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Record W1941019985

Quantitative Determination of Ochratoxin a in Must

2015· article· en· W1941019985 on OpenAlexaboutno aff
Valon Durguti, Aneliya Georgieva, Angel Angelov, Zyri Bajrami

Bibliographic record

VenueAgriculture & Food · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
Fundersnot available
KeywordsOchratoxin AMycotoxinWineOchratoxinWinemakingOchratoxinsPenicilliumFood scienceChemistryHuman healthAspergillusBiotechnologyBiologyBotany
DOInot available

Abstract

fetched live from OpenAlex

For quantitative analysis of ochratoxin A in must we have used the analytical method known as HPLC-FD and previously used the immunoaffinity clean-up procedure for extraction of ochratoxin A by high immunoaffinity columns. We have determined the quantity of OTA in 30 must samples. Samples that are analyzed have been taken in the vineyards of Kosovo, concretely in southern part of Kosovo, in Rahoveci and Suhareka region. The results of all analyzed samples have been below the limit allowed by EU for ochratoxin A i.e. 2 ng/ml and as such in the future do not pose a risk to human health. The must grape represents the grape juice from which by means of certain technological processes, in particular alcoholic fermentation process created wine booze. A quantitative determination of ochratoxin A in must is very important to have in early stage of the technological process of winemaking an overview regarding the presence or not of substances dangerous to human health as is in this case the mycotoxin known as ochratoxin A. Ochratoxin A, N-[(3R)-(5-chloro-8-hydroxy-3-methyl-1-oxo-7-isochromanyl) carbonyl]-L-phenylalanine, is a mycotoxin produced by certain species of Aspergillus and Penicillium filamentous fungi. The OTA levels in must and wine depend on different factors such as the climate, the date of harvesting and different wine-making procedures. The Penicillium species that is associated with ochratoxin A production, Penicillium verrucosum, is a common storage spices and is the source of ochratoxin A in crops in the cool temperate regions such as Canada, eastern and north western Europe and parts of South America. It grows only at temperatures below 30°C and at a lower water activity. Penicillium species may produce ochratoxin at temperatures as low as 5°C (Risk Assessment Studies, http://www.cfs.gov.hk/). Aspergillus species appears to be limited to conditions of high humidity and temperature growing in the tropical and subtropical climates and is the source of contamination for coffee and cocoa beans, spices, dried vine fruit, grape juice and wine. Aspergillus ochraceus is the best known species of ochratoxin – producing Aspergillus. It grows at moderate temperatures and at a high water activity and is a significant source of ochratoxin A in cereals. It infects coffee beans usually during sun-drying causing contamination in green coffee (Risk Assessment Studies, http://www.cfs.gov.hk/). Aspergillus carbonarius is highly resistant to sunlight and survives sun-drying because of its black spores and therefore grows at high temperatures. It is associated with maturing fruits and is the source of ochratoxin A in grapes, dried vine fruits, and wine and is also another source of ochratoxin A in coffee (Risk Assessment Studies, http://www.cfs.gov.hk/) Aspergillus niger is another minor source of ochratoxin A production in infected coffee beans and dried vine fruits. The mycotoxin has been detected in various food stuffs such as dried fruits, coffee, maize, sorghum, wheat, pulses and wine (Marquardt et al, 1992; Steyn et al, 1999). Therefore, using musts with low OTA levels will be possible to produce wines with toxin levels below the limits set by the European Commission (EC) 2 μg/kg (Ponsone et al.2010).

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.043
GPT teacher head0.246
Teacher spread0.203 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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