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Record W4384817751 · doi:10.1002/fsh3.12007

Novel technologies for improving quality of tofu products and their packaging: A critical overview

2023· article· en· W4384817751 on OpenAlexaff
Jie Du, Min Zhang, Liqing Qiu, Arun S. Mujumdar, Yamei Ma

Bibliographic record

VenueFood Safety and Health · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNanocomposite Films for Food Packaging
Canadian institutionsMcGill University
FundersHigher Education Discipline Innovation ProjectState Key Laboratory of Food Science and TechnologyGovernment of Jiangsu Province
KeywordsActive packagingPackaging engineeringShelf lifeQuality (philosophy)Computer scienceFood packagingBiochemical engineeringBusinessFood scienceEngineeringChemistryMarketing

Abstract

fetched live from OpenAlex

Abstract There are many kinds of tofu products, and they are mainly found in Asia. This paper mainly summarizes and classifies the tofu products of different countries through the analysis of the characteristics of different products and the differences between them. The storage conditions for most bean products are typically harsh, resulting in the shelf life of some fresh beans limited to 3–5 days. The new packaging technology avoids the shortcomings of traditional packaging, which makes it more effective to store. In this paper, biodegradable packaging materials, nanomaterials, active packaging, and intelligent packaging were reviewed. The significance of this review is to introduce various types of tofu products to gain a more comprehensive understanding of their advantages and disadvantages and to apply different packaging techniques to improve the preservation environment of tofu products. The purpose is to find suitable packaging technology, materials, and development route of tofu products.

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)0.002
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 score0.104
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.120
GPT teacher head0.370
Teacher spread0.251 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2023
Admission routes1
Has abstractyes

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