期刊
ANALYTICA CHIMICA ACTA
卷 1114, 期 -, 页码 66-73出版社
ELSEVIER
DOI: 10.1016/j.aca.2020.04.005
关键词
Laser-induced breakdown spectroscopy (LIBS); Big data; Hyperspectral imaging; Clustering
资金
- Pulsalys [L0978-L1294]
- French region Grand Est
- French region Rhones Alpes Auvergne (Optolyse, CPER2016)
Today, Laser-Induced Breakdown Spectroscopy (LIBS) imaging is in full change. Indeed, always more stable instrumentations are developed, which significantly increases the signal quality and naturally the analytical potential of the technique for the characterization of complex and heterogeneous samples at the micro-scale level. Obviously, other intrinsic features such as a limit of detection in the order of ppm, a high field of view and high acquisition rate make it one of the most complete chemical imaging techniques to date. It is thus possible in these conditions to acquire several million spectra from one single sample in just hours. Managing big data in LIBS imaging is the challenge ahead. In this paper, we put forward a new spectral analysis strategy, called embedded k-means clustering, for simultaneous detection of major and minor compounds and the generation of associated localization maps. A complex rock section with different phases and traces will be explored to demonstrate the value of this approach. (C) 2020 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据