Classification of Copper Minerals by Handheld Laser-Induced Breakdown Spectroscopy and Nonnegative Tensor Factorisation
| Autor: | Katarzyna Cieślik, Sven Merk, Arkadiusz J. Antończak, Rafal Zdunek, Toralf Beitz, Daniel Riebe, Pia Brinkmann, David Mory, Michał R. Wójcik |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
copper minerals
Computer science Stability (learning theory) 010501 environmental sciences lcsh:Chemical technology 01 natural sciences Biochemistry Spectral line Article Analytical Chemistry Tensor (intrinsic definition) lcsh:TP1-1185 Tensor Laser-induced breakdown spectroscopy Electrical and Electronic Engineering Spectroscopy Instrumentation 0105 earth and related environmental sciences LIBS business.industry 010401 analytical chemistry Pattern recognition Linear discriminant analysis Atomic and Molecular Physics and Optics 0104 chemical sciences Support vector machine ComputingMethodologies_PATTERNRECOGNITION NTF classification Artificial intelligence HALS business Mobile device |
| Zdroj: | Sensors Volume 20 Issue 18 Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 5152, p 5152 (2020) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s20185152 |
| Popis: | Laser-induced breakdown spectroscopy (LIBS) analysers are becoming increasingly common for material classification purposes. However, to achieve good classification accuracy, mostly noncompact units are used based on their stability and reproducibility. In addition, computational algorithms that require significant hardware resources are commonly applied. For performing measurement campaigns in hard-to-access environments, such as mining sites, there is a need for compact, portable, or even handheld devices capable of reaching high measurement accuracy. The optics and hardware of small (i.e., handheld) devices are limited by space and power consumption and require a compromise of the achievable spectral quality. As long as the size of such a device is a major constraint, the software is the primary field for improvement. In this study, we propose a novel combination of handheld LIBS with non-negative tensor factorisation to investigate its classification capabilities of copper minerals. The proposed approach is based on the extraction of source spectra for each mineral (with the use of tensor methods) and their labelling based on the percentage contribution within the dataset. These latent spectra are then used in a regression model for validation purposes. The application of such an approach leads to an increase in the classification score by approximately 5% compared to that obtained using commonly used classifiers such as support vector machines, linear discriminant analysis, and the k-nearest neighbours algorithm. |
| Databáze: | OpenAIRE |
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