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Record W4255846816 · doi:10.7455/ijfs.v5i2.359

The influence of thermal treatments on the antioxidant activity and colour of chokeberry (aronia menocarpa) extract

2016· article· en· W4255846816 on OpenAlexfundno aff
E Cristea

Bibliographic record

VenueInternational Journal of Food Studies · 2016
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsChemistryPolyphenolAntioxidantFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Natural ingredients are an important trend in the present day food industry since manufacturers are demanding natural food dyes originating from concentrates of plant origin. However, if the use of plant extracts as technological additives is intended, their stability should be evaluated. This paper presents research on the stability of a 50% ethanolic extract of chokeberry (Aronia melanocarpa) from the Republic of Moldova. The extract was subjected to various thermal regimes, i.e. -2 oC for 12 hours; 4 oC for 12 hours; 40 oC for 15 minutes, 60 oC for 15 minutes, 80 oC for 15 minutes and 100 oC for 2 minutes, after which the antioxidant activity and the colour parameters (CIELab) were evaluated. Three sets of extracts were also stored for 2 weeks at -2 oC; 4 oC; and 25-30 oC and afterwards the parameters mentioned above were measured once again. Furthermore, total content of polyphenols was determined using the Folin-Ciocalteu method. The antioxidant activity was stable at temperatures under 0oC and temperatures as high as 100oC, as well as during storage at -2oC, 4oC and 25-30oC. High temperatures however decreased the value of luminosity, and increased the redness, the yellowness and the chroma value which reached a maximum value of 55.66 after the extract was subjected to 100oC for 2 minutes. Storage at room temperature significantly affected the luminosity of the extract by increasing its value from 42.36 to 50.22. For most treatments, the colour was stable and was not significantly modified.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.143

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 head0.031
GPT teacher head0.320
Teacher spread0.288 · 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
Published2016
Admission routes1
Has abstractyes

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