Thermal Processing of Acidified Vegetables: Effect on Process Time-Temperature, Color and Texture
Bibliographic record
Abstract
The objective of this study was to compare the quality of low-acid vegetables conventionally thermal processed with those subjected to modified thermal processing following acidification to pH < 4.6. For conventional processing, a process lethality (Fo value) equivalent of 5 min at 121.1 °C (commercially sterilization) was used, while those that are acidified were pasteurized, such as acidic foods, to a lethality value of 10 min at 90 °C. Acidification was performed with citric acid by immersion of vegetables in an ultrasonic bath. The quality of raw, blanched, acidified, pasteurized and sterilized products were compared for color and textural characteristics. The acidified thermal processing yielded significantly better retained color and textural properties, almost similar to blanched vegetables, while those subjected to the conventional processing resulted in significant texture loss. The process temperatures were significantly lower, and corresponding process intensities were significantly less severe with the acidified thermal process, providing significant energy saving opportunities. The absorbed acid could easily be leached out by heating/holding the vegetables in tap water, if it was desired, to reduce the acidity level in the processed vegetables. There is significant current interest in acidified thermal processing of low acid- foods with quality retention being the main focus. While it is possible that some meat products may suffer quality loss, for vegetables, in general, the negative influence is significantly low, and the positive potential for quality retention, energy savings and process efficiency are very high.
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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 | 0.001 |
| 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 | 0.001 | 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".