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Record W2138326612

Osmotically dehydrated microwave vacuum drying of carrots

2010· article· en· W2138326612 on OpenAlexaffabout
Valérie Orsat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsMcGill University
Fundersnot available
KeywordsOsmotic dehydrationThermal diffusivityMicrowaveShrinkageChemistryDehydrationWater contentMoistureVacuum dryingWater activityFood scienceFreeze-dryingMaterials scienceChromatographyComposite materialThermodynamicsBiochemistryPhysics
DOInot available

Abstract

fetched live from OpenAlex

Changrue, V. and V. Orsat. 2009. Osmotically dehydrated microwave vacuum drying of carrots. Canadian Biosystemes Engineering/Le genie des biosystemes au Canada. 51: 3.11 3.19 Osmotic dehydration prior to drying was able to remove free water, which accounts for around 50% of the product’s moisture. The combination of osmotic and microwave vacuum drying was investigated. The advantage of microwave vacuum drying is that it provides faster drying times with a low-temperature process. Since solid gain from osmotic agents might cause a decrease in diffusivity of the osmotically dehydrated product and lower qualities of dried product, it is important to know the effects of osmotic treatment prior to microwave vacuum drying. Two levels of microwave input power (1 and 1.5W/g) and three power modes (continuous, 45 s on/15 s off and 30 s on/30 s off) were studied at an absolute pressure of 8 kPa for the microwave vacuum drying of carrots. Drying kinetics, energy consumption, and quality in terms of water activity, shrinkage, rehydration capacity, color characteristics, and sensory evaluation were studied. Empirical models were established to fit the observed data. In general, osmotic dehydration was able to decrease drying time and energy consumption. Less shrinkage and improved appearance were the advantages in terms of quality. Page’s model showed the best fit among the tested models.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.289

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 head0.198
Teacher spread0.186 · 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

Citations11
Published2010
Admission routes2
Has abstractyes

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