Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling

Jahan B. Ghasemi

• Publisher: Elsevier
• Year: 2022
• Print ISBN: 9780323904087
• eBook ISBN: 9780323907064
• Topic: Physical Sciences
• Subtopic: Physical & Theoretical Chemistry

Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling outlines key knowledge in this area, combining critical introductory approaches with the latest advanced techniques. Beginning with an introduction of univariate and multivariate statistical analysis, the book then explores multivariate calibration and validation methods. Soft modeling in chemical data analysis, hyperspectral data analysis, and autoencoder applications in analytical chemistry are then discussed, providing useful examples of the techniques in chemistry applications. Drawing on the knowledge of a global team of researchers, this book will be a helpful guide for chemists interested in developing their skills in multivariate data and error analysis. - Provides an introductory overview of statistical methods for the analysis and interpretation of chemical data - Discusses the use of machine learning for recognizing patterns in multidimensional chemical data - Identifies common sources of multivariate errors

Table of contents

• Title of Book
• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Chapter 1 Soft constraints in curve resolution problems
• Chapter 2 Multivariate predictive modeling and validation
• Chapter 3 Multivariate pattern recognition by machine learning methods
• Chapter 4 Tuning the apparent thermodynamic parameters of chemical systems
• Chapter 5 The analytical/measurement sources of multivariate errors. A case study: Detecting microplastics in sand
• Chapter 6 Autoencoders in generative modeling, feature extraction, regression, and classification
• Chapter 7 Uniqueness in resolving multivariate chemical data
• Appendix. Introduction to python
• Index