Maximum Likelihood Principal Component Analysis as initial projection step in Multivariate Curve Resolution analysis of noisy data

Dadashi, Mahsa; Abdollahi, Hamid; Tauler, Romà

doi:10.1016/j.chemolab.2012.07.009

Cited by 32 publications

(12 citation statements)

References 33 publications

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“…Results obtained in the analysis of three experimental data sets confirmed the conclusions raised in our previous work on simulated data sets with different noise structures and complexities [18]. MLPCA-MCR-ALS and MCR-WALS differ in the fact that MCR-WALS uses simultaneously "chemical" constraints (nonnegativity and others) and noise information during the ALS optimization, whereas MLPCA-MCR-ALS uses them sequentially, first noise information as a data pretreatment and then, separately, during the ALS optimization, the chemical constraints; it has been shown that MLPCA can be used as a preliminary projection step on ordinary MCR-ALS standard algorithms, with equivalent results than applying MCR-WALS.…”

Section: Discussionsupporting

confidence: 87%

“…This work is the continuation of a previous recent work [18] in which this comparison was systematically presented for simulated data having different noise structures, from homocedastic noise to heterocedastic, and correlated noise of different intensities and structures. Additionally, the results obtained by these two strategies will be compared with the results obtained when only MCR-ALS is applied in the traditional way.…”

Section: Introductionmentioning

confidence: 78%

“…Additionally, the results obtained by these two strategies will be compared with the results obtained when only MCR-ALS is applied in the traditional way. This work is the continuation of a previous recent work [18] in which this comparison was systematically presented for simulated data having different noise structures, from homocedastic noise to heterocedastic, and correlated noise of different intensities and structures. In the present paper, this comparison is extended to experimental data that covers different typical situations encountered in the investigation of analytical data.…”

Section: Introductionmentioning

confidence: 78%

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Application of maximum likelihood multivariate curve resolution to noisy data sets

Dadashi

Abdollahi

Tauler

2013

Journal of Chemometrics

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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.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 78%

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Application of maximum likelihood multivariate curve resolution to noisy data sets

Dadashi

Abdollahi

Tauler

2013

Journal of Chemometrics

Self Cite

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“…They published these two works in one of the top chemistry journals, Analytical Chemistry [3,4]. The research group of Abdollahi has developed different factor analysis-based curve resolution methods to resolve different chemical systems [27][28][29][30][31]. Tautomerization equilibria in aqueous micellar solutions and acid dissociation equilibria in mixed solvent media were studied using factor analysis methods by this group [27,29,32].…”

Section: Multivariate Curve Resolution and Factor Analysismentioning

confidence: 99%

A perspective on the growth of chemometrics in Iran: a glance into activities between 2005 and 2012

Naseri

Bahram

2013

Journal of Chemometrics

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Although the first publications in the field of chemometrics by Iranian researchers go back nearly 23 years, Iran's chemometrics has experienced significant growth after the year 2000. More than 1500 papers have been published by Iranian chemometricians between 2005 and 2012 which is about seven times more than those published before 2005. The aim of this review is to present a perspective about Iran's chemometrics developments. The role of enthusiastic students, motivated professors, annual workshops, and international collaborations is discussed in this work. After introducing the first papers in each field of chememotrics, the papers published during 2005–2012 were screened, and some of them were introduced. Copyright © 2013 John Wiley & Sons, Ltd.

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“…MLPCA has been widely applied in several works within chemometrics and systems biology, e.g. to analyse reflectance FTIR microspectroscopic data [331] and ion mass spectroscopic data [332], to fault detection in process industry [333], to the characterization of measurement errors in nuclear magnetic resonance (NMR) data [334] and gene expression data [335], to determine the appropriate number of reactions in stoichiometric modelling [336], and as a useful preprocessing tool for metabolomic, proteomic, transcriptomic [337] and environmental [338] data analysis.…”

Section: Introductionmentioning

confidence: 99%

Chemometric Approaches for Systems Biology

Fortuny¹

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The present Ph.D. thesis is devoted to study, develop and apply approaches commonly used in chemometrics to the emerging field of systems biology. Existing procedures and new methods are applied to solve research and industrial questions in different multidisciplinary teams. The methodologies developed in this document will enrich the plethora of procedures employed within omic sciences to understand biological organisms and will improve processes in biotechnological industries integrating biological knowledge at different levels and exploiting the software packages derived from the thesis.This dissertation is structured in four parts. The first block describes the framework in which the contributions presented here are based. The objectives of the two research projects related to this thesis are highlighted and the specific topics addressed in this document via conference presentations and research articles are introduced. A comprehensive description of omic sciences and their relationships within the systems biology paradigm is given in this part, jointly with a review of the most applied multivariate methods in chemometrics, on which the novel approaches proposed here are founded.The second part addresses many problems of data understanding within metabolomics, fluxomics, proteomics and genomics. Different alternatives are proposed in this block to understand flux data in steady state conditions. Some are based on applications of multivariate methods previously applied in other chemometrics areas. Others are novel approaches based on a bilinear decomposition using elemental metabolic pathways, from which a GNU licensed toolbox is made freely available for the scientific community. As well, a framework for metabolic data understanding is proposed for non-steady state data, using the same bilinear decomposition proposed for steady state data, but modelling the dynamics of the experiments using novel two and three-way data analysis procedures. Also, the relationships between different omic levels are assessed in this part integrating different sources of information of plant viruses in data fusion models. Finally, an example of interaction between organisms, oranges and fungi, is studied via multivariate image analysis techniques, with future application in food industries.v The third block of this thesis is a thoroughly study of different missing data problems related to chemometrics, systems biology and industrial bioprocesses. In the theoretical chapters of this part, new algorithms to obtain multivariate exploratory and regression models in the presence of missing data are proposed, which serve also as preprocessing steps of any other methodology used by practitioners. Regarding applications, this block explores the reconstruction of networks in omic sciences when missing and faulty measurements appear in databases, and how calibration models between near infrared instruments can be transferred, avoiding costs and time-consuming full recalibrations in bioindustries and research laboratories. Finally,...

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Maximum Likelihood Principal Component Analysis as initial projection step in Multivariate Curve Resolution analysis of noisy data

Cited by 32 publications

References 33 publications

Application of maximum likelihood multivariate curve resolution to noisy data sets

Application of maximum likelihood multivariate curve resolution to noisy data sets

A perspective on the growth of chemometrics in Iran: a glance into activities between 2005 and 2012

Chemometric Approaches for Systems Biology

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