Identification of significant factors by an extension of ANOVA–PCA based on multi-block analysis

Bouveresse, Delphine Jouan-Rimbaud; Pinto, Rui; Schmidtke, Leigh; Locquet, Nathalie; Rutledge, Douglas N.

doi:10.1016/j.chemolab.2010.05.005

Cited by 69 publications

(50 citation statements)

References 22 publications

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“…() and described in detail by Jouan‐Rimbaud Bouveresse et al . (). The rationale behind this method is the existence of a common structure to the data tables.…”

Section: Methodsmentioning

confidence: 97%

“…Although, in certain cases, it can decrease the signal‐to‐noise ratio, the normalisation of the data matrices needs to be done to ensure that all data blocks have similar orders of magnitudes, so that no table predominates over the others, which would reduce the influence of the matrices with low orders of magnitude. As the p original data blocks are column‐centred and normalised, p also corresponds to the total variance of data at the beginning of the procedure (Jouan‐Rimbaud Bouveresse et al ., ). The formulation of the CCSWA model in terms of the association matrices ( W ) is represented in equation :

W_{i} = Q {normalΛ}_{i} Q^{'} + E_{i}

with Q as the matrix whose columns are vectors q 1 , q 2 ,…, q p (common underlying dimensions), Λ i as a diagonal matrix whose diagonal elements are denoted λ 1 ( i ) , λ 2 ( i ) , … λ n ( i ) and E i as the residual matrix.…”

Section: Methodsmentioning

confidence: 97%

“…In this context, common components and specific weights analysis (CCSWA) is a method of analysis that simplifies comparison between data tables and verifies relationships in analytical parameters more efficiently than linear regressions (Blackman et al ., ). Furthermore, multiblock analysis methods can efficiently relate data obtained from different techniques such as spectroscopic, chromatography and physicochemical analyses in the same sample (Jouan‐Rimbaud Bouveresse et al ., ). This method can obtain relationships between data sets for the global characterisation of food products, as they are complex systems and characterised by different parameters (Mazerolles et al ., ).…”

Section: Introductionmentioning

confidence: 97%

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Common components and specific weights analysis for the discrimination and evaluation of vegetable oil quality

Ferreira

Santos

Rampazzo

et al. 2017

Int J of Food Sci Tech

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Summary The aim of this study was to assess the quality of different vegetable oils by applying common components and specific weights analysis as a tool for the evaluation and discrimination of chromatography, spectral and physicochemical data. This multiblock method of data analysis divided the data into three common components, corresponding to 56.44%, 34.74% and 8.77% of variance, and it was influenced mostly by chromatography, physicochemical and spectral data, respectively. Gas chromatography, which was used for discrimination of the botanical origin of oil and the groups of saturated, monounsaturated and polyunsaturated fatty acids, was situated in the first common dimension; physicochemical analysis, which was applied to evaluate quality parameters such as acid and saponification value and determine the stability of the product, was situated in the second common dimension. FTIR analysis, by exerting a minor influence on the common dimensions, was considered dispensable in evaluating the quality of vegetable oils by common components and specific weights analysis. Therefore, multiblock analysis could efficiently discriminate vegetable oils.

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“…() and described in detail by Jouan‐Rimbaud Bouveresse et al . (). The rationale behind this method is the existence of a common structure to the data tables.…”

Section: Methodsmentioning

confidence: 97%

W_{i} = Q {normalΛ}_{i} Q^{'} + E_{i}

Section: Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Common components and specific weights analysis for the discrimination and evaluation of vegetable oil quality

Ferreira

Santos

Rampazzo

et al. 2017

Int J of Food Sci Tech

View full text Add to dashboard Cite

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“…In CCSWA the values of λ i are known as saliences and are set to 1 in the first iteration of the algorithm. In sequence, singular value decomposition (SVD) of the global matrix W G was performed:

W_{G} = boldUS V^{T}

…”

Section: Methodsmentioning

confidence: 99%

“…The objective of CCSWA is to identify within a set of tables for the same samples, but with different variables, a common space representation establishing different weights for each original table. Thus CCSWA uses the most important common components (CC) to explain the variance of the data, and it is possible to correlate CC values with the variables contained in the set of tables …”

Section: Introductionmentioning

confidence: 99%

Application of multi‐block analysis and mixture design with process variable for development of chocolate cake containing yacon (Smallanthus sonchifolius) and maca (Lepidium meyenii)

et al. 2017

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Chemometrics Applied to Nuclear Magnetic Resonance Analysis

Beavis

Rusilowicz

Donarski

et al. 2019

Encyclopedia of Analytical Chemistry

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Nuclear magnetic resonance (NMR) spectroscopy provides information about the physical and chemical properties of atoms within a sample and has become an essential tool in metabolomics. Although less sensitive than mass spectrometry, NMR spectroscopy is highly reproducible and gives wider coverage in comparison to other techniques used for the analysis of complex mixtures. Recent advances in technology, advances in methodology, and increased computer power have increased the need to develop mathematical and statistical methods to analyze and interpret the complex datasets acquired. This has resulted in developments in the field of chemical informatics known as chemometrics . In contrast to targeted analyses, chemometric approaches do not initially attempt to identify particular metabolites. Instead, statistical techniques and pattern recognition methods are used to identify spectral features that show consistent trends or provide discrimination between classes. These features can provide a fingerprint for a metabolic state to be used, for example, for disease diagnostics, and individual features of interest can then be related to specific compounds and metabolic pathways using database searches. NMR‐based chemometric methods are now used in a wide range of applications including clinical diagnostics, food science and traceability, process control, monitoring genetic modification, and predicting the side effects of pharmaceuticals and their environmental effects.

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Identification of significant factors by an extension of ANOVA–PCA based on multi-block analysis

Cited by 69 publications

References 22 publications

Common components and specific weights analysis for the discrimination and evaluation of vegetable oil quality

Common components and specific weights analysis for the discrimination and evaluation of vegetable oil quality

Application of multi‐block analysis and mixture design with process variable for development of chocolate cake containing yacon (Smallanthus sonchifolius) and maca (Lepidium meyenii)

Chemometrics Applied to Nuclear Magnetic Resonance Analysis

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