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Multivariate data analysis for root ...

Heller-Krippendorf, Danica.

Multivariate data analysis for root cause analyses and time-of-flight secondary ion mass spectrometry

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multivariate data analysis for root cause analyses and time-of-flight secondary ion mass spectrometryby Danica Heller-Krippendorf.
Author:	Heller-Krippendorf, Danica.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden :2019.
Description:	xix, 195 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Time-of-flight mass spectrometry.
Online resource:	https://doi.org/10.1007/978-3-658-28502-9
ISBN:	9783658285029$q(electronic bk.)

Multivariate data analysis for root cause analyses and time-of-flight secondary ion mass spectrometry
Heller-Krippendorf, Danica.

Multivariate data analysis for root cause analyses and time-of-flight secondary ion mass spectrometry[electronic resource] /by Danica Heller-Krippendorf. - Wiesbaden :Springer Fachmedien Wiesbaden :2019. - xix, 195 p. :ill. (some col.), digital ;24 cm.

Advantages of Correlation Loadings for MVA of ToF-SIMS Spectra -- Required Number of Replicate Measurements in an Industrial Context -- Selection of Suitable Data Pre-processing in Root Cause Analysis Including the Selection of an Efficient Peak List, Scaling, Normalization and Centering -- New Presentation of PCA Results in Order to Simplify Data Analysis.

Danica Heller-Krippendorf develops concepts and approaches optimizing the applicability of MVA on data sets from an industrial context. They enable more time-efficient MVA of the respective ToF SIMS data. Priority is given to two main aspects by the author: First, the focus is on strategies for a more time-efficient collection of the input data. This includes the optimal selection of the number of replicate measurements, the selection of input data and guidelines for the selection appropriate data preprocessing methods. Second, strategies for more efficient analysis of MVA results are presented. Contents Advantages of Correlation Loadings for MVA of ToF-SIMS Spectra Required Number of Replicate Measurements in an Industrial Context Selection of Suitable Data Pre-processing in Root Cause Analysis Including the Selection of an Efficient Peak List, Scaling, Normalization and Centering New Presentation of PCA Results in Order to Simplify Data Analysis Target Groups Scientists and students in the field of surface analysis, data evaluation, ToF-SIMS and methods of multivariate data analysis Practitioners, especially in industry, in the field of surface and error analysis About the Author Danica Heller-Krippendorf did her research and dissertation at the University of Siegen, Germany, in collaboration with a German analytical service company. Now she is engineer in analytics at a DAX company.

ISBN: 9783658285029$q(electronic bk.)

Standard No.: 10.1007/978-3-658-28502-9doiSubjects--Topical Terms:

698882
Time-of-flight mass spectrometry.

LC Class. No.: QC454.M3 / H455 2019

Dewey Class. No.: 543.65

Multivariate data analysis for root cause analyses and time-of-flight secondary ion mass spectrometry
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520 $a Danica Heller-Krippendorf develops concepts and approaches optimizing the applicability of MVA on data sets from an industrial context. They enable more time-efficient MVA of the respective ToF SIMS data. Priority is given to two main aspects by the author: First, the focus is on strategies for a more time-efficient collection of the input data. This includes the optimal selection of the number of replicate measurements, the selection of input data and guidelines for the selection appropriate data preprocessing methods. Second, strategies for more efficient analysis of MVA results are presented. Contents Advantages of Correlation Loadings for MVA of ToF-SIMS Spectra Required Number of Replicate Measurements in an Industrial Context Selection of Suitable Data Pre-processing in Root Cause Analysis Including the Selection of an Efficient Peak List, Scaling, Normalization and Centering New Presentation of PCA Results in Order to Simplify Data Analysis Target Groups Scientists and students in the field of surface analysis, data evaluation, ToF-SIMS and methods of multivariate data analysis Practitioners, especially in industry, in the field of surface and error analysis About the Author Danica Heller-Krippendorf did her research and dissertation at the University of Siegen, Germany, in collaboration with a German analytical service company. Now she is engineer in analytics at a DAX company.
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