(195d) Endotoxin Sensors Using Liquid Crystals and Machine Learning
- Conference: AIChE Annual Meeting
- Year: 2019
- Proceeding: 2019 AIChE Annual Meeting
- Group: Topical Conference: Applications of Data Science to Molecules and Materials
- Time: Monday, November 11, 2019 - 4:15pm-4:30pm
Liquid crystal (LC) droplets dispersed in water are sensitive to the presence of endotoxins and undergo internal ordering transitions when exposed to different endotoxin species and concentrations . Specifically, endotoxin accumulates at topological defects within the LC droplets, triggering a change from droplet configurations with bipolar symmetry to droplet configurations with radial symmetry. Different internal configurations of LC droplets have distinct optical properties . As such, flow cytometry of LC droplets can be used to generate rich forward scattering/side scattering (FSC/SSC) data. A variety of counting methods used in the analysis of FSC/SSC data can predict endotoxin concentration , however, the optimal method for characterizing the scatter plots is not known and some methods are subject to variability.
In this work, we demonstrate that machine learning techniques can be used to accurately predict endotoxin concentrations and species from FSCC/SSC data. Our approach uses a convolutional neural network to extract pattern information (features) from FSC/SSC density data. Our framework reveals that significant hidden information is available in FSC/SSC data. Our analysis also provides insights into the physical effects driving changes in the LC internal configurations.
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