Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends

This study explored the effects of sorbitol on the textural properties of soy protein concentrate-based high-moisture meat analogues (SPC-HMMA) using a novel approach that combines artificial intelligence (AI) and genetic algorithms (GA) to replicate the textures of chicken and beef, aiming to devel...

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Autores: Gulzar, Saqib, Tagrida, Mohamed, Martín Belloso, Olga, Soliva-Fortuny, Robert
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/467777
Acceso en línea:https://doi.org/10.1016/j.lwt.2025.117416
https://hdl.handle.net/10459.1/467777
Access Level:acceso abierto
Palabra clave:Meat analogues
Extrusion
Plant-based proteins
Artificial intelligence
Sorbitol
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spelling Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blendsGulzar, SaqibTagrida, MohamedMartín Belloso, OlgaSoliva-Fortuny, RobertMeat analoguesExtrusionPlant-based proteinsArtificial intelligenceSorbitolThis study explored the effects of sorbitol on the textural properties of soy protein concentrate-based high-moisture meat analogues (SPC-HMMA) using a novel approach that combines artificial intelligence (AI) and genetic algorithms (GA) to replicate the textures of chicken and beef, aiming to develop customized meat analogues with tailored textural properties. This method allows for the simultaneous adjustment of multiple parameters, effectively capturing the complex non-linear interactions between ingredients and processing conditions during extrusion. SPC with varying sorbitol concentrations and moisture levels was extruded under optimized screw speeds and temperatures. Texture profile analysis (TPA) revealed that hardness values decreased from 3893 ± 308 g at 0% sorbitol to 421 ± 54 g at 20% sorbitol while cutting strength values ranged from 5951 ± 544 g crosswise at 0% sorbitol to 1754 ± 134 g at 20% sorbitol. Moisture content played a significant role in the textural properties of the SPC-HMMA with lower moisture yielding harder and chewier analogues. Scanning electron microscopy (SEM) revealed alterations in the microstructure while FTIR spectroscopy and deconvolution analysis indicated significant alterations in protein secondary structure. Cooking yield increased from 142.56 ± 1.5% to 168.54 ± 2.12%, water absorption capacity increased from 329.41 ± 5.16% to 464.67 ± 5.28%, while oil absorption capacity decreased from 120.84 ± 1.89% to 93.39 ± 1.82% with increasing sorbitol levels.This project has received funding from the European Union\u2019s Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No 101034288.Elsevier2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1016/j.lwt.2025.117416https://hdl.handle.net/10459.1/467777reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a https://doi.org/10.1016/j.lwt.2025.117416LWT, 2025, vol. 217, núm.117416, p. 1-14info:eu-repo/grantAgreement/EC/H2020/101034288cc-by-nc (c) Gulzar et al., 2025Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/oai:repositori.udl.cat:10459.1/4677772026-06-24T12:42:17Z
dc.title.none.fl_str_mv Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends
title Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends
spellingShingle Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends
Gulzar, Saqib
Meat analogues
Extrusion
Plant-based proteins
Artificial intelligence
Sorbitol
title_short Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends
title_full Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends
title_fullStr Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends
title_full_unstemmed Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends
title_sort Optimizing high-moisture meat analogue textures through Artificial Intelligence: The effect of sorbitol in soy protein concentrate blends
dc.creator.none.fl_str_mv Gulzar, Saqib
Tagrida, Mohamed
Martín Belloso, Olga
Soliva-Fortuny, Robert
author Gulzar, Saqib
author_facet Gulzar, Saqib
Tagrida, Mohamed
Martín Belloso, Olga
Soliva-Fortuny, Robert
author_role author
author2 Tagrida, Mohamed
Martín Belloso, Olga
Soliva-Fortuny, Robert
author2_role author
author
author
dc.subject.none.fl_str_mv Meat analogues
Extrusion
Plant-based proteins
Artificial intelligence
Sorbitol
topic Meat analogues
Extrusion
Plant-based proteins
Artificial intelligence
Sorbitol
description This study explored the effects of sorbitol on the textural properties of soy protein concentrate-based high-moisture meat analogues (SPC-HMMA) using a novel approach that combines artificial intelligence (AI) and genetic algorithms (GA) to replicate the textures of chicken and beef, aiming to develop customized meat analogues with tailored textural properties. This method allows for the simultaneous adjustment of multiple parameters, effectively capturing the complex non-linear interactions between ingredients and processing conditions during extrusion. SPC with varying sorbitol concentrations and moisture levels was extruded under optimized screw speeds and temperatures. Texture profile analysis (TPA) revealed that hardness values decreased from 3893 ± 308 g at 0% sorbitol to 421 ± 54 g at 20% sorbitol while cutting strength values ranged from 5951 ± 544 g crosswise at 0% sorbitol to 1754 ± 134 g at 20% sorbitol. Moisture content played a significant role in the textural properties of the SPC-HMMA with lower moisture yielding harder and chewier analogues. Scanning electron microscopy (SEM) revealed alterations in the microstructure while FTIR spectroscopy and deconvolution analysis indicated significant alterations in protein secondary structure. Cooking yield increased from 142.56 ± 1.5% to 168.54 ± 2.12%, water absorption capacity increased from 329.41 ± 5.16% to 464.67 ± 5.28%, while oil absorption capacity decreased from 120.84 ± 1.89% to 93.39 ± 1.82% with increasing sorbitol levels.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.lwt.2025.117416
https://hdl.handle.net/10459.1/467777
url https://doi.org/10.1016/j.lwt.2025.117416
https://hdl.handle.net/10459.1/467777
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a https://doi.org/10.1016/j.lwt.2025.117416
LWT, 2025, vol. 217, núm.117416, p. 1-14
info:eu-repo/grantAgreement/EC/H2020/101034288
dc.rights.none.fl_str_mv cc-by-nc (c) Gulzar et al., 2025
Attribution-NonCommercial 4.0 International
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
rights_invalid_str_mv cc-by-nc (c) Gulzar et al., 2025
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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