Development and evaluation of an immunoassay for the quantification of N-acetylneuraminic acid (Neu5Ac) in foods and biosamples
Bibliographic record
Abstract
N -acetylneuraminic acid is an active ingredient in tonic foods and an important additive in foods and biopharmaceuticals. To address the limitations of existing methods of N -acetylneuraminic acid quantification, we developed an immunoassay based on antibodies induced in hens using artificial antigen, showing high sensitivity and specificity with no cross-reactivity with eight N -acetylneuraminic acid analogues. An IgY-based indirect competitive enzyme-linked immunosorbent assay showed a detection range of 1.14 to 70.08 ng/mL and a limit of detection of 0.57 ng/mL. In spiked samples, recoveries by the indirect competitive enzyme-linked immunosorbent assay ranged from 74.05% to 110.87% compared with HPLC (73.01% to 108.8%). Consistency between the indirect competitive enzyme-linked immunosorbent assay and HPLC was satisfactory (R 2 = 0.9736), demonstrating this established immunoassay as a rapid and reliable approach for N -acetylneuraminic acid analysis. The assay described in this study provides an important method for the screening of N -acetylneuraminic acid in biological samples and foodstuffs. • An IgY-based ic-ELISA for the analysis of Neu5Ac was developed for the first time. • This immunoassay exhibited no significant cross-reactivity with other Neu5Ac analogs. • The linear range was 1.14–70.08 ng/mL with recoveries ranging from 74.05% to 110.87%. • The assay detected Neu5Ac in food and clinical samples. • Immunoassay is a promising approach to analyse SA for broad biomedical purposes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".