Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms
In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024038556 |
| _version_ | 1797224222269898752 |
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| author | Thomas J. Tewes Mario Kerst Svyatoslav Pavlov Miriam A. Huth Ute Hansen Dirk P. Bockmühl |
| author_facet | Thomas J. Tewes Mario Kerst Svyatoslav Pavlov Miriam A. Huth Ute Hansen Dirk P. Bockmühl |
| author_sort | Thomas J. Tewes |
| collection | DOAJ |
| description | In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%–88%. The six remaining species were correctly predicted by only 0%–49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases. |
| first_indexed | 2024-04-24T13:49:41Z |
| format | Article |
| id | doaj.art-c065b0e4150944d293b357922c2477c7 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T13:49:41Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-c065b0e4150944d293b357922c2477c72024-04-04T05:06:05ZengElsevierHeliyon2405-84402024-03-01106e27824Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganismsThomas J. Tewes0Mario Kerst1Svyatoslav Pavlov2Miriam A. Huth3Ute Hansen4Dirk P. Bockmühl5Faculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, GermanyFaculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, GermanyFaculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, GermanyFaculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, GermanyFaculty of Communication and Environment, Rhine-Waal University of Applied Sciences, Friedrich-Heinrich-Allee, 47475, Kamp-Lintfort, GermanyFaculty of Life Sciences, Rhine-Waal University of Applied Sciences, Marie-Curie-Straße 1, 47533, Kleve, Germany; Corresponding author.In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%–88%. The six remaining species were correctly predicted by only 0%–49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.http://www.sciencedirect.com/science/article/pii/S2405844024038556RamanSingle cellBulkMicroorganismsConvolutional neural networkCNN |
| spellingShingle | Thomas J. Tewes Mario Kerst Svyatoslav Pavlov Miriam A. Huth Ute Hansen Dirk P. Bockmühl Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms Heliyon Raman Single cell Bulk Microorganisms Convolutional neural network CNN |
| title | Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms |
| title_full | Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms |
| title_fullStr | Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms |
| title_full_unstemmed | Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms |
| title_short | Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms |
| title_sort | unveiling the efficacy of a bulk raman spectra based model in predicting single cell raman spectra of microorganisms |
| topic | Raman Single cell Bulk Microorganisms Convolutional neural network CNN |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024038556 |
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