A comprehensive review on novel synthetic foods: Potential risk factors, detection strategies, and processing technologies
Bibliographic record
Abstract
Nowadays, the food industry is facing challenges due to the simultaneous rise in global warming, population, and food consumption. As the integration of synthetic biology and food science, novel synthetic foods have obtained high attention to address these issues. However, these novel foods may cause potential risks related to human health. Four types of novel synthetic foods, including plant-based foods, cultured meat, fermented foods, and microalgae-based foods, were reviewed in the study. The original food sources, consumer acceptance, advantages and disadvantages of these foods were discussed. Furthermore, potential risk factors, such as nutritional, biological, and chemical risk factors, associated with these foods were described and analyzed. Additionally, the current detection methods (e.g., enzyme-linked immunosorbent assay, biosensors, chromatography, polymerase chain reaction, isothermal amplification, and microfluidic technology) and processing technologies (e.g., microwave treatment, ohmic heating, steam explosion, high hydrostatic pressure, ultrasound, cold plasma, and supercritical carbon dioxide) were reviewed and discussed critically. Nonetheless, it is crucial to continue innovating and developing new detection and processing technologies to effectively evaluate these novel synthetic foods and ensure their safety. Finally, approaches to enhance the quality of these foods were briefly presented. It will provide insights into the development and management of novel synthetic foods for food 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".