Photodynamic Inactivation of Foodborne Bacteria: Screening of 32 Potential Photosensitizers
Bibliographic record
Abstract
The development of novel antimicrobial technologies for the food industry represents an important strategy to improve food safety. Antimicrobial photodynamic disinfection (aPDD) is a method that can inactivate microbes without the use of harsh chemicals. aPDD involves the administration of a non-toxic, light-sensitive substance, known as a photosensitizer, followed by exposure to visible light at a specific wavelength. The objective of this study was to screen the antimicrobial photodynamic efficacy of 32 food-safe pigments tested as candidate photosensitizers (PSs) against pathogenic and food-spoilage bacterial suspensions as well as biofilms grown on relevant food contact surfaces. This screening evaluated the minimum bactericidal concentration (MBC), minimum biofilm eradication concentration (MBEC), and colony forming unit (CFU) reduction against Salmonella enterica, methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas fragi, and Brochothrix thermosphacta. Based on multiple characteristics, including solubility and the ability to reduce the biofilms by at least 3 log10 CFU/sample, 4 out of the 32 PSs were selected for further optimization against S. enterica and MRSA, including sunset yellow, curcumin, riboflavin-5′-phosphate (R-5-P), and erythrosin B. Optimized factors included the PS concentration, irradiance, and time of light exposure. Finally, 0.1% w/v R-5-P, irradiated with a 445 nm LED at 55.5 J/cm2, yielded a “max kill” (upwards of 3 to 7 log10 CFU/sample) against S. enterica and MRSA biofilms grown on metallic food contact surfaces, proving its potential for industrial applications. Overall, the aPDD method shows substantial promise as an alternative to existing disinfection technologies used in the food processing industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".