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Record W2332933510 · doi:10.1021/jf502824f

Deacidification of Cranberry Juice by Electrodialysis with Bipolar Membranes

2014· article· en· W2332933510 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2014
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrodialysisChemistryCRANBERRY JUICEChromatographyMembraneCitric acidLimitingMalic acidFood scienceBiochemistry

Abstract

fetched live from OpenAlex

Cranberry is recognized for its many benefits on human health; however, its high acidity may be a limiting factor for its consumption. This study aimed to investigate the deacidification of cranberry juice using a two simultaneous step electrodialysis with bipolar membranes (EDBM) process. In step 1 (deacidification), during the 6 h treatment, the pH of the juice increased from 2.47 to 2.71 and a deacidification rate of 22.84% was obtained, whereas in step 2 (pH lowering) the pH of juice 2 was almost stable. Citric, quinic, and malic acid were extracted with a maximum of 25% and were mainly transferred to the KCl 2 fraction. A significant loss of anthocyanins in juice 2 (step 2) was observed, due to their oxidation by oxygen incorporated by the centrifugal pump. This also affected its coloration. The first step of the EDBM process was successful for cranberry juice deacidification and could be improved by increasing the number of membranes stacked.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.264

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 head0.003
GPT teacher head0.169
Teacher spread0.165 · 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