Analysis) toolbox, a new software library that extends MATLAB and its Statistics Toolbox to support a robust and efficient analysis of complex datasets, affected by different sources of heterogeneity. As the name of the library indicates, the project was born around the Forward Search approach, but it has evolved to include the main traditional robust multivariate and regression techniques, including LMS, LTS, MCD, MVE, MM and S estimation. To address problems where data deviate from typical model assumptions, tools are available for robust data transformation and robust model selection. When different views of the data are available, e.g. a scatterplot of units and a plot of distances of such units from a fitted model, FSDA links such views and offers the possibility to interact with them. For example, selections of objects in a plot are highlighted in the other plots. This considerably simplifies the exploration of the data in view of extracting information and detecting patterns. We show the potential of the FSDA in chemometrics using data from chemical and pharmaceutical problems, where the presence of outliers, multiple groups, deviations from normality and other complex structures is not an exceptional circumstance.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Marco Riani, Domenico Perrotta, Francesca Torti
We present the FSDA (Forward Search for Data Analysis) toolbox, a new software library that extends MATLAB and its Statistics Toolbox to support a robust and efficient analysis of complex datasets, affected by different sources of heterogeneity. As the name of the library indicates, the project was born around the Forward Search approach, but it has evolved to include the main traditional robust multivariate and regression techniques, including LMS, LTS, MCD, MVE, MM and S estimation. To address problems where data deviate from typical model assumptions, tools are available for robust data transformation and robust model selection. When different views of the data are available, e.g. a scatterplot of units and a plot of distances of such units from a fitted model, FSDA links such views and offers the possibility to interact with them. For example, selections of objects in a plot are highlighted in the other plots. This considerably simplifies the exploration of the data in view of extracting information and detecting patterns. We show the potential of the FSDA in chemometrics using data from chemical and pharmaceutical problems, where the presence of outliers, multiple groups, deviations from normality and other complex structures is not an exceptional circumstance.
QSAR study of the DPPH radical scavenging activity of di(hetero)arylamines derivatives of benzo[b]thiophenes, halophenols and caffeic acid analogues
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Adam Lee, Andrew G. Mercader, Pablo R. Duchowicz, Eduardo A. Castro, Alicia B. Pomilio
We performed a predictive analysis based on Quantitative Structure–Activity Relationships (QSAR) of the radical scavenging activities of a set of compounds consisting of di(hetero)arylamine derivatives of benzo-[b]thiophenes, halophenols, and caffeic acid analogues. Given the importance of this activity in medicinal chemistry it is of interest to develop a theoretical method for its prediction. The selection of the descriptors from a pool containing more than a thousand geometrical, topological, quantum-mechanical and electronic types of descriptors was performed using a new advanced version of the Enhanced Replacement Method (ERM). The best QSAR linear model was constructed using 52 molecular structures not previously used in this type of quantitative structure–property study, and showed good predictive attributes. The model analysis suggested that the activity depends on the atomic van der Waals volumes and on the atomic electronegativity; and that the conformation of the molecule does not present a relevant role in the activity.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Adam Lee, Andrew G. Mercader, Pablo R. Duchowicz, Eduardo A. Castro, Alicia B. Pomilio
We performed a predictive analysis based on Quantitative Structure–Activity Relationships (QSAR) of the radical scavenging activities of a set of compounds consisting of di(hetero)arylamine derivatives of benzo-[b]thiophenes, halophenols, and caffeic acid analogues. Given the importance of this activity in medicinal chemistry it is of interest to develop a theoretical method for its prediction. The selection of the descriptors from a pool containing more than a thousand geometrical, topological, quantum-mechanical and electronic types of descriptors was performed using a new advanced version of the Enhanced Replacement Method (ERM). The best QSAR linear model was constructed using 52 molecular structures not previously used in this type of quantitative structure–property study, and showed good predictive attributes. The model analysis suggested that the activity depends on the atomic van der Waals volumes and on the atomic electronegativity; and that the conformation of the molecule does not present a relevant role in the activity.
Highlights
► Linear QSAR study of the DPPH radical scavenging activity of different compounds. ► Compounds: di(hetero)arylamines, halophenols, and caffeic acid analogues. ► Molecular structure translated into Dragon molecular descriptors. ► Validation of the QSAR models showed good predictive attributes. ► Atomic volumes and electronegativities relevant on the activity.Dynamic fault diagnosis using extended matrix and tensor locality preserving discriminant analysis
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Gang Rong, Su-Yu Liu, Ji-Dong Shao
It is well acknowledged that utilizing dynamic information can improve accuracy in fault diagnosis for dynamic processes. Conventional methods encode dynamic information by constructing an extended vector comprising current process data as well as past process data. Then the classic Linear Discriminant Analysis (LDA) is usually applied to extended vectors to reduce dimensionality and obtain a discriminative subspace where overlapping among different fault classes is minimized. However, using extended vectors aggravates the “curse of dimensionality” problem and loses structure information in variables. Besides, LDA probably provides suboptimal results when there are more than two candidate fault classes. This paper proposes using extended matrices to encoding dynamic information and using a novel dimensionality reduction method named Tensor Locality Preserving Discriminant Analysis (TLPDA) to perform dimensionality reduction on extend matrices directly. TLPDA is based on local structure in data and overcomes the main drawbacks of LDA. A new dynamic fault diagnosis scheme is developed based on extended matrices and TLPDA. Extensive simulations on the Tennessee Eastman (TE) benchmark simulation process clearly demonstrate the superiority of our methods in terms of misclassification rate and making use of extra training data.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Gang Rong, Su-Yu Liu, Ji-Dong Shao
It is well acknowledged that utilizing dynamic information can improve accuracy in fault diagnosis for dynamic processes. Conventional methods encode dynamic information by constructing an extended vector comprising current process data as well as past process data. Then the classic Linear Discriminant Analysis (LDA) is usually applied to extended vectors to reduce dimensionality and obtain a discriminative subspace where overlapping among different fault classes is minimized. However, using extended vectors aggravates the “curse of dimensionality” problem and loses structure information in variables. Besides, LDA probably provides suboptimal results when there are more than two candidate fault classes. This paper proposes using extended matrices to encoding dynamic information and using a novel dimensionality reduction method named Tensor Locality Preserving Discriminant Analysis (TLPDA) to perform dimensionality reduction on extend matrices directly. TLPDA is based on local structure in data and overcomes the main drawbacks of LDA. A new dynamic fault diagnosis scheme is developed based on extended matrices and TLPDA. Extensive simulations on the Tennessee Eastman (TE) benchmark simulation process clearly demonstrate the superiority of our methods in terms of misclassification rate and making use of extra training data.
Multi-response multi-factorial master ranking in non-linear replicated-saturated DOE for qualimetrics
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
George J. Besseris
Multi-response screening in chemometrics is greatly facilitated by fractional factorial analysis. A method is presented in this work that is an amalgamation of non-linear orthogonal arrays for planning data collection supported by multi-response order statistics to attain robust inference for replicated trials. The rank-sum estimator is the instrumental data compressor which is easily adapted to accommodate importance weights for concurrent optimization. Rank ordering allows the uniform scaling for all examined characteristics. As a result, a master response is created that carries all relevant information to a single manageable response. The concept of selective leveling is introduced to provide preference of a categorical factor setting over other choices. The technique has many advantages because simplifies the overall data planning and analysis while maintaining a distribution-free character with no sparsity assumptions to be imposed on the solution for effect contrasting to be successful. The technique is tested on screening three controlling factors and one interaction for profiling four quality characteristics of an epoxy product in a chemical laboratory of a resin manufacturer. Robustified resin qualimetrics are data mined from repeated trials properly weighted for synchronous screening. The data collector scheme was adapted to conform to an L 9(34) orthogonal array. The selective leveling property is applied on the three-setting substrate factor to demonstrate the influence of this property on terminal decision making. Results are discussed in the non-linear distribution-free domain. The rank ordering approach proposed in this work may supplement testing procedures for laboratory data analysis as required by ISO 17025:2005 standard.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
George J. Besseris
Multi-response screening in chemometrics is greatly facilitated by fractional factorial analysis. A method is presented in this work that is an amalgamation of non-linear orthogonal arrays for planning data collection supported by multi-response order statistics to attain robust inference for replicated trials. The rank-sum estimator is the instrumental data compressor which is easily adapted to accommodate importance weights for concurrent optimization. Rank ordering allows the uniform scaling for all examined characteristics. As a result, a master response is created that carries all relevant information to a single manageable response. The concept of selective leveling is introduced to provide preference of a categorical factor setting over other choices. The technique has many advantages because simplifies the overall data planning and analysis while maintaining a distribution-free character with no sparsity assumptions to be imposed on the solution for effect contrasting to be successful. The technique is tested on screening three controlling factors and one interaction for profiling four quality characteristics of an epoxy product in a chemical laboratory of a resin manufacturer. Robustified resin qualimetrics are data mined from repeated trials properly weighted for synchronous screening. The data collector scheme was adapted to conform to an L 9(34) orthogonal array. The selective leveling property is applied on the three-setting substrate factor to demonstrate the influence of this property on terminal decision making. Results are discussed in the non-linear distribution-free domain. The rank ordering approach proposed in this work may supplement testing procedures for laboratory data analysis as required by ISO 17025:2005 standard.
Highlights
► Non-linear order statistics treats saturated screening for replicated trials. ► Multi-response rank ordering transforms several quality traits concurrently. ► Three-factor screening with one interaction is applied for a four-response problem. ► Resin qualimetrics are data mined for weighted synchronous screening.Prediction of theaflavin and thearubigin content in black tea using a voltammetric electronic tongue
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Arunangshu Ghosh, Bipan Tudu, Pradip Tamuly, Nabarun Bhattacharyya, Rajib Bandyopadhyay
The two most important chemical groups that decide the liquor characteristics of black CTC (cut, torn and curled) tea are theaflavins (TF) and thearubigins (TR). Hence, a quick estimation of concentration of these compounds can significantly contribute to the evaluation process for the quality of finished tea in an objective manner. In this paper, a scheme for rapid measurement of concentration of TF and TR is described using a voltammetric electronic tongue with five working electrodes made of noble metals. The results indicate good correlation of electronic tongue predictions with the actual concentrations obtained using ultraviolet–visible spectrophotometer.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Arunangshu Ghosh, Bipan Tudu, Pradip Tamuly, Nabarun Bhattacharyya, Rajib Bandyopadhyay
The two most important chemical groups that decide the liquor characteristics of black CTC (cut, torn and curled) tea are theaflavins (TF) and thearubigins (TR). Hence, a quick estimation of concentration of these compounds can significantly contribute to the evaluation process for the quality of finished tea in an objective manner. In this paper, a scheme for rapid measurement of concentration of TF and TR is described using a voltammetric electronic tongue with five working electrodes made of noble metals. The results indicate good correlation of electronic tongue predictions with the actual concentrations obtained using ultraviolet–visible spectrophotometer.
Research Highlights
► Theaflavins (TF) and Thearubigins (TR) determine the liquor characteristics of tea. ► Concentrations of TF and TR are measured using a voltammetric electronic tongue. ► Correlation models developed using PLSR, SVR and BP-MLP. ► The results indicate good correlation with UV-Vis spectrophotometer readings.Combining fundamental knowledge and latent variable techniques to transfer process monitoring models between plants
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Emanuele Tomba, Pierantonio Facco, Fabrizio Bezzo, Salvador García-Muñoz, Massimiliano Barolo
In this paper we explore the issue of the transfer of process monitoring models between different plants that exploit the same manufacturing process to manufacture the same product. Given a source plant A and a target plant B, the objective is to use the data available from plant A to monitor the operation of plant B, until a sufficient amount of data entirely representative of the operation in plant B is collected to allow building a process monitoring model based on these data only. Two different model transfer methodologies are proposed, which depend on the nature of the measured process variables (namely, on whether they are common between the two plants or not). Both the proposed approaches combine fundamental engineering knowledge on the system (derived from mass or energy balances) with latent variable modeling techniques (namely, principal component analysis and joint-Y partial least-squares regression). Both approaches are based on adaptive algorithms, which make them practical for online use, and are tested on a benchmark problem related to the scale-up of the monitoring model for an industrial spray-drying process. Results show that both proposed procedures provide robust and prompt fault detection, even when very few data are available from plant B.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Emanuele Tomba, Pierantonio Facco, Fabrizio Bezzo, Salvador García-Muñoz, Massimiliano Barolo
In this paper we explore the issue of the transfer of process monitoring models between different plants that exploit the same manufacturing process to manufacture the same product. Given a source plant A and a target plant B, the objective is to use the data available from plant A to monitor the operation of plant B, until a sufficient amount of data entirely representative of the operation in plant B is collected to allow building a process monitoring model based on these data only. Two different model transfer methodologies are proposed, which depend on the nature of the measured process variables (namely, on whether they are common between the two plants or not). Both the proposed approaches combine fundamental engineering knowledge on the system (derived from mass or energy balances) with latent variable modeling techniques (namely, principal component analysis and joint-Y partial least-squares regression). Both approaches are based on adaptive algorithms, which make them practical for online use, and are tested on a benchmark problem related to the scale-up of the monitoring model for an industrial spray-drying process. Results show that both proposed procedures provide robust and prompt fault detection, even when very few data are available from plant B.
Highlights
► Two approaches are proposed to transfer process monitoring models between plants. ► The approaches combine fundamental process knowledge with latent variable methods. ► Data available from a source plant are exploited to monitor a target plant. ► An application to the scale-up of an industrial spray-drying process is presented. ► Reliable fault detection is achieved even when very few samples are available.Application of ‘multivariate curve resolution alternating least square (MCR–ALS)’ analysis to extract pure component synchronous fluorescence spectra at various wavelength offsets from total synchronous fluorescence spectroscopy (TSFS) data set of dilute aqueous solutions of fluorophores
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Keshav Kumar, Ashok Kumar Mishra
In the present study, the possibility of using the multivariate curve resolution alternating least square (MCR–ALS) analysis for the simultaneous extraction of the pure synchronous fluorescence spectra at various wavelength offsets (Δλ) for each fluorophore from the total synchronous fluorescence spectroscopy (TSFS) data set of the dilute aqueous mixtures of the three fluorophores, was explored. The present work was based on the assumption that unfolded TSFS data has a bilinear structure and therefore it can be subjected to MCR–ALS analysis. Three fluorophores, benzo[a]pyrene(BaP), perylene(PE), and pyrene(PY), were chosen. These three fluorophores show fluorescence at all the seven wavelength offsets (Δλ) used to create the TSFS data set. In addition, Raman scattering due to solvent molecules (i.e. water) also appear in the wavelength ranges where these fluorophores show fluorescence. These two factors make the simultaneous extraction of synchronous spectral profile at various Δλs from the TSFS data set relatively difficult. The appearance of the diagonal signals in the three‐dimensional landscapes of TSFS shows the presence of the Raman scattering. The Raman signal due to solvent molecules was found to influence the synchronous profile of a fluorophore to different extents at different Δλs. TSFS data set of dimension, sample×wavelength×Δλ, was unfolded along the first mode to obtain the unfolded TSFS data set. Pure synchronous spectral profiles at various Δλs were obtained for each fluorophore by performing the MCR–ALS analysis on the unfolded TSFS data. However Raman scattering signals could not be eliminated from the synchronous spectral profiles of the PE and PY. For the mitigation of Raman scattering from the calculated spectral profiles, TSFS data of solvent blank were subtracted from all the samples before performing the MCR–ALS analysis. The obtained spectral profiles of BaP, PE, and PY were found to match with their actual spectral profiles which verifies that unfolded TSFS data set has a bilinear structure. To test the strength of the present work, MCR–ALS analysis was also performed on the unfolded TSFS data set of 12 groundwater samples which were contaminated with the BaP‐ and PY‐spiked gasoline. The obtained results show that it is possible to monitor the presence of BaP and PY in groundwater samples.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Keshav Kumar, Ashok Kumar Mishra
In the present study, the possibility of using the multivariate curve resolution alternating least square (MCR–ALS) analysis for the simultaneous extraction of the pure synchronous fluorescence spectra at various wavelength offsets (Δλ) for each fluorophore from the total synchronous fluorescence spectroscopy (TSFS) data set of the dilute aqueous mixtures of the three fluorophores, was explored. The present work was based on the assumption that unfolded TSFS data has a bilinear structure and therefore it can be subjected to MCR–ALS analysis. Three fluorophores, benzo[a]pyrene(BaP), perylene(PE), and pyrene(PY), were chosen. These three fluorophores show fluorescence at all the seven wavelength offsets (Δλ) used to create the TSFS data set. In addition, Raman scattering due to solvent molecules (i.e. water) also appear in the wavelength ranges where these fluorophores show fluorescence. These two factors make the simultaneous extraction of synchronous spectral profile at various Δλs from the TSFS data set relatively difficult. The appearance of the diagonal signals in the three‐dimensional landscapes of TSFS shows the presence of the Raman scattering. The Raman signal due to solvent molecules was found to influence the synchronous profile of a fluorophore to different extents at different Δλs. TSFS data set of dimension, sample×wavelength×Δλ, was unfolded along the first mode to obtain the unfolded TSFS data set. Pure synchronous spectral profiles at various Δλs were obtained for each fluorophore by performing the MCR–ALS analysis on the unfolded TSFS data. However Raman scattering signals could not be eliminated from the synchronous spectral profiles of the PE and PY. For the mitigation of Raman scattering from the calculated spectral profiles, TSFS data of solvent blank were subtracted from all the samples before performing the MCR–ALS analysis. The obtained spectral profiles of BaP, PE, and PY were found to match with their actual spectral profiles which verifies that unfolded TSFS data set has a bilinear structure. To test the strength of the present work, MCR–ALS analysis was also performed on the unfolded TSFS data set of 12 groundwater samples which were contaminated with the BaP‐ and PY‐spiked gasoline. The obtained results show that it is possible to monitor the presence of BaP and PY in groundwater samples.
Highlights
► It was shown that Unfolded-TSFS has bilinear structure. ► Raman scattering signals was found to appear diagonally in TSFS. ► At low concentration of fluorophores Raman scattering becomes a problem in TSFS. ► Pure SF profile at various Δλs from TSFS data set can be extracted using MCR-ALS.QSAR and evaluation of molecular electrostatic potential for N-nitrosopiperidinone semicarbazones
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
T. Hemalatha, P.K.M. Imran, A. Gnanamani, S. Nagarajan
A series of N-nitrosopiperidone semicarbazones were synthesized and tested for their antifungal activity against the plant pathogens viz., Fusarium oxysporum and Rhizoctonia solani. Majority of the compounds displayed very high activity against the tested organisms. A good reactivity trend was observed with varying substituting moieties of the compounds. More enhanced antifungal activities were found in the thiosemicarbazones than the semicarbazones. Effective dosage (ED50) values were used to build statistical models with the help of molecular descriptors. QSAR equations were developed and they worked well for all the predictions. Molecular electrostatic potentials were calculated by grid based method at 3–21G* level of DFT. The results were correlated with predicted activity. The surface potential of the molecules were calculated and included in the models. Predicted molecular electrostatic potential surface values and the pictures have provided a good insight into the hydrophobic/hydrophilic nature of the molecular surface.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
T. Hemalatha, P.K.M. Imran, A. Gnanamani, S. Nagarajan
A series of N-nitrosopiperidone semicarbazones were synthesized and tested for their antifungal activity against the plant pathogens viz., Fusarium oxysporum and Rhizoctonia solani. Majority of the compounds displayed very high activity against the tested organisms. A good reactivity trend was observed with varying substituting moieties of the compounds. More enhanced antifungal activities were found in the thiosemicarbazones than the semicarbazones. Effective dosage (ED50) values were used to build statistical models with the help of molecular descriptors. QSAR equations were developed and they worked well for all the predictions. Molecular electrostatic potentials were calculated by grid based method at 3–21G* level of DFT. The results were correlated with predicted activity. The surface potential of the molecules were calculated and included in the models. Predicted molecular electrostatic potential surface values and the pictures have provided a good insight into the hydrophobic/hydrophilic nature of the molecular surface.
Graphical abstract
Highlights
► New and efficient antifungal N-nitrosopiperidone semicarbazones. ► QSAR equations were developed. ► Surface potential of the molecules were calculated and included in the models. ► Results were well correlated with predictions.Multivariate industrial process monitoring based on the integration method of canonical variate analysis and independent component analysis
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Yinghua Yang, Yonglu Chen, Xiaobo Chen, Xiaozhi Liu
Tennessee Eastman (TE) process is a typical multivariate chemical process. It has some characteristics of complexity and nonlinearity. Therefore, it is an ideal research platform substituted for the real industrial process whose data is difficult to be achieved. Many scholars have done a lot of studies on monitoring approaches and applied these methods on the platform. However, it is not an easy work to obtain some ideal simulation results on detecting some special faults in TE process, such as the fault 3. In this paper, an integration of canonical variate analysis and independent component analysis method (CV-ICA) is proposed. It combines the advantages of canonical variate analysis (CVA) and independent component analysis (ICA) to solve these problems. CV-ICA applies CVA to calculate the canonical variates from the process data, and then employs ICA to extract independent components (ICs). The monitoring simulation demonstrates the availability of the proposed method.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Yinghua Yang, Yonglu Chen, Xiaobo Chen, Xiaozhi Liu
Tennessee Eastman (TE) process is a typical multivariate chemical process. It has some characteristics of complexity and nonlinearity. Therefore, it is an ideal research platform substituted for the real industrial process whose data is difficult to be achieved. Many scholars have done a lot of studies on monitoring approaches and applied these methods on the platform. However, it is not an easy work to obtain some ideal simulation results on detecting some special faults in TE process, such as the fault 3. In this paper, an integration of canonical variate analysis and independent component analysis method (CV-ICA) is proposed. It combines the advantages of canonical variate analysis (CVA) and independent component analysis (ICA) to solve these problems. CV-ICA applies CVA to calculate the canonical variates from the process data, and then employs ICA to extract independent components (ICs). The monitoring simulation demonstrates the availability of the proposed method.
Highlights
► An integration method of CV-ICA is proposed for process monitoring. ► CV-ICA method provides a more efficient fault detection method for TE process. ► We achieve high fault detection rate for specific fault which is difficult to detect.Application of wavelet analysis and decision tree in UTDR data for diagnosis of membrane filtration
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Junghui Chen, Yun-Chen Yang, Tsong-Yang Wei
Fouling is readily acknowledged to be one of the most critical problems with respect to wider applications of membranes in liquid separation. It is a significant hindrance to successful membrane operation. It may also result in extensive time and maintenance costs. The detection of the defects is crucial to preventing the filtration system from malfunction that could cause damage or entire system halt. This study presents the technical detection results using the ultrasonic frequency domain reflectometry (UTDR), which is capable of diagnosing membrane fouling in various operation conditions. The detection technique is the combination of the wavelet transforms (WT) and the decision tree (DT). It is proposed to exploit the virtues from UTDR signals. WT is used to represent all the possible types of transients in generated vibration signals for feature extraction. Its relative effectiveness in feature extraction is compared. DT is used for feature selection as well as classification. This work is the first-ever attempt to develop UTDR for direct and unambiguous diagnosis of membrane fouling in the practical operating condition. Experimental results show that the algorithms are indeed efficient and effective.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Junghui Chen, Yun-Chen Yang, Tsong-Yang Wei
Fouling is readily acknowledged to be one of the most critical problems with respect to wider applications of membranes in liquid separation. It is a significant hindrance to successful membrane operation. It may also result in extensive time and maintenance costs. The detection of the defects is crucial to preventing the filtration system from malfunction that could cause damage or entire system halt. This study presents the technical detection results using the ultrasonic frequency domain reflectometry (UTDR), which is capable of diagnosing membrane fouling in various operation conditions. The detection technique is the combination of the wavelet transforms (WT) and the decision tree (DT). It is proposed to exploit the virtues from UTDR signals. WT is used to represent all the possible types of transients in generated vibration signals for feature extraction. Its relative effectiveness in feature extraction is compared. DT is used for feature selection as well as classification. This work is the first-ever attempt to develop UTDR for direct and unambiguous diagnosis of membrane fouling in the practical operating condition. Experimental results show that the algorithms are indeed efficient and effective.
Highlights
► Non-invasive UTDR for the fouling status in different filtration conditions. ► Successful application of wavelet transform to extracting the features of UTDR. ► A decision tree that represents different operational modes for UTDR signalsPARAFAC analysis of front-face fluorescence data: Absorption and scattering effects assessed by means of Monte Carlo simulations
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Lyes Lakhal, Victor Acha, Thierry Aussenac
Three-way fluorescence data originating from mixtures of fluorophores embedded in turbid media such as biological media get strongly modulated by wavelength dependent absorption and scattering phenomena. Thus the consistent resolution and quantitative determination of the mixture becomes a difficult task. In this study two chemometric methodologies frequently used to deal with this type of data were applied to fluorescence simulated data sets qualitatively similar to those measured in biological samples: Parallel Factor Analysis (PARAFAC) that does require the fulfillment of trilinearity, and multivariate curve resolution‐alternating least squares (MCR‐ALS) which decomposes the data according to a model lacking this structure. Monte Carlo simulations were used to simulate fluorescence excitation–emission matrices (EEMs) of known fluorescent mixtures under separated and simultaneous variations of the absorption parameter μa and the scattering parameter μs. PARAFAC and constrained MCR-ALS models were then fitted to the simulated data. Both algorithms failed the recover the true profiles. The results obtained with PARAFAC and MCR-ALS models are similar and the recovered profiles exhibit severe distortions due to the absorption and scattering effects. Finally, qualitative and quantitative effects of the absorption and scattering on the fluorescence data were assessed and discussed.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Lyes Lakhal, Victor Acha, Thierry Aussenac
Three-way fluorescence data originating from mixtures of fluorophores embedded in turbid media such as biological media get strongly modulated by wavelength dependent absorption and scattering phenomena. Thus the consistent resolution and quantitative determination of the mixture becomes a difficult task. In this study two chemometric methodologies frequently used to deal with this type of data were applied to fluorescence simulated data sets qualitatively similar to those measured in biological samples: Parallel Factor Analysis (PARAFAC) that does require the fulfillment of trilinearity, and multivariate curve resolution‐alternating least squares (MCR‐ALS) which decomposes the data according to a model lacking this structure. Monte Carlo simulations were used to simulate fluorescence excitation–emission matrices (EEMs) of known fluorescent mixtures under separated and simultaneous variations of the absorption parameter μa and the scattering parameter μs. PARAFAC and constrained MCR-ALS models were then fitted to the simulated data. Both algorithms failed the recover the true profiles. The results obtained with PARAFAC and MCR-ALS models are similar and the recovered profiles exhibit severe distortions due to the absorption and scattering effects. Finally, qualitative and quantitative effects of the absorption and scattering on the fluorescence data were assessed and discussed.
Highlights
► The acquisition with the front face geometry does not necessarily guarantee the trilinear structure of the fluorescence three-way data. ► Monte Carlo approach for modeling fluorescence in turbid media and derive an analytical model of the measured EEM. ► PARAFAC analysis of front-face fluorescence data simulated by Monte Carlo method.Computer-assisted assessment of potentially useful non-peptide HIV-1 protease inhibitors
12 June 2012,
18:32:13
Publication year:
2012
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Omar Deeb, Elaine F.F. da Cunha, Rodrigo A. Cormanich, Teodorico C. Ramalho, Matheus P. Freitas
Quantitative structure–activity relationship (QSAR) studies were recently performed to model the bioactivities of two different series of non-peptide HIV-1 protease inhibitors. The sum of the substructures of these two compound classes giving rise to new actives can cause synergistic effects on bioactivities and enhanced pharmacokinetic parameters. Therefore, the two congeneric series were joined and a MIA-QSAR model was built and used to estimate the biological activities of new compounds derived from the miscellany of substructures of the most active compounds of both series. The QSAR model was validated through leave-one-out cross-validation and external validation, and its robustness attested by means of a Y-randomization test. One of the proposed compounds was very promising and, therefore, submitted to ADME evaluation, demonstrating improved properties in comparison to the existing compounds. Docking studies demonstrated the high affinity of the novel compound towards HIV-1 protease, especially due to interactions with catalytic Asp dyad, in agreement with the expected trend obtained by QSAR for the proposed compounds and by the experimental data of the most active ligands.
Source:Chemometrics and Intelligent Laboratory Systems, Volume 116
Omar Deeb, Elaine F.F. da Cunha, Rodrigo A. Cormanich, Teodorico C. Ramalho, Matheus P. Freitas
Quantitative structure–activity relationship (QSAR) studies were recently performed to model the bioactivities of two different series of non-peptide HIV-1 protease inhibitors. The sum of the substructures of these two compound classes giving rise to new actives can cause synergistic effects on bioactivities and enhanced pharmacokinetic parameters. Therefore, the two congeneric series were joined and a MIA-QSAR model was built and used to estimate the biological activities of new compounds derived from the miscellany of substructures of the most active compounds of both series. The QSAR model was validated through leave-one-out cross-validation and external validation, and its robustness attested by means of a Y-randomization test. One of the proposed compounds was very promising and, therefore, submitted to ADME evaluation, demonstrating improved properties in comparison to the existing compounds. Docking studies demonstrated the high affinity of the novel compound towards HIV-1 protease, especially due to interactions with catalytic Asp dyad, in agreement with the expected trend obtained by QSAR for the proposed compounds and by the experimental data of the most active ligands.
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