Biosensors for Sustainable Food Engineering: Challenges and Perspectives
Bibliographic record
Abstract
Current food production faces tremendous challenges from growing human population, maintaining clean resources and food qualities, and protecting climate and environment. Food sustainability is mostly a cooperative effort resulting in technology development supported by both governments and enterprises. Multiple attempts have been promoted in tackling challenges and enhancing drivers in food production. Biosensors and biosensing technologies with their applications, are being widely applied to tackling top challenges in food production and its sustainability. Consequently, a growing demand in biosensing technologies exists in food sustainability. Microfluidics represents a technological system integrating multiple technologies. Nanomaterials, with its technology in biosensing, is thought to be the most promising tool in dealing with health, energy, and environmental issues closely related to world populations. The demand of point of care (POC) technologies in this area focus on rapid, simple, accurate, portable, and low-cost analytical instruments. This review provides current viewpoints from the literature on biosensing in food production, food processing, safety and security, food packaging and supply chain, food waste processing, food quality assurance, and food engineering. The current understanding of progress, solution, and future challenges, as well as the commercialization of biosensors are summarized.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".