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Record W2523290305 · doi:10.1002/jsfa.8040

Drying based on temperature‐detection‐assisted control in microwave‐assisted pulse‐spouted vacuum drying

2016· article· en· W2523290305 on OpenAlexaff
Xiaohuang Cao, Min Zhang, He Qian, Arun S. Mujumdar

Bibliographic record

VenueJournal of the Science of Food and Agriculture · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Drying and Modeling
Canadian institutionsMcGill University
Fundersnot available
KeywordsTemperature controlMicrowaveVacuum dryingMaterials scienceAnalytical Chemistry (journal)Process engineeringChemistryChromatographyFreeze-dryingThermodynamicsComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: An online temperature-detection-assisted control system of microwave-assisted pulse-spouted vacuum drying was newly developed. By using this system, temperature control can be automatically and continuously adjusted based on the detection of drying temperature and preset temperature. Various strategies for constant temperature control, linear temperature control and three-step temperature control were applied to drying carrot cubes. Drying kinetics and the quality of various temperature-controlled strategies online are evaluated for the new drying technology as well as its suitability as an alternative drying method. RESULTS: Drying time in 70 °C mode 1 had the shortest drying time and lowest energy consumption in all modes. A suitable colour, highest re-hydration ratio and fracture-hardness, and longest drying time occurred in 30-40-50 °C mode 3. The number of hot spots was reduced in 40-50-60 °C mode 3. Acceptable carrot snacks were obtained in 50-60-70 °C mode 3 and 70 °C mode 2. All temperature curves showed that the actual temperatures followed the preset temperatures appropriately. CONCLUSIONS: With this system, a linear temperature-controlled strategy and a three-step temperature-controlled strategy can improve product quality and heating non-uniformity compared to constant temperature control, but need greater energy consumption and longer drying time. A temperature-detection-assisted control system was developed for providing various drying strategies as a suitable alternative in making a snack product. © 2016 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)0.001
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 score0.129
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.199
Teacher spread0.187 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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