ComDim for explorative multi-block data analysis of Cantal-type cheeses: Effects of salts, gentle heating and ripening

Loudiyi, M.; Rutledge, Douglas N.; Aït‐Kaddour, Abderrahmane

doi:10.1016/j.foodchem.2018.05.039

Cited by 12 publications

(9 citation statements)

References 31 publications

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“…principal component analysis -PCA), or a data fusion followed by PCA, with a single multi-block data analysis applied to all of them, with the advantage to bring the 'saliences' of each block indicating the importance of each one and its variability in each dimension (Bouveresse et al, 2011;Mishra et al, 2020). In the literature, ComDim has been applied to characterize complex samples in several ways (Bouveresse et al, 2011;Loudiyi, Rutledge, & Aït-Kaddour, 2018;Martin et al, 2015;Ríos-Reina, Callejón, Savorani, Amigo, & Cocchi, 2019;Rosa et al, 2017). However, the potential of multi-block methods is little explored due to the lack of experience of many analysts (Mishra et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

et al. 2021

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Coffee quality is determined by several factors and, in the chemometric domain, the multi-block data analysis methods are valuable to study multiple information describing the same samples. In this industrial study, the Common Dimension (ComDim) multi-block method was applied to evaluate metabolite fingerprints, nearinfrared spectra, sensory properties, and quality parameters of coffee blends of different cup and roasting profiles and to search relationships between these multiple data blocks. Data fusion-based Principal Component Analysis was not effective in exploiting multiple data blocks like ComDim. However, when a multi-block was applied to explore the data sets, it was possible to demonstrate relationships between the methods and techniques investigated and the importance of each block or criterion involved in the industrial quality control of coffee. Coffee blends were distinguished based on their qualities and metabolite composition. Blends with high cup quality and lower roasting degrees were generally differentiated from those with opposite characteristics.

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Section: Introductionmentioning

confidence: 99%

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

et al. 2021

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“…ComDim assumes that several tables, or blocks, describe the same fundamental phenomena, but these can have different contributions to each block. Unlike other methods of multivariate analysis using the variables‐based variance–covariance matrix, ComDim calculates a weighted sum of the samples‐based variance–covariance matrices of all the normed blocks and then extracts the first component, called the “common dimension” or common component (CC) 31 . The weights of all blocks are initially set to 1, and the extracted components are used in an iterative process to optimize the weights.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Unlike other methods of multivariate analysis using the variables-based variancecovariance matrix, ComDim calculates a weighted sum of the samples-based variance-covariance matrices of all the normed blocks and then extracts the first component, called the "common dimension" or common component (CC). 31 The weights of all blocks are initially set to 1, and the extracted components are used in an iterative process to optimize the weights. The components are extracted such that they correspond to the maximum amount of variance that is common to the largest number of blocks.…”

Section: Common Components and Specific Weights Analysis Comdimmentioning

confidence: 99%

“…Initially developed for sensory analysis, 29 ComDim or common components and specific weights analysis is a method of multiblock analysis, used for the simultaneous analysis of several matrices with different variables describing the same samples. [30][31][32] ComDim assumes that several tables, or blocks, describe the same fundamental phenomena, but these can have different contributions to each block. Unlike other methods of multivariate analysis using the variables-based variancecovariance matrix, ComDim calculates a weighted sum of the samples-based variance-covariance matrices of all the normed blocks and then extracts the first component, called the "common dimension" or common component (CC).…”

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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A synthetic review of some recent extensions of ComDim

Bouveresse

Rutledge

2022

Journal of Chemometrics

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The simultaneous analysis of several data matrices related to the same set of samples can be done with the use of multiblock methods. Common components and specific weights analysis (CCSWA), also called ComDim, is one method enabling the analysis of such data. CCSWA, which is a multiblock version of principal components analysis (PCA), is particularly interesting as it can easily be modified and transformed, either by replacing its PCA‐based data decomposition by another multivariate method, such as independent components analysis or partial least‐squares regression, for example, or by segmenting the data matrices before multiblock analysis, or both. These modifications aim at improving the models and their interpretation. This article will give a concise description of ComDim and then a presentation of several recent extensions of the method, showing its usefulness as both a supervised and a non‐supervised method.

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ComDim for explorative multi-block data analysis of Cantal-type cheeses: Effects of salts, gentle heating and ripening

Cited by 12 publications

References 31 publications

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends

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

A synthetic review of some recent extensions of ComDim

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