scholarly journals Rapid nuclear forensics analysis via machine-learning-enabled laser-induced breakdown spectroscopy (LIBS)

2019 ◽  
Author(s):  
Bobby Bhatt ◽  
Alix Dehayem-Kamadjeu ◽  
Kalambuka Hudson Angeyo
Keyword(s):  
Machine Learning ◽  
Nuclear Forensics ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

scholarly journals Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1241
Author(s):  
Nikolaos Gyftokostas ◽  
Eleni Nanou ◽  
Dimitrios Stefas ◽  
Vasileios Kokkinos ◽  
Christos Bouras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Olive Oil ◽  
Absorption Spectra ◽  
Absorption Spectroscopy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Spectroscopic Techniques ◽  
Breakdown Spectroscopy ◽  
Olive Oils ◽  
Laser Induced Breakdown

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.

scholarly journals Imaging margins of skin tumors using laser-induced breakdown spectroscopy and machine learning

2021 ◽  
Author(s):  
Kateřina Kiss ◽  
Anna Šindelářová ◽  
Lukáš Krbal ◽  
Václav Stejskal ◽  
Kristýna Mrázová ◽  
...  
Keyword(s):  
Machine Learning ◽  
Significant Role ◽  
Skin Tumors ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Nowadays, laser-based techniques play a significant role in medicine, mainly in the ophthalmology, dermatology, and surgical fields.

Four-metal-element quantitative analysis and pollution source discrimination in atmospheric sedimentation by laser-induced breakdown spectroscopy (LIBS) coupled with machine learning

2020 ◽  
Vol 35 (2) ◽  
pp. 403-413 ◽  
Author(s):  
Xin Zhang ◽  
Nan Li ◽  
Chunhua Yan ◽  
Jiahui Zeng ◽  
Tianlong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Analysis ◽  
Pollution Source ◽  
Laser Induced Breakdown Spectroscopy ◽  
Source Discrimination ◽  
Metal Elements ◽  
Breakdown Spectroscopy ◽  
Metal Element ◽  
Laser Induced Breakdown

The laser-induced breakdown spectroscopy (LIBS) technique coupled with machine learning was proposed to perform four metal elements quantitative analysis and pollution source discrimination in atmospheric sedimentation.

scholarly journals Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1393 ◽  
Author(s):  
Yanwei Yang ◽  
Xiaojian Hao ◽  
Lili Zhang ◽  
Long Ren
Keyword(s):  
Machine Learning ◽  
Iron Ore ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Iron Ores ◽  
K Nearest Neighbor ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

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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