Optimization of microwave-vacuum dryingprocessing parameters on the physical propertiesof dried Saskatoon berries
Bibliographic record
Abstract
Abstract The objective of this study is to optimize the microwave-vacuum drying parameters (microwave power, drying time and fruit load) on the physical properties of dried Saskatoon berries. Response surface methodology combined with central composite rotatable design was used to observe the effect of microwave-vacuum drying processing variables and optimize the drying conditions for the physical properties as response variables (moisture content, rehydration ratio, hardness, L value and total color difference) of the microwave-vacuum dried Saskatoon berries. The response variables were effectively modeled as the function of independent variables for the regression as well as response surface modeling. The regression and response surface modeling indicated that increasing microwave power and drying time and deceasing fruit load decreased moisture content but increased rehydration ratio, hardness, L value and total color difference of the microwave-vacuum dried Saskatoon berries. Also, the numerical combining with graphical optimization indicated that microwave power (5.8 – 6.5 kW), drying time (52-59 min) and fruit load (10-10.25 Kg) were very effective to improve the studied physical properties of the dried Saskatoon berries. The findings are expected to be helpful for the process development of commercial scale microwave-vacuum drying of Saskatoon berries within the experimental range.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".