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Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and
Zongyu Huang1, Yang Ni1, Qun Yu1
1School of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, China.
Advances in Colloid and Interface Science
|April 17, 2022
Summary
Deep learning object detection accurately classifies and quantifies Pickering emulsion microstructures. This method analyzes emulsion droplet changes, revealing mechanisms at varying concentrations and temperatures.
Area of Science:
- Colloid and Surface Science
- Materials Science
- Chemical Engineering
Background:
- Understanding complex emulsion microstructures is crucial for mechanism elucidation.
- Microscopic image analysis is key, but classification and quantification remain challenging.
- Deep learning offers a potential solution for analyzing intricate emulsion systems.
Purpose of the Study:
- To develop a novel technique for evaluating Pickering emulsion properties.
- To classify and quantify emulsion microstructures using deep learning object detection.
- To enable detailed statistical analysis of emulsion droplet characteristics.
Main Methods:
- Utilizing an object detection algorithm based on deep learning.
- Training neural network models to characterize emulsion droplets from microscopic images.
- Distinguishing individual droplets and morphological mechanisms.
Main Results:
- Successful classification and quantification of emulsion microstructures.
- Characterization of emulsion droplets and their morphological mechanisms.
- Statistical analysis of droplet changes with Pickering interface concentration and storage temperature.
Conclusions:
- The developed deep learning methodology provides a new quantitative approach for emulsion analysis.
- This technique facilitates the elucidation of mechanisms governing Pickering emulsions.
- The findings offer insights into emulsion properties based on microstructure analysis.
