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Record W2143505678 · doi:10.5539/jfr.v2n6p89

Effect of High-Oxygen Packaging on Respiratory Physiology and Sensorial Qualities of Fresh Shiitake Mushrooms (Lentinus edodes)

2013· article· en· W2143505678 on OpenAlexvenueno aff
Yanjie Li, Yutaka Ishikawa, Takaaki Satake, H. Kitazawa, Xiaoli Qiu

Bibliographic record

VenueJournal of Food Research · 2013
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMushroomAromaFood scienceOxygenRespiration rateChemistryRespirationLentinusAnaerobic exerciseSensory analysisAnaerobic respirationBotanyBiology

Abstract

fetched live from OpenAlex

<p>In this research, the effect of high-oxygen packaging (HOP) with initial 80% and 100% oxygen on fresh shiitake mushrooms was studied. Initial air in package was the control treatment. All the samples were stored at 10 °C with RH 90% for 9 days. Respiration rate, hardness, TSS, and color were determined, and sensory quality was evaluated during the storage. Results indicated that high-oxygen packaging retarded the anaerobic metabolism occurrence and HOP with initial 100% oxygen could maintain the lightness of shitake mushroom better than 80% oxygen. Hardness and TSS did not show significant difference between high-oxygen packaging and control treatment. However, neither initial 100% O<sub>2</sub> nor 80% O<sub>2</sub> could reduce the respiration rate of fresh shiitake mushroom. Sensory quality especially the aroma, cap color and gill color of fresh shiitake mushroom could be better maintained in high-oxygen packaging than control treatment. The acceptability of the shiitake mushrooms after storage was the highest in HOP with initial 100% oxygen. In conclusion, high-oxygen packaging especially with initial 100% oxygen showed the obvious effect maintaining the sensory quality of fresh shiitake mushrooms although it could not reduce the respiration rate at 10 °C.<strong></strong></p>

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)0.001
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 score0.074
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.053
GPT teacher head0.381
Teacher spread0.328 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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