Molecularly imprinted polymers-based colorimetric-SERS dual biosensor for the detection of atrazine in apple juice
Bibliographic record
Abstract
Atrazine is a harmful herbicide that can disrupt the hormonal system in humans and animals. Contamination of atrazine in various agri-food products occurs due to illegal uses. Detection of atrazine in foods is time consuming and expensive when the official methods such as gas chromatography-mass spectrometry are applied. In this thesis project, we developed a dual biosensor integrating molecularly imprinted polymers (MIPs) with gold nanoparticles (AuNPs)-based colorimetric assay and surface enhanced Raman spectroscopy (SERS), which can be applied for rapid, high-throughput and sensitive determination of trace levels of atrazine in agri-food products (e.g., apple juice). This biosensor includes three functions: separation, screening, and quantification. For separation, MIPs were synthesized using molecular imprinting technology by employing atrazine as the template molecule. MIPs-based solid phase extraction (MIPs-SPE) could selectively separate atrazine from apple juice with high recoveries (~93%). AuNPs-based colorimetric assays were able to rapidly detect atrazine due to ligands between atrazine molecules and the surface of AuNPs. High-throughput screening of a large number of samples could be achieved with simple color variation with the limit of detection (LOD) as low as 0.01 mg L-¹. For quantification, SERS tests were conducted using AuNPs for Raman spectral collection. Raman spectra of apple juice samples with different concentrations of atrazine were rapidly collected and analyzed by chemometrics. The calculated LOD equals to 0.0012 mg L-¹ and limit of quantification (LOQ) was 0.0040 mg L-¹, both of which meet the guidelines set up by Health Canada (i.e., 0.005 mg L-¹). Three types of AuNPs with different diameters (i.e., 43 nm, 27 nm, 11 nm) were synthesized and compared for the use of this biosensor. The largest AuNPs worked best for colorimetric assays while the medium-size AuNPs were the most suitable candidate for SERS tests. The extremely low LOD and LOQ strongly validate the potential application of this innovative dual biosensor for accurate and high-throughput determination of atrazine in foods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".