Cinnamil- and Quinoxaline-Derivative Indicator Dyes for Detecting Volatile Amines in Fish Spoilage
Bibliographic record
Abstract
Colorimetric indicators are versatile for applications such as intelligent packaging. By interacting with food, package headspace, and/or the ambient environment, color change in these indicators can be useful for reflecting the actual quality and/or monitoring distribution history (e.g., time and temperature) of food products. In this study, indicator dyes based on cinnamil and quinoxaline derivatives were synthesized using aroma compounds commonly present in food: diacetyl, benzaldehyde, p-tolualdehyde and p-anisaldehyde. The identities of cinnamil and quinoxaline derivatives were confirmed by Fourier transform infrared (FT–IR) spectroscopy, mass spectrometry (MS), 1H nuclear magnetic resonance (NMR) and 13C NMR analyses. Photophysical evaluation showed that the orange-colored cinnamil derivatives in dimethylsulfoxide (DMSO) turned to dark brownish coloration when exposed to strong alkalis. The cinnamil and acid-doped quinoxaline derivatives were sensitive to volatile amines commonly present during the spoilage in seafood. Quinoxaline derivatives doped by strong organic acid were effective as pH indicators for volatile amine detection, with lower detection limits than cinnamil. However, cinnamil exhibited more diverse color profiles than the quinoxaline indicators when exposed to ammonia, trimethylamine, triethylamine, dimethylamine, piperidine and hydrazine. Preliminary tests of acid-doped quinoxaline derivatives on fresh fish demonstrated their potential as freshness indicators in intelligent packaging applications.
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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) | 0.001 | 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 | 0.001 | 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".