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Record W2064117181 · doi:10.2174/2212798411002020096

Patented Techniques for Detoxification of Mycotoxins in Feeds and Food Matrices

2010· review· en· W2064117181 on OpenAlexaff
Jianwei He, Ting Zhou

Bibliographic record

VenueRecent Patents on Food Nutrition & Agriculture · 2010
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMycotoxinDetoxification (alternative medicine)AflatoxinContaminationBioavailabilityFood safetyFood scienceBiotechnologyBiologyEnvironmental scienceMedicineEcology

Abstract

fetched live from OpenAlex

Mycotoxins are found worldwide in cereal grains, animal feed and human food that are produced from contaminated grains. This presents food safety risks. A variety of methods has been developed to reduce the safety risks by removing mycotoxins from contaminated grain products, eliminate the bioavailability of mycotoxins in the gastrointestinal system, degrade mycotoxins in food and feed, reduce mycotoxins pre- and post-harvest, and prevent (mycotoxin involved) plant disease. These techniques include physical methods such as flotation, extraction and adsorption, chemical reactions such as alkalization, oxidation, heat and irradiation, as well as biological detoxifications using microorganisms, enzymes and resistant plants. Most of the recent patented techniques for detoxification of mycotoxins in feeds and food matrices are discussed for an improved understanding of the patent techniques and insights into detoxification of mycotoxins.

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 categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.053
GPT teacher head0.275
Teacher spread0.222 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations39
Published2010
Admission routes1
Has abstractyes

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