Effect of the mechanical harvest of drupes on the quality characteristics of green fermented table olives
Bibliographic record
Abstract
BACKGROUND: Because of damage caused by mechanical harvesting, the drupes for table olive production are traditionally hand harvested. Until now, no data have been available on the microbiological and chemical features of mechanically harvested drupes during fermentation. RESULTS: Drupes mechanically harvested and inoculated with Lactobacillus pentosus OM13 were characterized by the lowest concentrations of potential spoilage microorganisms. On the other hand, drupes mechanically harvested and subjected to spontaneous fermentation showed the highest concentration of Enterobacteriaceae and pseudomonads during transformation. The lowest decrease of pH (4.20) was registered for the trials inoculated with the starter culture. Differences in terms of volatile organic compounds were estimated among trials. Multivariate analysis showed that the olives processed from the drupes mechanically harvested and inoculated with starter were closely related to control production (drupes manually harvested) in terms of microbiological and pH values. Sensory analysis evidenced negative evaluations only for the uninoculated trials. CONCLUSION: Drupes mechanically harvested and subjected to a driven fermentation with Lactobacillus pentosus OM13 determined the production of table olives with appreciable organoleptic features. Thus mechanical harvesting performed using a trunk shaker equipped with an inverse umbrella and the addition of starter lactic acid bacteria represents a valuable alternative to manual harvesting for table olive production at the industrial level.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".