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Record W2764221049 · doi:10.1080/15538362.2017.1381873

Investigation of Antioxidant Content and Capacity in Yellow European Plums

2017· article· en· W2764221049 on OpenAlexafffund
Andrea DiNardo, Jayasankar Subramanian, Ashutosh Singh

Bibliographic record

VenueInternational Journal of Fruit Science · 2017
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Agriculture, Food and Rural AffairsUniversity of Guelph
KeywordsChlorogenic acidAscorbic acidChemistryDPPHFood scienceAntioxidantNutraceuticalExtraction (chemistry)ChromatographyBiochemistry

Abstract

fetched live from OpenAlex

Phenolic content and antioxidant capacity of five Yellow European plums (Prunus domestica) were studied using heat reflux extraction. Fresh plums were extracted at 50°C and 70°C, while freeze dried plums were extracted at 50°C, 60°C, and 70°C. Quantification of phenolic compounds such as ascorbic acid, neochlorogenic acid, and chlorogenic acid, was performed using high performance liquid chromatography. Antioxidant activity was determined by evaluating the scavenging ability of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric (Fe3+) free radicals. Total phenolic content and ferric reducing antioxidant potential were highest for freeze dried samples extracted at 60°C whereas extraction at 70°C resulted in the lowest yield. Neochlorogenic acid was the predominant phenolic compound in each plum genotype followed by ascorbic acid and chlorogenic acid. This study demonstrates that there is an adequate amount of health promoting phytochemicals within European plums, hence extraction of these compounds have potential for use towards functional food, nutraceutical, and pharmaceutical industries.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.124
GPT teacher head0.308
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2017
Admission routes2
Has abstractyes

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