Artificial intelligence and spectroscopic techniques for gemology applications /
edited by Ashutosh Kumar Shu.
- 1 online resource (various pagings) : illustrations (some color).
- [IOP release $release] IOP series in spectroscopic methods and applications IOP ebooks. [2022 collection] .
- IOP (Series). Release 22. IOP series in spectroscopic methods and applications. IOP ebooks. 2022 collection. .
"Version: 20221201"--Title page verso.
Includes bibliographical references.
1. Laser-induced breakdown spectroscopy for gemological testing / Francesco Poggialini, Beatrice Campanella, Stefano Legnaioli, Simona Raneri and Vincenzo Palleschi -- 2. Raman spectroscopy for the non-destructive analysis of gemstones / Danilo Bersani, Laura Fornasini, Peter Vandenabeele and Anastasia Rousaki -- 3. Application of Fourier-transformed infrared spectroscopy and machine learning algorithm for gem identification / Pimthong Thongnopkun, Kanet Wongravee, Prompong Pienpinijtham and Aumaparn Phlayrahan -- 4. A ruby stone grading inspection using an optical tomography system / Syarfa Najihah Raisin, Juliza Jamaludin and Fatinah Mohd Rahalim -- 5. Trace elements and big data application to gemology by x-ray fluorescence / Yujie Gao, Moqing Lin, Xu Li and Xueying Sun.
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.
Gemologists/mineralogists in academia and industry.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Ashutosh Kumar Shukla has more than two decades of physics teaching and research experience. He has had numerous articles and review articles published in peer-reviewed journals.
9780750339278 9780750339261
10.1088/978-0-7503-3927-8 doi
Gemology--Data processing.
Artificial intelligence--Industrial applications.
Spectrum analysis.
Spectrum analysis, spectrochemistry, mass spectrometry.
SCIENCE / Spectroscopy & Spectrum Analysis.
QE392 / .A785 2022eb
553.8
"Version: 20221201"--Title page verso.
Includes bibliographical references.
1. Laser-induced breakdown spectroscopy for gemological testing / Francesco Poggialini, Beatrice Campanella, Stefano Legnaioli, Simona Raneri and Vincenzo Palleschi -- 2. Raman spectroscopy for the non-destructive analysis of gemstones / Danilo Bersani, Laura Fornasini, Peter Vandenabeele and Anastasia Rousaki -- 3. Application of Fourier-transformed infrared spectroscopy and machine learning algorithm for gem identification / Pimthong Thongnopkun, Kanet Wongravee, Prompong Pienpinijtham and Aumaparn Phlayrahan -- 4. A ruby stone grading inspection using an optical tomography system / Syarfa Najihah Raisin, Juliza Jamaludin and Fatinah Mohd Rahalim -- 5. Trace elements and big data application to gemology by x-ray fluorescence / Yujie Gao, Moqing Lin, Xu Li and Xueying Sun.
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.
Gemologists/mineralogists in academia and industry.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Ashutosh Kumar Shukla has more than two decades of physics teaching and research experience. He has had numerous articles and review articles published in peer-reviewed journals.
9780750339278 9780750339261
10.1088/978-0-7503-3927-8 doi
Gemology--Data processing.
Artificial intelligence--Industrial applications.
Spectrum analysis.
Spectrum analysis, spectrochemistry, mass spectrometry.
SCIENCE / Spectroscopy & Spectrum Analysis.
QE392 / .A785 2022eb
553.8