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Record W7118127747 · doi:10.11648/j.jfns.20251306.17

Effect of Fermentation Time on the Quality of Tea Samples Produced from Sweet Orange Peel Powder

2025· article· en· W7118127747 on OpenAlexaff
Joseph Buraimoh, Ufuoma Akpezi Orieruo, Samuel Buraimoh

Bibliographic record

VenueJournal of Food and Nutrition Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFermentationTitratable acidOrange (colour)AntioxidantDPPHMouthfeelTaste

Abstract

fetched live from OpenAlex

This study investigated the effect of fermentation time on tea produced from sweet orange peel powder. Orange peels were processed into powder, with a portion fermented for 1 day and another for 2 days. Teas from fermented and unfermented powders were analyzed for proximate composition, phytochemicals, antioxidant activity (DPPH), sensory, and physicochemical properties. Lipton tea served as the control in sensory evaluation. Fermentation increased ash, fat, crude fiber, and protein, while reducing carbohydrate content. Moisture content increased slightly during fermentation compared with unfermented powder and was lowest in Lipton tea. Although some phytochemicals declined, fermentation increased flavonoid content and improved the tea's antioxidant activity, with DPPH values increasing across samples, though the day 2 sample showed a significant decrease. Fermentation also reduced pH and total soluble solids but increased total titratable acidity. Sensory scores for color, flavor, and mouthfeel improved, while taste and overall acceptability decreased with longer fermentation. Significant differences (p<0.05) were found only in taste and overall acceptability, where unfermented tea was preferred. The study concludes that sweet orange peel is suitable for producing fermented and unfermented teas. Tea fermented for 1 day is recommended for its higher phytochemical content and antioxidant activity, as a short fermentation period appears most suitable for balancing nutritional quality, bioactive compounds, and sensory appeal.

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)0.001
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 score0.135
Threshold uncertainty score0.113

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.046
GPT teacher head0.364
Teacher spread0.318 · 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
Published2025
Admission routes1
Has abstractyes

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