Enzyme Inhibition-Based Determination of Pesticide Residues in Vegetable and Soil in Centrifugal Microfluidic Devices
Bibliographic record
Abstract
Pesticide residue is of concern as an environmental pollutant when present at medium to high concentrations. Such residue was quantified in both vegetable and soil samples by an enzyme inhibition technique. The multistep reactions were integrated into centrifugal microfluidic devices allowing automated simultaneous analysis of several samples or of replicates. The small sample size inherent to microfluidic devices allowed for less reagent to be used including less of the expensive enzyme which is key to this method. Liquid-solid magnetically actuated extraction, filtration, sedimentation, and detection were all integrated on the same device. Several parameters were optimized including the concentration of enzyme, substrate, chromatic agent, and reaction time. In this environmental application of centrifugal microfluidics, the percent inhibition of enzyme activity is logarithmically proportional to the demonstration pesticide concentration (in this case carbofuran). This meant that as the pesticide concentration increased in the samples, the reaction was more inhibited and the final product absorbed less light at 525 nm. Two versions of the centrifugal microfluidic devices were made. One version was designed for the analysis of vegetable samples (cabbage) and the other for the analysis of soil samples. Each version provided results that were statistically similar to the conventional benchtop method with a carbofuran limit of detection of 0.1 ppm or 0.1 μg g(-1) (5 ng absolute limit of detection).
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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".