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  • Chemometrics in GC-MS & LC-MS - Eigenvector Research, Inc.
    LC MS Course Description Chemometric Methods for GC MS and LC MS covers methods for dealing with the discontinuous spectra produced by mass spectrometry coupled with preseparation by gas or liquid chromatography Participants will learn how overlapping peaks can be resolved into separate peaks for each of the components and their associated single component spectra using a number of methods including Multivariate Curve Resolution MCR and Parallel Factor Analysis PARAFAC Methods for extracting high quality mass chromatograms from complex data such as resulting from LC MS with electro spray will also be covered Methods that extract small differences between very similar samples such as different batches of the same material will also be discussed Methods for dealing with retention time variations will be discussed including alignment methods and PARAFAC2 The course includes hands on computer time for participants to work example problems using PLS Toolbox Prerequisites Linear Algebra for Chemometricians MATLAB for Chemometricians or equivalent experience Course Outline Self modeling Mixture Analysis and Self modeling Curve Resolution Pure variable method Pure spectrum method Analysis of multiple samples simultaneously with PARAFAC the PARAFAC model interpreting results potential problems due to retention shifts etc CODA COmponent Detection Algorithm the Durbin and Watson

    Original URL path: http://eigenvector.com/courses/EigenU_GCMS.html (2016-04-27)
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  • Advanced Preprocessing for Spectroscopic Applications Course - Eigenvector Research, Inc.
    of a successful multivariate calibration or classification scheme Preprocessing is often the critical step in the development of multivariate regression and classification models Spectroscopic data poses its own unique problems and also opportunities due to its highly structured nature The objective of spectroscopic data preprocessing is to maximize signal to clutter S C where clutter is defined as extraneous variance and data anomalies that can distract model development Maximizing S C is a different paradigm than maximizing signal to noise and a firm understanding of the preprocessing algorithms and objectives can lead to more efficient and effective model development Advanced Preprocessing for Spectroscopic Applications starts with a brief review of basic preprocessing methods to demonstrate how they work within the objective of maximizing S C and how they can be misused The course then delves into more advanced topics such as multiplicative scatter correction extended multiplicative scatter correction and generalized least squares like weighting Examples will be focused on spectroscopic applications although many methods are directly extensible to other types of data The mathematical principles for the preprocessing methods will also be covered The course includes hands on computer time for participants to work example problems using PLS Toolbox EMSC

    Original URL path: http://eigenvector.com/courses/EigenU_Prepro.html (2016-04-27)
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  • Clustering and Classification Course - Eigenvector Research, Inc.
    determine the similarity or dissimilarity among samples Clustering methods are usually exploratory analysis methods which elucidate the similarity within a set of samples They are often used to determine if there are natural groupings and or particularly unique individuals or groups within a set Classification on the other hand uses data with known group assignments and attempts to determine which group s if any a new sample belongs to This

    Original URL path: http://eigenvector.com/courses/EigenU_Cluster.html (2016-04-27)
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  • Variable Selection Course - Eigenvector Research, Inc.
    number of variables and for other purposes such as reducing costs But how to do it Genetic algorithms forward selection and jack knifing are just few of the possible ways to do variable selection In this short course the theory behind when to use what is given and an outline of different possible approaches is given Through examples and exercises it is shown how some approaches work well in some

    Original URL path: http://eigenvector.com/courses/EigenU_VarSel.html (2016-04-27)
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  • Bring Your Own Data Workshop - Eigenvector Research, Inc.
    your data and learn from others in this all day workshop EVRI instructors will provide their hands on experience with everything from how to import data include additional information in data and as time allows outline paths that reaches the actual data analytical goals You will also learn important insider tricks on data analysis and MATLAB scripting You may not get concrete results in this workshop but this is a rare opportunity to gain invaluable skills insight and confidence quickly This is a hands on course with limited time Therefore the outcomes for participants will vary based on how complicated their datasets and goals are To get more out of the workshop we strongly suggest you bring a representative subset of your data if you have a very large dataset You will also get more out of the class if you prepare clear questions ahead of time The course is limited to twelve participants In order to join this course you need to send in your data by May 1 2013 Your data file should be in Excel format with no empty rows or columns although other formats can be accommodated if necessary as long as they can be read

    Original URL path: http://eigenvector.com/courses/BYOD.html (2016-04-27)
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  • Multivariate Curve Resolution Course - Eigenvector Research, Inc.
    on mixtures Unlike standard quantification methods MCR attempts to determine the composition of the mixtures without or with incomplete prior knowledge of the components of the system or their response in the variables i e pure component spectra This course will discuss the relationship of MCR to Classical Least Squares CLS and Principal Component Analysis PCA and discuss various MCR methods Central to this course s objectives are an understanding

    Original URL path: http://eigenvector.com/courses/EigenU_MCR.html (2016-04-27)
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  • Online Course Terms and Conditions
    individual named in the Account information The course material may not be privately or publicly broadcast presented or otherwise accessed by any non licensed individuals without written permission of Eigenvector Research Inc EVRI The videos may only be viewed via streaming from the Eigenvector Research web site and no physical or electronic copies may be made of their content other than those necessary to view the video while streaming The

    Original URL path: http://eigenvector.com/courses/EigenU_Online_Terms.html (2016-04-27)
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  • White Papers - Eigenvector Research, Inc.
    provide brief technical descriptions of Eigenvector software and consulting applications Although these papers represent a small portion of the projects and applications developed by our staff we hope that they provide some insight into the solutions we can provide If you have questions or would like information on a subject not listed please contact us at bmw eigenvector com Documents Adaptive Multi Way Principal Components Analysis Applied to Monitoring a Semiconductor Etch Process PDF 134KB Chemometric Analysis of LC MS Electrospray Data PDF 127KB Deploying your PCA Model Online using PLS Toolbox with OPC PDF 65KB Detection and Classification with Overlapping Signals PDF 29KB Development and Implementation of Effective Multivariate Calibrations for Process Analytical Applications PDF 176KB Enhanced Classification of Placebo and Active Formulations via Hierarchical Modeling PDF 1 8MB This article was printed in the November December 2013 issue of American Pharmaceutical Review Volume 16 Issue 7 Copyright rests with the publisher Extended Multiplicative Scatter Correction Applied to Mid Infrared Reflectance Measurements of Soil PDF 2 8MB Generalized Weighting to Account for Sampling Artifacts PDF 121KB Introduction to Preprocessing Calibration and Application PDF 40KB NIR Method for Determining the Active Ingredient in Pharmaceutical Tablets PDF 210KB PARAFAC for Analysis

    Original URL path: http://eigenvector.com/whitepapers.htm (2016-04-27)
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