Basic Chemometric Techniques in Atomic Spectroscopy
The first edition of this book was a first book for atomic spectroscopists to present the basic principles of experimental designs, optimization and multivariate regression. Multivariate regression is a valuable statistical method for handling complex problems (such as spectral and chemical interferences) which arise during atomic spectrometry. However, the technique is underused as most spectroscopists do not have time to study the often complex literature on the subject. This practical introduction uses conceptual explanations and worked examples to give readers a clear understanding of the technique. Mathematics is kept to a minimum but, when required, is kept at a basic level. Practical considerations, interpretations and troubleshooting are emphasized and literature surveys are included to guide the reader to further work. The same dataset is used for all chapters dealing with calibration to demonstrate the differences between the different methodologies. Readers will learn how to handle spectral and chemical interferences in atomic spectrometry in a new, more efficient and cost-effective way.
Basic Chemometric Techniques in Atomic Spectroscopy, The Royal Society of Chemistry, 2013.
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Table of contents
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CHAPTER 1: An Overview of Atomic Spectrometric Techniquesp1-51ByJosé Manuel Costa‐FernándezJosé Manuel Costa‐FernándezSearch for other works by this author on:
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CHAPTER 2: Classical Linear Regression by the Least Squares Methodp52-122ByJosé Manuel Andrade‐Garda;José Manuel Andrade‐GardaDepartment of Analytical ChemistryUniversity of A Coruña, A CoruñaSpainSearch for other works by this author on:Alatzne Carlosena‐Zubieta;Alatzne Carlosena‐ZubietaDepartment of Analytical ChemistryUniversity of A Coruña, A CoruñaSpainSearch for other works by this author on:Rosa María Soto‐Ferreiro;Rosa María Soto‐FerreiroDepartment of Analytical ChemistryUniversity of A Coruña, A CoruñaSpainSearch for other works by this author on:Javier Teran‐Baamonde;Javier Teran‐BaamondeDepartment of Analytical ChemistryUniversity of A Coruña, A CoruñaSpainSearch for other works by this author on:Michael ThompsonMichael ThompsonSchool of Biological and Chemical SciencesBirkbeck University of London, United Kingdom[email protected]Search for other works by this author on:
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CHAPTER 3: Implementing a Robust Methodology: Experimental Designs and Optimisationp123-255ByXavier Tomàs‐Morer;Xavier Tomàs‐MorerDepartment of Applied StatisticsInstitut Químic de Sarrià, Universitat Ramon Llull, BarcelonaSpainSearch for other works by this author on:Lucinio González‐Sabaté;Lucinio González‐SabatéDepartment of Applied StatisticsInstitut Químic de Sarrià, Universitat Ramon Llull, BarcelonaSpainSearch for other works by this author on:Laura Fernández‐Ruano;Laura Fernández‐RuanoDepartment of Applied StatisticsInstitut Químic de Sarrià, Universitat Ramon Llull, BarcelonaSpainSearch for other works by this author on:María Paz Gómez‐CarracedoMaría Paz Gómez‐CarracedoSearch for other works by this author on:
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CHAPTER 4: Ordinary Multiple Linear Regression and Principal Components Regressionp256-279ByJoan Ferré‐Baldrich;Joan Ferré‐BaldrichDepartment of Analytical and Organic ChemistryUniversitat Rovira i VirgiliTarragona, Spain[email protected]Search for other works by this author on:Ricard Boqué‐MartíRicard Boqué‐MartíDepartment of Analytical and Organic ChemistryUniversitat Rovira i VirgiliTarragona, Spain[email protected]Search for other works by this author on:
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CHAPTER 5: Partial Least‐Squares Regressionp280-347ByJosé Manuel Andrade‐Garda;José Manuel Andrade‐GardaDepartment of Analytical ChemistryUniversity of A Coruña, A CoruñaSpainSearch for other works by this author on:Alatzne Carlosena‐Zubieta;Alatzne Carlosena‐ZubietaDepartment of Analytical ChemistryUniversity of A Coruña, A CoruñaSpainSearch for other works by this author on:Ricard Boqué‐Martí;Ricard Boqué‐MartíDepartment of Analytical and Organic ChemistryUniversitat Rovira i Virgili, TarragonaSpain[email protected]Search for other works by this author on:Joan Ferré‐BaldrichJoan Ferré‐BaldrichDepartment of Analytical and Organic ChemistryUniversitat Rovira i Virgili, TarragonaSpain[email protected]Search for other works by this author on:
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CHAPTER 6: Multivariate Regression using Artificial Neural Networks and Support Vector Machinesp348-397ByJosé Manuel Andrade‐Garda;José Manuel Andrade‐GardaDepartment of Analytical ChemistryUniversity of A Coruña, A CoruñaSpainSearch for other works by this author on:Marcos Gestal‐Pose;Marcos Gestal‐PoseDepartment of Information and Communications TechnologiesUniversity of A CoruñaA Coruña, Spain[email protected]Search for other works by this author on:Francisco Abel Cedrón‐Santaeufemia;Francisco Abel Cedrón‐SantaeufemiaDepartment of Information and Communications TechnologiesUniversity of A CoruñaA Coruña, Spain[email protected]Search for other works by this author on:Julián Dorado‐de‐la‐Calle;Julián Dorado‐de‐la‐CalleDepartment of Information and Communications TechnologiesUniversity of A CoruñaA Coruña, Spain[email protected]Search for other works by this author on:María Paz Gómez‐CarracedoMaría Paz Gómez‐CarracedoDepartment of Analytical ChemistryUniversity of A Coruña, A CoruñaSpainSearch for other works by this author on:
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