Effect of Hot Water Treatments on Quality of Highbush Blueberries
Bibliographic record
Abstract
Highbush blueberries, cv 'Burlington', were treated with 22, 45, 50, or 60 degrees C water for 15 or 30 s along with an untreated control. Fruit were then stored for 0, 1, 2, or 4 wk at 0 degrees C and 2 or 9 d at 20 degrees C prior to evaluation of microbial population and fruit quality. After 4 wk of storage, the hot water treatment at 60 degrees C resulted in 92% marketable berries, followed by 90% at 50 degrees C, 88% at 45 degrees C, and 83% at 22 degrees C compared with 76% in untreated controls. Decay incidence was reduced to 0.6%, 1.2%, 1.4%, or 2.8% with 60, 50, 45, or 22 degrees C water treatments, respectively, compared with 5.1% in controls following 4 wk at 0 degrees C and 2 d at 20 degrees C. After an additional 7 d at 20 degrees C, decay in fruit treated at 60 degrees C for 15 or 30 s remained at 1.8% and 0.4%, respectively, compared to 37.4% in controls. Weight loss of berries treated with hot water was 0.4% against 3.8% in controls, and shriveled and split berries were also reduced compared to controls (P<0.001). Aerobic plate count and yeast and mold count were reduced by 0.45 to 0.7 log at 60 degrees C for 30 s. Botrytis cinerea and Colletotrichum sp. were the dominant fungal pathogens causing decay of Burlington blueberries during storage. Hot water treatments also immediately induced an increase in ethanol and reduced fruit titratable acidity and soluble solids content, but had no significant effect on fruit firmness, pH, or most flavor volatile concentrations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".