A Survey of Recent Patents in Engineering Technology for the Screening, Separation and Processing of Eggshell
Bibliographic record
Abstract
The chicken egg is a well-known complete food of human daily consumption which serves as a cost-effective, high-quality nutrient resource. About 30% of table eggs are directed to breaker plants in developed countries, leading to the generation of substantial eggshell (ES) waste, which is increasingly explored for potential value-added applications. The number of patents describing ES-based applications has increased dramatically in recent years. This review provides insight into the most recent patents published between 2015 and 2020, with focus on different engineering technologies for the screening, separation, and processing of ES. Screening technologies include detection of ES surface spots and glossiness, ES cracks, and mechanical properties, along with identification of chicken breed and enumeration of surface bacterial count. Collection and separation technologies describe separation strategies of ES from egg white (EW), egg yolk (EY), liquid egg, eggshell membrane (ESM), hatchlings, and cooked egg. Separation of ES from liquid eggs utilizes gravity, rotational forces, or air pressure. Processing of ES involves washing and sterilization along with cutting, crushing, and pulverization technologies that enable the collection of ES suitable for value-added applications. In addition, ES carving (mechanical and laser) opens up the realm of artwork and decoration. Furthermore, intact ES can be utilized for food serving. The exponential increase in innovative screening, separation, collection, and processing technologies reflects industrial interest to upscale low-value ES waste material, and is a first crucial step in the emergence of advanced technologies that exploit the biomedical, chemical, engineering, and environmental applications for ES.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".