Improving quantitative analysis of spark-induced breakdown spectroscopy: Multivariate calibration of metal particles using machine learning

2022 ◽  
Vol 159 ◽  
pp. 105874
Author(s):  
Hanyang Li ◽  
Leonardo Mazzei ◽  
Christopher D. Wallis ◽  
Anthony S. Wexler
Keyword(s):  
Machine Learning ◽  
Quantitative Analysis ◽  
Multivariate Calibration ◽  
Metal Particles ◽  
Breakdown Spectroscopy

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.

Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning

2021 ◽  
Author(s):  
Ashwin P. Rao ◽  
Phillip R. Jenkins ◽  
Dung M Vu ◽  
John D. Auxier ◽  
Anil K Patnaik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Trace Elements ◽  
Quantitative Analysis ◽  
Chemometric Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

We present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld...

scholarly journals Quantitative analysis of laser induced breakdown spectroscopy of Pb in navel orange based on multivariate calibration

2014 ◽  
Vol 63 (10) ◽  
pp. 104213
Author(s):  
Chen Tian-Bing ◽  
Yao Ming-Yin ◽  
Liu Mu-Hua ◽  
Lin Yong-Zeng ◽  
Li Wen-Bing ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Multivariate Calibration ◽  
Laser Induced Breakdown Spectroscopy ◽  
Navel Orange ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Quantitative analysis of chromium in potatoes by laser-induced breakdown spectroscopy coupled with linear multivariate calibration

2015 ◽  
Vol 54 (25) ◽  
pp. 7807 ◽  
Author(s):  
Tianbing Chen ◽  
Lin Huang ◽  
Mingyin Yao ◽  
Huiqin Hu ◽  
Caihong Wang ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Multivariate Calibration ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Quantitative analysis of Lead Zirconate Titanate (PZT) ceramics by laser-induced breakdown spectroscopy (LIBS) in combination with multivariate calibration

2017 ◽  
Vol 130 ◽  
pp. 21-26 ◽  
Author(s):  
Rafael Hernández-García ◽  
Margarita E. Villanueva-Tagle ◽  
Francisco Calderón-Piñar ◽  
María D. Durruthy-Rodríguez ◽  
Francisco W.B. Aquino ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Lead Zirconate Titanate ◽  
Multivariate Calibration ◽  
Lead Zirconate ◽  
Laser Induced Breakdown Spectroscopy ◽  
Pzt Ceramics ◽  
Breakdown Spectroscopy ◽  
Zirconate Titanate ◽  
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.

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