DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins
To classify raw SERS Raman spectra from biological materials, we propose DeepRaman, a new architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation approach. Unlike standard machine learning approaches such as PCA, LDA, SVM, RF, GBM etc, DeepRaman fu...
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2025
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| Online Access: | http://dx.doi.org/10.1016/j.heliyon.2025.e42550 https://www.sciencedirect.com/science/article/pii/S2405844025009302 http://hdl.handle.net/10576/64058 |
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| _version_ | 1857415086613725184 |
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| author | Samir Brahim, Belhaouari |
| author2 | Talbi, Abdelhamid Elgamal, Mahmoud Elmagarmid, Khadija Ahmed Ghannoum, Shaimaa Yang, Yanjun Zhao, Yiping Zughaier, Susu M. Bensmail, Halima |
| author2_role | author author author author author author author author |
| author_facet | Samir Brahim, Belhaouari Talbi, Abdelhamid Elgamal, Mahmoud Elmagarmid, Khadija Ahmed Ghannoum, Shaimaa Yang, Yanjun Zhao, Yiping Zughaier, Susu M. Bensmail, Halima |
| author_role | author |
| dc.creator.none.fl_str_mv | Samir Brahim, Belhaouari Talbi, Abdelhamid Elgamal, Mahmoud Elmagarmid, Khadija Ahmed Ghannoum, Shaimaa Yang, Yanjun Zhao, Yiping Zughaier, Susu M. Bensmail, Halima |
| dc.date.none.fl_str_mv | 2025-04-08T06:59:49Z 2025-02-28 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.heliyon.2025.e42550 24058440 https://www.sciencedirect.com/science/article/pii/S2405844025009302 http://hdl.handle.net/10576/64058 4 11 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Raman spectroscopy CNN Fourier transform Progressive fourier transform Scalogram SVM k-NN ImageNet Dense CNN |
| dc.title.none.fl_str_mv | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | To classify raw SERS Raman spectra from biological materials, we propose DeepRaman, a new architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation approach. Unlike standard machine learning approaches such as PCA, LDA, SVM, RF, GBM etc, DeepRaman functions independently, requiring no human interaction, and can be used to much smaller datasets than traditional CNNs. Performance of DeepRaman on 14 endotoxins bacteria and on a public data achieved an extraordinary accuracy of 99 percent. This provides exact endotoxin classification and has tremendous potential for accelerated medical diagnostics and treatment decision-making in cases of pathogenic infections. BackgroundBacterial endotoxin, a lipopolysaccharide exuded by bacteria during their growth and infection process, serves as a valuable biomarker for bacterial identification. It is a vital component of the outer membrane layer in Gram-negative bacteria. By employing silver nanorod-based array substrates, surface-enhanced Raman scattering (SERS) spectra were obtained for two separate datasets: Eleven endotoxins produced by bacteria, each having an 8.75 pg average detection quantity per measurement, and three controls chitin, lipoteichoic acid (LTA), bacterial peptidoglycan (PGN), because their structures differ greatly from those of LPS. ObjectiveThis study utilized various classical machine learning techniques, such as support vector machines, k-nearest neighbors, and random forests, in conjunction with a modified deep learning approach called DeepRaman. These algorithms were employed to distinguish and categorize bacterial endotoxins, following appropriate spectral pre-processing, which involved novel filtering techniques and advanced feature extraction methods. ResultMost traditional machine learning algorithms achieved distinction accuracies of over 99 percent, whereas DeepRaman demonstrated an exceptional accuracy of 100 percent. This method offers precise endotoxin classification and holds significant potential for expedited medical diagnoses and therapeutic decision-making in cases of pathogenic infections. ConclusionWe present the effectiveness of DeepRaman, an innovative architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation method, in classifying raw SERS Raman spectral data from biological specimens with unparalleled accuracy relative to conventional machine learning algorithms. Notably, this Convolutional Neural Network (CNN) operates autonomously, requiring no human intervention, and can be applied with substantially smaller datasets than traditional CNNs. Furthermore, it exhibits remarkable proficiency in managing challenging baseline scenarios that often lead to failures in other techniques, thereby promoting the broader clinical adoption of Raman spectroscopy. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_e3de9b66b161d00921f68fe76f1554e4 |
| identifier_str_mv | 24058440 4 11 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/64058 |
| publishDate | 2025 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxinsSamir Brahim, BelhaouariTalbi, AbdelhamidElgamal, MahmoudElmagarmid, Khadija AhmedGhannoum, ShaimaaYang, YanjunZhao, YipingZughaier, Susu M.Bensmail, HalimaRaman spectroscopyCNNFourier transformProgressive fourier transformScalogramSVMk-NNImageNetDense CNNTo classify raw SERS Raman spectra from biological materials, we propose DeepRaman, a new architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation approach. Unlike standard machine learning approaches such as PCA, LDA, SVM, RF, GBM etc, DeepRaman functions independently, requiring no human interaction, and can be used to much smaller datasets than traditional CNNs. Performance of DeepRaman on 14 endotoxins bacteria and on a public data achieved an extraordinary accuracy of 99 percent. This provides exact endotoxin classification and has tremendous potential for accelerated medical diagnostics and treatment decision-making in cases of pathogenic infections. BackgroundBacterial endotoxin, a lipopolysaccharide exuded by bacteria during their growth and infection process, serves as a valuable biomarker for bacterial identification. It is a vital component of the outer membrane layer in Gram-negative bacteria. By employing silver nanorod-based array substrates, surface-enhanced Raman scattering (SERS) spectra were obtained for two separate datasets: Eleven endotoxins produced by bacteria, each having an 8.75 pg average detection quantity per measurement, and three controls chitin, lipoteichoic acid (LTA), bacterial peptidoglycan (PGN), because their structures differ greatly from those of LPS. ObjectiveThis study utilized various classical machine learning techniques, such as support vector machines, k-nearest neighbors, and random forests, in conjunction with a modified deep learning approach called DeepRaman. These algorithms were employed to distinguish and categorize bacterial endotoxins, following appropriate spectral pre-processing, which involved novel filtering techniques and advanced feature extraction methods. ResultMost traditional machine learning algorithms achieved distinction accuracies of over 99 percent, whereas DeepRaman demonstrated an exceptional accuracy of 100 percent. This method offers precise endotoxin classification and holds significant potential for expedited medical diagnoses and therapeutic decision-making in cases of pathogenic infections. ConclusionWe present the effectiveness of DeepRaman, an innovative architecture inspired by the Progressive Fourier Transform and integrated with the scalogram transformation method, in classifying raw SERS Raman spectral data from biological specimens with unparalleled accuracy relative to conventional machine learning algorithms. Notably, this Convolutional Neural Network (CNN) operates autonomously, requiring no human intervention, and can be applied with substantially smaller datasets than traditional CNNs. Furthermore, it exhibits remarkable proficiency in managing challenging baseline scenarios that often lead to failures in other techniques, thereby promoting the broader clinical adoption of Raman spectroscopy.This research was funded by the Qatar National Research Fund (QNRF now QRDI) under Grant NPRP12S-0224-190144. Computational resources were provided by the Qatar Research Computing Institute (QCRI).Elsevier2025-04-08T06:59:49Z2025-02-28Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.heliyon.2025.e4255024058440https://www.sciencedirect.com/science/article/pii/S2405844025009302http://hdl.handle.net/10576/64058411enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/640582025-04-08T19:06:14Z |
| spellingShingle | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins Samir Brahim, Belhaouari Raman spectroscopy CNN Fourier transform Progressive fourier transform Scalogram SVM k-NN ImageNet Dense CNN |
| status_str | publishedVersion |
| title | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins |
| title_full | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins |
| title_fullStr | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins |
| title_full_unstemmed | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins |
| title_short | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins |
| title_sort | DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins |
| topic | Raman spectroscopy CNN Fourier transform Progressive fourier transform Scalogram SVM k-NN ImageNet Dense CNN |
| url | http://dx.doi.org/10.1016/j.heliyon.2025.e42550 https://www.sciencedirect.com/science/article/pii/S2405844025009302 http://hdl.handle.net/10576/64058 |