Central European Institute of Technology, Brno University of Technology, Purkyňova 123, 612 00, Brno, Czech Republic.
Sci Data. 2020 Feb 13;7(1):53. doi: 10.1038/s41597-020-0396-8.
In this work, we present an extensive dataset of laser-induced breakdown spectroscopy (LIBS) spectra for the pre-training and evaluation of LIBS classification models. LIBS is a well-established spectroscopic method for in-situ and industrial applications, where LIBS is primarily applied for clustering and classification tasks. As such, our dataset is aimed at helping with the development and testing of classification and clustering methodologies. Moreover, the dataset could be used to pre-train classification models for applications where the amount of available data is limited. The dataset consists of LIBS spectra of 138 soil samples belonging to 12 distinct classes. The spectra were acquired with a state-of-the-art LIBS system. Lastly, the composition of each sample is also provided, including estimated uncertainties.
在这项工作中，我们提供了一个广泛的激光诱导击穿光谱（LIBS）光谱数据集，用于 LIBS 分类模型的预训练和评估。LIBS 是一种用于原位和工业应用的成熟光谱方法，主要应用于聚类和分类任务。因此，我们的数据集旨在帮助开发和测试分类和聚类方法。此外，该数据集可用于预训练分类模型，适用于可用数据量有限的应用。该数据集由属于 12 个不同类别的 138 个土壤样本的 LIBS 光谱组成。光谱是用最先进的 LIBS 系统获得的。最后，还提供了每个样本的组成，包括估计的不确定度。
