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Artificial intelligence and spectroscopic techniques for gemology applications / edited by Ashutosh Kumar Shu.

Contributor(s): Shu, Ashutosh Kumar [editor.] | Institute of Physics (Great Britain) [publisher.].
Material type: materialTypeLabelBookSeries: IOP (Series)Release 22: ; IOP series in spectroscopic methods and applications: ; IOP ebooks2022 collection: Publisher: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2022]Description: 1 online resource (various pagings) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9780750339278; 9780750339261.Subject(s): Gemology -- Data processing | Artificial intelligence -- Industrial applications | Spectrum analysis | Spectrum analysis, spectrochemistry, mass spectrometry | SCIENCE / Spectroscopy & Spectrum AnalysisAdditional physical formats: Print version:: No titleDDC classification: 553.8 Online resources: Click here to access online Also available in print.
Contents:
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.
Abstract: 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.
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"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.

Also available in print.

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.

Title from PDF title page (viewed on January 9, 2023).

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