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Record W3041354878 · doi:10.5539/jfr.v9n4p50

Physicochemical Properties and Antioxidant Activity of Mixed Oil of Safou (Dacryodes edulis (G. Don) H.J. Lam) from Several Trees

2020· article· en· W3041354878 on OpenAlexvenueno aff
Alain Serges Ondo-Azi, Crépin Ella Missang, Thomas Silou

Bibliographic record

VenueJournal of Food Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAfrican Botany and Ecology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDPPHChemistryAntioxidantFood sciencePeroxide valueCarotenoidAcid valueBotanyOrganic chemistryBiochemistryBiology

Abstract

fetched live from OpenAlex

The valorization of lipids can be highlighted by industrial exploitation of safou pulp, very rich in these. However, the low yield of safou trees is lacking. It’s necessary to estimate the technological potentialities of the oil obtained in order to describe the nutritional value and potential exploitation of this oil. Physical and chemical characteristics of this oil were examined. The aim of our study was to extract oil from fruits of several safou fruits and prepared a unique sample. This sample was essayed for its physicochemical properties and antioxidant activity. Results showed that the refractive index was 1.4693. The density and viscosity values were 0.9 mg/mL and 31.08 mPa/s, respectively. Acid and peroxide values were 6.17 mg KOH/g and 31.46 meq O2/kg. Gas chromatography revealed that the major fatty acids were C16:0 (44.23%), C18:1 (30.50%), and C18:2 (19.62%). Triacylglycerols were the most important lipids (88.88% of total lipids). Spectrometric assessment of color led to the remarkable presence of the peaks associated with the visible absorption of carotenoids near 530 nm and chlorophyll pigments located between 610 and 670 nm. Antioxidant activity and DPPH radical scavenging activities of safou oil were exanimate. So, oil mixtures can be used, while varietal delimitation and mix some varieties for oil industry.

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 score0.329
Threshold uncertainty score0.173

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)0.0000.000

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 head0.117
GPT teacher head0.280
Teacher spread0.163 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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