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  • Correlation Spectroscopy Course - Eigenvector Research, Inc.
    collected in the presence of any sample perturbation led to the concept of Generalized 2D Correlation Spectroscopy which is the subject of this course The course will begin with an explanation of the principles of classical Correlation Spectroscopy This will include a discussion of the two complementary methods to display the temporal correlation behavior in spectral data sets the synchronous and asynchronous maps Several chemometric methods can be particularly useful when combined with Correlation Spectroscopy including Self Modeling Curve Resolution a series of methods designed to resolve mixture data into their pure components and their contributions and multi block PLS modeling Several different Curve Resolution methods will be discussed including the Pure Variable Method and it will be shown that these methods can be used with correlation spectroscopy to greatly simplify the interpretability of correlation maps This will be followed with a discussion and demonstration of hetero spectral and hetero analytical correlations where the above mentioned techniques are used to analyze data that was generated from the measurement of the same set of samples by two different spectroscopic or analytical methods i e mid IR and near IR or near IR and DSC For these cases it will be shown

    Original URL path: http://eigenvector.com/courses/EigenU_Correlation_Spec.html (2016-04-27)
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  • Design of Experiments Course - Eigenvector Research, Inc.
    and 6 Sigma quality initiatives This DOE course will cover the fundamental aspects of experimental design and the practical application of those designs The major topics to be covered include screening DOEs fractional factorial designs for the efficient identification of important factors response surface optimization designs CCD Box Behnkin etc for identifying the optimum factor settings for your problem and how to choose and construct the appropriate design Other practically important aspects of DOE will be discussed throughout the course including the identification of factors for investigation identification of potential sources of variation characterization of the measurement system proper execution of the DOE experiments statistical evaluation of factor significance and construction of the model and uses of the regression models Special attention will be paid to developing designs that work well with multivariate calibration methods including cross validatable designs and selection of candidate reference samples based on measured values reduceNNsamples Examples will be used throughout the course to illustrate the various aspect of the DOE modeling process Examples will be used throughout the course to illustrate the various aspect of the DOE modeling process The course includes hands on computer time for participants to work example problems using PLS Toolbox

    Original URL path: http://eigenvector.com/courses/EigenU_DOE.html (2016-04-27)
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  • Hierarchical and Optimized Models - Eigenvector Research, Inc.
    models are used when standard models perform poorly on data from complicated systems Hierarchical models utilize empirical or first principle based segregation of the data to break the problem into smaller easier to model portions They are frequently used with both classification and regression problems Optimized models use combinatorial methods to identify preprocessing and modeling conditions that provide the most flexible and stable models for a given task This course

    Original URL path: http://eigenvector.com/courses/EigenU_Hierarchical.html (2016-04-27)
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  • Chemometrics in PAT Course - Eigenvector Research, Inc.
    to date have been in the development deployment and support of multivariate Process Analytical Technologies PAT However this application area involves some unique considerations including regulatory compliance on line model deployment logistics and model performance monitoring Implementing Chemometrics in PAT is designed to familiarize the student with the various chemometrics tool and other associated tools that are needed for effective PAT applications Deployment software demonstrations and PAT case studies will be used to help illustrate the course material The course includes hands on computer time for participants to work example problems using PLS Toolbox and MATLAB Prerequisites Chemometrics I PCA and Chemometrics II Regression and PLS or equivalent experience Course Outline Background The typical PAT Project Timeline exploration understanding development deployment support Regulatory Issues CFR Part 11 Compliance Pharma ISO Review of Six Sigma and Quality by Design QbD principles Administrative and Interpersonal Issues Review of Chemometrics Tools for PAT Design of Experiments DOE Exploratory Analysis methods PCA MCR Model Building Methods Inverse regression MLR PLS Direct regression CLS and extensions thereof Model Maintenance Tools outlier detection The NEW Model Robustness Tool Chemometrics in Exploratory PAT Goals product and or process development scale up Case Studies Chemometrics to support PAT

    Original URL path: http://eigenvector.com/courses/EigenU_PAT.html (2016-04-27)
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  • Modeling Fluorescence EEM Data - Eigenvector Research, Inc.
    of data Ideally such data will follow Beers Law in which case the so called PARAFAC model allows one to extract the underlying chemical components directly from mixture measurements Sometimes when measuring complex and possibly optically dense samples artifacts will occur in the fluorescence data These need to be handled before a meaningful PARAFAC model can be obtained In this course the participant will learn how to critically analyze EEM

    Original URL path: http://eigenvector.com/courses/EigenU_Fluorescence_EEM.html (2016-04-27)
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  • Multivariate Statistical Process Control Course - Eigenvector Research, Inc.
    this data overload and extracting critical information about process health The course covers monitoring and fault detection in chemical and manufacturing processes Methods for monitoring continuous batch and transient processes are covered Using diagnostic plot to track down root causes is covered along with methods for dealing with process drift The course includes hands on computer time for participants to work example problems using PLS Toolbox Prerequisites Linear Algebra for

    Original URL path: http://eigenvector.com/courses/EigenU_MSPC.html (2016-04-27)
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  • PLS_Toolbox Beyond the Interfaces - Eigenvector Research, Inc.
    Toolbox Beyond the Interfaces Course Description Although many data analysis tasks can be performed within the Graphical User Interfaces GUIs in PLS Toolbox there is another layer of tools that can only be accessed from the Matlab command line or other scripting environment Users of all levels find this flexibility helpful for automating analyses increasing control of their analyses accessing less commonly used tools or just because they are more comfortable in a text driven computer language environment This course will discuss how to access PLS Toolbox functionality that is not available from or less convenient in the graphical interfaces Users will have an opportunity to work at the command line to walk through an entire data analysis procedure while introducing commonly used scripting notation theoretical guidelines and approaches for learning about new tools and methods Hands on exercises will be done using MATLAB and PLS Toolbox Prerequisites Linear Algebra for Chemometricians MATLAB for Chemometricians Chemometrics I PCA and Chemometrics II Regression and PLS or equivalent experience Course Outline 1 0 Introduction Outside the Interfaces 1 1 Why use command line scripting 1 2 Architecture of PLS Toolbox 1 3 Demos as learning tools 2 0 Walk through From Import

    Original URL path: http://eigenvector.com/courses/EigenU_Beyond_Interfaces.html (2016-04-27)
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  • Robust Analysis Course - Eigenvector Research, Inc.
    data models are not an accurate representation of the bulk of the data Alternately outlier samples are sometimes the most interesting samples in a data set revealing unique properties or trends If these samples are not identified opportunities for discovery can be missed Robust Methods deal with the problem of outliers by determining which samples represent the consensus in the data and basing the models on those samples while ignoring

    Original URL path: http://eigenvector.com/courses/EigenU_Robust.html (2016-04-27)
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