Towards Microbial Food Safety of Sprouts: Photodynamic Decontamination of Seeds
Bibliographic record
Abstract
The climate crisis is one of the biggest challenges for humanity in the 21st century. Production and consumption of meat contributes to global warming by causing emissions of climate-relevant gases. Freshly grown sprouts are part of an alternative, as they are less polluting but still a nutritious food. However, warm humid sprouting conditions may cause pathogenic microorganisms to thrive. Decontamination methods for raw sprouts are therefore relevant. Photodynamic Inactivation (PDI) is a novel approach that uses photoactivatable molecules (photosensitisers, PS) and visible or near-infrared light to produce reactive oxygen species (ROS). These ROS kill microorganisms by oxidative processes. Here, we test the application of PDI based on sodium-magnesium-chlorophyllin (Chl, approved as food additive E140) for photo-decontamination of mung bean, radish, and buckwheat seeds. Seeds were contaminated with Listeria innocua, serving as a model system for Listeria monocytogenes, subjected to PDI using an LED array with 395 nm and tested for remaining bacterial contamination by CFU counting. PDI based on 100 µM Chl reduces the bacterial load of mung bean and radish seeds by 99.9% (radiant exposure 56.4 J/cm2 and 28.2 J/cm2, respectively), and of buckwheat seeds by <90% reduction after illumination with 28.2 J/cm2. Neither weight nor the germination rates of seeds are affected by PDI. Interestingly, the effect of PDI on seeds is partially maintained on stored sprouts after germination: The bacterial load on mung bean sprouts is reduced by more than 99.9% after phototreatment of seeds with 100 µM Chl and illumination at 56.4 J/cm2. In conclusion, we suggest PDI based on Chl as an effective and biocompatible method for the decontamination of seeds and sprouts for human consumption from Listeria.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".