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Record W2178388699 · doi:10.1002/jsfa.7536

Recycling of osmotic solutions in microwave–osmotic dehydration: product quality and potential for creation of a novel product

2015· article· en· W2178388699 on OpenAlexafffund
Derek Wray, Hosahalli S. Ramaswamy

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsOsmotic dehydrationChemistryDehydrationLeaching (pedology)Pulp and paper industryProcess engineeringMicrowaveBy-productFood scienceEnvironmental scienceComputer scienceBiochemistryEngineeringSoil waterSoil science

Abstract

fetched live from OpenAlex

BACKGROUND: Despite osmotic dehydration being a cost effective process for moisture removal, the cost implications of making, regenerating, and properly disposing of the spent osmotic solutions contributes greatly to the economic feasibility of the drying operation. The potential for recycling of osmotic solutions and their use for creation of a novel product was explored using microwave-osmotic dehydration under continuous flow spray (MWODS) conditions. RESULTS: Identical runs were repeated 10 times to determine the progressive physical and compositional effects of the thermal treatment and leaching from the cranberry samples. The microbiological stability and constant drying performance indicated that MWODS would be well suited for employing recycled solutions. While the anthocyanin content of the solution never approached that of cranberry juice concentrate, it is demonstrated that the spent syrup can infuse these health positive components into another product (apple). CONCLUSION: This study found that re-using osmotic solutions is a viable option to reduce cost in future MWODS applications, with no detriment to product quality and potential to use the spent solution for novel products. © 2015 Society of Chemical Industry.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.191
Threshold uncertainty score0.098

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.082
GPT teacher head0.281
Teacher spread0.199 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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