Parallel co-ordinate geometry and principal component analysis for the interpretation of large multi-response experimental designs

Heyden, Yvan Vander; Pravdova, V.; Questier, Frederik; Tallieu, L.; Scott, Andy; Massart, D.L.

doi:10.1016/s0003-2670(02)00080-6

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The first technique used for data visualisation is parallel coordinate geometry (PCG) [27]. In PCG, the axes relating to individual variables are plotted parallel to each other (Fig.…”

Section: Linking Late Developmental and Early Commercial Manufacturinmentioning

confidence: 99%

Application of Multivariate Tools in Pharmaceutical Product Development to Bridge Risk Assessment to Continuous Verification in a Quality by Design Environment

2010

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An important aspect of a quality by design approach to pharmaceutical product formulation and process development is continuous quality verification. This is an innovative way of validating the process where manufacturing performance is continuously monitored, evaluated and adjusted as necessary. For new drug products, the body of knowledge accumulated through the development cycle and formalised via risk assessment forms the natural basis of this activity. This paper shows how multivariate tools can be used as part of a continuous quality verification approach for a new drug product relying on the information that summarises the control strategy, i.e. the subset of critical variables selected via risk assessment and the related proven acceptable ranges determined during developmental studies.

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“…The first technique used for data visualisation is parallel coordinate geometry (PCG) [27]. In PCG, the axes relating to individual variables are plotted parallel to each other (Fig.…”

Section: Linking Late Developmental and Early Commercial Manufacturinmentioning

confidence: 99%

Application of Multivariate Tools in Pharmaceutical Product Development to Bridge Risk Assessment to Continuous Verification in a Quality by Design Environment

2010

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“…Although there are some works dealing with high-dimensional visualizations in chemistry and materials science [16,17], it is still in its infancy in the area of PV. With parallel coordinates as an example of information visualization, we demonstrate how to combine data-mined results with raw data and then to identify QPPPRs with respect to HT combinatorial arrays of the ZnO:Co thin films.…”

Section: High-dimensional Visualization Using Parallel Coordinatesmentioning

confidence: 99%

Data-mining-aided mapping of structure-property relationships for combinatorially generated Co-doped ZnO thin films

Suh

Gorrie

Perkins

et al. 2010

2010 35th IEEE Photovoltaic Specialists Conference

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We demonstrate the use of multivariate analysis and highdimensional visualization to uncover and exploit nonobvious quantitative structure-property relationships or processing-property-performance relationships in vast arrays of multidimensional photovoltaic (PV) datasets. Under the research framework of PV informatics, the critical role of these mapping techniques, for design of transparent conducting oxides in particular, is discussed in the context of combinatorially generated Co-doped ZnO thin films. Multidimensional maps are generated using principal component analysis, a dimensional reduction technique in data mining. We present high-dimensional information visualization techniques such as parallel coordinates for mapping relationships that exist in the huge amounts of heterogeneous high-throughput data of thin films.

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“…Information visualizations of multidimensional data sets have been recognized as very crucial to depicting multiple gene expressions in a field of biotechnology or genetics. There are some pioneering works for visualization of high-dimensional or combinatorially derived data in chemistry; − however, visualization of materials science data generated from a combinatorial approach is still in the early stages despite combinatorial approaches with high-throughput synthesis in materials design and synthesis developing quickly. As a test bed in this paper, we provide a demonstration of different sophisticated visualization strategies of high-dimensional catalysis data obtained through combinatorial experiments to visualize composition-activity relationships, which are highly desirable to understand heterogeneous catalysts.…”

Section: Introductionmentioning

confidence: 99%

Visualization of High-Dimensional Combinatorial Catalysis Data

et al. 2009

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The role of various techniques for visualization of high-dimensional data is demonstrated in the context of combinatorial high-throughput experimentation (HTE). Applying visualization tools, we identify which constituents of catalysts are associated with final products in a huge combinatorially generated data set of heterogeneous catalysts, and catalytic activity regions are identified with respect to pentanary composition spreads of catalysts. A radial visualization scheme directly visualizes pentanary composition spreads in twodimensional (2D) space and catalytic activity of a final product by combining high-throughput results from five slate libraries. A glyph plot provides many possibilities for visualizing high-dimensional data with interactive tools. For catalyst discovery and lead optimization, this work demonstrates how large multidimensional catalysis data sets are visualized in terms of quantitative composition activity relationships (QCAR) to effectively identify the relevant key role of compositions (i.e., lead compositions) of catalysts. Keywords VRAC, Materials Science and Engineering Disciplines Materials Science and Engineering | Mechanical Engineering CommentsReprinted with permission from ACS Combinatorial Chemistry 11 (2009) The role of various techniques for visualization of high-dimensional data is demonstrated in the context of combinatorial high-throughput experimentation (HTE). Applying visualization tools, we identify which constituents of catalysts are associated with final products in a huge combinatorially generated data set of heterogeneous catalysts, and catalytic activity regions are identified with respect to pentanary composition spreads of catalysts. A radial visualization scheme directly visualizes pentanary composition spreads in two-dimensional (2D) space and catalytic activity of a final product by combining high-throughput results from five slate libraries. A glyph plot provides many possibilities for visualizing high-dimensional data with interactive tools. For catalyst discovery and lead optimization, this work demonstrates how large multidimensional catalysis data sets are visualized in terms of quantitative composition activity relationships (QCAR) to effectively identify the relevant key role of compositions (i.e., lead compositions) of catalysts.

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Parallel co-ordinate geometry and principal component analysis for the interpretation of large multi-response experimental designs

Cited by 7 publications

References 12 publications

Application of Multivariate Tools in Pharmaceutical Product Development to Bridge Risk Assessment to Continuous Verification in a Quality by Design Environment

Application of Multivariate Tools in Pharmaceutical Product Development to Bridge Risk Assessment to Continuous Verification in a Quality by Design Environment

Data-mining-aided mapping of structure-property relationships for combinatorially generated Co-doped ZnO thin films

Visualization of High-Dimensional Combinatorial Catalysis Data

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