Comparison of acidified and conventional low acid thermal processing of white mushroom in glass jars as influenced by reciprocating agitation
Bibliographic record
Abstract
The objective of this study was to evaluate the quality advantage that can be gained by combining acidified low-acid food thermal processing (ALTP) (pH < 4.6) and reciprocating agitation thermal processing (RATP) of mushroom. Mushroom was selected as a low acid food which is conventionally subjected to low-acid food conventional thermal processing (LACP) (pH > 4.6). Glass jars were used as containers. LACP was established to target a commercial sterilization process (F o value of 5.0 min at 121.1 °C), while ALTP was established to get an equivalent pasteurization process (F o value of 10.0 min at 90 °C). Heat penetration data was obtained to establish the above targeted processes and to assess the influence of processing conditions on the heating rate index and heating lag factor. Once processed, the associated quality parameters were evaluated and compared. Process variables included three processing temperatures (115, 120, 125 °C for LACP and 90, 95 and 100 °C for ALTP) and three RATP conditions (0, 1.0 and 2.0 Hz agitation). The results revealed higher rates of heat penetration and lower processing times for both LACP and ALTP processes as the reciprocation frequency increased. Quality retention advantages were realized as the process temperature was increased in each case. The best advantage was delivered with ALTP. Although the rate of heat transfer was the highest at 2.0 Hz, the best quality was observed at a moderate agitation frequency (1.0 Hz) as product texture breakdown was avoided. This is the first study that combined ALTP and RATP and compared them with LACP. • Conventional and acidified thermal processing compared for mushrooms. • Effect of reciprocation agitation processing & product variable were evaluated. • A glass jar was used as the container in order to be compatible for home canning use. • Higher intensity agitation improves heat transfer, but also damages product quality. • Optimal conditions were assessed for maximizing the product quality.
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 | 0.001 |
| 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".