Mixture design methodology and predictive modeling for developing active formulations using essential oils and citrus extract against foodborne pathogens and spoilage microorganisms in rice
Bibliographic record
Abstract
The antibacterial and antifungal effects of six plant-derived essential oils (EOs) and two types of citrus extracts (CEs) were studied against two pathogenic bacteria (Salmonella Typhimurium and Escherichia coli O157:H7) and three fungi (Aspergillus niger, Penicillium chrysogenum, and Mucor circinelloides). A broth microdilution assay and checkerboard method were used to measure the minimal inhibitory concentration (MIC) of each extract and the possible interactions between them. The MIC assay showed that cinnamon EO, Mediterranean EO, Southern formulation, citrus EO, organic citrus extract (OCE), and natural citrus extract (NCE) had the highest antimicrobial and antifungal activity. The checkerboard method showed that the Mediterranean EO+OCE combination acted in synergy against all tested pathogens. A centroid mixture design was used to develop active formulations by predicting optimal concentrations of EO/CEs for increased antibacterial/antifungal activity. A mixture of four formulations (625 ppm OCE, 313 ppm Mediterranean EO, 625 ppm citrus EO, and 313 ppm cinnamon EO) named as active formulation 1, and the mixture from five formulations (625 ppm NCE, 625 ppm Asian formulation, 313 ppm Southern formulation, 625 ppm cinnamon EO, and 313 ppm savory thyme EO) named as active formulation 2, were formulated and tested because of their high microbicidal effectiveness. In situ tests with rice showed a significant reduction (P ≤ 0.05) of all tested pathogenic bacteria and fungi from the vapor of active formulations 1 and 2 after 28 days of storage. PRACTICAL APPLICATION: Active formulations (essential oils and citrus extracts) developed in the study are highly effective against foodborne pathogens. Active formulations in this study could be used as natural preservatives in the food industry for controlling foodborne diseases and spoilage organisms in stored foods.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".