Identification and classification of polymer e-waste using laser-induced breakdown spectroscopy (LIBS) and chemometric tools

2017 ◽  
Vol 59 ◽  
pp. 390-395 ◽  
Author(s):  
Vinicius Câmara Costa ◽  
Francisco Wendel Batista Aquino ◽  
Caio Marcio Paranhos ◽  
Edenir Rodrigues Pereira-Filho
Keyword(s):  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Chemometric Tools

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 Laser Induced Breakdown Spectroscopy (LIBS): Application to Geological Materials-=SUP=-*-=/SUP=-

2021 ◽  
Vol 129 (10) ◽  
pp. 1336
Author(s):  
Sonali Dubey ◽  
Rohit Kumar ◽  
Abhishek K. Rai ◽  
Awadhesh K. Rai
Keyword(s):  
Planetary Exploration ◽  
Field Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Data Set ◽  
Geological Materials ◽  
Breakdown Spectroscopy ◽  
Spot Detection ◽  
Huge Data ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy (LIBS) is emerging as an analytical tool for investigating geological materials. The unique abilities of this technique proven its potential in the area of geology. Detection of light elements, portability for in-field analysis, spot detection, and no sample preparation are some features that make this technique appropriate for the study of geological materials. The application of the LIBS technique has been tremendously developed in recent years. In this report, results obtained from previous and most recent studies regarding the investigation of geological materials LIBS technique are reviewed. Firstly, we introduce investigations that report the advancement in LIBS instrumentation, its applications, especially in the area of gemology and the extraterrestrial/planetary exploration have been reviewed. Investigation of gemstones by LIBS technique is not widely reviewed in the past as compared to LIBS application in planetary exploration or other geological applications. It is anticipated that for the classification of gemstones samples, huge data set is appropriate and to analyze this data set, multivariate/chemometric methods will be useful. Recent advancement of LIBS instrumentation for the study of meteorites, depth penetration in Martian rocks and its regolith proved the feasibility of LIBS used as robotic vehicles in the Martian environment. Keywords: LIBS, Gemstone, geological samples, Extra-terrestrial

In situ classification of rocks using stand-off laser-induced breakdown spectroscopy with a compact spectrometer

2018 ◽  
Vol 33 (3) ◽  
pp. 461-467 ◽  
Author(s):  
W. T. Li ◽  
Y. N. Zhu ◽  
X. Li ◽  
Z. Q. Hao ◽  
L. B. Guo ◽  
...  
Keyword(s):  
High Accuracy ◽  
Remote Detection ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

The ASPI-LDA algorithm combined with a compact spectrometer to achieve high accuracy classification, which has a great potential for field in situ remote detection.

Fast classification of tobacco based on laser-induced breakdown spectroscopy

2019 ◽  
Vol 12 (5) ◽  
pp. 1139-1146 ◽  
Author(s):  
李昂泽 LI Ang-ze ◽  
王宪双 WANG Xian-shuang ◽  
徐向君 XU Xiang-jun ◽  
何雅格 HE Ya-ge ◽  
郭 帅 GUO Shuai ◽  
...  
Keyword(s):  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Fast Classification

The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods

2020 ◽  
Vol 22 (7) ◽  
pp. 074012 ◽  
Author(s):  
Zhongqi FENG ◽  
Dacheng ZHANG ◽  
Bowen WANG ◽  
Jie DING ◽  
Xuyang LIU ◽  
...  
Keyword(s):  
Laser Induced Breakdown Spectroscopy ◽  
Chemometric Methods ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

scholarly journals Laser-Induced Breakdown Spectroscopy and Principal Component Analysis for the Classification of Spectra from Gold-Bearing Ores

2019 ◽  
Vol 74 (1) ◽  
pp. 42-54 ◽  
Author(s):  
Daniel Diaz ◽  
Alejandro Molina ◽  
David W. Hahn
Keyword(s):  
Principal Component Analysis ◽  
Limit Of Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Lines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Single Shot ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) were applied to the classification of LIBS spectra from gold ores prepared as pressed pellets from pulverized bulk samples. For each sample, 5000 single-shot LIBS spectra were obtained. Although the gold concentrations in the samples were as high as 7.7 µg/g, Au emission lines were not observed in most single-shot LIBS spectra, rendering the application of the usual ensemble-averaging approach for spectral processing to be infeasible. Instead, a PCA approach was utilized to analyze the collection of single-shot LIBS spectra. Two spectral ranges of 21 nm and 0.15 nm wide were considered, and LIBS variables (i.e., wavelengths) reduced to no more than three principal components. Single-shot spectra containing Au emission lines (positive spectra) were discriminated by PCA from those without the spectral feature (negative spectra) in a spectral range of less than 1 nm wide around the Au(I) 267.59 nm emission line. Assuming a discrete gold distribution at very low concentration, LIBS sampling of gold particles seemed unlikely; therefore, positive spectra were considered as data outliers. Detection of data outliers was possible using two PCA statistical parameters, i.e., sample residual and Mahalanobis distance. Results from such a classification were compared with a standard database created with positive spectra identified with a filtering algorithm that rejected spectra with an Au intensity below the smallest detectable analytical LIBS signal (i.e., below the LIBS limit of detection). The PCA approach successfully identified 100% of the data outliers when compared with the standard database. False identifications in the multivariate approach were attributed to variations in shot-to-shot intensity and the presence of interfering emission lines.

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