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

Yang, Yaqi; Hao, Xiaojian; Zhang, Lili; Ren, Long

doi:10.3390/s20051393

Cited by 58 publications

(22 citation statements)

References 35 publications

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“…Although the clusterisation quality obtained by us is better than in earlier studies [ 74 , 75 ], it would have been remiss to not investigate the capabilities of the chemometric techniques (PCA, NMF, and ComDim) in finding the latent characteristic features in the spectra of animals. Similar works do exist [ 5 , 76 ], but they usually either do not go further than conducting PCA or, on the contrary, use complicated algorithms requiring big training datasets (such as neural networks).…”

Section: Resultsmentioning

confidence: 61%

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Sushkov

Galbács

Janovszky

et al. 2022

Sensors

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Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

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Section: Resultsmentioning

confidence: 61%

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Sushkov

Galbács

Janovszky

et al. 2022

Sensors

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“…Compared with traditional agricultural heavy metal detection methods, such as atomic fluorescence spectrometry (AFS), atomic absorption spectrometry (AAS), X-ray fluorescence spectroscopy (XRFS), and inductively coupled plasma atomic emission spectrometry (ICP-AES), LIBS has the advantages of a fast detection speed, simultaneous detection of multiple elements, and avoiding secondary contamination, which are unique advantages [ 2 , 3 , 4 , 5 , 6 , 7 ]. These advantages can make the LIBS technique shine in the fields of agriculture, industry, food, biology, medicine, and aerospace [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review

Yang

Ren

et al. 2022

Sensors

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Heavy metal pollution in agriculture is a significant problem that endangers human health. Laser-induced breakdown spectroscopy (LIBS) is an emerging technique for material and elemental analysis, especially heavy metals, based on atomic emission spectroscopy. The LIBS technique has been widely used for rapid detection of heavy metals with its advantages of convenient operation, simultaneous detection of multi-elements, wide range of elements, and no requirement for the state and quantity of samples. However, the development of LIBS is limited by its detection sensitivity and limit of detection (LOD). Therefore, in order to improve the detection sensitivity and LOD of LIBS, it is necessary to enhance the LIBS signal to achieve the purpose of detecting heavy metal elements in agriculture. This review mainly introduces the basic instruments and principles of LIBS and summarizes the methods of enhanced LIBS signal detection of heavy metal elements in agriculture over the past 10 years. The three main approaches to enhancing LIBS are sample pretreatment, adding laser pulses, and using auxiliary devices. An enhanced LIBS signal may improve the LOD of heavy metal elements in agriculture and the sensitivity and stability of the LIBS technique. The enhanced LIBS technique will have a broader prospect in agricultural heavy metal monitoring and can provide technical support for developing heavy metal detection instruments.

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Section: Introductionmentioning

confidence: 99%

“…The technology is simple in sample preparation, easy in operation, and comprehensive in elements detected. Laser-induced breakdown spectroscopy (LIBS) technology is widely used in material detection, − environmental monitoring, − gas composition analysis, − and other fields. − Traditional LIBS has the disadvantages of low detection sensitivity and high spectral line background noise. Therefore, many methods have been proposed to enhance spectral intensity and improve the sensitivity of LIBS systems, such as nanoparticle enhancement, increasing sample temperature, multipulse, and other methods.…”

Section: Introductionmentioning

confidence: 99%

Effect of Plane Mirrors Combined with Au-Nanoparticle Confinement on the Spectral Properties of Fe Plasma Induced by Laser-Induced Breakdown

et al. 2022

Self Cite

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To overcome the shortcomings of low detection sensitivity and high spectral line background noise of traditional laser-induced breakdown spectroscopy (LIBS), a method of combining flat mirrors with gold nanoparticles (Au-NPs) was proposed. First, independent plane mirror and Au-NPs experiments were performed by using aluminum alloy samples. After that, the samples were placed under four conditions (None-LIBS; Three mirrors-LIBS; 20 nm Au-NPs-LIBS; 20 nm Au-NPs and Three mirrors-LIBS), and the differences between various spectral parameters were analyzed. The experimental results show that the optimal number of plane mirrors is 3, and the optimal size of gold nanoparticles is 20 nm. When 20 nm Au-NPs and Three mirrors are used in combination, the plasmonic spectral intensity can be effectively enhanced. The enhancement factor is up to 2.98 (Fe II 240.45 nm), and the signal-to-noise ratio (SNR) is significantly improved up to 10.03. The variation of the plasma temperature between 1 and 5 μs was also investigated, and the experimental results showed that the plasma temperature could be increased by the flat mirror, while the electron temperature was almost unchanged under the action of Au-NPs. It is shown that the combination of the two enhancement methods can effectively increase the spectral intensity and improve the signal-to-noise ratio, which will help to improve the detection performance of the LIBS system.

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

Cited by 58 publications

References 35 publications

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Enhanced Laser-Induced Breakdown Spectroscopy for Heavy Metal Detection in Agriculture: A Review

Effect of Plane Mirrors Combined with Au-Nanoparticle Confinement on the Spectral Properties of Fe Plasma Induced by Laser-Induced Breakdown

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