Physical, chemical, and toxicological characterization of sulfated cellulose nanocrystals for food-related applications using <i>in vivo</i> and <i>in vitro</i> strategies
Bibliographic record
Abstract
Abstract Cellulose nanocrystals (CNCs) are a next-generation cellulose product with many unique properties including applications in the food industry as a food additive, food coating, and in food-contact packaging material. While CNC is anticipated to be safe due to its similarity to the many forms of cellulose currently used as food additives, special consideration is given to it as it is the first manufactured form of cellulose that is nanoscale in both length and width. A proactive approach to safety has been adopted by manufacturers to demonstrate CNC safety toward responsible commercialization. As part of the safety demonstration, in vivo and in vitro testing strategies were commissioned side-by-side with conventional cellulose, which has been safely used in food for decades. Testing included a 90-day rodent feeding study as well as additional physical, chemical, and biological studies in vitro that follow European Food Safety Authority (EFSA) guidance to demonstrate the safe use of novel food ingredients. The strategy includes assessment of neat materials side-by-side with simulated digestion, mimicking conditions that occur along the gastrointestinal tract as well as intracellularly. An intestinal co-culture model examined any potential toxicological effects from exposure to either pristine or digested forms of CNC including cytotoxicity, metabolic activity, membrane permeability, oxidative stress, and proinflammatory responses. None of the studies demonstrated any toxicity via oral or simulated oral exposure. These studies demonstrate that CNC produced by InnoTech Alberta is similarly safe by ingestion as conventional cellulose with a no-observed-adverse-effect level of 2085.3 (males) and 2682.8 (females) mg/kg/day.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".