Rapid Determination of the Gross Calorific Value of Coal Using Laser-Induced Breakdown Spectroscopy Coupled with Artificial Neural Networks and Genetic Algorithm

2017 ◽  
Vol 31 (4) ◽  
pp. 3849-3855 ◽  
Author(s):  
Zhimin Lu ◽  
Juehui Mo ◽  
Shunchun Yao ◽  
Jingbo Zhao ◽  
Jidong Lu
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rapid Determination ◽  
Calorific Value ◽  
Laser Induced Breakdown Spectroscopy ◽  
Gross Calorific Value ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

scholarly journals Rapid Determination of Cadmium Contamination in Lettuce Using Laser-Induced Breakdown Spectroscopy

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2930 ◽  
Author(s):  
Tingting Shen ◽  
Wenwen Kong ◽  
Fei Liu ◽  
Zhenghui Chen ◽  
Jingdong Yao ◽  
...  
Keyword(s):  
Limit Of Detection ◽  
Rapid Determination ◽  
Univariate Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Growth Environment ◽  
Breakdown Spectroscopy ◽  
Contamination Degree ◽  
Cd Contamination ◽  
Laser Induced Breakdown

Quick access to cadmium (Cd) contamination in lettuce is important to supervise the leafy vegetable growth environment and market. This study aims to apply laser-induced breakdown spectroscopy (LIBS) technology for fast determination of Cd content and diagnosis of the Cd contamination degree in lettuce. Emission lines Cd II 214.44 nm, Cd II 226.50 nm, and Cd I 228.80 nm were selected to establish the univariate analysis model. Multivariate analysis including partial least squares (PLS) regression, was used to establish Cd content calibration models, and PLS model based on 22 variables selected by genetic algorithm (GA) obtained the best performance with correlation coefficient in the prediction set Rp2 = 0.9716, limit of detection (LOD) = 1.7 mg/kg. K-Nearest Neighbors (KNN) and random forest (RF) were used to analyze Cd contamination degree, and RF model obtained the correct classification rate of 100% in prediction set. The preliminary results indicate LIBS coupled with chemometrics could be used as a fast, efficient and low-cost method to assess Cd contamination in the vegetable industry.

Classification and identification of brands of iron ores using laser-induced breakdown spectroscopy combined with principal component analysis and artificial neural networks

2020 ◽  
Vol 12 (10) ◽  
pp. 1316-1323 ◽  
Author(s):  
Yawen Yang ◽  
Chen Li ◽  
Shu Liu ◽  
Hong Min ◽  
Chenglin Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Iron Ores ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Ann Models

In this work, PCA-ANN models of LIBS spectra were developed to classify and identify iron ores according to the production countries and brands.

Rapid determination of zinc in soils by laser-induced breakdown spectroscopy

2013 ◽  
Vol 39 (1) ◽  
pp. 81-83 ◽  
Author(s):  
A. M. Popov ◽  
M. O. Kozhnov ◽  
T. A. Labutin ◽  
S. M. Zaytsev ◽  
A. N. Drozdova ◽  
...  
Keyword(s):  
Rapid Determination ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

A Comparative Study of Two Data Reduction Methods for Steel Classification Based on LIBS

2014 ◽  
Vol 644-650 ◽  
pp. 4722-4725 ◽  
Author(s):  
Hai Yang Kong ◽  
Lan Xiang Sun ◽  
Jing Tao Hu ◽  
Yong Xin ◽  
Zhi Bo Cong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Characteristic Lines ◽  
Laser Induced Breakdown ◽  
Reduction Methods ◽  
Artificial Neural ◽  
Priori Knowledge

Spectra of 27 steel samples were acquired by Laser-Induced Breakdown Spectroscopy (LIBS) for steel classification. Two methods were used to reduce dimensions: the first is to select characteristic lines of elements contained in the samples manually and the second is to do principal component analysis (PCA) of original spectra. Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples. The results show that, the better result can be achieved by selecting peak lines manually, but this solution needs much priori knowledge and wastes much time. The principal components (PCs) of original spectra were utilized as the input of artificial neural networks can also attain a good result nevertheless and this method can be developed into an automatic solution without any priori knowledge.

Rapid determination of water COD using laser-induced breakdown spectroscopy coupled with partial least-squares and random forest

2018 ◽  
Vol 10 (40) ◽  
pp. 4879-4885 ◽  
Author(s):  
Song Ye ◽  
Xiao Chen ◽  
Daming Dong ◽  
Jiejun Wang ◽  
Xinqiang Wang ◽  
...  
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Chemical Analysis ◽  
Rapid Determination ◽  
Oxygen Demand ◽  
Laser Induced Breakdown Spectroscopy ◽  
Water Quality Indicator ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Chemical oxygen demand (COD) is a water quality indicator that is typically measured by lengthy chemical analysis methods in the laboratory, which indicates that obtaining rapid results is difficult.

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