Process Technologies for Disinfection of Food-Contact Surfaces in the Dry Food Industry: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it