Problem-Oriented Analytical Chemistry Driven by Chemometrics

Luis Cuadros Rodríguez, Ana María Jimenez-Carvelo, Jose M Andrade-Garda

• Publisher: Elsevier Science
• Year: 2025
• eBook ISBN:
  9780443221644
• Topic: Physical Sciences
• Subtopic: Science Research & Methodology

Problem-Oriented Analytical Chemistry Driven by Chemometrics covers challenges that are relevant to many fields using analytical techniques, including food quality and safety, environmental studies, product authentication, and petrochemistry using an analytical data science approach. The book's novel approach starts from case studies, allowing analytical chemists to understand how seemingly impenetrable chemometrics techniques can be applied to solve problems. It is written primarily to assist graduate and postdoc analytical chemists and early researchers, but has applicability to industry, too.Many analytical chemists dealing with complex, real-world problems face significant difficulties extracting information from their data. In many cases, solutions are available in the chemometrics arena but relevant books are usually too technical and do not always focus on the 'general perspective' needed by the analyst when facing those complex tools. - Presents a novel, problem-oriented approach to using chemometrics based on case studies - Considers the most common analytical techniques, looking at case studies of applications and how to get sound information from their signals - Provides an accessible perspective on chemometrics rather than simply presenting complex Mathematics and/or algorithms

Table of contents

• Title of Book
• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• Foreword
• Preface
• Chapter 1 A multivariate approach to Analytical Chemistry
• Chapter 2 Analytical signals, analytical data, and instruments
• Chapter 3 Preprocessing of analytical signals and reduction of analytical data
• Chapter 4 Simultaneous multioptimization of operational variables
• Chapter 5 Multiobjective and multiresponse optimization
• Chapter 6 Analytical similarity analysis
• Chapter 7 Analytical qualification of materials
• Chapter 8 Analytical quantification of analytes and materials
• Chapter 9 Variable selection for analytical chemistry data
• Chapter 10 Validation of multivariate analytical methods
• Chapter 11 Multivariate curve resolution
• Chapter 12 Standardization and transfer of multivariate analytical models
• Chapter 13 N-way signals and data
• Chapter 14 A picture is worth one thousand spectra: multivariate and hyperspectral image analysis of complex matrices
• Chapter 15 Data analysis for microfluidic based analytical devices
• Chapter 16 Data analysis in applications of chromatography coupled with mass spectrometry and ion mobility spectrometry
• Chapter 17 Data analysis for analytical applications of nuclear magnetic resonance
• Index