Electrochemical quantification of Ochratoxin A in canadian grain
Bibliographic record
Abstract
The objective of this thesis is to quantitatively detect ochratoxin A (OTA) in Canadian grain by electrochemistry. To this end, two distinct sensing approaches were developed. In the first approach, a simple label-free sensor was designed to detect OTA. The oxidation mechanism of OTA was investigated using cyclic voltammetry (CV), and the impact of pH on the sensor's response to OTA was explored. Experimental design techniques were employed to optimize the analytical signal. OTA was successfully detected in wheat extracts using differential pulse voltammetry (DPV). The calculated analytical limit of detection (LOD) and limit of quantification (LOQ) were 57.2 nM and 190.6 nM, respectively. For the second approach, a gold electrode (GE) was modified with an OTA-specific aptamer to create a selective aptasensor. Methylene blue (MB) was linked to the aptamer, and the cathodic peak resulting from the reduction of MB at the aptasensor's surface was recorded as the analytical signal using square wave voltammetry (SWV). The surface packing density of the sensor was calculated to optimize the OTA aptamer concentration during sensor modification. The proposed aptasensor detected OTA in phosphate-buffered saline (PBS) media with a lowest measurable concentration (LMC) of 3.44 µM and a lowest quantifiable concentration (LQC) of 10.42 µM. The aptasensor demonstrated its capability to detect OTA in spiked wheat extracts with acceptable accuracy. The thesis concludes by comparing the two developed methods and discussing future research opportunities to enhance the performance of the aptasensor.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".