The Experts below are selected from a list of 41589 Experts worldwide ranked by ideXlab platform
Roma Tauler - One of the best experts on this subject based on the ideXlab platform.
-
Balanced scaling as a pretreatment step in Multivariate Curve Resolution analysis of noisy data
Microchemical Journal, 2021Co-Authors: Jamile Mohammad Jafari, Roma Tauler, Hamid AbdollahiAbstract:Abstract Analysis of data sets with heteroscedastic error has been a challenging problem in the chemometrics literature. Different methods have been proposed for analyzing this type of data, in particular, using the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method. The present paper introduces the Balanced Scaling (BS) approach as a pretreatment step combined with the Multivariate Curve Resolution Alternating Least Squares (BS-MCR-ALS) method as an adequate procedure to analyze data with heteroscedastic noise. In particular, for the analysis of environmental data, the Balanced Scaling (BS) method can be a useful approach to provide an optimal individual data scaling. The performance of the BS-MCR-ALS method is compared with the performance of the Maximum Likelihood Principal Component Analysis Multivariate Curve Resolution Alternating Least Squares (MLPCA-MCR-ALS) method, and also with the performance of the traditional Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method in the analysis of data sets with different type of error structures. The results obtained in this comparison revealed that the solutions obtained by BS-MCR-ALS and MLPCA-MCR-ALS were very similar.
-
Data Fusion by Multivariate Curve Resolution
Data Handling in Science and Technology, 2019Co-Authors: A. De Juan, Roma TaulerAbstract:Abstract Data fusion implies often the concatenation of data sets that present an enormous diversity in terms of information, size, and behavior. The pieces of information connected reflect the variation apportioned by components, events, or sources that are differently represented and, yet, complement each other in the data blocks analyzed simultaneously. Multivariate Curve Resolution (MCR) was born as a tool to unmix the information in a single data set into a bilinear model of chemically meaningful profiles associated with pure components or sources. With the increase of complexity of chemical problems and the need to perform data fusion to understand all the aspects related to a particular scenario, multiset analysis by MCR came into play. Multiset analysis performed by MCR has two main advantages, the first stemming from the intrinsic versatile multiset structure and the second linked to the per block, per component, and per mode flexible application of constraints to model pure profiles by MCR, which covers the specific needs of the diverse blocks of information present in a data fusion framework. These two essential aspects are extensively developed in this chapter, and a final representative report on the main fields of application of data fusion by MCR is also provided.
-
Multivariate Curve Resolution-Alternating Least Squares for Spectroscopic Data
Data Handling in Science and Technology, 2016Co-Authors: A. De Juan, Roma TaulerAbstract:Abstract The chapter describes the algorithm multivariate Curve Resolution-alternating least squares (MCR-ALS), paying special attention to applications to analyze spectroscopic data. The main application fields addressed are process and image analysis. A brief comment on the specific use of MCR for quantitative analysis is done. Finally, a small note on how MCR-ALS compares to similar bilinear or multilinear decomposition methods is written as a conclusion of this chapter.
-
Ambiguities in Multivariate Curve Resolution
Data Handling in Science and Technology, 2016Co-Authors: A. Malik, Roma TaulerAbstract:Abstract The presence of ambiguities and unique solutions in multivariate Curve Resolution (MCR) chemometric methods along with several methods for the estimation of the range or area of feasible solutions (AFS) associated to ambiguous MCR solution is discussed. The MCR-BANDS method provides an easy and flexible estimation of the extension of rotation ambiguities for any number of components and constraints. Other methods provide a more accurate display of the geometrical space spanned by all feasible solutions.
-
Potential use of multivariate Curve Resolution for the analysis of mass spectrometry images
The Analyst, 2015Co-Authors: Joaquim Jaumot, Roma TaulerAbstract:In this work the application of multivariate Curve Resolution is proposed for the analysis of Mass Spectrometry Imaging (MSI) data. Recently, developments in the ionization of samples have dramatically expanded the number of applications of MSI due to the possibility of collecting the mass spectrum for each pixel of a considered surface in a reasonable time. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. However, there are major drawbacks inherent to MSI related to the high complexity of the data obtained from real samples and to the extremely huge size of the datasets generated by this technique. Therefore, the potential of chemometrical tools in different steps of the analysis process is unquestionable, from data compression to data Resolution of the different components present at each pixel of the image. In this work, this data analysis is carried out by means of the multivariate Curve Resolution method. The benefits of the application of this method are shown for two examples consisting of a MS image of two platted microbes and a MS image of a mouse lung section. The results show that multivariate Curve Resolution allows us to obtain distribution maps of different components and their identification from resolved high-Resolution mass spectra.
Romà Tauler - One of the best experts on this subject based on the ideXlab platform.
-
Multivariate Curve Resolution of hyphenated and multidimensional chromatographic measurements: a new insight to address current chromatographic challenges.
Analytical chemistry, 2013Co-Authors: Hadi Parastar, Romà TaulerAbstract:In this Feature, the capabilities and versatility of multivariate Curve Resolution methods are discussed in light of the current challenges in chromatographic measurements, with special emphasis on...
-
Application of maximum likelihood multivariate Curve Resolution to noisy data sets
Journal of Chemometrics, 2013Co-Authors: Mahsa Dadashi, Hamid Abdollahi, Romà TaulerAbstract:In this work, two different maximum likelihood approaches for multivariate Curve Resolution based on maximum likelihood principal component analysis (MLPCA) and on weighted alternating least squares (WALS) are compared with the standard multivariate Curve Resolution alternating least squares (MCR-ALS) method. To illustrate this comparison, three different experimental data sets are used: the first one is an environmental aerosol source apportionment; the second is a time-course DNA microarray, and the third one is an ultrafast absorption spectroscopy. Error structures of the first two data sets were heteroscedastic and uncorrelated, and the difference between them was in the existence of missing values in the second case. In the third data set about ultrafast spectroscopy, error correlation between the values at different wavelengths is present. The obtained results confirmed that the resolved component profiles obtained by MLPCA-MCR-ALS are practically identical to those obtained by MCR-WALS and that they can differ from those resolved by ordinary MCR-ALS, especially in the case of high noise. It is shown that methods that incorporate uncertainty estimations (such as MLPCA-ALS and MCR-WALS) can provide more reliable results and better estimated parameters than unweighted approaches (such as MCR-ALS) in the case of the presence of high amounts of noise. The possible advantage of using MLPCA-MCR-ALS over MCR-WALS is then that the former does not require changing the traditional MCR-ALS algorithm because MLPCA is only used as a preliminary data pretreatment before MCR analysis. Copyright © 2013 John Wiley & Sons, Ltd.
-
uniqueness and rotation ambiguities in multivariate Curve Resolution methods
Chemometrics and Intelligent Laboratory Systems, 2011Co-Authors: Hamid Abdollahi, Romà TaulerAbstract:Abstract The presence of rotation ambiguities and unique solutions in Multivariate Curve Resolution (MCR) chemometric methods is discussed in detail. Using recently proposed graphical approaches to display the bands and areas of feasible solutions in a subspace of reduced dimensions, the results obtained by different MCR methods are compared. These results show that in the presence of rotation ambiguities and under a particular set of constraints, the solutions obtained by the different MCR methods can differ among them and also from the true solution depending on initial estimates and on the applied algorithm. In absence of rotational ambiguities, all MCR methods should give the same unique solution which should be equal to the true one. Many of the MCR methods proposed in the literature like MCR-ALS, RFA, MCR-FMIN, or MCR-BANDS are confirmed to give a valid solution within the band or area of feasible solutions. On the contrary, and according to the results of this study, in its present implementation, the minimum volume simplex analysis, MVSA method can give unfeasible solutions when resolving bilinear data systems with more than two components, because it only applies non-negativity constraints to concentration profiles and not to spectral profiles.
-
Kinetic studies of nitrofurazone photodegradation by multivariate Curve Resolution applied to UV-spectral data.
International Journal of Pharmaceutics, 2009Co-Authors: Michele De Luca, Sílvia Mas, Giuseppina Ioele, F Oliverio, Gaetano Ragno, Romà TaulerAbstract:This work aims at describing the kinetic model of nitrofurazone photodegradation by a novel chemometric technique, hybrid hard–soft multivariate Curve Resolution (HS-MCR). The study was applied to UV-spectral data from the photolysis of nitrofurazone solutions at different concentrations and exposed under varying illuminance power. The HS-MCR method was able to elucidate the kinetics of the photodegradation process and to determine the rate constants, and estimating at the same time the pure spectra of the degradation products. Exposure to light of the drug gave a first rapid isomerization to the syn-form that in turn underwent degradation furnishing a mixture of yellow-red products. The photodegradation process could be explained with a kinetic model based on three consecutive first-order reactions (A > B, B > C and C > D). These results were confirmed by application of the MCR procedure to the analysis of the data obtained from HPLC-DAD analysis of the nitrofurazone samples at different reaction times. The kinetic model was observed to be dependent on experimental conditions. The samples at higher concentrations showed rate constants lower than the diluted samples, whereas an increase of the rate of all degradation processes was observed when the light power also increased. This work shows the power of the hybrid hard- and soft-multivariate Curve Resolution as a method to deeply study degradation processes of photolabile drugs.
-
Exploratory data analysis of DNA microarrays by multivariate Curve Resolution.
Analytical biochemistry, 2006Co-Authors: Joaquim Jaumot, Romà Tauler, Raimundo GargalloAbstract:In this work, the application of a multivariate Curve Resolution procedure based on alternating least squares optimization (MCR-ALS) for the analysis of data from DNA microarrays is proposed. For this purpose, simulated and publicly available experimental data sets have been analyzed. Application of MCR-ALS, a method that operates without the use of any training set, has enabled the Resolution of the relevant information about different cancer lines classification using a set of few components; each of these defined by a sample and a pure gene expression profile. From resolved sample profiles, a classification of samples according to their origin is proposed. From the resolved pure gene expression profiles, a set of over- or underexpressed genes that could be related to the development of cancer diseases has been selected. Advantages of the MCR-ALS procedure in relation to other previously proposed procedures such as principal component analysis are discussed.
Hamid Abdollahi - One of the best experts on this subject based on the ideXlab platform.
-
Balanced scaling as a pretreatment step in Multivariate Curve Resolution analysis of noisy data
Microchemical Journal, 2021Co-Authors: Jamile Mohammad Jafari, Roma Tauler, Hamid AbdollahiAbstract:Abstract Analysis of data sets with heteroscedastic error has been a challenging problem in the chemometrics literature. Different methods have been proposed for analyzing this type of data, in particular, using the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method. The present paper introduces the Balanced Scaling (BS) approach as a pretreatment step combined with the Multivariate Curve Resolution Alternating Least Squares (BS-MCR-ALS) method as an adequate procedure to analyze data with heteroscedastic noise. In particular, for the analysis of environmental data, the Balanced Scaling (BS) method can be a useful approach to provide an optimal individual data scaling. The performance of the BS-MCR-ALS method is compared with the performance of the Maximum Likelihood Principal Component Analysis Multivariate Curve Resolution Alternating Least Squares (MLPCA-MCR-ALS) method, and also with the performance of the traditional Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method in the analysis of data sets with different type of error structures. The results obtained in this comparison revealed that the solutions obtained by BS-MCR-ALS and MLPCA-MCR-ALS were very similar.
-
On the restrictiveness of equality constraints in multivariate Curve Resolution
Chemometrics and Intelligent Laboratory Systems, 2020Co-Authors: Mathias Sawall, Hamid Abdollahi, Armin Börner, Christoph Kubis, Somaye Vali Zade, Henning Schröder, Denise Meinhardt, Alexander Brächer, Robert Franke, Klaus NeymeyrAbstract:Abstract Multivariate Curve Resolution methods suffer from the non-uniqueness of the solutions of the nonnegative matrix factorization problem. The solution ambiguity can be considerably reduced by equality constraints in the form of known spectra or concentration profiles. Two measures are suggested that indicate the impact of the equality constraints. The representation of these measures in the area of feasible solutions show strong variations in the restrictiveness of equality constraints. The measures are tested for a three-component model problem and experimental data sets from the hydroformylation process and a catalyst cluster formation.
-
A conceptual view to the area correlation constraint in multivariate Curve Resolution
Chemometrics and Intelligent Laboratory Systems, 2019Co-Authors: Mahdiyeh Ghaffari, Hamid AbdollahiAbstract:Abstract Multivariate Curve Resolution (MCR) techniques are widely employed to study chemical systems and unravel underlying physic-chemical information. Different constraints have been introduced to reduce the extent of uncertainty in the results of MCR-ALS. It is important to study the effect of each constraint on the range of feasible solutions in the Curve-Resolution studies. As each constraint uses specific information, constraints have different effects on the amount of uncertainty. In the present contribution, condition which resulted in uniqueness applying area correlation constraint (ACC) was illustrated. Additionally, the effect of area correlation constraint (relative known-value constraints) on the amount of uncertainty in the resolved profiles was investigated and visualized in detail. Besides, some rules were established for determining the required number and composition of calibration samples. To illustrate how ACC leads to the uniqueness of a MCR result in the presence of validation samples, the extent of rotational ambiguity was evaluated by analyzing different simulated and experimental data sets. The latter involves the quantification of Phenylalanine and Tyrosine in a second-order excitation-emission fluorescence data sets.
-
Introducing the monotonicity constraint as an effective chemistry-based condition in self-modeling Curve Resolution
Chemometrics and Intelligent Laboratory Systems, 2019Co-Authors: Somaye Vali Zade, Klaus Neymeyr, Mathias Sawall, Hamid AbdollahiAbstract:Abstract The results by soft modeling multivariate Curve Resolution methods often are not unique and are questionable because of the rotational ambiguity. It means a range of feasible solutions equally fits experimental data and fulfills the constraints. Regarding to chemometric literature, the reduction of the rotational ambiguity in multivariate Curve Resolution problems is a major challenge in order to construct effective chemometric methods. It is worth to study the effects of applying constraints on the reduction of the rotational ambiguity, since it can help us to choose the useful constraints for multivariate Curve Resolution methods for analyzing data sets. The aim of this work is to demonstrate the impact of monotonicity and unimodality constraints on the full set of all feasible, nonnegative solutions. We compared the results of two constraints in different two- and three-component systems. To reach this goal, two simulated kinetic and equilibrium data sets are used. Moreover, an experimental data set related to a mixture of two uniprotic acid solutions at different pH values as a model for equilibrium systems is used to extend the discussions to real cases. It is shown in this work that monotonicity is a meaningful chemistry-based constraint which in some cases is more effective than unimodality.
-
Known-value constraint in multivariate Curve Resolution
Analytica chimica acta, 2018Co-Authors: Mahsa Akbari Lakeh, Hamid AbdollahiAbstract:Abstract Multivariate Curve Resolution (MCR) methods are powerful chemometric approaches that have been significantly involved to study the complex chemical systems. The accuracy of MCR results is directly related to the applied constraints which determine the properties of the resolved profiles. Constraints have been, and still are, an active field of research in MCR studies. Different constraints have different impacts on the range of feasible solutions, so it is important to study the effect of each constraint, to examine its compliance with physico-chemical principles, and to find sufficient conditions to ensure unique solutions. In this study, we focus on known-value constraint and the requirements for reducing the range of feasible solutions under this constraint. In addition, several theoretical rules are presented to determine the minimum number of required known-values in order to get a unique solution. The theory of known-values was accessed using several simulated and experimental datasets. As shown previously, known-value information can be applied in quantitative analysis of first-order data. The prediction performance of MCR-ALS method under known-value constraint was compared to the results of the well stablished partial least squares (PLS) method in the presence of minimum number of required calibration samples. The comparison shows the advantage of using MCR-ALS method under known-value constraint over the PLS results in such experiments.
Joaquim Jaumot - One of the best experts on this subject based on the ideXlab platform.
-
Potential use of multivariate Curve Resolution for the analysis of mass spectrometry images
The Analyst, 2015Co-Authors: Joaquim Jaumot, Roma TaulerAbstract:In this work the application of multivariate Curve Resolution is proposed for the analysis of Mass Spectrometry Imaging (MSI) data. Recently, developments in the ionization of samples have dramatically expanded the number of applications of MSI due to the possibility of collecting the mass spectrum for each pixel of a considered surface in a reasonable time. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. However, there are major drawbacks inherent to MSI related to the high complexity of the data obtained from real samples and to the extremely huge size of the datasets generated by this technique. Therefore, the potential of chemometrical tools in different steps of the analysis process is unquestionable, from data compression to data Resolution of the different components present at each pixel of the image. In this work, this data analysis is carried out by means of the multivariate Curve Resolution method. The benefits of the application of this method are shown for two examples consisting of a MS image of two platted microbes and a MS image of a mouse lung section. The results show that multivariate Curve Resolution allows us to obtain distribution maps of different components and their identification from resolved high-Resolution mass spectra.
-
multivariate Curve Resolution mcr solving the mixture analysis problem
Analytical Methods, 2014Co-Authors: A. De Juan, Joaquim Jaumot, Roma TaulerAbstract:This article is a tutorial that focuses on the main aspects to be considered when applying Multivariate Curve Resolution to analyze multicomponent systems, particularly when the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm is used. These aspects include general MCR comments on the potential fields of application and construction of data structures and details linked to each of the steps in the application workflow of the MCR-ALS algorithm (e.g., selection of initial estimates, choice and application of constraints, quality parameters of models and assessment of ambiguity,…). Two examples with downloadable data sets are shown for orientation on the practical use of this methodology.
-
MCR-BANDS: A user friendly MATLAB program for the evaluation of rotation ambiguities in Multivariate Curve Resolution
Chemometrics and Intelligent Laboratory Systems, 2010Co-Authors: Joaquim Jaumot, Roma TaulerAbstract:A new user friendly graphical interface and a command line MATLAB computer program for the evaluation of the extent of rotation ambiguities associated to Multivariate Curve Resolution solutions are presented. Different examples of application are shown including the simultaneous analysis of multiple data sets and the implementation of local rank and trilinearity constraints, basic tools to reduce and eliminate rotation ambiguities. The program allows for an easy check of the extent of rotation ambiguity remaining in Multivariate Curve Resolution solutions in the investigation of a particular system and it also allows for the checking of the effect of applied constraints. In this way, conditions and limitations to achieve optimal solutions in Multivariate Curve Resolution are easily assessed.
-
Application of multivariate Curve Resolution to the analysis of yeast genome-wide screens
Chemometrics and Intelligent Laboratory Systems, 2010Co-Authors: Joaquim Jaumot, Benjamin Piña, Roma TaulerAbstract:In this work, the application of Multivariate Curve Resolution to the analysis of yeast genome-wide screens obtained by means of DNA microarray technology is shown. In order to perform the analysis of this type of data, two algorithms based on Alternating Least Squares (MCR-ALS) and on its maximum likelihood weighted projection (MCR-WALS) variant are compared. The utilization of the modified weighted alternating least (WALS) squares algorithm is motivated by the rather poor quality, uncertainties and experimental noise associated to DNA microarray data. Moreover, a large number of missing values are usually present in these data sets and the weighted WALS approach allowed circumventing this problem. Two different experimental datasets were used for this comparison. In the first dataset, gene expression values in budding yeast were monitored in-response to glucose limitation. In the second dataset, the changes in the gene expression caused by the daunorubicin drug were monitored as a function of time. Results obtained by application of Multivariate Curve Resolution in the two cases allowed a good recovery of the evolving gene expression profiles and the identification of metabolic pathways and individual genes involved in these gene expression changes.
-
Exploratory data analysis of DNA microarrays by multivariate Curve Resolution.
Analytical biochemistry, 2006Co-Authors: Joaquim Jaumot, Romà Tauler, Raimundo GargalloAbstract:In this work, the application of a multivariate Curve Resolution procedure based on alternating least squares optimization (MCR-ALS) for the analysis of data from DNA microarrays is proposed. For this purpose, simulated and publicly available experimental data sets have been analyzed. Application of MCR-ALS, a method that operates without the use of any training set, has enabled the Resolution of the relevant information about different cancer lines classification using a set of few components; each of these defined by a sample and a pure gene expression profile. From resolved sample profiles, a classification of samples according to their origin is proposed. From the resolved pure gene expression profiles, a set of over- or underexpressed genes that could be related to the development of cancer diseases has been selected. Advantages of the MCR-ALS procedure in relation to other previously proposed procedures such as principal component analysis are discussed.
A. De Juan - One of the best experts on this subject based on the ideXlab platform.
-
Multivariate Curve Resolution for hyperspectral image analysis
Data Handling in Science and Technology, 2020Co-Authors: A. De JuanAbstract:Abstract Multivariate Curve Resolution (MCR) designs a family of methods able to provide qualitative (spectral signatures) and quantitative (concentration maps) information from image constituents using only the spectroscopic image measurement as initial information. MCR is a chemometric tool that uses constraints related to natural chemical or mathematical properties to drive the optimization of the concentration and spectral profiles sought. Progress in the use of MCR to analyze hyperspectral images has implied the development of constraints that take into account spatial image properties, or the use of the method for the analysis of image multisets formed by related images collected with the same platform or for image fusion scenarios involving images of different platforms. Besides, MCR scores and loadings are excellent compressed, noise-filtered, and compound-specific image information that can be used for further analysis purposes, such as segmentation, identification, quantitative analysis, or discriminant analysis.
-
Data Fusion by Multivariate Curve Resolution
Data Handling in Science and Technology, 2019Co-Authors: A. De Juan, Roma TaulerAbstract:Abstract Data fusion implies often the concatenation of data sets that present an enormous diversity in terms of information, size, and behavior. The pieces of information connected reflect the variation apportioned by components, events, or sources that are differently represented and, yet, complement each other in the data blocks analyzed simultaneously. Multivariate Curve Resolution (MCR) was born as a tool to unmix the information in a single data set into a bilinear model of chemically meaningful profiles associated with pure components or sources. With the increase of complexity of chemical problems and the need to perform data fusion to understand all the aspects related to a particular scenario, multiset analysis by MCR came into play. Multiset analysis performed by MCR has two main advantages, the first stemming from the intrinsic versatile multiset structure and the second linked to the per block, per component, and per mode flexible application of constraints to model pure profiles by MCR, which covers the specific needs of the diverse blocks of information present in a data fusion framework. These two essential aspects are extensively developed in this chapter, and a final representative report on the main fields of application of data fusion by MCR is also provided.
-
Multivariate Curve Resolution-Alternating Least Squares for Spectroscopic Data
Data Handling in Science and Technology, 2016Co-Authors: A. De Juan, Roma TaulerAbstract:Abstract The chapter describes the algorithm multivariate Curve Resolution-alternating least squares (MCR-ALS), paying special attention to applications to analyze spectroscopic data. The main application fields addressed are process and image analysis. A brief comment on the specific use of MCR for quantitative analysis is done. Finally, a small note on how MCR-ALS compares to similar bilinear or multilinear decomposition methods is written as a conclusion of this chapter.
-
Multivariate Curve Resolution for Quantitative Analysis
Data Handling in Science and Technology, 2015Co-Authors: Roma Tauler, A. De JuanAbstract:Abstract This chapter starts describing the basics of Multivariate Curve Resolution, including the modus operandi, description of constraints and use for analysis of single data sets and multiset analysis. An exhaustive section is devoted to the use of MCR for quantitative purposes. In this last part, classical extraction of quantitative information in second-order calibration problems is described and adaptation of classical analytical strategies, such as standard addition or the use of internal standards is also reported. Additionally, the power of the correlation constraint, an internal calibration procedure within the ALS optimization is presented for first-order and second-order methods. Finally, a thorough explanation on validation, sources of error and figures of merit associated with calibration problems tackled with MCR closes this chapter.
-
multivariate Curve Resolution mcr solving the mixture analysis problem
Analytical Methods, 2014Co-Authors: A. De Juan, Joaquim Jaumot, Roma TaulerAbstract:This article is a tutorial that focuses on the main aspects to be considered when applying Multivariate Curve Resolution to analyze multicomponent systems, particularly when the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm is used. These aspects include general MCR comments on the potential fields of application and construction of data structures and details linked to each of the steps in the application workflow of the MCR-ALS algorithm (e.g., selection of initial estimates, choice and application of constraints, quality parameters of models and assessment of ambiguity,…). Two examples with downloadable data sets are shown for orientation on the practical use of this methodology.
