Artificial intelligence-based prediction of the rheological properties of hydrocolloids for plant-based meat analogues.

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Abstract

Methylcellulose has been applied as a primary binding agent to control the quality attributes of plant-based meat analogues, however a great deal of efforts have been made to search for hydrocolloids for replacing methylcellulose due to increasing awareness of clean-labels. In this study, a machine learning framework was proposed in order to describe and predict the flow behaviors of six hydrocolloid solutions, and the predicted viscosities were correlated with the textural features of their corresponding plant-based meat analogues.Different shear-thinning and Newtonian behaviors were observed depending on the type of hydrocolloids and the shear rates. Methylcellulose exhibited an increasing viscosity pattern with increasing temperatures, compared to the other hydrocolloids. The machine learning algorithms (random forest and multilayer perceptron models) showed a better viscosity fitting performance than the constitutive equations (Power-law and Cross models). In addition, three hyperparameters of the multilayer perceptron model (optimizer, learning rate, and the number of hidden layers) were tuned using the Bayesian optimization algorithm.The optimized multilayer perceptron model exhibited the superior performance of the viscosity prediction (R2 = 0.9944 – 0.9961/RMSE = 0.0545 – 0.0708). Furthermore, the machine learning-predicted viscosities overall showed similar patterns with the textural parameters of the meat analogues. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.