Hydroxypropyl Guar/Cellulose Nanocrystal Film with Ionic Liquid and Anthocyanin for Real-Time and Visual Detection of NH<sub>3</sub>
Bibliographic record
Abstract
Simple, fast, and accurate detection of food freshness is of considerable significance to ensure food safety. The pH values of foods can be good indicators of their freshness, which can be used for real-time detection of food quality. Herein, we fabricated a pH-sensing film for flexible smart labels based on hydroxypropyl guar (HPG), cellulose nanocrystal (CNC), 1-butyl-3-methylimidazolium chloride (BmimCl), a kind of ionic liquid (IL), and anthocyanin (Anth). We investigated the structure, optical properties, and mechanical strength of the composite films. These films can be used to detect ammonia (NH 3 ) generated from seafood during storage by pH-sensing capability and monitor the real-time freshness of seafood. The HPG/CNC/IL/Anth film exhibited several advantages, such as wide color-change range, easy identification, high sensitivity, good reversibility, excellent stability, and low detection limit. The pH-sensing films can function as flexible smart labels for real-time, visual, and accurate detection of food freshness in cold chain logistics and other fields.
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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 | 0.000 | 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".