Quality Optimization of Canned Potatoes during Rotary Autoclaving
Bibliographic record
Abstract
Abstract The study was conducted to evaluate the optimal processing conditions for maximizing quality retention in canned potatoes during agitation thermal processing. A range of process parameters employed in this study were: retort temperature 115–125C; rotation speed 20 rpm; can headspace 10 mm; and still and agitating modes (end‐over‐end, fixed and free axial). Potatoes were prepared as 1.6 × 1.6 × 1.6‐cm cubes, filled in 307 × 409 cans, covered with 1% aqueous solution of carboxy‐methyl cellulose and the processing times were adjusted to provide an equivalent lethality (Fo value) of 10 min. During the study, selected color (L, b, h angle and ΔE) parameters and textural (hardness, gumminess and chewiness) attributes, as well as the heat penetration parameters (fh and jch) and cooking quality index (Co/Fo) were evaluated. The study indicated that potato particles processed at higher temperatures and under better agitating conditions demonstrated better quality retention. Practical Applications The food processing industries aim at achieving balance between the conducive and harmful effects of the thermal processing. Hence, for designing a thermal process, time–temperature combinations are chosen to impart required lethal effect to ensure microbiological safety and to simultaneously reduce the extent of thermal damage to color, texture and nutritional attributes. Optimization on the basis of color and texture becomes even more important because acceptance or rejection of most foods rely on the mouth feel and external appearance. Hence, in this study, the color and textural attributes were studied during retorting of potatoes and the optimal conditions for preparing canned potatoes were identified.
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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".