<i>Aspergillus niger</i> inactivation in microwave rotary drum drying of whole garlic bulbs and effect on quality of dried garlic powder
Bibliographic record
Abstract
Microwave rotary drum drying of whole garlic bulbs was investigated for the Aspergillus niger inactivation and moisture removal. The Weibull and Bigelow models were applied to microbial inactivation data. Garlic bulbs with initial moisture content in the range 1.95–2.14 g water g−1 dry matter were dried up to 0.06 g water g−1 dry matter. The microwave power density (PD) was varied from 1.03 to 2.67 Wg−1 at 1.5 and 2.0 pulsation ratios (PRs). Effect of PD and PR on A. niger inactivation, product temperature, moisture diffusivity, moisture ratio, drying rate, color, and sensory parameters was studied. Page model was found to be a better fit for microwave rotary drying characteristics of whole garlic bulbs. Microwave rotary drum drying resulted in the average log reduction of A. niger between 1.12 and 1.60. Weibull model predicted A. niger inactivation better than the Bigelow model as it considered the nonlinearity associated with a microbial population in the sterilization process. Garlic powder prepared at 2.0 PR and 1.85 Wg−1 PD was chosen as the best process based on sensory score. The cracking and peeling of garlic cloves were observed during microwave rotary drum drying. The SEM images confirmed the increase in the pore size of the microwave treated garlic sample than the untreated garlic which might be the reason for cracking and loosening of peel in garlic.
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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".