Photodynamic Decontamination of Food: Assessing Surface Challenges Against Listeria monocytogenes
Bibliographic record
Abstract
Listeria monocytogenes is a foodborne pathogen of significant concern. While it typically causes mild, self-limiting gastroenteritis, it poses a much higher threat to immunocompromised individuals and pregnant women, where it may lead to miscarriage. Numerous outbreaks have been linked to ready-to-eat foods. Although heat treatment is commonly used for microbial decontamination, it is unsuitable for fresh produce such as fruits and vegetables. Other physical (e.g., UV, gamma irradiation) and chemical (e.g., NaOCl, ozone) methods can compromise sensory qualities or face limited consumer acceptance. Photodynamic Inactivation (PDI) has emerged as a promising alternative, particularly when using natural photosensitizers. Because PDI efficacy depends on photosensitizer diffusion, there is a need to further explore how different and complex fruit surface structures may influence its performance. Three fruit models were therefore selected to represent distinct surface textures and were evaluated in situ: apples (smooth), strawberries (irregular), and kiwis (fuzzy and hairy surface). The influence of contamination order was also evaluated, as this factor is highly relevant to real-world supply-chain scenarios but has been largely overlooked in prior research. Additionally, the study investigated how the order of contamination affected the decontamination outcome. Sodium-magnesium-chlorophyllin (Na-Mg-Chl), an approved food additive (E140), was used as photosensitizer. Fruits were cut into 1 cm2 squares and inoculated with L. monocytogenes. A 100 µM Na-Mg-Chl solution was applied either before or after bacterial inoculation. All samples were then illuminated using a 395 nm LED (radiant exposure 15 J/cm2). When L. monocytogenes was applied first, followed by the addition of Na-Mg-Chl, a 5.96 log reduction was observed in apples, a 5.71 log reduction in strawberries, and a 6.02 log reduction in kiwis. Conversely, when Na-Mg-Chl was applied prior to bacterial deposition, apples showed a 5.61 log reduction, strawberries demonstrated a 6.34 log reduction, and kiwis achieved the highest inactivation, at 6.74 log units. These results indicate that PDI consistently achieved substantial bacterial reductions across all fruit types, regardless of surface characteristics or application order. This supports PDI as a powerful method for fruit surface decontamination, reducing public health risks and economic losses while preserving product quality and consumer confidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".