ArticleMicroorganisms2022
Raman Microscopic Identification of Microorganisms on Metal Surfaces via Support Vector Machines.
Article in Microorganisms, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Raman Spectroscopic Classification of Polyethylene Glycol Samples of Varying Molecular Weights Using Machine Learning.Molecules (Basel, Switzerland) · 2026Article
- Lighting the Path: Raman Spectroscopy's Journey Through the Microbial Maze.Molecules (Basel, Switzerland) · 2024Article
- Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms.Heliyon · 2024Article
- Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms.Molecules (Basel, Switzerland) · 2024Review
- Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy.Molecules (Basel, Switzerland) · 2024Article
- Pilot SERS Monitoring Study of Two Natural Hypersaline Lake Waters from a Balneary Resort during Winter-Months Period.Biosensors · 2023Article
- Recent advances of Raman spectroscopy for the analysis of bacteria.Analytical science advances · 2023Review
- Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy.Frontiers in microbiology · 2023Article
- Raman Microscopic Analysis of Dry-Surface Biofilms on Clinically Relevant Materials.Microorganisms · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An easy, inexpensive, and rapid method to identify microorganisms is in great demand in various areas such as medical diagnostics or in the food industry. In our study, we show the development of several predictive models based on Raman spectroscopy combined with support vector machines (SVM) for 21 species of microorganisms. The microorganisms, grown under standardized conditions, were placed on a silver mirror slide to record the data for model development. Additional data was obtained from microorganisms on a polished stainless-steel slide in order to validate the models in general and to assess possible negative influences of the material change on the predictions. The theoretical prediction accuracies for the most accurate models, based on a five-fold cross-validation, are 98.4%. For practical validation, new spectra (from stainless-steel surfaces) have been used, which were not included in the calibration data set. The overall prediction accuracy in practice was about 80% and the inaccurate predictions were only due to a few species. The development of a database provides the basis for further investigations such as the application and extension to single-cell analytics and for the characterization of biofilms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.