Mobile Water Kit 2.0: A Field Deployable Solution for E. coli Detection in Potable Water
Bibliographic record
Abstract
Escherichia coli (E.coli) is an indicator organism, the presence of which in potable water suggests that the water source has been compromised. Consumption of such water would cause huge health problem and even human mortality. However, one of the key challenges for water quality monitoring is to detect such indicator organism right at the source or at the point of consumption. Current technologies rely on collecting water samples and sending samples to microbiological laboratories, which through membrane filtration and plate count, provide conclusive results, typically within 24 – 48 hrs. Such lengthy wait period is very much precarious to the communities, particularly in rural Canada, counties in US, and limited resource regions of developing economies (i.e., India, China, Brazil), where even access to a reliable water quality monitoring laboratory is questionable. In response to this global issue, we have developed a low-cost and extremely efficient test method known as the Mobile Water Kit (MWK) [Gunda et al., Analytical Methods, 6, (16), 6236-6246, 2014], which has greatly simplified the process of E.coli detection in the field, which wasn’t feasible until recently. The MWK can accurately detect extremely low concentrations of E.coli (2 CFU/100 mL) under an hour and is faster than the current conventional laboratory methods, which requires 24 to 48 hours to produce proper results. However, MWK may require a trained technician to perform the test as there are number of steps involved for water testing starting from collecting the water sample to broadcasting of test results. Moreover, MWK contains the chemical reagents in liquid form to be stored and handled in a careful and manageable way. In the present work, we resolved these two impediments by the following modifications to MWK: (a) reduced the number of steps required to perform water testing; (b) converted the liquid based system to a hydrogel impregnated chemical reagent based system (i.e., porous soft material) for ease of handling and deployment. This revised version of MWK (i.e., MWK 2.0) simplifies the testing method and make it amenable for regular field use without the help of a trained technician. In addition, the MWK 2.0 replaces syringes, filters, centrifuge tubes, pipette tips, etc., which were essential components for the original version of MWK. Moreover, MWK 2.0 allows sample concentration and detection in one single step. With MWK 2.0, we were able to detect E.coli concentrations of 4x106 CFU/mL to 4x105 CFU/mL within 5 min and 4x104 CFU/mL to 40 CFU/mL within 60 min.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".