Study of Heat Treatment in Processing of Pumpkin Puree (Cucurbita moschata)
Bibliographic record
Abstract
Pumpkins have richness of nutrients and in puree form may contribute to their greater appreciation in the vegetable processing industry. However, studies are necessary for its processing, since it is part of the group of risk of microbial contamination (pH > 4.5). Thus, this work aimed to study the pumpkin puree processing by heat treatment in autoclave wherein the product was subjected to sterilization by autoclaving at 121 °C to check their microbial load and physicochemical characteristics and to identify the volatile compounds of headspace in stand up pouch packaging. The heat treatment in autoclave for 10, 20 and 30 minutes was effective in eliminating microbial load of the sample (< 1.0 × 101) and also induced the decrease of pH and increase of titratable acidity (TA), total soluble solids (TSS) and total carotenoids. The compounds identified in the headspace after autoclaving were alcohols, aldehydes, alkenes, ketones, esters, ethers and terpenes, many of them aromatic compounds that characterize pumpkin and carotenoid degradation products and acids that justify physicochemical changes after processing.
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) | 0.000 | 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".