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Record W2796842720 · doi:10.1111/jfpe.12689

Study on 3D printing of orange concentrate and material characteristics

2018· article· en· W2796842720 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Food Process Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsExtrusionRheologyNozzleOrange (colour)Materials scienceStarchShear forceComposite materialShear rateFood scienceChemistryMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Three‐dimensional (3D) food printing is a promising technology that attracted the attention of both academia and industry since it is considered as a major paradigm shift in the fabrication of intricate and personalized food design with the choices of altering the nutritional profile. In this study, 3D printing (3DP) properties of orange leather (OL) were characterized and simultaneously comparative assessment was carried out while making it from orange concentrate (OC) by adding varying proportions (15, 20, 25, and 30%) of wheat starch (WS). Rheological data suggest that steam cooking of OC–WS mixture for 16 ± 0.5 min exhibit shear‐thinning behavior, which is essential for extrusion‐type 3DP of food mixtures. A variation of 5% WS with OC significantly increase the yield stress (τ 0 ) and viscosity ( n ). Nuclear magnetic resonance (NMR) study revealed that the maximum amount of partially immobilized water was converted to bound water and developed the highest mechanical strength but poor extrudability for 30% WS containing sample. Texture profile analysis suggests that the 20% WS containing samples provide the best mastication properties among the four samples. To optimize the printing conditions and test the reproducibility of OL 3DP process effects of nozzle diameter ( d n ), nozzle tip‐print bed height ( h c ), extrusion rate ( v d ), and nozzle moving speed ( v n ) were tested experimentally. It was found that, at d n = 1.5 mm, h c = 1.54 ± 0.02 mm, v d = 245 mm 3 /s, and v n = 35 mm/s the printed objects remain consistent, achieve the best resolution and maximum fidelity. Practical applications The 3D food printing process has a great potential to improve the quality and utility of the food and food products. The 3DP of fruit concentrates combined with healthy additives, bioactive compounds could be a novel attractive way for fabricating food to serve people with special requirements, as a snack item or cold dish before main meal. This study suggests that, food mixtures with similar rheological, moisture, and textural properties at similar printing conditions could be used as supply material, that is, the “ink” for an extrusion‐type 3D food printer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.231
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it