Rapid Detection of Melamine in Tap Water and Milk Using Conjugated “One‐Step” Molecularly Imprinted Polymers‐Surface Enhanced Raman Spectroscopic Sensor
Bibliographic record
Abstract
An innovative "one-step" sensor conjugating molecularly imprinted polymers and surface enhanced Raman spectroscopic-active substrate (MIPs-SERS) was investigated for simultaneous extraction and determination of melamine in tap water and milk. This sensor was fabricated by integrating silver nanoparticles (AgNPs) with MIPs synthesized by bulk polymerization of melamine (template), methacrylic acid (functional monomer), ethylene glycol dimethacrylate (cross-linking agent), and 2,2'-azobisisobutyronitrile (initiator). Static and kinetic adsorption tests validated the specific affinity of MIPs-AgNPs to melamine and the rapid adsorption equilibration rate. Principal component analysis segregated SERS spectral features of tap water and milk samples with different melamine concentrations. Partial least squares regression models correlated melamine concentrations in tap water and skim milk with SERS spectral features. The limit of detection (LOD) and limit of quantification (LOQ) of melamine in tap water were determined as 0.0019 and 0.0064 mmol/L, while the LOD and LOQ were 0.0165 and 0.055 mmol/L for the determination of melamine in skim milk. However, this sensor is not ideal to quantify melamine in tap water and skim milk. By conjugating MIPs with SERS-active substrate (that is, AgNPs), reproducibility of SERS spectral features was increased, resulting in more accurate detection. The time required to determine melamine in tap water and milk were 6 and 25 min, respectively. The low LOD, LOQ, and rapid detection confirm the potential of applying this sensor for accurate and high-throughput detection of melamine in tap water and milk.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".