Novel fluorescent sensor with 2,5-furandicarboxylic acid as the ligand for histamine detection
Bibliographic record
Abstract
Using effective and sustainable technologies for food quality evaluation has become imperative due to the growing concerns regarding food safety and security. Fluorescent-based sensing is a rapid monitoring technique for food spoilage detection; however, the technique employs fossil-based compounds for sensor development. Here, a fluorescent sensor for histamine (biogenic amine) as an indicator for the detection of spoilage in the fish matrix was fabricated using 2,5-furandicarboxylic acid (FDCA) as the organic ligand. FDCA is a biobased molecule and serves as a replacement for petroleum-derived terephthalic acid, which is widely used in the synthesis of fluorescent metal-organic frameworks (MOF). Herein we report a MOF that was synthesized using two organic ligands: FDCA and adenine with Fe as the metal center, denoted as Fe-AD-FDCA. The fluorescent sensor was created by covalently bonding methyl red (MR) with an NH 2 -rich Fe-AD-FDCA framework by post-synthetic modification. When exposed to histamine, the sensor showed a fluorescent emissive response that increased MR emission and produced a distinctly visible color shift from dull pink to blue. By spreading and solidifying MR@Fe-AD-FDCA in the water-phase sodium salt of carboxy methyl cellulose (CMC-Na), portable sensory hydrogels were produced. This fluorescence sensor's ability could be utilized for real-time monitoring of the freshness of raw fish samples. • A fluorescent sensor for the detection of spoilage in fish matrices was fabricated. • Fluorescent MOF was synthesized with FDCA and adenine with Fe as metal center. • The MOF sensor showed a fluorescent emissive response when exposed to histamine. • This MOF sensor could be used for real-time monitoring of the freshness of raw fish.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".