A framework for sequential multiblock component methods

Smilde, Age K.; Westerhuis, Johan A.; Jong, S. De

doi:10.1002/cem.811

Cited by 194 publications

(145 citation statements)

References 27 publications

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“…Unsupervised multi-block methods include various extensions of PCA, such as SUM PCA (SMILDE et al, 2003), consensus PCA (WOLD et al, 1996), and hierarchical PCA . Corresponding methods perform data integration in two steps approach providing (1) scores and loadings for each block, and (2) consensus information derived from combination of all blocks.…”

Section: Multi-block Analysis or Data Fusionmentioning

confidence: 99%

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Trends in the application of chemometrics to foodomics studies

Khakimov¹,

Gürdeniz²,

Engelsen³

2015

Acta Alimentaria

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There is an ever-increasing trend in advanced food analysis and foodomics to use more and more sophisticated analytical platforms that generate large and complex data structures, which in turn require more and more sophisticated data analysis tools for converting data into information. The choice of multivariate chemometric methods is primarily determined by the design of the study, type of the data, and the conclusions sought. In order to validate multivariate models, scientists are required to have basic chemometric knowledge and to be familiar with the variance structure of the investigated data. This review outlines some of the key aspects of applying common chemometric methods used within foodomics and provides selected examples of current applications. The review aims to provide simple insight into various multivariate methods and to illustrate pros and cons of unsupervised and supervised methods. The main analytical platforms used in foodomics are briefl y discussed from the application point of view and the utilization of the generated data is illustrated. In addition, advanced data pre-processing tools, prior to multivariate analysis, are explained and relevant tools are demonstrated.

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Section: Multi-block Analysis or Data Fusionmentioning

confidence: 99%

“…The fi rst issue to consider is whether the primary interest is to study the variation between the blocks or the variation within each block of data is also of interest. Another point to consider is fairness, that is, whether each block contributes equally (SMILDE et al, 2003).…”

Section: Multi-block Analysis or Data Fusionmentioning

confidence: 99%

Trends in the application of chemometrics to foodomics studies

Khakimov¹,

Gürdeniz²,

Engelsen³

2015

Acta Alimentaria

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“…In practice, the {˛ k } k, coefficients are initialized by the compromise coefficients obtained with L STATIS methods computed on the L multiblocks separately. Then, deduced from Equations (9) and (12), the computation of the global compromise (W c D) is a diagonalization.…”

Section: Algorithmmentioning

confidence: 99%

A new linear method for analyzing four‐way multiblock tables: STATIS‐4

Sabatier¹,

Vivien²

2008

Journal of Chemometrics

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A new four-way multiblock method is proposed to study the links between more than two sets of data tables (several multiblocks) measured on the same observations. This method, called STATIS-4, generalizes the STATIS method to more than one set of matrices. In its first step, STATIS-4 is searching for one consensus for each multiblock and a global consensus summarizing all the previous ones as good as possible. Some graphical representations can be made to visualize the proximities between the tables within a multiblock and to visualize those between all the multiblocks. Moreover, plots of the observations for each table, each multiblock and global observations can be made. The theory of STATIS-4 and the algorithm used to obtain the optimal solutions are presented. Moreover, a real sensory dataset is studied with STATIS-4.

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“…These methods have been investigated by several authors [6][7][8] which show that (1) CPCA is the PCA of the supermatrix which contains all blocks and (2) HPCA appears as a method which have objectives difficult to understand. In addition the algorithm of HPCA presents some computational weakness.…”

Section: Introductionmentioning

confidence: 99%

Common components and specific weight analysis and multiple co‐inertia analysis applied to the coupling of several measurement techniques

Hanafi

Mazerolles

Dufour

et al. 2006

Journal of Chemometrics

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The present paper compares two multiblock techniques: the Common Components and Specific Weights Analysis (CCSWA) and the Multiple Co-inertia Analysis (MCoA). Both methods are used to (1) to investigate the relationships among various data tables and (2) to extract latent variables from information of different nature, reflecting different facets of a food product. Our objective is to study the ability of these methods to extract, from a set of data tables, latent characteristics which are representative of the whole modifications brought to a complex system (food product) by a modification of a given process factor. The comparison of these methods is based on the investigation of their conceptual framework by particularly highlighting new properties of CCSWA. Moreover, the two techniques of analysis are compared on the basis of a case study in cheese processing where each cheese sample is described by different kinds of measurements.

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A framework for sequential multiblock component methods

Cited by 194 publications

References 27 publications

Trends in the application of chemometrics to foodomics studies

Trends in the application of chemometrics to foodomics studies

A new linear method for analyzing four‐way multiblock tables: STATIS‐4

Common components and specific weight analysis and multiple co‐inertia analysis applied to the coupling of several measurement techniques

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