Artificial Intelligence and Spectroscopic Techniques for Gemology Applications

This collection highlights gemstone identification and analysis using spectroscopic techniques. It also includes the exciting applications of artificial intelligence and machine learning technologies that are being developed and used to enhance the efficiency of identification and analysis techniques. Laser-induced breakdown spectroscopy, Raman spectroscopy and FTIR spectroscopy applications are discussed in separate chapters. Ruby stone grading stone using optical tomography is the theme of another chapter. Chapters introduce the relevant theoretical concepts and present typical experimental methodologies with a focus on gemological applications and include experimental results and potential for future developments. A reader-friendly approach has been adopted throughout the book and basics of techniques have been included wherever appropriate. It provides a unique addition to the knowledge base in view of emerging applications of spectroscopic and information techniques in gemology. It not only suits the need of novice researchers but also intends to connect the experts to the state of the art in spectroscopic technology and associated machine learning applications.
Key Features:

• Includes case studies, recent trends and future prospects.
• Includes experimental set up as well as theoretical description.
• Encompasses applications and potential of AI and ML In gemology.
• Individual chapter content level designed to address the needs of novice researchers, as well as experienced researchers and technicians.