A Mini Review: Key Applications and Advances of Photodynamic Inactivation Against Bacteria in the Food Industry
Bibliographic record
Abstract
ABSTRACT With the increasingly severe global food safety issues, food‐borne pathogenic bacteria pose a significant threat. The traditional thermal inactivation approach has drawbacks such as high energy consumption and potential damage to food quality. Moreover, the advantages of current non‐thermal inactivation techniques need to be further enhanced and extended. Photodynamic inactivation (PDI), an innovative non‐thermal inactivation approach, has garnered significant interest in the realm of food preservation owing to its eco‐friendly nature and remarkable antimicrobial efficacy. This paper aims to discuss the applications of PDI technology in the food antibacterial field. It encompasses the capacity to eliminate planktonic bacteria and biofilms, along with its potential for application in antibacterial packaging films. Furthermore, this study systematically examines the utilization of various natural photosensitizers within PDI systems, analyzes their distinct antimicrobial activities against both Gram‐positive and Gram‐negative bacterial strains, and evaluates innovative approaches to optimize the bactericidal performance of PDI technology. Overall, PDI technology exhibits a broad‐spectrum antibacterial effect. However, its inactivation potency against Gram‐negative bacteria and biofilms is relatively weak. Through measures such as optimizing photosensitizer performance, adding adjuvants, and adjusting the reaction environment, the inactivation efficiency of PDI technology can be notably enhanced. This comprehensive review aims to provide groundbreaking perspectives and strategic guidance for the implementation of PDI technology in food safety applications, as well as to inspire the advancement of next‐generation antimicrobial solutions. Significantly, this contributes to effectively addressing food microbial contamination issues and safeguarding food safety. Moreover, it holds promise for promoting the widespread application and in‐depth development of PDI technology in related fields like food packaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".