Development of microfluidic “lab-on-a-chip” devices to detect mycotoxin in agri-foods
Bibliographic record
Abstract
Mycotoxins, secondary metabolites of certain fungi, are highly prevalent contaminants in agri-food systems and pose various health risks to humans and animals.Monitoring the residue level of these compounds is critical for ensuring food safety.While liquid chromatography-mass spectrometry (LC-MS) remains the standard detection method due to its sensitivity and specificity, it faces limitations such as high operational costs, complex sample preparation, and the requirement of specialized equipment and expertise.These challenges emphasize the growing demand for innovative analytical tools that offer greater accessibility, efficiency, and affordability.Therefore, this PhD thesis focuses on developing microfluidic "lab-on-a-chip" devices as novel solutions to improving the monitoring of mycotoxins in agri-foods.This is a manuscript-based thesis.Chapter 1 reviews the background information related to mycotoxins and thesis-related techniques that are not fully covered in the manuscripts, wrapped up with the research hypothesis and objectives.Then, three published/submitted individual manuscripts are included in Chapters 2 to 4, corresponding to the projects proposed in the objectives.Chapter 2 reports the development of a microfluidic device to enrich and detect zearalenone using quantum dot-embedded molecularly imprinted polymers (QD@MIPs).Chapter 3 introduces repackable microfluidic molecularly imprinted solid-phase extraction coupled with mass spectrometry (μMISPE-MS) for rapid mycotoxin analysis.Chapter 4 presents a microfluidic optical aptasensor for small molecules based on analyte-tuned growth of gold nanoseeds and machine-learning-enhanced spectrum analysis, demonstrated by rapid mycotoxin detection.Chapter 5 provides more information on the three projects with additional discussion on the thesis topic.Chapter 6 summarizes the accomplishments and original contributions, vii proposes potential future work, and concludes the thesis with a full list of publications contributed during the doctoral program.The presented research covers pattern design, integration with automation, and assay innovation in the development of microfluidic devices.This thesis not only contributes to the knowledge of sensor design and prototyping but also offers promising analytical tools for practical applications in mycotoxin detection to improve food safety.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".