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根据地理位置对未经过分级的蓝宝石进行分层判别时,选择拉曼或 LIBS 光谱信息的替代方法,以提高准确性。

Alternative selection of Raman or LIBS spectral information in hierarchical discrimination of raw sapphires according to geographical origin for accuracy improvement.

机构信息

Department of Chemistry and Research Institute for Convergence of Basic Sciences, Hanyang University, Seoul, 133-791, Republic of Korea.

Department of Mathematics and Research Institute for Convergence of Basic Sciences, Hanyang University, Seoul, 133-791, Republic of Korea.

出版信息

Talanta. 2021 Jan 1;221:121555. doi: 10.1016/j.talanta.2020.121555. Epub 2020 Sep 8.

Abstract

Both Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) were cooperatively utilized to improve the geographical origin identification of raw sapphires from five different countries (Mozambique, Laos, Australia, Rwanda, and Congo). A hierarchical support vector machine (H-SVM) was used for multi-group identification. Initially, accuracy improved to 87.5% using merged Raman-LIBS data compared to those of using only Raman (82.8%) or LIBS (71.9%) information. This improvement was attributed to incorporating two complimentary spectroscopic datasets that provided molecular vibrational and elemental information. However, merging both spectroscopic datasets is may not be the best choice since it would make distinct and sample-descriptive information in one spectroscopic dataset less recognized for analysis by the inclusion of less characteristic information in another spectroscopic dataset; using only Raman or LIBS information at each discrimination stage would be more effective. When Raman information was utilized during the first three discrimination stages followed by LIBS data during the fourth (last) discrimination stage in H-SVM, the accuracy improved to 90.6%. The proper selection of molecular vibrational or elemental sample information at different discrimination stages is attributed to this improvement.

摘要

拉曼光谱和激光诱导击穿光谱(LIBS)被协同用于提高来自五个不同国家(莫桑比克、老挝、澳大利亚、卢旺达和刚果)的蓝宝石原石的地理起源识别。采用分层支持向量机(H-SVM)进行多组识别。最初,与仅使用拉曼(82.8%)或 LIBS(71.9%)信息相比,合并的 Raman-LIBS 数据将准确率提高到 87.5%。这种改进归因于合并了两个互补的光谱数据集,这些数据集提供了分子振动和元素信息。然而,合并这两个光谱数据集可能不是最佳选择,因为它会使一个光谱数据集中的独特和样本描述信息因另一个光谱数据集中的特征信息较少而难以识别;在每个判别阶段仅使用拉曼或 LIBS 信息会更有效。在 H-SVM 的前三个判别阶段使用拉曼信息,然后在第四个(最后一个)判别阶段使用 LIBS 数据时,准确率提高到 90.6%。这种改进归因于在不同判别阶段正确选择分子振动或元素样本信息。

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