Feature‐level fusion of laser‐induced breakdown spectroscopy and Raman spectroscopy for improving support vector machine in clinical bacteria identification

2021 ◽  
Author(s):  
Geer Teng ◽  
Qianqian Wang ◽  
Xutai Cui ◽  
Kai Wei ◽  
Wenting Xiangli ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Raman Spectroscopy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Bacteria Identification ◽  
Breakdown Spectroscopy ◽  
Feature Level Fusion ◽  
Laser Induced Breakdown ◽  
Level Fusion

Accuracy improvement for multimetal analysis by laser‐induced breakdown spectroscopy with least squares support vector machine

2021 ◽  
Vol 63 (6) ◽  
pp. 1635-1641
Author(s):  
Changjin Che ◽  
Xiaomei Lin ◽  
Xun Gao ◽  
Jingjun Lin ◽  
Haoran Sun ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Accuracy Improvement ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

2016 ◽  
Vol 25 (6) ◽  
pp. 065201 ◽  
Author(s):  
Hong-Xing Yang ◽  
Hong-Bo Fu ◽  
Hua-Dong Wang ◽  
Jun-Wei Jia ◽  
Markus W Sigrist ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Principal Component ◽  
Component Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Quantitative Analysis of Laser-Induced Breakdown Spectroscopy of Heavy Metals in Water Based on Support-Vector-Machine Regression

2013 ◽  
Vol 33 (3) ◽  
pp. 0330002 ◽  
Author(s):  
王春龙 Wang Chunlong ◽  
刘建国 Liu Jianguo ◽  
赵南京 Zhao Nanjing ◽  
马明俊 Ma Mingjun ◽  
王寅 Wang Yin ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Support Vector Machine ◽  
Quantitative Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Support Vector Machine Regression ◽  
Breakdown Spectroscopy ◽  
Water Based ◽  
Laser Induced Breakdown

scholarly journals Edible Gelatin Diagnosis Using Laser-Induced Breakdown Spectroscopy and Partial Least Square Assisted Support Vector Machine

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4225 ◽  
Author(s):  
Hao Zhang ◽  
Shun Wang ◽  
Dongxian Li ◽  
Yanyan Zhang ◽  
Jiandong Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Partial Least Square ◽  
Least Square ◽  
Computational Time ◽  
Laser Induced Breakdown Spectroscopy ◽  
Accurate Estimation ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Svm Model ◽  
Laser Induced Breakdown

Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square–support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration.

scholarly journals Identification of plastics by laser-induced breakdown spectroscopy combined with support vector machine algorithm

2013 ◽  
Vol 62 (21) ◽  
pp. 215201
Author(s):  
Yu Yang ◽  
Hao Zhong-Qi ◽  
Li Chang-Mao ◽  
Guo Lian-Bo ◽  
Li Kuo-Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Identification of Aluminum Alloy Grades by Fiber-Laser Induced Breakdown Spectroscopy Combined with Support Vector Machine

2018 ◽  
Vol 55 (6) ◽  
pp. 063002 ◽  
Author(s):  
周中寒 Zhou Zhonghan ◽  
田雪咏 Tian Xueyong ◽  
孙兰香 Sun Lanxiang ◽  
张鹏 Zhang Peng ◽  
郭志卫 Guo Zhiwei ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Aluminum Alloy ◽  
Fiber Laser ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

scholarly journals Correction: Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model

2020 ◽  
Vol 35 (7) ◽  
pp. 1487-1487
Author(s):  
Y. M. Guo ◽  
L. B. Guo ◽  
Z. Q. Hao ◽  
Y. Tang ◽  
S. X. Ma ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Iron Ore ◽  
Support Vector Machine Model ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Accuracy Improvement ◽  
Machine Model ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Correction for ‘Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model’ by Y. M. Guo et al., J. Anal. At. Spectrom., 2018, 33, 1330–1335, DOI: 10.1039/C8JA00119G.

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