Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling
- 1st Edition - October 20, 2022
- Imprint: Elsevier
- Editor: Jahan B. Ghasemi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 4 0 8 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 7 0 6 - 4
Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling outlines key knowledge in this area, combining critical introduct… Read more
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Request a sales quoteMachine 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
Students, teachers and researchers across chemistry interested in developing their data analysis skills
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter 1: Soft constraints in curve resolution problems
- Abstract
- 1: Introduction
- 2: Basic concepts and theory
- 3: Applications
- 4: Conclusions
- References
- Chapter 2: Multivariate predictive modeling and validation
- Abstract
- 1: Regression
- 2: Classification
- 3: Validation
- References
- Chapter 3: Multivariate pattern recognition by machine learning methods
- Abstract
- 1: Introduction
- 2: Feature extraction
- 3: Task prediction
- 4: Conclusion
- References
- Chapter 4: Tuning the apparent thermodynamic parameters of chemical systems
- Abstract
- 1: Introduction
- 2: General strategy for tuning the apparent constant (Karimvand, Maeder, & Abdollahi, 2019)
- 3: The application of proposed strategy in practice
- References
- Chapter 5: The analytical/measurement sources of multivariate errors. A case study: Detecting microplastics in sand
- Abstract
- Acknowledgments
- 1: Introduction
- 2: Materials and methods
- 3: Results and discussion
- 4: Spectral insights on the problem
- 5: Future work
- References
- Chapter 6: Autoencoders in generative modeling, feature extraction, regression, and classification
- Abstract
- 1: What is an autoencoder?
- 2: Applications
- References
- Chapter 7: Uniqueness in resolving multivariate chemical data
- Abstract
- 1: Introduction
- 2: Non-negativity constraint
- 3: Trilinearity constraint
- 4: Nonexistence information
- 5: Equality constraint
- 6: Hard models constraint
- 7: General rule for uniqueness (GRU) (Karimvand et al., 2020)
- References
- Appendix: Introduction to python
- 1: Introduction
- 2: ChemoPy
- 3: PyDPI
- 4: pyMCR
- 5: DeepChem
- 6: Scikit-learn
- 7: Examples
- 8: Linear regression code
- 9: Logistic regression code
- 10: Support vector machine
- 11: Random forest code
- 12: Deep learning for regression task
- 13: Neural network on classification task
- 14: Convolutional neural network
- References
- Index
- Edition: 1
- Published: October 20, 2022
- Imprint: Elsevier
- No. of pages: 216
- Language: English
- Paperback ISBN: 9780323904087
- eBook ISBN: 9780323907064
JG
Jahan B. Ghasemi
Dr. Jahan B Ghasemi received his PhD from Shiraz University. He was a visiting Scientist at the University of Chalmers in 2001 and Delaware University in 2006. His current research interests are focused on chemometrics and data analysis and computational drug design. He is the author of more than 200 papers and 4 chapter books in international journals and books.
Affiliations and expertise
Faculty of Chemistry, University of Tehran, Tehran, IranRead Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling on ScienceDirect