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Record W2978143862 · doi:10.23986/afsci.77257

Occurrence of mycotoxin patulin and polyphenol profile of Nordic apple juices in relation to apple cultivation system and pre-processing storage temperature

2019· article· en· W2978143862 on OpenAlexfundno aff
Lagle Heinmaa, P. Põldma, Seyed Mahyar Mirmajlessi, H. Kaldmäe, E. Vangdal, Ulla Kidmose, Marianne G. Bertelsen, Roberto Lo Scalzo, Marta Fibiani, Ulvi Moor

Bibliographic record

VenueAgricultural and Food Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
FundersCoordination of European Transnational Research in Organic Food and Farming SystemsMinistry of Rural Affairs
KeywordsPatulinPasteurizationFood scienceMycotoxinChemistryPolyphenolAntioxidant

Abstract

fetched live from OpenAlex

The aims of this study were to find out if organic apple juice (AJ) contained higher contents of polyphenols or patulin compared to conventional AJ, and if higher storage temperature before processing increases patulin content in juice. AJ was pressed from Estonian, Danish and Norwegian apples. Additionally, three cultivars from Estonian organic and conventional orchards were stored at 3±2 °C and 9±2 °C before processing. Patulin, polyphenol content and antioxidant capacity were determined in pasteurized juices. In 2015, 33% of conventional (n=6) and 46% of organic (n=11) juices contained patulin; two of the organic juices above the legal limit (191 and 64µg l-1). In 2016, none of the AJs contained patulin. Patulin occurrence was more affected by weather conditions two weeks before harvest than by cultivation system and apple storage temperature. Polyphenol content was higher in organic than in conventional juices and was reduced at higher apple storage temperature.

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 score0.932
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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