Optimization of Process Parameters for Foam-Mat Drying of Peaches
Bibliographic record
Abstract
Peach (Prunus persica) is a highly perishable fruit with short shelf-life and susceptible to mechanical damage during harvest and post-harvest operations such as sorting, processing, packaging, and transport. Therefore, converting peaches into dehydrated products will not only reduce their post-harvest losses but also retain their nutritional and sensory qualities. The current study aimed at the optimization of process parameters of foam-mat drying for the production of peach powder from the peaches grown in Ontario, Canada. The operating parameters of foam-mat dryer such as temperatures (65°C, 70°C, and 75°C), foam thickness (3, 5, and 7 mm) and the concentration of foaming agents (soy and pea protein isolates (0.5% w/w, 1% w/w, and 1.5% w/w), were optimized using response surface methodology. The resulting peach powder obtained after drying was assessed for moisture, color, total phenols, antioxidant activity, microstructure, and thermal properties. It was observed that drying time increased with an increase in foam thickness and decreased with an increase in temperature and foaming agent concentrations. The optimum drying rate obtained for both proteins based foams was observed at 65°C. Physico-chemical analysis of peach powder showed significantly higher retention of bioactive components such as total phenols and antioxidants in foam mat dried peach powder containing PPI as the foaming agent. The differential scanning calorimetry thermogram for both protein isolates suggested a denaturation temperature of proteins was around 85°C. No significant difference was observed in the morphological structure of powders obtained by using both protein isolates as foaming agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".