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Characterization of Phenolic Compounds of Olea europaea L. andCeratonia siliqua L. Leaf Extracts by HPLC-ESI-MS

2022· article· en· W4311127538 on OpenAlexaff
Saïda Meziani, B. Dave Oomah, Cédric Bertrand

Bibliographic record

VenueCurrent Functional Foods · 2022
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCeratonia siliquaOleaOleuropeinPolyphenolChemistryHigh-performance liquid chromatographyMyricetinFood scienceBotanyOlive leafChromatographyBiologyFlavonoidBiochemistryOlive oilAntioxidantKaempferol

Abstract

fetched live from OpenAlex

Background: Olive (Olea europaea L.) and carob (Ceratonia siliqua L.), which contain considerable amounts of phenolic compounds, are the most important nutritional and therapeutic plants in the Mediterranean basin. Objectives: The goal of this work is to revalue carob and olive leaves as key sources of polyphenols, hence increasing the value of waste goods. Methods: In this study, aqueous acetone or ethanol (80% v/v) extracts of olive (O. europaea L. cultivar aimel) and carob (C. siliqua L.) leaves from Algeria were evaluated for phenolic content, and the extracts were characterized by reverse-phase high-performance liquid chromatography-electrospray ionization mass spectrometry (HPLC-ESI-MS). Discussion and Results: The total phenolic content of olive and carob leaf extracts ranged from 5.6 to 23 mg GAE/g. The use of HPLC-ESI-MS to investigate phenolics revealed that the extracts included a variety of phenolic compounds, including 23 compounds in olive leaf extracts and 17 compounds in carob leaf extracts. In olive and carob, the major phenolic components are oleuropein and myricetin rhamnoside, respectively. Conclusion: According to our findings, olea europaea and Ceratonia siliqua appear to be rich suppliers of natural chemicals. These plants have a lot of potential in terms of medications and functional foods.

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 score0.222
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.261
Teacher spread0.233 · 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
Published2022
Admission routes1
Has abstractyes

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