Rapid gemstone mineral identification using portable Raman spectroscopy.

  • Published In: Journal of Raman Spectroscopy, 2023, v. 54, n. 6. P. 640 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tsai, Tsung‐Han; Xu, Wenxing 3 of 3

Abstract

Mineral identification is the first step in gemstone characterization, which is the foundation for maintaining transparency in gemstone trading. Current techniques have complex protocols and require expensive instrumentation, severely restricting their use to trained experts or large gemological laboratories and limiting the processing speed. A simple and inexpensive automatic identification instrument can fulfill the needs of the Gemology Society. Hence, we propose a portable setup for the automatic mineral identification of gemstones based on a custom‐built 405 nm Raman spectroscopy probe system. This device enables the noninvasive mineral identification of commonly encountered loose or mounted gemstone materials in the market and generates the results in seconds. It is mechanically designed to allow safe operation under normal office conditions, and the pre‐aligned sample stage significantly simplifies the sample alignment process. Fifty‐seven types of gemstone materials in the trading market were tested with the device to prove the usability of the system. The primary purpose of this device is to automatically identify the types of minerals in the gemstones. It can also be extended to identify lab‐grown gemstones in the selected mineral species. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Raman Spectroscopy. 2023/06, Vol. 54, Issue 6, p640
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2023
  • ISSN:0377-0486
  • DOI:10.1002/jrs.6518
  • Accession Number:164094771
  • Copyright Statement:Copyright of Journal of Raman Spectroscopy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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