Abstract
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.
Highlights
Background & SummaryLaser-induced breakdown spectroscopy (LIBS) is an emission spectroscopic method that uses a high-powered laser pulse to ignite a microplasma
This is achieved by focusing laser pulses with lengths in the fs–ns range into spots with diameters of tens of μm
laser-induced breakdown spectroscopy (LIBS) is often preferred in industrial settings that are unfavourable for most common spectroscopic methods, such as charged-particle-based techniques and variations of mass spectrometry
Summary
Laser-induced breakdown spectroscopy (LIBS) is an emission spectroscopic method that uses a high-powered laser pulse to ignite a microplasma. The dispersed light intensities can be related to the composition of the target material[1,2] Owing to these relatively simple principles, LIBS instrumentations are generally robust. A common approach to classification is the randomized division of the complete dataset into training, validation, and testing subsets. We propose a dataset where the training and testing data is sampled from distinct materials. The classification problem is shown schematically in Fig. 1: Various ore samples belong to the same geological class, e.g., the class hematite is represented by six samples Four of these samples are provided in the training dataset, while the remaining two are included in the test dataset. The dataset could be used to develop feature-engineering and dimensionality-reduction methodologies
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