A comprehensive review of food rheology: analysis of experimental, computational, and machine learning techniques

Nnyigide, Osita Sunday; Hyun, Kyu

doi:10.1007/s13367-023-00075-w

Korea-Aust. Rheol. J.

2023

DOI: 10.1007/s13367-023-00075-w

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A comprehensive review of food rheology: analysis of experimental, computational, and machine learning techniques

Osita Sunday Nnyigide,

Kyu Hyun

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2024

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Cited by 3 publications

References 111 publications

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Effect of urea on the linear and nonlinear rheological properties of human serum albumin hydrogels

Nnyigide,

Hyun

2024

Rheol Acta

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Effect of urea on the linear and nonlinear rheological properties of human serum albumin hydrogels

Nnyigide,

Hyun

2024

Rheol Acta

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Comparative study of the rheology and texture of konjac glucomannan-xanthan gum complexes in water and simulated gastric fluid and their effect on gastric emptying

Chen,

An,

Youssef

et al. 2024

Food Hydrocolloids

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Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis

Bahiuddin,

Mazlan,

Imaduddin

et al. 2024

Journal of the Mechanical Behavior of Materials

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Machine learning’s prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial–temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction.

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A comprehensive review of food rheology: analysis of experimental, computational, and machine learning techniques

Cited by 3 publications

References 111 publications

Effect of urea on the linear and nonlinear rheological properties of human serum albumin hydrogels

Effect of urea on the linear and nonlinear rheological properties of human serum albumin hydrogels

Comparative study of the rheology and texture of konjac glucomannan-xanthan gum complexes in water and simulated gastric fluid and their effect on gastric emptying

Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis

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