Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties
This study aimed to optimize the 3D printing parameters of salmon gelatin gels (SGG) using artificial neural networks with the genetic algorithm (ANN-GA) and response surface methodology (RSM). In addition, the influence of the optimal parameters obtained using the two different methodologies was ev...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Gels |
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| Online Access: | https://www.mdpi.com/2310-2861/9/9/766 |
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| author | Nailín Carvajal-Mena Gipsy Tabilo-Munizaga Marleny D. A. Saldaña Mario Pérez-Won Carolina Herrera-Lavados Roberto Lemus-Mondaca Luis Moreno-Osorio |
| author_facet | Nailín Carvajal-Mena Gipsy Tabilo-Munizaga Marleny D. A. Saldaña Mario Pérez-Won Carolina Herrera-Lavados Roberto Lemus-Mondaca Luis Moreno-Osorio |
| author_sort | Nailín Carvajal-Mena |
| collection | DOAJ |
| description | This study aimed to optimize the 3D printing parameters of salmon gelatin gels (SGG) using artificial neural networks with the genetic algorithm (ANN-GA) and response surface methodology (RSM). In addition, the influence of the optimal parameters obtained using the two different methodologies was evaluated for the physicochemical and digestibility properties of the printed SGG (PSGG). The ANN-GA had a better fit (R<sup>2</sup> = 99.98%) with the experimental conditions of the 3D printing process than the RSM (R<sup>2</sup> = 93.99%). The extrusion speed was the most influential parameter according to both methodologies. The optimal values of the printing parameters for the SGG were 0.70 mm for the nozzle diameter, 0.5 mm for the nozzle height, and 24 mm/s for the extrusion speed. Gel thermal properties showed that the optimal 3D printing conditions affected denaturation temperature and enthalpy, improving digestibility from 46.93% (SGG) to 51.52% (PSGG). The secondary gel structures showed that the β-turn structure was the most resistant to enzymatic hydrolysis, while the intermolecular β-sheet was the most labile. This study validated two optimization methodologies to achieve optimal 3D printing parameters of salmon gelatin gels, with improved physicochemical and digestibility properties for use as transporters to incorporate high value nutrients to the body. |
| first_indexed | 2024-03-10T22:43:54Z |
| format | Article |
| id | doaj.art-f7669a9d784642069dc5f96146d077a8 |
| institution | Directory Open Access Journal |
| issn | 2310-2861 |
| language | English |
| last_indexed | 2024-03-10T22:43:54Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Gels |
| spelling | doaj.art-f7669a9d784642069dc5f96146d077a82023-11-19T10:51:31ZengMDPI AGGels2310-28612023-09-019976610.3390/gels9090766Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility PropertiesNailín Carvajal-Mena0Gipsy Tabilo-Munizaga1Marleny D. A. Saldaña2Mario Pérez-Won3Carolina Herrera-Lavados4Roberto Lemus-Mondaca5Luis Moreno-Osorio6Department of Food Engineering, Universidad del Bío-Bío, Avenida Andrés Bello 720, Chillán 3780000, ChileDepartment of Food Engineering, Universidad del Bío-Bío, Avenida Andrés Bello 720, Chillán 3780000, ChileDepartment of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB T6G 2P5, CanadaDepartment of Food Engineering, Universidad del Bío-Bío, Avenida Andrés Bello 720, Chillán 3780000, ChileDepartment of Food Engineering, Universidad del Bío-Bío, Avenida Andrés Bello 720, Chillán 3780000, ChileDepartment of Food Science and Chemical Technology, Universidad de Chile, Santos Dumont 964, Santiago 8330015, ChileDepartment of Basic Sciences, Universidad del Bío-Bío, Avenida Andrés Bello 720, Chillán 3780000, ChileThis study aimed to optimize the 3D printing parameters of salmon gelatin gels (SGG) using artificial neural networks with the genetic algorithm (ANN-GA) and response surface methodology (RSM). In addition, the influence of the optimal parameters obtained using the two different methodologies was evaluated for the physicochemical and digestibility properties of the printed SGG (PSGG). The ANN-GA had a better fit (R<sup>2</sup> = 99.98%) with the experimental conditions of the 3D printing process than the RSM (R<sup>2</sup> = 93.99%). The extrusion speed was the most influential parameter according to both methodologies. The optimal values of the printing parameters for the SGG were 0.70 mm for the nozzle diameter, 0.5 mm for the nozzle height, and 24 mm/s for the extrusion speed. Gel thermal properties showed that the optimal 3D printing conditions affected denaturation temperature and enthalpy, improving digestibility from 46.93% (SGG) to 51.52% (PSGG). The secondary gel structures showed that the β-turn structure was the most resistant to enzymatic hydrolysis, while the intermolecular β-sheet was the most labile. This study validated two optimization methodologies to achieve optimal 3D printing parameters of salmon gelatin gels, with improved physicochemical and digestibility properties for use as transporters to incorporate high value nutrients to the body.https://www.mdpi.com/2310-2861/9/9/766salmon gelatin3D printingdimensional stabilityartificial neural networksecondary structuredigestibility |
| spellingShingle | Nailín Carvajal-Mena Gipsy Tabilo-Munizaga Marleny D. A. Saldaña Mario Pérez-Won Carolina Herrera-Lavados Roberto Lemus-Mondaca Luis Moreno-Osorio Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties Gels salmon gelatin 3D printing dimensional stability artificial neural network secondary structure digestibility |
| title | Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties |
| title_full | Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties |
| title_fullStr | Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties |
| title_full_unstemmed | Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties |
| title_short | Three-Dimensional Printing Parameter Optimization for Salmon Gelatin Gels Using Artificial Neural Networks and Response Surface Methodology: Influence on Physicochemical and Digestibility Properties |
| title_sort | three dimensional printing parameter optimization for salmon gelatin gels using artificial neural networks and response surface methodology influence on physicochemical and digestibility properties |
| topic | salmon gelatin 3D printing dimensional stability artificial neural network secondary structure digestibility |
| url | https://www.mdpi.com/2310-2861/9/9/766 |
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