Conformational changes and physicochemical attributes of texturized pea protein isolate-konjac gum: With a new perspective of residence time during extrusion

Sun, Dongyu; Wu, Min; Zhang, Tong; Wei, Dongxue; Zhou, Chengyi; Shang, Nan

doi:10.1016/j.foodres.2023.112500

Cited by 9 publications

(1 citation statement)

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“…This phenomenon specifically manifests in the product's image features as an overall richness in yellow hue, less green hue, and a gradual reduction in brightness (a* < 0, b* > 0, L* gradually decreases). Simultaneously, the hardness of the extruded products gradually increases due to the promotion of protein melting in the barrel with rising temperature, reducing viscosity, and thus extending the residence time [31] in the barrel and prolonging the Maillard reaction time [32], which deepens the browning degree of the extruded products. This illustrates a mechanistic correlation between the image features and the hardness of the extruded products.…”

Section: Prediction Deviation In Optimized Modelmentioning

confidence: 99%

Study on the Residence Time and Texture Prediction of Pea Protein Extrusion Based on Image Analysis

Wu,

Zhang,

Gao

et al. 2023

Foods

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This paper initially involves three main processing parameters: screw speed, feeding speed, and initial material moisture content, exploring the RTD of materials inside the extruder barrel under varying parameters and clarifying the impact of parameter variations on RTD. Finally, machine vision technology was utilized to link extruded product images to texture features, and a texture prediction model based on image features was established using a Back Propagation (BP) neural network. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to optimize the BP neural network. The results indicate that the feeding speed has a stronger impact than the screw speed on the extrusion process, and an increase in the initial material moisture content tends to shorten the RTD. Specifically, an increase in screw speed results in a denser product structure, while higher feeding speeds lead to reduced pore size in the microstructure. As the initial material moisture content increased from 55% to 70%, the average residence time MRT decreased from 265.21 s to 166.62 s. Additionally, elevated moisture content causes a more porous microstructure. After optimizing the texture prediction model of extruded products through the application of Particle Swarm Optimization and Genetic Algorithm models, it was discovered that the Genetic Algorithm was more effective in reducing errors (p < 0.05) than the Particle Swarm Optimization algorithm. It was found that the Particle Swarm Optimization model exhibited better prediction performance. The results of the prediction indicated a significant association between the image features of the product and hardness, resilience, and chewiness, as corroborated by correlation coefficients of 0.93913, 0.94040, and 0.94724, respectively.

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Section: Prediction Deviation In Optimized Modelmentioning

confidence: 99%