Raman Spectroscopy

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Intro

Raman Spectroscopy is a type of spectroscopy that uses monchromatic infared light (laser) to determine the Raman Spectra of substances. However, it must be noted that not all substances can be recognized using Raman spectroscopy. The theory behind Raman spectroscopy is very complex and well outside the scope of these tutorials. Overall, only "Raman active" molecules have Raman spectra. I've included some links, at the bottom of the page, that I found helpful while researching this topic. I know they are numerous and lengthy, but they explain the theory far better than I can in these few short pages... I prefer video format but the published articles are more comprehensive and are likely more accurate.

Below, is the spectrometer used at FLC. It is a Stellarnet 785nm Raman Spectrometer (I think this is the one we are working with).


Figure 1: FLC Raman spectrometer.


Current Progress and Future Plans:

We have collected spectrums for a range of samples shown on "current datasets." When these spectrums were fed into the neural network, we got 100% accuracy. We then verified the network on spectrums of the same samples but at different laser powers. We verified it on PL: 8, 6, 4, 2, and 1. All of the spectrums above level 4 had no errors. Below that, some samples were mistaken for other materials.

Current Datasets
Literature Review
Presentations

Our data collection plan, moving forward, is as follows:

Solids (90 Spectrums per Sample):

Liquids (90 Spectrums per Sample):

Previous Work:

I spent several months trying to get Ecoli spectrums. Neither our current spectrometer nor Dr. Grubb's spectrometer produced any recogonizable peaks. One of our biggest problems is the poor signal to noise ratio. Even our best Maltol spectrums have significant noise levels. The E. Coli spectrums also had a significant upward shift. The oils I tried later on had similar upward shifts. I found that applying a polynomial background subraction algorithm significanty improved the spectrums. We might try this for E. Coli spectrums, in the future.

Figure 2: E. Coli under microscope (4x objective).


Figure 3: E. Coli spectrums.


Figure 4: Bacteria spectrums from Ho. et.al's paper [1].


Tutorials

These tutorials could serve many projects but the purpose of my research is to recognize E. Coli bacteria while maximizing accuracy and minimizing pre-processsing. This final goal is likely several months in the future. In the meantime, we can verify the spectrometers accuracy and repeatability using some common materials. These tutorials will introduce some Raman spectroscopy basics, walk through sample preparation, how to run the spectrometer, and some of the required safety procedures. Please review the included manuals before you start the first tutorial:

StellarNet Manual- 2019
RPB Manual Version StellarNet
StellarNet Ramulaser


Tutorials:
Tutorial 1
Tutorial 2

Helpful Resources:
Youtube Playlist Collection of Stellarnet's Raman Videos Youtube Playlist Collection of Stellarnet's Spectrawiz Videos General Raman Spectroscopy Videos

Sources Used on This Page:

[1] C. S. Ho et al., “Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning,” Nat. Commun., vol. 10, no. 1, pp. 1–13, 2019, doi: 10.1038/s41467-019-12898-9.

Other Useful Sources:

  1. M. J. Pelletier, “Quantitative analysis using Raman spectrometry,” Appl. Spectrosc., vol. 57, no. 1, pp. 20-42, 2003.
  2. S. Yu, H. Li, X. Li, Y. V. Fu, and F. Liu, “Classification of pathogens by Raman spectroscopy combined with generative adversarial networks,” Sci. Total Environ., vol. 726, p. 138477, 2020, doi: 10.1016/j.scitotenv.2020.138477.
  3. W. Lu, X. Chen, L. Wang, H. Li, and Y. V. Fu, “Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification,” Anal. Chem., vol. 92, no. 9, pp. 6288–6296, 2020, doi: 10.1021/acs.analchem.9b04946.
  4. S. Yan et al., “Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level,” Talanta, vol. 226, no. November 2020, p. 122195, 2021, doi: 10.1016/j.talanta.2021.122195.
  5. A. K. Boardman et al., “HHS Public Access,” vol. 88, no. 16, pp. 8026–8035, 2017, doi: 10.1021/acs.anal-chem.6b01273.A.
  6. S. A. Strola et al., “Single bacteria identification by Raman spectroscopy,” J. Biomed. Opt., vol. 19, no. 11, p. 111610, 2014, doi: 10.1117/1.jbo.19.11.111610.
  7. F. Uysal Ciloglu, A. M. Saridag, I. H. Kilic, M. Tokmakci, M. Kahraman, and O. Aydin, “Identification of methicillin-resistant: Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques,” Analyst, vol. 145, no. 23, pp. 7559–7570, 2020, doi: 10.1039/d0an00476f.
  8. H. Lu, S. Tian, L. Yu, X. Lv, and S. Chen, “Diagnosis of hepatitis B based on Raman spectroscopy combined with a multiscale convolutional neural network,” Vib. Spectrosc., vol. 107, no. January, p. 103038, 2020, doi: 10.1016/j.vibspec.2020.103038.
  9. J. C. Baritaux, A. C. Simon, E. Schultz, C. Emain, P. laurent, and J. M. Dinten, “A study on identification of bacteria in environmental samples using single-cell Raman spectroscopy: feasibility and reference libraries,” Environ. Sci. Pollut. Res., vol. 23, no. 9, pp. 8184–8191, 2016, doi: 10.1007/s11356-015-5953-x.
  10. W. R. Premasiri, J. C. Lee, A. Sauer-Budge, R. Théberge, C. E. Costello, and L. D. Ziegler, “The biochemical origins of the surface-enhanced Raman spectra of bacteria: a metabolomics profiling by SERS,” Anal. Bioanal. Chem., vol. 408, no. 17, pp. 4631–4647, 2016, doi: 10.1007/s00216-016-9540-x

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Page created: December 28, 2020
Page Last Updated: 8-3-2021