Rheological analysis in food processing: factors, applications, and future outlooks with machine learning integration

Area of Science:

• Food Science and Technology
• Rheology
• Machine Learning Applications

Background:

• Food rheology is crucial for determining texture, taste, stability, and overall quality.
• Complex food production and market demands require advanced characterization methods.
• Machine learning (ML) offers powerful tools for analyzing complex food systems.

Purpose of the Study:

• To review the integration of machine learning with food rheology.
• To examine ML applications in texture analysis (large and small deformation rheology).
• To summarize factors influencing food rheology and component interactions.

Main Methods:

• Review of existing literature on food rheology and machine learning.
• Analysis of rheological measurements, including large and small deformation.
• Exploration of machine learning algorithms for rheological data analysis.

Main Results:

• Machine learning effectively predicts and analyzes food rheological properties.
• Integration of ML with rheology aids in food flow analysis and deformation characterization.
• ML facilitates product formulation optimization, process monitoring, and sensory analysis.

Conclusions:

• Machine learning significantly enhances the characterization and optimization of food rheology.
• Despite challenges with large datasets and complex conditions, ML shows high efficacy.
• Further development of ML-based rheological approaches holds substantial potential for the food industry.