Machine Learning and Artificial Intelligence in Chemical and Biological Sensing
- 1st Edition - July 7, 2024
- Imprint: Elsevier Science
- Editors: Jeong-Yeol Yoon, Chenxu Yu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 0 0 1 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 0 0 0 - 5
Machine Learning and Artificial Intelligence in Chemical and Biological Sensing covers the theoretical background and practical applications of various ML/AI methods toward ch… Read more
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Request a sales quoteMachine 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 book's editors have written the first three chapters to firmly introduce the reader to fundamental ML theories that can be used for chemical/biosensing. Subsequent chapters then cover the practical applications with contributions by various experts in the field.
Sections 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.
- Presents the first comprehensive reference text on the use of ML and AI for chemical and biological sensing
- Provides a firm grounding in the fundamental theories on ML and AI before covering the practical applications with contributions by various experts in the field
- Includes a wide array of practical applications covered, including: E-nose, Raman, SERS, lens-free imaging, multi/hyperspectral imaging, NIR/optical imaging, receptor-free biosensing, paper microfluidics, single molecule analysis in biomedicine, in situ protein characterization, microbial population dynamics, and all-in-one sensor systems
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. Research scientists, engineers, and technicians at governmental research laboratories and those in industry working on chemical sensors, biosensors, analytical instruments, and diagnostic devices including research scientists, engineers, and marketing engineers in industry, especially start-up companies
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Introduction
- 1. Fundamentals of chemical sensors and biosensors
- Abstract
- 1.1 Introduction
- 1.2 Fundamentals of chemical sensors and biosensors
- 1.3 Machine learning and artificial intelligence in sensor applications
- 1.4 Future directions
- 1.5 Conclusion
- References
- 2. Fundamentals of machine learning
- Abstract
- 2.1 What is machine learning?
- 2.2 Types of machine learning: which machine learning technique should be used?
- 2.3 Naive Bayes classifier
- 2.4 Logistic regression
- 2.5 Decision tree
- 2.6 Random forests
- 2.7 Support vector machines
- 2.8 k-Nearest neighbors
- 2.9 k-Means clustering
- 2.10 eXtreme gradient boosting
- 2.11 Artificial neural network
- 2.12 Machine learning with Python and scikit-learn libary
- 2.13 Machine learning example of logistic regression
- 2.14 Machine learning example of random forests
- 2.15 Machine learning example of support vector machines
- 2.16 Machine learning example of eXtreme gradient boosting
- 2.17 Concluding remarks
- References
- 3. Use of machine learning/artificial intelligence in chemical sensors and biosensors
- Abstract
- 3.1 Introduction
- 3.2 Array data from multiple sensors: electronic nose
- 3.3 Spectroscopy data: FTIR, Raman, and SERS
- 3.4 Image data
- 3.5 Unconventional data
- 3.6 An extensive array of conventional sensor data
- References
- 4. Machine learning-assisted electronic nose and gas sensors
- Abstract
- 4.1 Introduction to electronic nose
- 4.2 Electronic nose sensing principle
- 4.3 Conducting polymer gas sensors
- 4.4 Metal-oxide semiconductor gas sensors
- 4.5 Carbon nanotube and graphene gas sensors
- 4.6 Optical gas sensors
- 4.7 Piezoelectric gas sensors
- 4.8 Recent literature on electronic nose
- 4.9 Machine learning used in recent electronic nose systems
- 4.10 Concluding remarks
- References
- 5. Machine learning-assisted Fourier transform infrared and Raman spectroscopic sensing in agricultural and food systems
- Abstract
- 5.1 Introduction
- 5.2 Spectral data processing
- 5.3 Case study No. 1: evaluation of meat quality using machine learning-driven Raman spectroscopic screening
- 5.4 Case study No. 2: detection and identification of microorganisms in mixed cultures by Fourier transform infrared spectroscopy and machine learning-driven discriminant analysis
- 5.5 Case study No. 3: machine learning-assisted characterization of cervid skin tissues with chronic wasting disease by Raman spectroscopic biosensing
- 5.6 Conclusions
- References
- 6. Machine learning-assisted surface-enhanced Raman spectroscopic characterization of biological systems
- Abstract
- 6.1 Introduction
- 6.2 Overview of technologies
- 6.3 Application of surface-enhanced Raman spectroscopy in different biological systems
- 6.4 Case study: machine learning-enabled surface-enhanced Raman spectroscopy mapping of plant cell wall pectin distribution and interaction
- 6.5 Conclusion and future perspectives
- References
- 7. Artificial intelligence-assisted microscopic imaging analysis for high-throughput plant phenotyping
- Abstract
- 7.1 Introduction
- 7.2 Artificial intelligence for microscopic image analysis
- 7.3 A case study for the identification of grape disease resistance quantitative trait loci
- 7.4 Discussion
- 7.5 Summary
- Acknowledgments
- References
- 8. Artificial intelligence/machine learning-assisted near-infrared/optical biosensing for plant phenotyping
- Abstract
- 8.1 Introduction
- 8.2 Imaging technology for plant phenotyping
- 8.3 Image-based machine learning/deep learning for plant phenomics
- 8.4 Summary
- Acknowledgement
- References
- 9. Machine learning–assisted multispectral and hyperspectral imaging
- Abstract
- 9.1 Introduction
- 9.2 Hyperspectral image acquisition
- 9.3 Hyperspectral image preprocessing
- 9.4 Hyperspectral image processing and analysis
- 9.5 Applications of hyperspectral imaging for food safety and quality assessment
- References
- 10. Machine learning–assisted biosensors utilizing a set of biological polymers
- Abstract
- 10.1 Introduction
- 10.2 Peptide sequences to identify bacterial species
- 10.3 Polysaccharides and proteins to identify bacterial species
- 10.4 Amino acids and proteins to identify environmental toxicants
- 10.5 Potential applications to other environmental toxicants
- 10.6 Other platforms: sensor arrays
- 10.7 Conclusion
- References
- 11. Machine learning–assisted flow velocity analysis in paper microfluidics
- Abstract
- 11.1 Paper microfluidics
- 11.2 Fluid transportation in paper microfluidics
- 11.3 Flow profile measurement
- 11.4 Capillary flow velocity-based detection of bacteria and viruses on paper microfluidics
- 11.5 Machine learning analysis of capillary flow velocity profiles
- 11.6 Conclusion and future direction
- References
- 12. Advanced sensor platforms and machine learning tools for real-time contaminant monitoring
- Abstract
- 12.1 Introduction
- 12.2 Overview of machine learning
- 12.3 Environmental biosensors
- 12.4 Machine learning in environmental biosensing
- 12.5 Conclusion
- References
- 13. Machine learning–driven descriptions of protein dynamics at solid–liquid interfaces
- Abstract
- 13.1 Introduction
- 13.2 Summary
- Acknowledgments
- References
- 14. Artificial intelligence/machine learning tools for single molecule data analysis in biomedicine
- Abstract
- 14.1 Introduction
- 14.2 The history of single-molecule technologies
- 14.3 Nanopore-based single-molecule analysis
- 14.4 Digital single-molecule bioassay
- 14.5 CRISPR/Cas-based single molecule detection
- 14.6 Application of aritificial intelligence/machine learning tools in nanopore sequencing
- 14.7 Applications of artificial intelligence/machine learning tools in digital enzyme-linked immunosorbent assay
- 14.8 Applications of AlphaFold2 in protein structure prediction
- 14.9 Conclusion and future perspectives
- References
- Index
- Edition: 1
- Published: July 7, 2024
- Imprint: Elsevier Science
- No. of pages: 408
- Language: English
- Paperback ISBN: 9780443220012
- eBook ISBN: 9780443220005
JY
Jeong-Yeol Yoon
Jeong-Yeol Yoon received his Ph.D. in biomedical engineering from the University of California, Los Angeles (UCLA), USA in 2004. He joined the faculty at the University of Arizona, USA in the same year. Dr Yoon has worked on smartphone-based biosensors. He was the President of the Institute of Biological Engineering in 2015 and is currently the Editor-in-Chief of the Journal of Biological Engineering (Springer Nature), Associate Editor of Biosensors and Bioelectronics (Elsevier), and Editorial Board Member for several other journals.
Affiliations and expertise
Professor, Department of Biomedical Engineering and Department of Biosystems Engineering, The University of Arizona, USACY
Chenxu Yu
Chenxu Yu received his Ph.D. in biological systems engineering from University of Wisconsin-Madison, USA in 2003. He then did post-doctoral training at Pennsylvania State University and Purdue University, USA before joined the faculty at Iowa State University, USA in 2007. He has been serving as an editorial board member of seven reputed journals. His research interests are in the areas of bionanotechnology and biosensors, food and bioprocess engineering, spectroscopic imaging and biodiagnostics, environmental monitoring and engineering.
Affiliations and expertise
Affiliation: Associate Professor, Department of Agricultural & Biosystems Engineering, Iowa State University, USARead Machine Learning and Artificial Intelligence in Chemical and Biological Sensing on ScienceDirect