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Record W4387133915 · doi:10.3390/molecules28196852

Green Extraction of Polyphenols via Deep Eutectic Solvents and Assisted Technologies from Agri-Food By-Products

2023· review· en· W4387133915 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMolecules · 2023
Typereview
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsPolyphenolBiochemical engineeringEutectic systemBusinessSustainable productionChemistryBiotechnologyFood productsExtraction (chemistry)Food scienceComputer scienceProduction (economics)Organic chemistryEngineeringBiologyEconomicsAntioxidant

Abstract

fetched live from OpenAlex

Polyphenols are the largest group of phytochemicals with important biological properties. Their presence in conveniently available low-cost sources, such as agri-food by-products, has gained considerable attention in their recovery and further exploitation. Retrieving polyphenols in a green and sustainable way is crucial. Recently, deep eutectic solvents (DESs) have been identified as a safe and environmentally benign medium capable of extracting polyphenols efficiently. This review encompasses the current knowledge and applications of DESs and assisted technologies to extract polyphenols from agri-food by-products. Particular attention has been paid to fundamental mechanisms and potential applications in the food, cosmetic, and pharmaceutical industries. In this way, DESs and DESs-assisted with advanced techniques offer promising opportunities to recover polyphenols from agri-food by-products efficiently, contributing to a circular and sustainable economy.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.047
GPT teacher head0.283
Teacher spread0.236 · 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