Quality Retention Enhancement in Canned Potato and Radish Using Reciprocating Agitation Thermal Processing
Bibliographic record
Abstract
Abstract This study focusses on evaluating the effects of reciprocating agitation thermal processing (RA-TP) on the quality of canned potato cubes and whole radish packed in brine solution (1 % NaCl+1 % CaCl 2 solution). Experimental cans were subjected to RA-TP in a lab-scale steam retort at different temperatures (110–130 °C) and reciprocation frequencies (0–3 Hz). Color, texture, antioxidant activity and solids leached into the liquid were evaluated to characterize the quality of processed product. RA-TP resulted in superior quality retention in processed vegetables as compared to static retort (0 Hz) due to the associated shorter (up to 70 %) process times. In general, higher operating temperatures and reciprocation frequencies resulted in better retention of color and antioxidant activity. However, RA-TP also increased texture damage and nutrients/solids leaching, but these negative effects were milder at higher process temperatures. Therefore, high-temperature and high agitation frequency RA-TP concept with shorter process time could be effectively used for better quality retention. The optimal product quality was obtained at 130 °C retort temperature and 3.0 Hz reciprocation frequency for whole radish and at 130 °C retort temperature and 1.5 Hz reciprocation frequency for potato cubes.
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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".