MétaCan
Menu
Back to cohort
Record W2946727296 · doi:10.5539/jfr.v8n4p1

Changes in Quality Attributes Related to Browning during Storage of Litchi Juice Fermented by Lactobacillus

2019· article· en· W2946727296 on OpenAlexvenueno aff
Xingxing Yuan, Yuanshan Yu, Yujuan Xu, Gengsheng Xiao, Jijun Wu

Bibliographic record

VenueJournal of Food Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Quality and Safety Studies
Canadian institutionsnot available
FundersNatural Science Foundation of Guangdong Province
KeywordsBrowningChemistryFood scienceFermentationLactobacillus caseiRutinLactobacillusMaillard reactionAntioxidantBiochemistry

Abstract

fetched live from OpenAlex

Litchi juice fermented by Lactobacillus casei was heated (95°C, 1 min) and stored in a dark place at 25°C. Changes in quality attributes (color, 5-hydroxymethylfurfural (5-HMF) phenolic compounds, antioxidant capacity, sugars, free amino acids, and others) related to browning in fermented litchi juice were investigated during the six months of storage. Noticeable visual changes due to browning were observed during storage of fermented litchi juice, especially in the upper part of the juice bottle, and the value of color difference (△E) increased to 7.12±0.04 after six months of storage. The 5-HMF content increased with the increase in storage time, which rose from 0 to 2.31±0.16 mg/L after six months of storage. Five soluble phenolic compounds (rutin, narcissoside, quercetin, kaempferol-rutinose-rhamnoside, and isorhamnetin-rutinose-rhamnoside) were identified in fermented litchi juice, none of which showed a significant decrease (P>0.05), whereas a tendency for total phenolic content to decrease was observed during storage of fermented litchi juice. Adding 0.3 g/L of sodium sulfite can inhibit the browning reaction in fermented litchi juice and decrease the formation of 5-HMF as well as the loss of total phenolics.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.111
GPT teacher head0.358
Teacher spread0.247 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Food ResearchSame topicFood Quality and Safety StudiesFrench-language works237,207