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Ultrasound improves the low-sodium salt curing of sea bass: Insights into the effects of ultrasound on texture, microstructure, and flavor characteristics

2023· article· en· W4386609882 on OpenAlexaff
Hengli Bai, Laihao Li, Yanyan Wu, Shengjun Chen, Yongqiang Zhao, Qiuxing Cai, Yueqi Wang

Bibliographic record

VenueUltrasonics Sonochemistry · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMeat and Animal Product Quality
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsChewinessFlavorMarinationSea bassUltrasoundSonicationFood scienceMicrostructureCuring (chemistry)ChemistrySodiumBass (fish)FisheryChromatographyBiologyFish <Actinopterygii>Organic chemistryMedicineCrystallographyRadiology

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the effects of ultrasonic pretreatment on the quality of cured sea bass. Compared to static marination, ultrasonication significantly increased the rate of NaCl transfer, reduced the hardness and chewiness of fish, and improved water retention. Microstructural observations revealed that the sea bass muscle fibers were severely fragmented, with their borders becoming increasingly blurred with increasing ultrasonic intensity. In addition, ultrasound-assisted marination significantly increased the degradation of proteins, total free amino acid levels, and relative levels of volatile flavor substances such as aldehydes and esters. Therefore, the use of an appropriate ultrasound treatment for the salt curing of fish has a positive effect on the textural and flavor characteristics of sea bass, with the most optimal approach being 300.W ultrasound treatment for 60 min. Overall, the results of this study provide technical evidence for improving the quality of lightly cured low-salt content seafood.

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)0.002
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 score0.168
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations50
Published2023
Admission routes1
Has abstractyes

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