Rheological, structural and textural characteristics of 3D-printed and conventionally-produced gluten-free snack made with chickpea and lupin flour

Studying the impact of 3d printing technology on safety indicators of plant-based burger

This study investigate the impacts of 3D printing technology on safety indicators in plant-based burgers. Experiments were performed in the refrigerator on days 0, 5, 7, 12, and 14 and at room temperature at 0, 24, and 96 h after printing. On days 7 and 12 in the refrigerator, PTC of printed samples was significantly higher than conventional. On day 1, the printed sample had higher pH, but on the other days, the pH decreased and no significant difference was reported. TVBN showed a significant (p < 0.05) increase in the refrigerator and room. Peroxide value and TBARS increased (p < 0.05) in all groups and the printed sample showed a higher value, which could be due to porosity during printing. The results of the sensory showed that the appearance and overall acceptance of the 3D printed sample were significantly (p < 0.05) higher than the conventional sample.

Influence of Infill Level and Post-Processing on Physical Parameters and Betaine Content of Enriched 3D-Printed Sweet Snacks

Betaine is a non-essential amino acid with proven functional properties and untapped potential for cereal food enrichment. While 3D printing represents a viable approach for manufacturing enriched cereal-based foods with novel shapes and textures, it is crucial to consider the impact of printing parameters and post-processing on the betaine content and properties of these products. The aim of this study was to investigate the influence of the infill level (20, 30 and 40%) of 3D-printed cuboid shapes and the post-processing techniques (drying oven, vacuum dryer, air fryer) of betaine-enriched oat-based snacks on the print quality, texture, and sensory properties, as well as the content of preserved betaine. The interaction of post-processing technique and infill level influenced the length deviation and texture properties, as well as the betaine content of snacks. Height stability was only influenced by post-processing technique. In general, oven-dried snacks showed the best dimensional stability, having the lowest width/length deformation (about 8%) at the infill level of 20%. Betaine was best preserved (19-31% loss) in snacks post-processed in a vacuum dryer (1281-1497 mg/g), followed by an air fryer and a drying oven, where betaine loss was in the range 28-55%. Air-fried snacks with 40% infill level had the highest values of instrumentally measured crunchiness (38.9 Nmm) as well as sensory test values for liking of texture (7.5), intensity of odor (6) and overall flavor (6). Overall, air frying proved to be a convenient and quick post-processing technique for 3D-printed snacks, but infill patterns for preserving betaine should be further explored. Vacuum drying could be used to preserve bioactive compounds, but efforts should be made to minimize its negative impact on the physical deformations of the 3D-printed products.

Quantitative analysis of 3D food printing layer extrusion accuracy: Contextualizing automated image analysis with human evaluations: Quantifying 3D food printing accuracy

3D food printing can customize food appearance, textures, and flavors to tailor to specific consumer needs. Current 3D food printing depends on trial-and-error optimization and experienced printer operators, which limits the adoption of the technology by general consumers. Digital image analysis can be applied to monitor the 3D printing process, quantify printing errors, and guide optimization of the printing process. We here propose an automated printing accuracy assessment tool based on layer-wise image analysis. Printing inaccuracies are quantified based on over- and under-extrusion with reference to the digital design. The measured defects are compared to human evaluations via an online survey to contextualize the errors and identify the most useful measurements to improve printing efficiency. The survey participants marked oozing and over-extrusion as inaccurate printing which matched the results obtained from automated image analysis. Although under-extrusion was also quantified by the more sensitive digital tool, the survey participants did not perceive consistent under-extrusion as inaccurate printing. The contextualized digital assessment tool provides useful estimations of printing accuracy and corrective actions to avoid printing defects. The digital monitoring approach may accelerate the consumer adoption of 3D food printing by improving the perceived accuracy and efficiency of customized food printing.