Principal component-generalized spectrum-machine learning approach for quantifying gallium in a surrogate plutonium matrix using LIBS

Authors

• Ashwin P. Rao, Air Force Institute of TechnologyFollow
• Anil K. Patnaik, Air Force Institute of TechnologyFollow

Document Type

Article

Publication Date

6-1-2025

Comments

The "Link to Full Text" on this page opens or downloads the accepted manuscript (postprint) of  the work, provided through the CHORUS program, and hosted at the Optica website.

A 12-month embargo was observed for posting the accepted manuscript.

DOI

10.1364/AO.554537

Source Publication

Applied Optics (ISSN 1559-128X | eISSN 2155-3165)

Recommended Citation

Ashwin P. Rao and Anil K. Patnaik, "Principal component-generalized spectrum-machine learning approach for quantifying gallium in a surrogate plutonium matrix using LIBS," Appl. Opt. 64, D77-D84 (2025)