A Covalently Anchored Biobased Nanofiber Label for Self-Reporting Fish Spoilage with High Stability and Radiometric Visibility
Bibliographic record
Abstract
The development of efficient and reliable strategies for real-time monitoring of seafood freshness is crucial for ensuring food safety and reducing waste. Herein, a novel biobased colorimetric film was designed and fabricated for visual and intelligent detection of fish spoilage. A pH-responsive chromogenic monomer (TGI) was first synthesized from tributyl citrate and then covalently incorporated into polyester-based polyurethane (PCCU) to prevent dye leaching and enhance stability. Experiments demonstrate that TGI enhances the spinnability of PCCU. The resulting PCCU-TGI nanofiber membrane exhibits a high specific surface area (SSA = 12.65 m 2 /g) and porous structure ( V total = 24.28 cm 3 /g), enabling rapid gas permeation and responsiveness. The mechanistic studies indicate that under the influence of total volatile organic compounds (TVB-N), the alkaline hydrolysis reaction of TGI ester groups expands the π-conjugated system and reduces the bandgap of excited electrons. This enables the colorimetric film to undergo a color change linearly correlated with the degree of spoilage under natural light through intrinsic means. The optimized film (PCCU-9%TGI) exhibited a highly linear correlation (R 2 > 0.93) between the color difference (ΔE) and the TVB-N content of cod fillets during storage at both 25 and 4 °C. Furthermore, an electronic eye system was integrated for real-time color capture and data processing, which successfully demonstrated remote, quantitative spoilage monitoring and alert capabilities. This work provides a robust and feasible platform combining smart material design with intelligent sensing technology for next-generation food packaging applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".