Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection

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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Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

Sensors ◽

10.3390/s20051393 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1393 ◽

Cited By ~ 3

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.

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A modified backward elimination approach for the rapid classification of Chinese ceramics using laser-induced breakdown spectroscopy and chemometrics

Journal of Analytical Atomic Spectrometry ◽

10.1039/c9ja00371a ◽

2020 ◽

Vol 35 (3) ◽

pp. 518-525 ◽

Cited By ~ 3

Author(s):

Fangqi Ruan ◽

Lin Hou ◽

Tianlong Zhang ◽

Hua Li

Keyword(s):

Feature Selection ◽

Laser Induced Breakdown Spectroscopy ◽

Archaeological Ceramics ◽

Breakdown Spectroscopy ◽

Backward Elimination ◽

Laser Induced Breakdown

A modified backward elimination approach was proposed for feature selection (FS) to eliminate the redundant and irrelevant features from laser-induced breakdown spectroscopy (LIBS) spectra for the rapid classification of Chinese archaeological ceramics.

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Classification of lung cancer using ensemble-based feature selection and machine learning methods

Molecular BioSystems ◽

10.1039/c4mb00659c ◽

2015 ◽

Vol 11 (3) ◽

pp. 791-800 ◽

Cited By ~ 53

Author(s):

Zhihua Cai ◽

Dong Xu ◽

Qing Zhang ◽

Jiexia Zhang ◽

Sai-Ming Ngai ◽

...

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Learning Methods ◽

Machine Learning Methods

The ensemble-based feature selection method presents the merit of acquisition of more informative and compact features than those obtained by individual methods.

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