Common components and specific weights analysis: A tool for metabolomic data pre-processing

Dubin, Elodie; Spiteri, Marc; Dumas, Anne-Sophie; Ginet, Jérôme; Lees, M.; Rutledge, Douglas N.

doi:10.1016/j.chemolab.2015.11.005

Cited by 22 publications

(9 citation statements)

References 15 publications

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“…The components are extracted such that they correspond to the maximum amount of variance that is common to the largest number of blocks. In other words, it tries to find a common space for all the blocks, where each block has a specific weight (called salience) for the definition of each direction in that space, as represented by Equation 33 :

W_{i} = {QΛ}_{i} Q^{T} = {falsefalse}_{\sum}^{boldj = bold1} λ_{j}^{(i)} q_{j} q_{j}^{T},

where Q is the matrix with columns that are the common components defined by q j . Λ i is the diagonal matrix of which the j th diagonal element is

λ_{j}^{(bold-italici)}

, is the salience/weight of the i th table for the j th CC generated by q j , and W i is the i th sample‐based variance–covariance matrix.…”

Section: Methodsmentioning

confidence: 99%

Section: Common Components and Specific Weights Analysis Comdimmentioning

confidence: 99%

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Extraction of information about structural changes in a semisolid pharmaceutical formulation from near‐infrared and Raman images by multivariate curve resolution–alternating least squares and ComDim

Mitsutake

Neves

Rutledge

et al. 2020

Journal of Chemometrics

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Solid dispersions are an interesting option to improve the solubility of Class II (high permeability, low water solubility) drugs of the Biopharmaceutical Classification System without the use of organic solvents. However, structural changes (polymorphism) may be present in both active pharmaceutical ingredients (API) and excipients, which require evaluation during pharmaceutical development. Thus, the aim of this work was to demonstrate the feasibility of using Raman and near-infrared (NIR) mapping associated with multivariate curve resolution-alternating least squares (MCR-ALS) and common components and specific weights analysis (CCSWA or ComDim) chemometric methods to assess the solid dispersions of atorvastatin calcium in Gelucire ® 48/16 under controlled (3.0 ± 1.0 C, sealed flask) and accelerated (33.0 ± 1.0 C, 75% of relative humidity, open flask) aging conditions. MCR-ALS allowed the identification of the amorphous and crystalline fractions of the drug within the solid dispersion, which is not easily achieved by traditional techniques such as differential scanning calorimetry and X-ray diffraction, especially for semisolid formulations. ComDim results indicated that Raman seemed more sensitive to the presence of the API, whereas NIR was found to be more sensitive to alterations in the spectra of the excipient. The use of the ComDim method is not yet widespread in the pharmaceutical area; therefore, this paper shows its potential to support pharmaceutical development in preformulation stages for stability studies.

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W_{i} = {QΛ}_{i} Q^{T} = {falsefalse}_{\sum}^{boldj = bold1} λ_{j}^{(i)} q_{j} q_{j}^{T},

where Q is the matrix with columns that are the common components defined by q j . Λ i is the diagonal matrix of which the j th diagonal element is

λ_{j}^{(bold-italici)}

, is the salience/weight of the i th table for the j th CC generated by q j , and W i is the i th sample‐based variance–covariance matrix.…”

Section: Methodsmentioning

confidence: 99%

Section: Common Components and Specific Weights Analysis Comdimmentioning

confidence: 99%

Extraction of information about structural changes in a semisolid pharmaceutical formulation from near‐infrared and Raman images by multivariate curve resolution–alternating least squares and ComDim

Mitsutake

Neves

Rutledge

et al. 2020

Journal of Chemometrics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the number of samples was the same for all blocks, all W i matrices had the same size (n×n). The common dimensions of all matrix were calculated iteratively according to 35,46 where d is the number of dimensions which has to be fixed, λdim (i) is the specific weight (=“salience”) of the matrix X i in the construction of the common component q dim in the dimension dim , and R i is the residual matrix of X i . So, each common component q dim is weighted by a scalar λdim (i) reflecting the contribution of the matrix X i in the construction of q dim .…”

Section: Methodsmentioning

confidence: 99%

Multiangle near infrared spectroscopy associated with common components and specific weights analysis for in line monitoring

Rey-Bayle

Bendoula

Caillol³

et al. 2019

Journal of Near Infrared Spectroscopy

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Near infrared spectroscopy offers a number of important advantages for process monitoring. In addition to its numerous practical advantages, an important reason to use near infrared spectroscopy for process monitoring is its ability to supply versatile and multivariate information. However, in heterogeneous samples the interaction of light is complex and includes transmission, absorption, and scattering simultaneously which all affect spectra. The measurement of the signal at one point may be insufficient. A solution is to measure the medium at several points and to use specific multivariate analysis. In our study we propose to associate multipoint measurements with a common components and specific weight analysis. We monitored two media online by angular multipoint near infrared spectroscopy. For the first medium, in which only the scattering varies over time, the precipitation of silica was chosen to illustrate such a medium. For the second medium, both scattering and absorption vary, whereby microemulsions implemented for enhanced oil recovery illustrate this medium. The results showed, by combining multiangle measurements to common components and specific weight analysis, the interest of measuring at different angles. In the first case, two scattering regimes have been identified and it was possible to access the anisotropy coefficient during the silica precipitation reaction. In the second case study, on microemulsions, it was possible to identify the different phases and to separate the phenomena related to absorption and those related to diffusion. These encouraging results validate the interest of coupling multiangle measurements with multivariate multiblock analysis tools.

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“…Common Components and Specific Weights Analysis (CCSWA) is proposed as a novel method for correction of analytical bias such as intensity variability (both within‐ and between‐batch effects) in LC‐MS‐based metabolomics studies . Using two different, LC‐ToF‐MS study datasets the authors compared against locally weighted scatterplot smoothing (LOWESS/ LOESS) normalization, and demonstrated the superiority of CCSWA in handling noncontinuous effects in a batch as well as between batch effects.…”

Section: Data Preprocessing Toolsmentioning

confidence: 99%

Review of emerging metabolomic tools and resources: 2015–2016

Misra

Fahrmann

2017

Electrophoresis

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Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

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Common components and specific weights analysis: A tool for metabolomic data pre-processing

Cited by 22 publications

References 15 publications

Extraction of information about structural changes in a semisolid pharmaceutical formulation from near‐infrared and Raman images by multivariate curve resolution–alternating least squares and ComDim

Extraction of information about structural changes in a semisolid pharmaceutical formulation from near‐infrared and Raman images by multivariate curve resolution–alternating least squares and ComDim

Multiangle near infrared spectroscopy associated with common components and specific weights analysis for in line monitoring

Review of emerging metabolomic tools and resources: 2015–2016

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