Related references
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Article
Automation & Control Systems
Gian Alix et al.
Summary: The exploration and analysis of chemical components in (extra-)terrestrial materials play an important role in modern research. Laser-induced breakdown spectroscopy (LiBS) is a widely used method for analyzing the chemical attributes of geological samples. This study compared the performances of different machine learning models in predicting elemental abundances and found that partial least squares (PLS) and extreme gradient boost machines (XGB) outperformed others in terms of predictive power, generalization ability, and runtime efficiency. The study also proposed the use of ensemble models to aggregate predictions from top-performing models, which showed promising results.
JOURNAL OF CHEMOMETRICS
(2023)
Article
Geochemistry & Geophysics
Tong Chen et al.
Summary: In this study, a new method for identifying multiple types of rocks was proposed by using LIBS technology combined with deep learning theory. The method achieved characterization of spatial distribution of elements on rock surface through LIBS-based images and classification using the Inception-v3 network. Specific data augmentation methods were proposed to address the small scale of the image dataset obtained in the laboratory. The superiority of this method was verified through three classification experiments of shale, gneiss and granite.
APPLIED GEOCHEMISTRY
(2022)
Article
Geochemistry & Geophysics
Jeannet A. Meima et al.
Summary: A new method using scanning Laser Induced Breakdown Spectroscopy (LIBS) was developed to analyze cryptic variations in mineral chemistry. This fast and easy-to-use method is particularly useful for comparing the lateral variability of important diagnostic horizons such as Merensky Reef and UG-2.
Article
Chemistry, Analytical
Weijie Xu et al.
Summary: The application of LIBS in geological materials analysis is crucial, especially for light elements like hydrogen, where X-ray fluorescence spectrometry may not be effective. This study aims to improve the accuracy of determining structural water in rocks by addressing the matrix effect through exploring the sensitivity of the H-alpha line to plasma properties. By combining physical and statistical data mining approaches, a significant improvement in H2O determination in rocks was achieved, with a RMSE of about 0.11 wt% for prediction with independent test samples.
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
(2022)
Article
Optics
Xiaoyan Song et al.
Summary: The RF-ANN method combines random forest algorithm with artificial neural network for improved analytical performance of LIBS in low-alloy steel composition evaluation. It outperforms traditional calibration curve method, providing more accurate predictions for the concentrations of chromium and vanadium in low-alloy steel samples.
Article
Physics, Multidisciplinary
Sara Kanzi et al.
Summary: This paper studies the GUP-modified Hawking radiation of rotating polytropic black hole given in the Boyer-Lindquist coordinates. The GUP-modified Klein-Gordon equation is applied to investigate the quantum tunneling of scalar particles. The reflection and transmission probabilities of the radiation are derived and discussed in detail.
EUROPEAN PHYSICAL JOURNAL PLUS
(2022)
Article
Multidisciplinary Sciences
Promode R. Bandyopadhyay
Summary: The origin of scale coupling may be explained by the understanding of multistability, which involves shifts between stable points via unstable equilibrium points due to stimulus. Research has shown that under specific conditions, corn cobs and other smooth fruits can undergo multistable autonomous motion, primarily manifested as rolling and yawing. The thickness of the viscous lubricant film is proportional to the cob's mass, tangential velocity, and temperature, with heavier cobs experiencing thinner films and lighter cobs favoring positive feedback leading to more spinning.
SCIENTIFIC REPORTS
(2022)
Article
Chemistry, Analytical
Deborah Musyoka Wayua et al.
Summary: This study developed a peak-free LIBS measurement strategy for the direct rapid determination of trace biometals in blood, such as copper, zinc, iron, and magnesium. The chemometrics approach, including artificial neural networks and partial least squares, was used to select spectral features and evaluate the analytical performance. The results demonstrated that this method achieved high accuracy in determining biometals in blood.
ANALYTICAL LETTERS
(2022)
Article
Chemistry, Analytical
Chenwei Zhu et al.
Summary: This study used laser-induced breakdown spectroscopy (LIBS) to accurately and quickly measure the arsenic concentration in niuhuangjiedu tablets (NHJDTs), and analyzed the health risks associated with arsenic. The results showed that LIBS is a rapid and accurate method for arsenic determination, and indicated potential health risks in the NHJDTs.
ANALYTICAL LETTERS
(2022)
Review
Agriculture, Multidisciplinary
Jie Ren et al.
Summary: Laser Induced Breakdown Spectroscopy (LIBS) technology is widely used in agro-ecological monitoring due to its in-situ and non-destructive nature, fast and accurate detection, and minimal sample pre-processing. This review summarizes the applications of LIBS in agriculture from 2017 to 2021, including soil and fertilizers elemental composition analysis, water quality detection, and pests, diseases, and elements in crops. The challenges and developing prospects of LIBS in agriculture are also discussed.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Food Science & Technology
Dimitrios Stefas et al.
Summary: In this study, the effects of artificial feeding of bees on honey were investigated using LIBS for the first time. The emission spectral characteristics of plasma on honey samples were analyzed and correlation plots were constructed to indicate the importance of spectral lines of elements. Machine learning algorithms based on LDA and RFC were employed for honey sample classification, with accuracies exceeding 90% and validation through HPLC-RID.
Article
Food Science & Technology
Banu Sezer et al.
Summary: This study explored the possibility of using LIBS to discriminate between durum and common wheat samples in flour and pasta, as well as determine the adulteration ratio. The results showed that LIBS is a promising analytical technique that is rapid, reliable, environmentally friendly, and nondestructive for identifying and detecting wheat flour adulteration in pasta.
JOURNAL OF CEREAL SCIENCE
(2022)
Article
Spectroscopy
Fan Yang et al.
Summary: In this research, the performance of a designed deep convolutional neural network (CNN) on datasets consisting of multi-distance spectra is investigated. The results show that the CNN can surpass four alternative chemometrics in terms of overall prediction accuracy. Moreover, the CNN model can behave even better if an extremely simple distance correction procedure is supplemented.
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
(2022)
Article
Spectroscopy
Vincent Detalle et al.
Summary: LIBS technology has matured in the field of cultural heritage, providing important elemental analysis data for artifacts conservation and archaeological research. Researchers have made progress by constantly improving the technology, overcoming challenges, and encouraging more people to use and study this technique.
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
(2022)
Article
Spectroscopy
Simon Mueller et al.
Summary: This study proposes a novel semi-supervised learning classification model to separate known minerals from unknown materials in geological drill cores, resolving quantitative analysis challenges in LIBS applications. By combining supervised linear discriminant analysis and semi-supervised support vector machines, main minerals and known accessory minerals were effectively separated, with unknown materials correctly identified.
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
(2022)
Review
Energy & Fuels
Guofa Wang et al.
Summary: This study examines the role of coal as China's basic energy source and focuses on the intelligent development of coal mines. It introduces the Chinese mode of intelligent mining and proposes classification and grading standards. The study establishes a digital logic model and information entity and knowledge map construction method to promote the development of intelligent coal mine systems. It also describes the top-level architecture of 5G+ intelligent coal mine systems and the progress in intelligent mining technology. The study analyzes the development of intelligent coal mines and proposes ideas for promoting their development.
INTERNATIONAL JOURNAL OF COAL SCIENCE & TECHNOLOGY
(2022)
Article
Green & Sustainable Science & Technology
Yongli Zhang
Summary: This study investigates the factors influencing China's energy efficiency, considering energy consumption and environmental factors. The research finds that energy efficiency exhibits spatial correlation and agglomeration characteristics, with energy consumption structure being the main influencing factor. Regions with abundant energy resources bear the environmental cost of energy consumption. Additionally, research and development (R&D) investment only affects local energy efficiency, whereas foreign direct investment (FDI) has a negative impact on energy efficiency.
JOURNAL OF CLEANER PRODUCTION
(2022)
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Environmental Studies
Rashed Poormirzaee et al.
Summary: New technologies have a significant impact on the efficiency and productivity of the mining industry. This research proposes a framework for assessing and prioritizing smart mining strategies by integrating the Z-number theory and fuzzy-VIKOR technique. The results show that the third strategy (A3) is the most appropriate for implementing Industry 4.0 technologies in large-scale mines in Iran.
Article
Optics
Chong Wang et al.
Summary: The study used LIBS technology to classify 13 types of rocks, reduced dimensionality using principal component analysis, and applied three classification algorithms (linear discriminant analysis, random forest classification, and support vector machine) for classification. The results showed that the support vector machine algorithm can be applied to LIBS rock classification, with average accuracies of 27%, 91%, and 100% for the three algorithms, respectively.
Article
Environmental Sciences
Changqing Liu et al.
Summary: This study developed a laboratory standoff LIBS system combined with a Martian simulation chamber to examine the geochemical characteristics of igneous rocks and applied three multivariate analysis methods to characterize the samples. Results show that PCA is better suited for identifying igneous rock lithology with higher correlation than using predicted elemental ratios derived by PLS and LASSO. This preliminary study provides suitable methods for the elemental prediction and geochemical identification of Martian rocks.
Review
Geochemistry & Geophysics
Ashwin Kumar Myakalwar et al.
Summary: The review article discusses the latest advancements in monitoring molten phases in metallurgical processes using LIBS, covering modern experimental arrangements, patents, industrial applications, and the importance of temperature, pressure, and atmospheric composition in melting processes. It also highlights the need for development and limitations, as well as the use of machine learning and data processing techniques to enhance the effectiveness of LIBS systems.
Article
Spectroscopy
Zainab Gazali et al.
Summary: Compositional analysis of gallstone samples was conducted using LIBS and PAS, with regression plots for classification and observation of atomic and molecular features in the spectra. Relative hardness of gallstones was estimated from LIBS spectra, molecular constituents were detected using PAS, and discrimination was performed using PCA. The combination of PAS and LIBS was found to be a suitable method for gallstone compositional analysis.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Review
Spectroscopy
Ying Zhang et al.
Summary: Global environmental pollution has become more challenging in recent years, highlighting the importance of environmental monitoring for reflecting environmental quality. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique with advantages such as rapid, real-time, and simultaneous detection of multiple elements, making it highly valuable for environmental analysis. This review article focuses on the progress of basic research and applications of LIBS technology in environmental analysis over the past decade, providing insights into its current research status.
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY
(2021)
Proceedings Paper
Nanoscience & Nanotechnology
Sun Peng et al.
Summary: The study introduced an Adaptive Iterative Re-weighted Partial Least Squares (air-PLS) baseline correction method to improve the accuracy of Laser Induced Breakdown Spectroscopy (LIBS) quantitative analysis. The method significantly enhanced the accuracy of LIBS quantitative analysis for metal elements in soil samples.
Article
Chemistry, Multidisciplinary
Xu Wang et al.
Summary: The low-cost, multi-channel smartphone-based spectrometer can achieve accurate rock type prediction through deep learning model processing.
CHEMICAL COMMUNICATIONS
(2021)
Article
Chemistry, Analytical
Patrick Janovszky et al.
Summary: This study demonstrates the efficiency of LIBS mapping and spatially resolved local analysis for mineral classification and geological prospecting. Statistical approaches like CT, LDA, and RF show good classification accuracies, with RF and LDA performing better. Multiple analytical locations within each grain can provide more reliable discrimination, and LIBS elemental mapping combined with matrix-matched calibration can offer valuable information on element distribution. Mining for Be and Li in granitoid rocks should target biotite and amphibole grains based on the results.
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
(2021)
Article
Chemistry, Analytical
Pavel Jahoda et al.
Summary: Machine Learning has been applied in spectroscopy for mineral recognition and elemental composition estimation. A novel approach combining measurements from different spectroscopic methods using Deep Learning algorithms and neural networks for mineral classification was proposed. Experimental results demonstrate that combining different spectral methods can significantly improve the accuracy of mineral classification.
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(2017)
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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
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Y Wu et al.
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