Technologies to Enhance Food Safety for Food Produced in Space Environments – A Review
Bibliographic record
Abstract
Food safety during space missions is a critical concern that encompasses the challenges of food preservation, preparation and consumption. Additional research and development are required to address the unique set of food safety challenges that space-based plant cultivation systems, which enable the production of fresh fruits and vegetables in situ, present. For example, in the unique conditions of space, where factors such as microgravity and limited resources redefine the parameters for food production and preservation, the risk of microbial contamination and the proliferation of pathogens present a significant challenge to astronaut health and mission success. Contaminant monitoring systems and control methods must be developed to assess the safety of extraterrestrial produced food and ensure that the presence of any microbial contaminants is eliminated. This review summarises progress towards detection technologies and control methods that could be implemented to improve the safety of food being produced in space. These approaches include diagnostic platforms for microbial profiling and detection, including genomic sequencing approaches (e.g., biomolecular sequencer, MinION, swab to sequencer platform, and PCR-based approaches) and physical control methods (e.g., thermal, irradiation, and cold plasma) currently in use on the International Space Station or being adapted for spaceflight conditions. While these diagnostic technologies and control methods are being investigated for space applications, additional research and development is likely to benefit food safety practices and protocols terrestrially.
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 | 0.003 | 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) | 0.004 | 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".