Atmospheric-cold plasma (ACP) performance on coliform inactivation, dehydration and functional properties in powder form materials (wheat flour and whey protein isolate)
Bibliographic record
Abstract
Abstract This study was conducted to determine the performance of atmospheric plasma on powder form biological materials including wheat flour (WF) and whey protein isolate (WP). Coliform bacteria inactivation and optimization were performed based on a central composite design with two variables, namely residence time and mass of the sample. The results indicated that both variables had significant effect on bacterial inactivation with more importance of residence time compared to mass of the substrate. Drying process was conducted for selected conditions including mild, moderate, and extreme conditions. The results indicated that plasma can be even used as a fast and effective tool for drying biological materials. Among all models used in this study, Henderson-Pabis model was more suitable in predicting the dehydration kinetics of both materials. Drying rate constants obtained using this model indicated that the ratios of residence time over mass of the material did not have a significant impact on this parameter. Analysis of the functional properties revealed that water absorption can be highly (≈ 70%) enhanced in WF. However, properties such as oil absorption (in WF and WP), protein solubility and emulsifying activity index (EAI) as well as stability (in WP) were slightly changed by the plasma treatment.
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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".