Dataset for "Augmenting corn starch gel printability for architectural 3D modeling for customized food"
This dataset results from a study that aimed to bolster the printability of normal corn starch (NCS) through integration with pregelatinized (PG) high-amylose starch (G50 and G70, with 55% and 68% amylose contents, respectively) and proteins (soy, wheat, pea protein isolates, and whey protein). The PG starch was prepared by disorganizing the high-amylose starches in 33% CaCl₂ solution and then precipitating them with ethanol.
The dataset contains all raw data for the characteristics (rheological properties, expansion rate, texture, digestibility, height, water loss, and moisture content) of different formulations involving the effects of PG high-amylose type, PG-G70 content, protein type, and soybean protein isolate (SPI) content. It also contains the Origin (Unicode) Project files used to generate the plots shown in the associated paper, "Augmenting corn starch gel printability for architectural 3D modeling for customized food".
Cite this dataset as:
Xian, D.,
Wu, L.,
Lin, K.,
Liu, P.,
Wu, S.,
Yuan, Y.,
Xie, F.,
2024.
Dataset for "Augmenting corn starch gel printability for architectural 3D modeling for customized food".
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01435.
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Data
Data to Share.zip
application/zip (15MB)
Creative Commons: Attribution 4.0
Creators
Dongni Xian
Guangzhou University
Linlin Wu
Guangzhou University
Keying Lin
Guangzhou University
Peng Liu
Guangzhou University
Silin Wu
Guangzhou University
Yang Yuan
Guangzhou University
Fengwei Xie
University of Bath
Contributors
University of Bath
Rights Holder
Documentation
Data collection method:
For details of the methodology, see the "Materials and methods" section of the associated paper.
Technical details and requirements:
The files were created using Excel and Origin software programs.
Additional information:
The names of the Origin (Unicode) Project (.opju) files correspond to the figure numbers in the associated paper. For the detailed information regarding the samples tested, refer to the respective figure captions in the associated paper.
Methodology link:
Xian, D., Wu, L., Lin, K., Liu, P., Wu, S., Yuan, Y., and Xie, F., 2024. Augmenting corn starch gel printability for architectural 3D modeling for customized food. Food Hydrocolloids, 156, 110294. Available from: https://doi.org/10.1016/j.foodhyd.2024.110294.
Funders
Engineering and Physical Sciences Research Council
https://doi.org/10.13039/501100000266
Breaking FROntiers for advanced engineering of bespoke, functional Biopolymer COmposite materials
EP/V002236/3
People's Government of Guangdong Province
https://doi.org/10.13039/501100002912
Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation ("Climbing Program" Special Funds) Award
pdjh2023b0413
Publication details
Publication date: 4 September 2024
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01435
URL for this record: https://researchdata.bath.ac.uk/id/eprint/1435
Related papers and books
Xian, D., Wu, L., Lin, K., Liu, P., Wu, S., Yuan, Y., and Xie, F., 2024. Augmenting corn starch gel printability for architectural 3D modeling for customized food. Food Hydrocolloids, 156, 110294. Available from: https://doi.org/10.1016/j.foodhyd.2024.110294.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Contact person: Fengwei Xie
Faculty of Engineering & Design
Chemical Engineering
