Process Technologies for Disinfection of Food-Contact Surfaces in the Dry Food Industry: A Review
Bibliographic record
Abstract
The survival characteristics of bacterial pathogens, including Salmonella spp., Listeria monocytogenes, Staphylococcus aureus, and Escherichia coli, in foods with a low water activity (aw) have been extensively examined and reported. Microbial attachment on the food-contact surfaces can result in cross-contamination and compromise the safety of low-aw foods. The bactericidal potential of various conventional and novel disinfection technologies has been explored in the dry food industry. However, the attachment behavior of bacterial pathogens to food-contact surfaces in low-aw conditions and their subsequent response to the cleaning and disinfection practices requires further elucidation. The review summarizes the elements that influence disinfection, such as the presence of organic residues, persistent strains, and the possibility of microbial biotransfer. This review explores in detail the selected dry disinfection technologies, including superheated steam, fumigation, alcohol-based disinfectants, UV radiation, and cold plasma, that can be used in the dry food industry. The review also highlights the use of several wet disinfection technologies employing chemical antimicrobial agents against surface-dried microorganisms on food-contact surfaces. In addition, the disinfection efficacy of conventional and novel technologies against surface-dried microorganisms on food-contact surfaces, as well as their advantages and disadvantages and underlying mechanisms, are discussed. Dry food processing facilities should implement stringent disinfection procedures to ensure food safety. Environmental monitoring procedures and management techniques are essential to prevent adhesion and allow the subsequent inactivation of microorganisms.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".