Industrial adoption of emerging food processing technologies: Insights from the Canadian agri-food sector
Bibliographic record
Abstract
This project aimed to gather practical insights into the industrial adoption of 12 emerging food processing technologies: high pressure processing, pulsed electric fields, cold plasma, foam mat drying, electrolyzed water, microwave, ohmic heating, ozone, pulsed light, supercritical fluid extraction, ultrasound, and ultraviolet light. This was achieved through an online survey conducted in the Canadian agri-food sector where ten questions were asked and a co-creation workshop with key stakeholders within the food industry were asked to prioritize the findings. The collaborative approach was designed to leverage diverse expertise to support innovation in food processing. Key findings reflect the predominance of CEOs and business owners among survey respondents, highlighting the influence of decision-makers. Small and start-up companies were the most represented across various food sectors. Notably, start-ups appeared more inclined to adopt emerging technologies, probably due to their agility and innovation-driven culture. Cold plasma, pulsed electric fields, and supercritical fluid extraction were identified as the ones requiring more science-supported data. Microwave, ozone, and ultraviolet light were seen as more mature, while ohmic heating, ultrasound, and electrolyzed water were less commonly mentioned, indicating earlier stages of adoption. Major barriers to adoption included high equipment and maintenance costs, R&D expenses, and limited government financial support. Reliable data on performance, energy use, and techno-economic analysis were deemed crucial for scaling technologies to commercial readiness. These insights can guide research, policy, and investment to support sustainable innovation in the agri-food sector.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".