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Record W2236205003

Ionizing irradiation post-harvest processing of chestnuts: effects of gamma and e-beam technologies on physico-chemical parameters

2012· article· en· W2236205003 on OpenAlexaboutno aff
Amílcar L. António, Elsa Ramalhosa, Márcio Carocho, Albino Bento, Iwona Kałuska, B. Quintana, M. Luísa Botelho, Isabel C.F.R. Ferreira

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2012
Typearticle
Languageen
FieldNursing
TopicNuts composition and effects
Canadian institutionsnot available
Fundersnot available
KeywordsIrradiationIonizing radiationRadiochemistryNuclear scienceBeam (structure)Nuclear physicsNuclear engineeringEngineeringPolitical scienceEnvironmental scienceEngineering physicsLibrary scienceMaterials sciencePhysicsChemistryComputer scienceOptics
DOInot available

Abstract

fetched live from OpenAlex

Chestnut fruit must be postharvest treated to meet the international fitossanitary regulations during exportation. Chemical fumigation with methyl bromide was the most common practice for elimination of insects in those fruits. Nevertheless, it is a toxic product for the operators and was recently banned by the european legislation (March 2010), following the international recommendations of Montreal Protocol on ozone depleting substances. Therefore, it becomes essential to find alternative preservation methodologies. Irradiation might be a good alternative; its use by several industries on different food products could confirm the viability of such treatment in chestnut fruits. The effects of storage time (0 and 30 days at 4 oC) and irradiation dose (gamma and e-beam) up to 3 kGy on physico-chemical parameters were evaluated. Those parameters included colour, texture, moisture, nutritional value, sugars, fatty acids and tocopherols [1-3]. After analysis of the results, it was observed that irradiation at up to 3 kGy did not affect the mentioned parameters, being more relevant the effects of storage time. Overall, the irradiation might be a promising alternative for post-harvest chestnuts processing, without altering the main physico-chemical characteristics. References [1] Antonio et al. Food Chem. Toxicol., 49 (2011) 1918-1923. [2] Fernandes et al. Food Chem. Toxicol. 49 (2011) 2429-2432. [3] Fernandes et al. J. Agric. Food Chem. 2011, 59, 10028–10033.

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 score0.004

Distilled classifier scores by category (both heads)

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 head0.046
GPT teacher head0.360
Teacher spread0.314 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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