Development of novel molecularly imprinting chemosensors for the detection of chemical hazards in foods
Bibliographic record
Abstract
Food chemical contamination remains one of the burdens to public health and may cause significant economic loss to agri-food industry and healthcare. Rapid, selective and sensitive determination of chemical contaminants in foods is of great importance to control food quality and ensure food safety. Traditional sample pretreatment methods have limited selectivity to the analytes and detection techniques (e.g., liquid chromatography-based methods) are usually labor-intensive, time-consuming, and even lack the specificity and/or sensitivity. Therefore, the overall objective of my Ph.D. thesis project was to investigate different chemosensors combining selective molecularly imprinted polymers (MIPs) with either surface enhanced Raman spectroscopy (SERS) or fluorescent quantum dots (QDs) to determine chemical hazards in different food matrices. Firstly, a MIPs-SERS/colorimetric dual sensor was developed using MIPs-packed solid phase extraction cartridge to isolate chlorpyrifos from apple juice. Silver nanoparticle (AgNPs) was applied for both colorimetric reaction for rapidly screening and semi-quantification of chlorpyrifos with concentrations ≥5 µg/mL by naked eye and SERS analysis for sensitive determination with a limit of detection (LOD) of 0.01 µg/mL based on aggregated AgNPs induced by chlorpyrifos. To further simplify SERS measurement, MIPs was wrapped on the surface of gold nanoparticles (AuNPs) as a dual functional nanomaterial (i.e., AuNPs@MIPs). A representative herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) was selectively separated from milk by AuNPs@MIPs. Without analyte elution step, SERS measurement was directly conducted on AuNPs@MIPs and a LOD of 0.011 µg/mL was achieved for sensing 2,4-D. Furthermore, a quantum dots (QDs) based fluorescence sensor was developed to determinate enrofloxacin in swine muscle. Taking advantage of fluorescence quenching effect of QDs, enrofloxacin was sensitively detected with a LOD of 0.059 µg/mL. An inertial separation microfluidic chip was designed and fabricated to integrate sample pretreatment, analyte separation and detection on the chip. The separation efficiency of the developed microfluidic chip for QDs@MIPs microparticles was as high as 97.7±1.6%. Taken together, MIPs were validated to be robust materials for selective separation and analysis of food chemical hazards. Combining MIPs with different detection tools has great potential for determination of chemical hazards in complex agri-food products.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".