Drying based on temperature‐detection‐assisted control in microwave‐assisted pulse‐spouted vacuum drying
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".