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.
Author(s): Ashutosh Kumar Shukla
Series: IOP Series in Spectroscopic Methods and Applications
Publisher: IOP Publishing
Year: 2022
Language: English
Pages: 167
City: Bristol
PRELIMS.pdf
Preface
Editor biography
Ashutosh Kumar Shukla
List of contributors
CH001.pdf
Chapter 1 Laser-induced breakdown spectroscopy for gemological testing
1.1 Introduction
1.2 What is LIBS
1.3 Applications of LIBS in gemology
1.4 Conclusion
References
CH002.pdf
Chapter 2 Raman spectroscopy for the non-destructive analysis of gemstones
2.1 Raman spectroscopy on gemstones
2.1.1 A short introduction to Raman spectroscopy
2.2 Benchtop and mobile Raman spectroscopy
2.3 Selected topics of Raman spectroscopy for gemological purposes, including forgeries
2.3.1 Garnets
2.3.2 Jade
2.3.3 Beryls
2.3.4 Corundum and other gemstones
2.3.5 Raman and photoluminescence emission
2.3.6 Glass
2.3.7 Pearls and corals
2.3.8 Forgeries
2.4 Conclusions
Acknowledgments
References
CH003.pdf
Chapter 3 Application of Fourier-transformed infrared spectroscopy and machine learning algorithm for gem identification
3.1 Introduction
3.2 Concept of IR spectroscopy
3.2.1 FT-IR sampling techniques for gem analysis
3.3 Diamond
3.3.1 Classification of diamond types
3.3.2 Characterization of synthetic diamonds and treated diamonds
3.3.3 Identification of diamond imitations
3.4 Rubies and sapphires
3.4.1 General information on rubies and sapphires
3.4.2 Application of FITR for corundum analysis
3.5 Emerald
3.5.1 Identification of natural and synthetic emeralds
3.5.2 Origin determination
3.5.3 Identification of resin-filled emeralds
3.6 Quartz
3.6.1 Identification of natural and synthetic quartz
3.6.2 Characterization of heat treatment and irradiation
3.7 Jade
3.7.1 Identification of jade enhancement
3.8 Turquoise
3.8.1 Characterization of turquoise
3.8.2 Identification of treated and imitated turquoise
3.9 Application of machine learning algorithm to gemstone classification
3.10 Conclusions
References
CH004.pdf
Chapter 4 A ruby stone grading inspection using an optical tomography system
4.1 Introduction
4.2 Methodology
4.2.1 Mathematical expression
4.2.2 Image reconstruction
4.3 Results and discussion
4.3.1 Analysis on image reconstruction
4.3.2 Statistical ANOVA test analysis
4.4 Conclusion
Acknowledgments
References
CH005.pdf
Chapter 5 Trace elements and big data application to gemology by x-ray fluorescence
5.1 Introduction to XRF technique
5.1.1 The fundamentals of XRF
5.1.2 The advantages of XRF
5.2 Trace elements and analysis in gemstones
5.3 Case study of XRF and big data gemology
5.3.1 Identification of species and varieties
5.3.2 Color origin and fluorescence
5.3.3 Treatment detection
5.4 Big data application in gemology: geographic origin determination
5.4.1 Ruby
5.4.2 Spinel
References and further reading
