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Record W3157462444 · doi:10.3390/membranes11050322

Concentration with Nanofiltration of Red Wine Cabernet Sauvignon Produced from Conventionally and Ecologically Grown Grapes: Effect on Phenolic Compounds and Antioxidant Activity

2021· article· en· W3157462444 on OpenAlexaboutno aff
Ivana Ivić, Mirela Kopjar, Dubravko Pichler, Ivana Buljeta, Anita Pichler

Bibliographic record

VenueMembranes · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsnot available
FundersHrvatska Zaklada za Znanost
KeywordsWineNanofiltrationChemistryPolyphenolAntioxidantFlavonoidFood scienceMembraneChromatographyAnthocyaninOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the influence of different operating conditions (four pressures: 2.5, 3.5, 4.5 and 5.5 MPa; two temperature regimes: with and without cooling) and wine type on phenolic compounds retention during the nanofiltration process of two Cabernet Sauvignon red wines (conventionally and ecologically produced). The nanofiltration process was conducted on Alfa Laval LabUnit M20 with plate module and six NF M20 membranes. In initial wines and obtained retentates, total polyphenol and flavonoid contents, monomeric anthocyanins content, antioxidant activity, individual phenolic compounds and CIELab colour parameters were determined. A loss of total phenolic compounds and decrease in antioxidant activity was observed in all retentates comparing to initial wine. However, retentate cooling and higher pressure increased their retention. Besides processing parameters, individual phenolic compound retention depended on several factors, such as the wine type, chemical properties of compounds and membrane type, and their combinations. Different chemical composition of initial conventional and ecological wine influenced the retention of individual compounds.

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 score0.625
Threshold uncertainty score0.183

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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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