Comparative Evaluation of Different Pretreatments on Tomato Slices Dried in a Cabinet Air Drier
Bibliographic record
Abstract
Effects on drying and other acceptability parameters when slices of tomatoes were subjected to different pretreatments before initiating the process of drying were studied. The study also included observation of effects of inherent moisture levels in the drying tomato on the attributes such as color, texture, shrinkage and appearance. The pretreatments consisted of solutions in water, of sugar (1%), calcium chloride (1,2 % ), common salt (2%), ascorbic acid (0.75%) and sodium benzoate (0.1%). The slices of tomato, 1 cm thick, were soaked in the specific solutions separately for 3, 6 and 12 hours. Untreated tomato slices served as control. Drying was stopped when tomato slices attained moisture levels of 30% and 10%. Drying rate (mr = m ebx where: mr - moisture ratio; m - model constant; b - drying rate; x time, minute) ranged between -0.007 (sugar solution 1%) and -0.029 (control). Pretreatments did in fact influence the shrinkage both at 10 and 30 % moisture levels. Effect on shrinkage was more pronounced at 30 % than 10 % moisture level. Visual assessment and photographs suggest that pretreatments with calcium chloride and sodium chloride had better texture and visual appeal than other pre-drying treatments. Duration of drying to reach 10% moisture level ranged between 180.0 and 241.67 minutes. Variations were observed between samples for parameters re-hydration ratio and water activity (0.462 0.550). It is suggested that pre-drying treatments do affect the different parameters that were studied and can help in improving the visual appeal and acceptability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".