About this article
- Submitted: Jan 08, 2024
- Final Revised: May 28, 2024
- Accepted: Jun 18, 2024
- Published: Jun 30, 2024
- Abstract view: 13
- PDF Download: 1
- Volumes: Vol. 2 No. 1 (2024) Pages 17-30
- DOI: 10.63208/21018-103
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright (c) 2024 Author(s)
Cite this article
Abstract
A growing body of literature demonstrates the feasibility of developing robust predictive models for microorganisms by integrating Raman micro-spectroscopy with chemometric techniques, frequently resulting in high-precision outcomes. While advancements in machine learning and broader software accessibility have simplified the generation of predictive models from intricate datasets, the underlying mechanisms driving such high accuracy remain insufficiently explored. This research utilizes Raman spectroscopic data from fungal spores and carotenoid-bearing microorganisms to reveal that precise classification often does not depend on specific peak positions or subtle variations in band ratios related to chemical composition. Instead, characteristic baseline effects in biochemically analogous microorganisms, which may be intensified by particular data pretreatment methods, or even seemingly neutral spectral regions, can hold significant weight for convolutional neural networks (CNNs). By employing Gradient-weighted Class Activation Mapping (Grad-CAM), this study seeks to demystify the "black box" of CNNs in microbiological contexts, identifying the specific Raman spectral regions fundamentally responsible for successful classification.
References
Stöckel, S.; Kirchhoff, J.; Neugebauer, U.; Rösch, P.; Popp, J. The application of Raman spectroscopy for the detection and identification of microorganisms. J. Raman Spectrosc. 2016, 47, 89–109.
Rösch, P.; Harz, M.; Krause, M.; Popp, J. Fast and reliable identification of microorganisms by means of Raman spectroscopy. In Biophotonics 2007: Optics in Life Science; Optical Society of America: Washington, DC, USA, 2007; pp. 6633–6645.
Pahlow, S.; Meisel, S.; Cialla-May, D.; Weber, K.; Rösch, P.; Popp, J. Isolation and identification of bacteria by means of Raman spectroscopy. Adv. Drug Deliv. Rev. 2015, 89, 105–120. [PubMed]
Ho, C.S.; Jean, N.; Hogan, C.A.; Blackmon, L.; Jeffrey, S.S.; Holodniy, M.; Banaei, N.; Saleh, A.A.; Ermon, S.; Dionne, J. Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nat. Commun. 2019, 10, 4927. [PubMed]
Meisel, S.; Stöckel, S.; Elschner, M.; Melzer, F.; Rösch, P.; Popp, J. Raman spectroscopy as a potential tool for detection of Brucella spp. in milk. Appl. Environ. Microbiol. 2012, 78, 5575–5583.
Rösch, P.; Harz, M.; Schmitt, M.; Peschke, K.D.; Ronneberger, O.; Burkhardt, H.; Motzkus, H.W.; Lankers, M.; Hofer, S.; Thiele, H.; et al. Chemotaxonomic identification of single bacteria by micro-Raman spectroscopy: Application to clean-room-relevant biological contaminations. Appl. Environ. Microbiol. 2005, 71, 1626–1637.
Strola, S.A.; Baritaux, J.-C.; Schultz, E.; Simon, A.C.; Allier, C.; Espagnon, I.; Jary, D.; Dinten, J.M. Single bacteria identification by Raman spectroscopy. J. Biomed. Opt. 2014, 19, 111610.
Hetjens, B.T.; Tewes, T.J.; Platte, F.; Wichern, F. The application of Raman spectroscopy in identifying Metarhizium brunneum, Metarhizium pemphigi and Beauveria bassiana. Biocontrol Sci. Technol. 2021, 32, 329–340.
Tewes, T.J.; Kerst, M.; Platte, F.; Bockmühl, D.P. Raman Microscopic Identification of Microorganisms on Metal Surfaces via Support Vector Machines. Microorganisms 2022, 10, 556.
Kanno, N.; Kato, S.; Ohkuma, M.; Matsui, M.; Iwasaki, W.; Shigeto, S. Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes. iScience 2021, 24, 102975.
Harz, M.; Rösch, P.; Popp, J. Vibrational spectroscopy-A powerful tool for the rapid identification of microbial cells at the single-cell level. Cytom. Part A 2009, 75, 104–113.
Mlynáriková, K.; Samek, O.; Bernatová, S.; Růžička, F.; Ježek, J.; Hároniková, A.; Šiler, M.; Zemánek, P.; Holá, V. Influence of culture media on microbial fingerprints using raman spectroscopy. Sensors 2015, 15, 29635–29647.
Harz, M.; Rösch, P.; Peschke, K.D.; Ronneberger, O.; Burkhardt, H.; Popp, J. Micro-Raman spectroscopic identification of bacterial cells of the genus Staphylococcus and dependence on their cultivation conditions. Analyst 2005, 130, 1543–1550. [PubMed]
Hutsebaut, D.; Maquelin, K.; De Vos, P.; Vandenabeele, P.; Moens, L.; Puppels, G.J. Effect of Culture Conditions on the Achievable Taxonomic Resolution of Raman Spectroscopy Disclosed by Three Bacillus Species. Anal. Chem. 2004, 76, 6274–6281. [PubMed]
Bocklitz, T.; Walter, A.; Hartmann, K.; Rösch, P.; Popp, J. How to pre-process Raman spectra for reliable and stable models? Anal. Chim. Acta 2011, 704, 47–56. [PubMed]
Schumacher, W.; Stöckel, S.; Rösch, P.; Popp, J. Improving chemometric results by optimizing the dimension reduction for Raman spectral data sets. J. Raman Spectrosc. 2014, 45, 930–940.
Kumar, B.N.V.; Kampe, B.; Rösch, P.; Popp, J. Characterization of carotenoids in soil bacteria and investigation of their photodegradation by UVA radiation via resonance Raman spectroscopy. Analyst 2015, 140, 4584–4593.
Burkart, N.; Huber, M.F. A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 2021, 70, 245–317.
Goodwin, N.L.; Nilsson, S.R.O.; Choong, J.J.; Golden, S.A. Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience. Curr. Opin. Neurobiol. 2022, 73, 102544.
Vinogradova, K.; Dibrov, A.; Myers, G. Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract). Proc. AAAI Conf. Artif. Intell. 2020, 34, 13943–13944.
Selvaraju, R.R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. Grad-cam: Why did you say that? Visual explanations from deep networks via gradient-based localization. Revista do Hospital das Clínicas 2016, 17, 331–336.
Tewes, T.J.; Centeleghe, I.; Maillard, J.-Y.; Platte, F.; Bockmühl, D.P. Raman Microscopic Analysis of Dry-Surface Biofilms on Clinically Relevant Materials. Microorganisms 2022, 10, 1369. [PubMed]
Bradski, G. The OpenCV Library. Dr. Dobb’s J. Softw. Tools 2000, 120, 122–125.
Gautam, R.; Vanga, S.; Ariese, F.; Umapathy, S. Review of multidimensional data processing approaches for Raman and infrared spectroscopy. EPJ Tech. Instrum. 2015, 2, 8.
Guo, S.; Popp, J.; Bocklitz, T. Chemometric analysis in Raman spectroscopy from experimental design to machine learning–based modeling. Nat. Protoc. 2021, 16, 5426–5459.
Chang, W.-C. On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions. J. R. Stat. Soc. Ser. C 1983, 32, 267–275.
De Siqueira e Oliveira, F.S.; Giana, H.E.; Silveira, L. Discrimination of selected species of pathogenic bacteria using near-infrared Raman spectroscopy and principal components analysis. J. Biomed. Opt. 2012, 17, 107004.
Huang, Z.; Lui, H.; Chen, M.X.; Alajlan, A.; McLean, D.I.; Zeng, H. Raman spectroscopy of in vivo cutaneous melanin. J. Biomed. Opt. 2004, 9, 1198–1205.
De Gelder, J.; De Gussem, K.; Vandenabeele, P.; Moens, L. Reference database of Raman spectra of biological molecules. J. Raman Spectrosc. 2007, 38, 1133–1147.
Schuster, K.C.; Reese, I.; Urlaub, E.; Gapes, J.R.; Lendl, B. Multidimensional Information on the Chemical Composition of Single Bacterial Cells by Confocal Raman Microspectroscopy. Anal. Chem. 2000, 72, 5529–5534.
Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; MIT Press: Cambridge, MA, USA, 2016.