ArticleFrontiers in chemistry2019
Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples.
Article in Frontiers in chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning assisted identification of antibiotic-resistantMicrochemical journal : devoted to the application of microtechniques in all branches of science · 2024Article
- Fast and accurate identification of pathogenic bacteria using excitation-emission spectroscopy and machine learning.Sensors & diagnostics · 2024Article
- Polymer-based chemical-nose systems for optical-pattern recognition of gut microbiota.Chemical science · 2022Article
- Simpler Procedure and Improved Performance for Pathogenic Bacteria Analysis with a Paper-Based Ratiometric Fluorescent Sensor Array.Analytical chemistry · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% without the need to acquire additional reference data. The described approach significantly expands the functionality of sensor arrays and provides important insights into data processing for the analysis of other complex samples.
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Registered trials
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