Soft Hydrogel Actuator for Fast Machine-Learning-Assisted Bacteria Detection

ACS Appl Mater Interfaces. 2022 Feb 9;14(5):7321-7328. doi: 10.1021/acsami.1c22470. Epub 2022 Jan 26.

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.

Keywords: eGaIn; hydrogel; interface; lactic acid bacteria; multilayer perceptron.

MeSH terms

  • Alloys / chemistry
  • Bacillus coagulans / isolation & purification*
  • Density Functional Theory
  • Electrochemical Techniques / methods*
  • Gallium / chemistry
  • Hydrogels / chemistry*
  • Indium / chemistry
  • Machine Learning*
  • Streptococcus thermophilus / isolation & purification*

Substances

  • Alloys
  • Hydrogels
  • Indium
  • Gallium