ArticleHeliyon2024
Prediction of textural properties of 3D-printed food using response surface methodology.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Image-Based Pore Space and Pore Network Characterization: A Review of 2D/3D Workflow in Foods.Journal of food science · 2026Review
- Machine Learning-Based Prediction of Textural Properties and Nonlinear Regulatory Pattern Analysis of 3D-Printed Dough Containing Konjac Glucomannan.Foods (Basel, Switzerland) · 2026Article
- Optimization of 3D Extrusion Printing Parameters for Raw and Extruded Dehulled Andean Fava Bean Flours Using Response Surface Methodology (RSM).Foods (Basel, Switzerland) · 2025Article
- Characteristics of Food Printing Inks and Their Impact on Selected Product Properties.Foods (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
3D printing has enabled modifying internal structures of the food affecting textural properties, but predicting desired texture remains challenging. To overcome this challenge, the use of response surface methodology (RSM) was demonstrated to develop empirical models relating 3D printing parameters to textural properties using aqueous inks containing cricket powders as a model system. Regression models were established for our key textural properties (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.