Support vector machine based classification of seafloor rock types measured underwater using Laser Induced Breakdown Spectroscopy

2016 ◽  
Author(s):  
Mallikarjun Yelameli ◽  
Blair Thornton ◽  
Tomoko Takahashi ◽  
Tharindu Weerkoon ◽  
Yasunori Takemura ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Breakdown Spectroscopy ◽  
Rock Types ◽  
Laser Induced Breakdown

Identification accuracy improvement for steel species using a least squares support vector machine and laser-induced breakdown spectroscopy

2018 ◽  
Vol 33 (9) ◽  
pp. 1545-1551 ◽  
Author(s):  
Jingjun Lin ◽  
Xiaomei Lin ◽  
Lianbo Guo ◽  
Yangmin Guo ◽  
Yun Tang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Identification Accuracy ◽  
Laser Induced Breakdown Spectroscopy ◽  
Support Vector ◽  
Classification Methods ◽  
Accuracy Improvement ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Two typical classification methods, partial least squares discriminant analysis (PLS-DA) and a support vector machine (SVM), were used to study the classification of steels with similar constituents.

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.

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.

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