Machine learning (ML) has recently become popular in chemical and biological sensing applications. ML is a subset of artificial intelligence (AI) and other AI techniques have been used in various chemical and biological sensing.
Machine Learning and Artificial Intelligence in Chemical and Biological Sensing covers the theoretical background and practical applications of various ML/AI methods toward chemical and biological sensing. No comprehensive reference text has been available previously to cover the wide breadth of this topic. The Editors have written the first three chapters to firmly introduce the reader to fundamental ML theories that can be used for chemical/biosensing. The subsequent chapters then cover the practical applications with contributions by various experts in the field. They show how ML and AI-based techniques can provide solutions for:
1) identifying and quantifying target molecules when specific receptors are unavailable 2) analyzing complex mixtures of target molecules, such as gut microbiome and soil microbiome 3) analyzing high-throughput and high-dimensional data, such as drug screening, molecular interaction, and environmental toxicant analysis, 4) analyzing complex data sets where fingerprinting approach is needed
This book is written primarily for upper undergraduate students, graduate students, research staff, and faculty members at teaching and research universities and colleges who are working on chemical sensing, biosensing, analytical chemistry, analytical biochemistry, biomedical imaging, medical diagnostics, environmental monitoring, and agricultural applications.
1. Machine Learning and Artificial Intelligence in Chemical and Biological Sensing - p. i 2. Cover - p. i 3. Title Page - p. ii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of Contributors - p. x 7. Preface - p. xiv 8. 1 Fundamentals of chemical sensors and biosensors - p. 1 9. 2 Fundamentals of machine learning - p. 22 10. 3 Use of machine learning/artificial intelligence in chemical sensors and biosensors - p. 71 11. 4 Machine learning-assisted electronic nose and gas sensors - p. 82 12. 5 Machine learning-assisted Fourier transform infrared and Raman spectroscopic sensing in agricultural and food systems - p. 113 13. 6 Machine learning-assisted surface-enhanced Raman spectroscopic characterization of biological systems - p. 146 14. 7 Artificial intelligence-assisted microscopic imaging analysis for high-throughput plant phenotyping - p. 176 15. 8 Artificial intelligence/machine learning-assisted near-infrared/optical biosensing for plant phenotyping - p. 202 16. 9 Machine learning–assisted multispectral and hyperspectral imaging - p. 226 17. 10 Machine learning–assisted biosensors utilizing a set of biological polymers - p. 259 18. 11 Machine learning–assisted flow velocity analysis in paper microfluidics - p. 275 19. 12 Advanced sensor platforms and machine learning tools for real-time contaminant monitoring - p. 292 20. 13 Machine learning–driven descriptions of protein dynamics at solid–liquid interfaces - p. 321 21. 14 Artificial intelligence/machine learning tools for single molecule data analysis in biomedicine - p. 341 22. Index - p. 369 23. Index - Continued - p. 369
Machine learning (ML) has recently become popular in chemical and biological sensing applications. ML is a subset of artificial intelligence (AI) and other AI techniques have been used in various chemical and biological sensing.
Machine Learning and Artificial Intelligence in Chemical and Biological Sensing covers the theoretical background and practical applications of various ML/AI methods toward chemical and biological sensing. No comprehensive reference text has been available previously to cover the wide breadth of this topic. The Editors have written the first three chapters to firmly introduce the reader to fundamental ML theories that can be used for chemical/biosensing. The subsequent chapters then cover the practical applications with contributions by various experts in the field. They show how ML and AI-based techniques can provide solutions for:
1) identifying and quantifying target molecules when specific receptors are unavailable 2) analyzing complex mixtures of target molecules, such as gut microbiome and soil microbiome 3) analyzing high-throughput and high-dimensional data, such as drug screening, molecular interaction, and environmental toxicant analysis, 4) analyzing complex data sets where fingerprinting approach is needed
This book is written primarily for upper undergraduate students, graduate students, research staff, and faculty members at teaching and research universities and colleges who are working on chemical sensing, biosensing, analytical chemistry, analytical biochemistry, biomedical imaging, medical diagnostics, environmental monitoring, and agricultural applications.
1. Machine Learning and Artificial Intelligence in Chemical and Biological Sensing - p. i 2. Cover - p. i 3. Title Page - p. ii 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of Contributors - p. x 7. Preface - p. xiv 8. 1 Fundamentals of chemical sensors and biosensors - p. 1 9. 2 Fundamentals of machine learning - p. 22 10. 3 Use of machine learning/artificial intelligence in chemical sensors and biosensors - p. 71 11. 4 Machine learning-assisted electronic nose and gas sensors - p. 82 12. 5 Machine learning-assisted Fourier transform infrared and Raman spectroscopic sensing in agricultural and food systems - p. 113 13. 6 Machine learning-assisted surface-enhanced Raman spectroscopic characterization of biological systems - p. 146 14. 7 Artificial intelligence-assisted microscopic imaging analysis for high-throughput plant phenotyping - p. 176 15. 8 Artificial intelligence/machine learning-assisted near-infrared/optical biosensing for plant phenotyping - p. 202 16. 9 Machine learning–assisted multispectral and hyperspectral imaging - p. 226 17. 10 Machine learning–assisted biosensors utilizing a set of biological polymers - p. 259 18. 11 Machine learning–assisted flow velocity analysis in paper microfluidics - p. 275 19. 12 Advanced sensor platforms and machine learning tools for real-time contaminant monitoring - p. 292 20. 13 Machine learning–driven descriptions of protein dynamics at solid–liquid interfaces - p. 321 21. 14 Artificial intelligence/machine learning tools for single molecule data analysis in biomedicine - p. 341 22. Index - p. 369 23. Index - Continued - p. 369
