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Record W3158798096 · doi:10.3389/fbioe.2021.677559

A Survey of Recent Patents in Engineering Technology for the Screening, Separation and Processing of Eggshell

2021· review· en· W3158798096 on OpenAlexafffund
Tamer A. Ahmed, Manar Younes, Ling Wu, Maxwell T. Hincke

Bibliographic record

VenueFrontiers in Bioengineering and Biotechnology · 2021
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaHubei University of TechnologyTongji UniversityHebei Agricultural UniversityHubei UniversityHuazhong Agricultural University
KeywordsEggshellNanotechnologyEnvironmental scienceProcess engineeringComputer scienceBiologyMaterials scienceEngineeringEcology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.052
GPT teacher head0.285
Teacher spread0.233 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations22
Published2021
Admission routes2
Has abstractyes

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