Effect of reciprocating agitation thermal processing (RA‐TP) on quality of canned tomato (<i>Solanum lycopersicum</i>) puree
Bibliographic record
Abstract
BACKGROUND: Reciprocating agitation thermal processing (RA-TP) is a recent innovation in the field of canning for obtaining high-quality canned food. The objective of this study was to compare RA-TP processing with conventional non-agitated (still) processing with respect to the impact on quality (color, antioxidant capacity, total phenols, carotenoid and lycopene contents) of canned tomato (Solanum lycopersicum) puree. RESULTS: Owing to a 63-81% reduction in process times as compared with still processing, tomato puree with a brighter red color (closer to fresh) was obtained during RA-TP. At 3 Hz reciprocation frequency, the loss of antioxidant, lycopene and carotenoid contents could be reduced to 34, 8 and 8% respectively as compared with 96, 41 and 52% respectively during still processing. In fact, the phenolic content for RA-TP at 3 Hz was 5% higher than in fresh puree. Quality retention generally increased with an increase in frequency, although the differences were less significant at higher reciprocation frequencies (between 2 and 3 Hz). CONCLUSION: Research findings indicate that RA-TP can be effective to obtain thermally processed foods with high-quality attribute retention. It can also be concluded that a very high reciprocation frequency (>3 Hz) is not necessarily needed and significant quality improvement can be obtained at lower frequencies (∼2 Hz). © 2016 Society of Chemical Industry.
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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 | 0.000 | 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".