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Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 76,
  • Issue 5,
  • pp. 609-619
  • (2022)

Pushing the Limits of Surface-Enhanced Raman Spectroscopy (SERS) with Deep Learning: Identification of Multiple Species with Closely Related Molecular Structures

Open Access Open Access

Abstract

Raman spectroscopy is a non-destructive and label-free molecular identification technique capable of producing highly specific spectra with various bands correlated to molecular structure. Moreover, the enhanced detection sensitivity offered by surface-enhanced Raman spectroscopy (SERS) allows analyzing mixtures of related chemical species in a relatively short measurement time. Combining SERS with deep learning algorithms allows in some cases to increase detection and classification capabilities even further. The present study evaluates the potential of applying deep learning algorithms to SERS spectroscopy to differentiate and classify different species of bile acids, a large family of molecules with low Raman cross sections and molecular structures that often differ by a single hydroxyl group. Moreover, the study of these molecules is of interest for the medical community since they have distinct pathological roles and are currently viewed as potential markers of gut microbiome imbalances. A convolutional neural network model was developed and used to classify SERS spectra from five bile acid species. The model succeeded in identifying the five analytes despite very similar molecular structures and was found to be reliable even at low analyte concentrations.

© 2022 The Author(s)

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References

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2021 (2)

J. Zhu, A.S. Sharma, J. Xu, Y. Xu, T. Jiao, Q. Ouyang, et al. “Rapid On-Site Identification of Pesticide Residues in Tea by One-Dimensional Convolutional Neural Network Coupled with Surface-Enhanced Raman Scattering”. Spectrochim. Acta, Part A. 2021. 246: 118994. doi:.
[Crossref]

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[Crossref]

2020 (3)

C. Gómez, S. Stücheli, D.V. Kratschmar, J. Bouitbir, A. Odermatt. “Development and Validation of a Highly Sensitive LC-MS/MS Method for the Analysis of Bile Acids in Serum, Plasma, and Liver Tissue Samples”. Metabolites. 2020. 10(7): 282. doi:.
[Crossref]

S. Guo, P. Rösch, J. Popp, T. Bocklitz. “Modified PCA and PLS: Towards a Better Classification in Raman Spectroscopy-Based Biological Applications”. J. Chemom. 2020. 34(4): e3202. doi:.
[Crossref]

F. Lussier, V. Thibault, B. Charron, G.Q. Wallace, J.-F. Masson. “Deep Learning and Artificial Intelligence Methods for Raman and Surface-Enhanced Raman Scattering”. TrAC, Trends Anal. Chem. 2020. 124: 115796. doi:.
[Crossref]

2019 (7)

X. Fan, W. Ming, H. Zeng, Z. Zhang, H. Lu. “Deep Learning-Based Component Identification for the Raman Spectra of Mixtures”. Analyst. 2019. 144(5): 1789‐1798. doi:.
[Crossref]

J.Y.L. Chiang, J.M. Ferrell. “Bile Acids as Metabolic Regulators and Nutrient Sensors”. Annu. Rev. Nutr. 2019. 39(1): 175‐200. doi:.
[Crossref]

D. Rovati, B. Albini, P. Galinetto, P. Grisoli, et al. “High Stability Thiol-Coated Gold Nanostars Monolayers with Photo-Thermal Antibacterial Activity and Wettability Control”. Nanomaterials. 2019. 9(9): 1288. doi:.
[Crossref]

F. Lussier, D. Missirlis, J.P. Spatz, J.-F. Masson. “Machine-Learning-Driven Surface-Enhanced Raman Scattering Optophysiology Reveals Multiplexed Metabolite Gradients Near Cells”. ACS Nano. 2019. 13(2): 1403‐1411. doi:.
[Crossref]

M. Fukuhara, K. Fujiwara, Y. Maruyama, H. Itoh. “Feature Visualization of Raman Spectrum Analysis with Deep Convolutional Neural Network”. Anal. Chim. Acta. 2019. 1087: 11‐19. doi:.
[Crossref]

P. Vijayvargiya, M. Camilleri, V. Chedid, P. Carlson, et al. “Analysis of Fecal Primary Bile Acids Detects Increased Stool Weight and Colonic Transit in Patients with Chronic Functional Diarrhea”. Clin. Gastroenterol. Hepatol. 2019. 17(5): 922-929.E2. doi:.
[Crossref]

P. Vijayvargiya, M. Camilleri. “Current Practice in the Diagnosis of Bile Acid Diarrhea”. Gastroenterology. 2019. 156(5): 1233‐1238. doi:.
[Crossref]

2018 (5)

S.R. Smith, J. Lipkowski. “Guided Assembly of Two-Dimensional Arrays of Gold Nanoparticles on a Polycrystalline Gold Electrode for Electrochemical Surface-Enhanced Raman Spectroscopy”. J. Phys. Chem. 2018. 122(13): 7303‐7311. doi:.
[Crossref]

E. Heřmánková, A. Žák, L. Poláková, R. Hobzová, et al. “Polymeric Bile Acid Sequestrants: Review of Design, In Vitro Binding Activities, and Hypocholesterolemic Effects”. Eur. J. Med. Chem. 2018. 144: 300‐317. doi:.
[Crossref]

P.B. Hylemon, S.C. Harris, J.M. Ridlon. “Metabolism of Hydrogen Gases and Bile Acids in the Gut Microbiome”. FEBS Lett. 2018. 592(12): 2070‐2082. doi:.
[Crossref]

A. Molinaro, A. Wahlström, H.-U. Marschall. “Role of Bile Acids in Metabolic Control”. Trends Endocrinol. Metab. 2018. 29(1): 31‐41. doi:.
[Crossref]

A.S. Moody, B. Sharma. “Multi-Metal, Multi-Wavelength Surface-Enhanced Raman Spectroscopy Detection of Neurotransmitters”. ACS Chem. Neurosci. 2018. 9(6): 1380‐1387. doi:.
[Crossref]

2017 (5)

O. Ramírez-Pérez, V. Cruz-Ramón, P. Chinchilla-López, N. Méndez-Sánchez. “The Role of the Gut Microbiota in Bile Acid Metabolism”. Ann. Hepatol. 2017. 16(1): 21‐26. doi:.
[Crossref]

S. He, J. Chua, E.K.M. Tan, J.C.Y. Kah. “Optimizing the SERS Enhancement of a Facile Gold Nanostar Immobilized Paper-Based SERS Substrate”. RSC Adv. 2017. 7(27): 16264‐16272. doi:.
[Crossref]

J. Liu, M. Osadchy, L. Ashton, M. Foster, et al. “Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution”. Analyst. 2017. 142(21): 4067‐4074. doi:.
[Crossref]

J. Acquarelli, T. Van Laarhoven, J. Gerretzen, T.N. Tran, et al. “Convolutional Neural Networks for Vibrational Spectroscopic Data Analysis”. Anal. Chim. Acta. 2017. 954: 22‐31. doi:.
[Crossref]

K. Wegner, S. Just, L. Gau, H. Mueller, et al. “Rapid Analysis of Bile Acids in Different Biological Matrices Using LC-ESI-MS/MS for the Investigation of Bile Acid Transformation by Mammalian Gut Bacteria”. Anal. Bioanal. Chem. 2017. 409(5): 1231‐1245. doi:.
[Crossref]

2016 (2)

A. Wahlström, S.I. Sayin, H.-U. Marschall, F. Bäckhed. “Intestinal Crosstalk Between Bile Acids and Microbiota and Its Impact on Host Metabolism”. Cell Metab. 2016. 24(1):41–50. doi:.
[Crossref]

S. Laing, K. Gracie, K. Faulds. “Multiplex In Vitro Detection Using SERS”. Chem. Soc. Rev. 2016. 45(7): 1901‐1918. doi:.
[Crossref]

2015 (3)

S. He, M.W.C. Kang, F.J. Khan, E.K.M. Tan, et al. “Optimizing Gold Nanostars as a Colloid-Based Surface-Enhanced Raman Scattering (SERS) Substrate”. J. Opt. 2015. 17(11): 114013. doi:.
[Crossref]

Y. Shen, T. Lai, R.E. Campbell. “Red Fluorescent Proteins (RFPS) and RFP-Based Biosensors for Neuronal Imaging Applications”. Neurophotonics. 2015. 2(3): 031203. doi:.
[Crossref]

Y. Lecun, Y. Bengio, G. Hinton. “Deep Learning”. Nat. 2015. 521(7553): 436‐444. doi:.
[Crossref]

2014 (3)

F. Tian, F. Bonnier, A. Casey, A.E. Shanahan, H.J. Byrne. “Surface Enhanced Raman Scattering with Gold Nanoparticles: Effect of Particle Shape”. Anal. Methods. 2014. 6(22): 9116‐9123. doi:.
[Crossref]

A.D.S. Indrasekara, S. Meyers, S. Shubeita, L.C. Feldman, et al. “Gold Nanostar Substrates for SERS-Based Chemical Sensing in the Femtomolar Regime”. Nanoscale. 2014. 6(15): 8891‐8899. doi:.
[Crossref]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”. J. Mach. Learn. Res. 2014. 15(1): 1929‐1958.

2013 (1)

M.B. Haddada, J. Blanchard, S. Casale, J.-M. Krafft, et al. “Optimizing the Immobilization of Gold Nanoparticles on Functionalized Silicon Surfaces: Amine- vs. Thiol-Terminated Silane”. Gold Bull. 2013. 46(4): 335‐341. doi:.
[Crossref]

2011 (4)

C. Muehlethaler, G. Massonnet, P. Esseiva. “The Application of Chemometrics on Infrared and Raman Spectra as a Tool for the Forensic Analysis of Paints”. Forensic Sci. Int. 2011. 209(1‐3): 173‐182. doi:.
[Crossref]

S. Castillo, P. Gopalacharyulu, L. Yetukuri, M. Orešič. “Algorithms and Tools for the Preprocessing of LC–MS Metabolomics Data”. Chemom. Intell. Lab. Syst. 2011. 108(1): 23‐32. doi:.
[Crossref]

Q. Su, X. Ma, J. Dong, C. Jiang, W. Qian. “A Reproducible SERS Substrate Based on Electrostatically Assisted APTES-Functionalized Surface-Assembly of Gold Nanostars”. ACS Appl. Mater. Interfaces. 2011. 3(6): 1873–1879. doi:.
[Crossref]

Z.D. Schultz. “Raman Spectroscopic Imaging of Cholesterol and Docosahexaenoic Acid Distribution in the Retinal Rod Outer Segment”. Aust. J. Chem. 2011. 64(5): 611‐616. doi:.
[Crossref]

2010 (2)

M.G. Blaber, M.D. Arnold, M.J. Ford. “A Review of the Optical Properties of Alloys and Intermetallics for Plasmonics”. J. Phys. Condens. Matter. 2010. 22(14): 143201. doi:.
[Crossref]

M. Knauer, N.P. Ivleva, X. Liu, R. Niessner, C. Haisch. “Surface-Enhanced Raman Scattering-Based Label-Free Microarray Readout for the Detection of Microorganisms”. Anal. Chem. 2010. 82(7): 2766‐2772. doi:.
[Crossref]

2008 (2)

Y. Wang, W. Qian, Y. Tan, S. Ding. “A Label-Free Biosensor Based on Gold Nanoshell Monolayers for Monitoring Biomolecular Interactions in Diluted Whole Blood”. Biosens. Bioelectron. 2008. 23(7): 1166‐1170. doi:.
[Crossref]

E. Le Ru, P. Etchegoin, J. Grand, N. Félidj, et al. “Surface Enhanced Raman Spectroscopy on Nanolithography-Prepared Substrates”. Curr. Appl. Phys. 2008. 8(3–4): 467‐470. doi:.
[Crossref]

2005 (1)

A.D. McFarland, M.A. Young, J.A. Dieringer, R.P. Van Duyne. “Wavelength-Scanned Surface-Enhanced Raman Excitation Spectroscopy”. J. Phys. Chem. B. 2005. 109(22): 11279‐11285. doi:.
[Crossref]

2003 (1)

C.L. Haynes, R.P. Van Duyne. “Plasmon-Sampled Surface-Enhanced Raman Excitation Spectroscopy”. J. Phys. Chem. B. 2003. 107(30): 7426‐7433. doi:.
[Crossref]

1984 (1)

P. Hildebrandt, M. Stockburger. “Surface-Enhanced Resonance Raman Spectroscopy of Rhodamine 6G Adsorbed on Colloidal Silver”. J. Phys. Chem. 1984. 88(24): 5935‐5944. doi:.
[Crossref]

Acquarelli, J.

J. Acquarelli, T. Van Laarhoven, J. Gerretzen, T.N. Tran, et al. “Convolutional Neural Networks for Vibrational Spectroscopic Data Analysis”. Anal. Chim. Acta. 2017. 954: 22‐31. doi:.
[Crossref]

Albini, B.

D. Rovati, B. Albini, P. Galinetto, P. Grisoli, et al. “High Stability Thiol-Coated Gold Nanostars Monolayers with Photo-Thermal Antibacterial Activity and Wettability Control”. Nanomaterials. 2019. 9(9): 1288. doi:.
[Crossref]

Arnold, M.D.

M.G. Blaber, M.D. Arnold, M.J. Ford. “A Review of the Optical Properties of Alloys and Intermetallics for Plasmonics”. J. Phys. Condens. Matter. 2010. 22(14): 143201. doi:.
[Crossref]

Ashton, L.

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ACS Appl. Mater. Interfaces (1)

Q. Su, X. Ma, J. Dong, C. Jiang, W. Qian. “A Reproducible SERS Substrate Based on Electrostatically Assisted APTES-Functionalized Surface-Assembly of Gold Nanostars”. ACS Appl. Mater. Interfaces. 2011. 3(6): 1873–1879. doi:.
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ACS Chem. Neurosci (1)

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ACS Nano (1)

F. Lussier, D. Missirlis, J.P. Spatz, J.-F. Masson. “Machine-Learning-Driven Surface-Enhanced Raman Scattering Optophysiology Reveals Multiplexed Metabolite Gradients Near Cells”. ACS Nano. 2019. 13(2): 1403‐1411. doi:.
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Anal. Bioanal. Chem (1)

K. Wegner, S. Just, L. Gau, H. Mueller, et al. “Rapid Analysis of Bile Acids in Different Biological Matrices Using LC-ESI-MS/MS for the Investigation of Bile Acid Transformation by Mammalian Gut Bacteria”. Anal. Bioanal. Chem. 2017. 409(5): 1231‐1245. doi:.
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Anal. Chem (1)

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Anal. Chim. Acta (2)

J. Acquarelli, T. Van Laarhoven, J. Gerretzen, T.N. Tran, et al. “Convolutional Neural Networks for Vibrational Spectroscopic Data Analysis”. Anal. Chim. Acta. 2017. 954: 22‐31. doi:.
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M. Fukuhara, K. Fujiwara, Y. Maruyama, H. Itoh. “Feature Visualization of Raman Spectrum Analysis with Deep Convolutional Neural Network”. Anal. Chim. Acta. 2019. 1087: 11‐19. doi:.
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Anal. Methods (1)

F. Tian, F. Bonnier, A. Casey, A.E. Shanahan, H.J. Byrne. “Surface Enhanced Raman Scattering with Gold Nanoparticles: Effect of Particle Shape”. Anal. Methods. 2014. 6(22): 9116‐9123. doi:.
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Analyst (2)

J. Liu, M. Osadchy, L. Ashton, M. Foster, et al. “Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution”. Analyst. 2017. 142(21): 4067‐4074. doi:.
[Crossref]

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Ann. Hepatol (1)

O. Ramírez-Pérez, V. Cruz-Ramón, P. Chinchilla-López, N. Méndez-Sánchez. “The Role of the Gut Microbiota in Bile Acid Metabolism”. Ann. Hepatol. 2017. 16(1): 21‐26. doi:.
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Annu. Rev. Nutr (1)

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Aust. J. Chem (1)

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Biosens. Bioelectron (1)

Y. Wang, W. Qian, Y. Tan, S. Ding. “A Label-Free Biosensor Based on Gold Nanoshell Monolayers for Monitoring Biomolecular Interactions in Diluted Whole Blood”. Biosens. Bioelectron. 2008. 23(7): 1166‐1170. doi:.
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Supplementary Material (1)

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Supplement 1       sj-pdf-1-asp-10.1177_00037028221077119 – Supplemental Material for Pushing the Limits of Surface-Enhanced Raman Spectroscopy (SERS) with Deep Learning: Identification of Multiple Species with Closely Related Molecular Structures

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