An edible genipin‐based sensor for biogenic amine detection
Bibliographic record
Abstract
Abstract BACKGROUND Food is often wasted due to real or perceived concerns about preservation and shelf life. Thus, precise, accurate, and consumer‐friendly methods of indicating whether food is safe for consumers are drawing great interest. The colorimetric sensing of biogenic amines released as food degrades is a potential way of determining the quality of the food. Herein, we report the use of the fruit extract genipin, a naturally occurring iridoid that reacts selectively with primary amines, as a colorimetric sensor for biogenic amines under conditions that mimic food degradation. RESULTS Genipin was shown to react with four biogenic amines: putrescine, cadaverine, tyramine, and histamine, resulting in a dark blue dye under atmospheric conditions (λ max ~ 600 nm). In the absence of oxygen gas, this reaction provided a different response, instead yielding a red dye. Genipin was immobilized in edible calcium alginate beads and the gel beads were exposed to putrescine vapors and chicken samples. In both cases, the formation of blue dye demonstrated proof‐of‐concept that immobilized genipin can sense gaseous biogenic amines in timeframes and conditions relevant to food spoilage. CONCLUSION Genipin can act as a highly selective, qualitative sensor for both biogenic amines and oxygen gas. Since food spoilage is triggered by O 2 , the dual colorimetric response that genipin has uniquely positions it as a promising sensor for food spoilage. By immobilizing the sensor in an edible calcium alginate matrix, biogenic amine vapors were detected at low concentrations. © 2020 Society of Chemical Industry (SCI)
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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".