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Record W4249159586 · doi:10.21608/ajfs.2020.105625

Utilization of Agro-Wastes to Produce Oyster Mushroom (Pleurotus ostreatus) with High Antioxidant and Antimicrobial Activities

2020· article· en· W4249159586 on OpenAlexafffund
Amal Abd El-Razek, Amel Ibrahim, Aisha El-Attar, Dalal Asker

Bibliographic record

VenueAlexandria Journal of Food Science and Technology · 2020
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoAlexandria UniversityAcademy of Scientific Research and Technology
KeywordsMushroomOysterPleurotus ostreatusAntimicrobialFood sciencePleurotusAntioxidantChemistryMicrobiologyBiologyFisheryBiochemistry

Abstract

fetched live from OpenAlex

Oyster mushroom (Pleurotus ostreatus) is a good source of bioactive compounds that have numerous healthbenefits and medicinal properties. In this study, the effect of P. ostreatus cultivation in agro-waste substrates on itspotential as a food additive with antioxidant and antimicrobial activities was investigated. Extracts of P. ostreatus cultivatedon mixed rice and wheat straws (RS+WS) substrates showed higher total polyphenols (TPC), total flavonoids(TFC),α-tocopherol content, ferric reducing antioxidant power and antibacterial activities than those cultivated on ricestraw (RS) alone. The HPLC analysis of their ethanolic extracts revealed 11 phenolic compounds including p-hydroxybenzoic acid, naringenin, kaempferol and apigenin as major compounds. The aqueous and ethanolic extracts of bothcultivates showed significant antibacterial activity against Salmonella typhimurium, Escherichia coli, and Staphylococcusaureus, while no inhibitory effect on Bacillus cereus was observed. The results indicated that P. ostreatuscultivation is an effective bioconversion process that is capable to transfer agro-wastes into potentially valuable sourceof natural antioxidant and antimicrobial additives for further use in functional food products.

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 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 score0.014
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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