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Record W4403364185 · doi:10.1016/j.lwt.2024.116899

A ratiometric indicator pad utilizing Alz/BRI-PVA for visual assessment of fish freshness

2024· article· en· W4403364185 on OpenAlexaff
Xiuying Liu, Binbin Guan, Min Yang, Xinwen Bai, Wei Zhang, Pingping Wang, Zaixi Shu, Yiwei Tang, Lijie Zhu

Bibliographic record

VenueLWT · 2024
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsFish <Actinopterygii>Food scienceChemistryBusinessFisheryBiology

Abstract

fetched live from OpenAlex

In this study, a novel intelligent water-absorbent cellulose pad with a specified area was developed for the visual and non-destructive monitoring of fish freshness. The pad’s substrate was composed of wood pulp cellulose (WPC), and the specified area contained a ratiometric mixture of brilliant blue (BRI) and alizarin (Alz)., fixed with Polyvinyl alcohol (PVA). During the freshness monitoring process, the Alz/BRI-PVA WPC pad exhibited a color transition from green to brown, followed by a shift to purple-red, corresponding to the increasing levels of total volatile base nitrogen (TVB-N) and total viable count (TVC). After testing, a BRI/Alz ratio of 1:8 was selected as the optimal ratio. Compared to the volatile gas-based sensing tags, the pad developed in this study demonstrated a high sensitivity of 53.4% by interacting with the sample exudate to produce signal changes. This study demonstrates the feasibility of using the developed dual-functional pad for water absorption and freshness indications. • A dual functional pad with indicating and water-absorbing properties is developed. • The indicating area shows color changes for visual detection of freshness. • The indicating area is more sensitive due to the Alz/BRI ratiometric indicator. • The indicating area can change color from green to brown, and then to purple.

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 categoriesInsufficient payload (model declined to judge)
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 score0.096
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.379
Teacher spread0.351 · 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.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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