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