Soft Hydrogel Actuator for Fast Machine-Learning-Assisted Bacteria Detection
Source abstract
Abstract
We demonstrate that our bio-electrochemical platform facilitates the reduction of detection time from the 3-day period of the existing tests to 15 min. Machine learning and robotized bioanalytical platforms require the principles such as hydrogel-based actuators for fast and easy analysis of bioactive analytes. Bacteria are fragile and environmentally sensitive microorganisms that require a special environment to support their lifecycles during analytical tests. Here, we develop a bio-electrochemical platform based on the soft hydrogel/eutectic gallium–indium alloy interface for the detection of Streptococcus thermophilus and Bacillus coagulans bacteria in various mediums. The soft hydrogel-based device is capable to support bacteria’ viability during detection time. Current–voltage data are used for multilayer perceptron algorithm training. The multilayer perceptron model is capable of detecting bacterial concentrations in the 104 to 108 cfu/mL range of the culture medium or in the dairy products with high accuracy (94%). Such a fast and easy biodetection is extremely important for food and agriculture industries and biomedical and environmental science.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Biosensors and Analytical Detection · Advanced Chemical Sensor Technologies · Cell Image Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Infochemistry Scientific Center of ITMO University, Lomonosova Street 9, St. Petersburg 191002, Russia
Institute for Functional Intelligent Materials, National University of Singapore, Singapore 117544, Singapore
Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore
Funding
Russian Science Foundation
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