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Record W4292336326 · doi:10.1080/87559129.2022.2094405

Valorization of Agri-Food By-Products from Plant Sources Using Pressure-Driven Membrane Processes to Recover Value-Added Compounds: Opportunities and Challenges

2022· article· en· W4292336326 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

VenueFood Reviews International · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
Fundersnot available
KeywordsFood productsFood scienceValue (mathematics)MembraneChemistryBusinessBiochemical engineeringEnvironmental scienceBiotechnologyBiologyMathematicsEngineeringBiochemistry

Abstract

fetched live from OpenAlex

Agri-food by-products are defined as secondary products derived from food manufacturing processes. They can be of plant or animal origin. When agri-food by-products are not valorized, they regularly end up in landfills or rivers and create pollution problems. However, many of these by-products contain high value-added compounds, including carbohydrates, oligosaccharides, proteins, peptides, fibers, phenolic compounds, and isoflavones, which can be recovered. In this context, membrane processes such as microfiltration, ultrafiltration, nanofiltration, and reverse osmosis are of interest for the valorization of agri-food by-products. In this review, the advantages and limitations of membrane processes for the valorization of agrifood by-products from plant sources are discussed as well as some research avenues to reduce membrane fouling, which remains the main limitation for large-scale industrial applications.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.083
GPT teacher head0.261
Teacher spread0.178 · 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