ArticleSensors (Basel, Switzerland)2020
Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS).
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Rapid Classification of Bauxite Ores from Different Mining Areas by BBO-XGBoost Combined with CC-LIBS.ACS omega · 2026Article
- Application of Laser-Induced Breakdown Spectroscopy (LIBS) technology in malignant tumor diagnosis.Discover oncology · 2026Review
- Research on the Dynamic Model of Fireball Thermal Dose Based on the Effective Band Integral Method.ACS omega · 2023Article
- Prediction of Compressive Strength of Biomass-Humic Acid Limonite Pellets Using Artificial Neural Network Model.Materials (Basel, Switzerland) · 2023Article
- Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics.Sensors (Basel, Switzerland) · 2022Article
- Article
- Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review.Sensors (Basel, Switzerland) · 2022Review
- Effect of Plane Mirrors Combined with Au-Nanoparticle Confinement on the Spectral Properties of Fe Plasma Induced by Laser-Induced Breakdown.ACS omega · 2022Article
- Laser-Induced Breakdown Spectroscopy Combined with Nonlinear Manifold Learning for Improvement Aluminum Alloy Classification Accuracy.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores' selection in the metallurgical industry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.