Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics
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 chem...
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ftpubmed:oai:pubmedcentral.nih.gov:9657760 2023-05-15T17:08:02+02:00 Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics Sushkov, Nikolai I. Galbács, Gábor Janovszky, Patrick Lobus, Nikolay V. Labutin, Timur A. 2022-10-27 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657760/ http://www.ncbi.nlm.nih.gov/pubmed/36365928 https://doi.org/10.3390/s22218234 en eng MDPI http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657760/ http://www.ncbi.nlm.nih.gov/pubmed/36365928 http://dx.doi.org/10.3390/s22218234 © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). CC-BY Sensors (Basel) Article Text 2022 ftpubmed https://doi.org/10.3390/s22218234 2022-11-20T02:38:29Z 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. Text Limacina helicina PubMed Central (PMC) Sensors 22 21 8234 |
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Article Sushkov, Nikolai I. Galbács, Gábor Janovszky, Patrick Lobus, Nikolay V. Labutin, Timur A. Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics |
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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. |
format |
Text |
author |
Sushkov, Nikolai I. Galbács, Gábor Janovszky, Patrick Lobus, Nikolay V. Labutin, Timur A. |
author_facet |
Sushkov, Nikolai I. Galbács, Gábor Janovszky, Patrick Lobus, Nikolay V. Labutin, Timur A. |
author_sort |
Sushkov, Nikolai I. |
title |
Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics |
title_short |
Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics |
title_full |
Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics |
title_fullStr |
Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics |
title_full_unstemmed |
Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics |
title_sort |
towards automated classification of zooplankton using combination of laser spectral techniques and advanced chemometrics |
publisher |
MDPI |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657760/ http://www.ncbi.nlm.nih.gov/pubmed/36365928 https://doi.org/10.3390/s22218234 |
genre |
Limacina helicina |
genre_facet |
Limacina helicina |
op_source |
Sensors (Basel) |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657760/ http://www.ncbi.nlm.nih.gov/pubmed/36365928 http://dx.doi.org/10.3390/s22218234 |
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© 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
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CC-BY |
op_doi |
https://doi.org/10.3390/s22218234 |
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Sensors |
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22 |
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21 |
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8234 |
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1766063597729873920 |