Influence of Hydrogen Introduced Drying Atmosphere on Drying Kinetics, Phenolic Profile, and Rehydration Behavior of Tomato Slices
Bibliographic record
Abstract
ABSTRACT The current study performed drying of tomato slices in a drying atmosphere with hydrogen gas (RAD MIX ; a gaseous mixture of 4% hydrogen, 5% carbon dioxide and 91% nitrogen), and compared with 100% air, 100% nitrogen drying environments. All the drying experiments were carried out at 60°C. Control samples and pretreated samples with 1% Potassium metabisulfite (KMS) treated samples were dried to a final moisture content of around 13.29% to 13.70% (wet basis), respectively. The drying behavior and quality characteristics including color change (Δ E ), lycopene retention, total phenolics and rehydration ratio of the dried products were studied. The better retention of color and quality was observed in the sulfite pretreated and RAD MIX dried sample. The color change (Δ E ) was found to be 10.1, lycopene retention by 94.28% (3.3 mg/100 g), and the maximum rehydration ratio of 4.67 were observed in samples subjected to reduced atmospheric drying. Additionally, the color change was lesser for samples pretreated with KMS than the control samples. It was found that the use of hydrogen gas at 4% concentration (RAD MIX ) in the drying environment significantly impacted quality parameters of tomato slices. The sulfite pretreatment was advantageous technique in terms of moisture diffusivity, antioxidant compounds retention and rehydration ratio of tomato slices.
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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".