2026 Calibration
Lume 1.2 – Method Calibration
Paired calibration of the Lume 1.2 sensor against two EPA-approved reference methods — Colilert (IDEXX defined-substrate, MPN) and membrane filtration (MF, CFU) — and the Aquagenx Compartment Bag Test (CBT, MPN) used in international monitoring, for E. coli and total coliform quantification.
Field Validation
—Field Records
—Sensor Sites
—Latest Sample
Lab Validation
—Lab Records
—Sensors Tested
Field Calibration Data
Live data from the mWater Lume 1.2 – 2026 Validation Data datagrid. Each water sample collection event is paired with a reference enumeration; the Method column distinguishes which reference was used — Colilert (IDEXX defined-substrate MPN), membrane filtration (MF, CFU), or compartment bag tests (CBT, MPN). The Use in Calibration column flags rows that are unusable because no /diagnostics record (water temperature, required by the CFU regression) was streaming within ±20 min of the sample. All date/time columns are displayed in UTC. Times are corrected from the mWater-stored value using the Timezone Entered column: mWater records times using the data-entry device’s local clock (Boulder, MDT = UTC−6); for samples collected in a different timezone the stored time is adjusted accordingly.
Boulder Creek E. coli Distribution — All Colilert Grabs
All unique Colilert grab samples collected at Boulder Creek sites (n = 39, deduplicated). Values in CFU/100 mL. EPA single-sample recreational threshold: 126 CFU/100 mL.
| Site |
n |
Min |
Median |
Max |
≥126 CFU |
All values (CFU/100 mL) |
| BC-CU |
12 |
12 |
47 |
1986 |
2 (17%) |
12, 15, 15, 21, 26, 44, 50, 53, 60, 75, 152, 1986 |
| BC-55 |
13 |
6 |
53 |
866 |
4 (31%) |
6, 20, 23, 24, 26, 28, 53, 75, 131, 145, 166, 378, 866 |
| BC-30 |
3 |
36 |
105 |
517 |
1 (33%) |
36, 105, 517 |
| BC-Can |
8 |
2 |
16 |
30 |
0 (0%) |
2, 3, 5, 7, 16, 17, 27, 30 |
| BC-Eben |
3 |
6 |
28 |
30 |
0 (0%) |
6, 28, 30 |
| All BC |
39 |
2 |
30 |
1986 |
7 (18%) |
median = 30 • mean = 171 • ≥126: 7 of 39 |
Observed vs. Predicted E. coli — Colilert
Pooled OLS: log10(colilert) ~ barcode + mon2_val + temperature + tof_mean + mon2_val×temperature (led_power = 512, sipm_bias ≈ 3000, reference barcode: 50046). Left: all matched grabs (n = 34). Right: post burn-in only (n = 21, sensors 50052 and 50066 excluded). Source data: ⬇ field_matched_512.csv
All matched data — n=34, R²=0.61, LOO R²=−0.92
Post burn-in only — n=21, R²=0.66, LOO R²=0.15
Binary Detection: ≥126 CFU/100 mL — Field Logistic + Sensor FE
Logistic regression: features mon2_val, temperature, tof_mean, mon2_val×temperature (z-scored per LOO fold) plus barcode fixed effects (reference: 50046). Left: all matched grabs (n = 34). Right: post burn-in only (n = 21). Metrics computed via LOO-CV. Source data: ⬇ field_matched_512.csv
Observed vs. Predicted E. coli — CBT
Paired calibration of the Lume sensor against the Aquagenx Compartment Bag Test (CBT), an MPN-based reference method used in international water quality monitoring. Kigali field deployment data.
View CBT Calibration →
Lab Calibration Data
Jan 2–6, 2026 calibration sessions (paper training range). Fluorescence signal (mon2_val) at the paper operating point: led_power = 1024, sipm_bias = 3040. CBT calibration data is in the field-calibration table above.
⬇ Download full dataset (CSV)
Loading lab validation data…
Lab: Predicted vs. Observed — Operating Point Comparison
Pooled OLS (Jan 2–6, n = 125): log₁₀(colilert) ~ barcode + signal × floor_temp × tof_mean. Barcode is a fixed-effect intercept shift (reference: 50030); slopes shared. Fit separately for each LED/bias operating point. In-sample R².
LED = 1024 · bias = 3040 — paper
LED = 512 · bias = 3000 — production
LED = 256 · bias = 3300 — original
Lab Binary Logistic — ≥126 CFU/100 mL
Logistic regression trained on the full lab dataset (Jan 2–21 2026, n = 300 rows with valid production-combo readings), binary label: Colilert ≥ 126 CFU/100 mL. Features: mon2_val_512, floor_temp, tof_mean (all z-scored; no sensor fixed effect so the model is sensor-agnostic). Performance estimated by leave-one-out cross-validation (LOO-CV): each fold re-standardizes from the training set of n = 299 before predicting the held-out row. Note: the 9 positive examples all come from one contamination event (Jan 8, three consecutive time points). LOO-CV performance for the positive class may be over-optimistic due to temporal correlation between the 9 rows.
Fitting logistic regression with LOO-CV…
Merged Lab + Field Binary Logistic — ≥126 CFU/100 mL
Logistic regression fit on the combined lab (Jan 2–21 2026, n = 300) and field (n = 30) datasets. Binary label: Colilert ≥ 126 CFU/100 mL (16 positives total: 9 lab, 7 field). Features: mon2_val, temperature, tof_mean (continuous, z-scored per LOO fold) plus a dummy variable for every sensor relative to reference 50030 (unscaled 0/1). All 9 sensors are included. Field sensors with very few samples (50052 n=2, 50062 n=1, 50066 n=1) have sparse dummy estimates; their LOO predictions fall back to the pooled signal when their dummy is unobserved in training.
Fitting merged logistic regression with LOO-CV…
Four-Panel Method Comparison
How does the Lume compare against the two EPA-approved laboratory methods — Colilert (IDEXX) and membrane filtration (MF) — and against itself when retrained on a different reference? Each column analyzes paired samples across three frameworks: log-log regression (top), Bland-Altman agreement (middle), and categorical classification (bottom).
Column 1 · MF vs. Colilert (n = 153)
The dedicated method comparison study pairs Colilert (n = 2 replicates) with membrane filtration (n = 3 replicates) across 161 datetimes; 8 zero-valued pairs are excluded from the log-scale analysis, yielding 153 observations. The two EPA-approved methods show R² = 0.572 with a +0.35 log10 bias — MF systematically reads ~2.2× higher than Colilert. 95% limits of agreement span [−0.64, +1.34], meaning paired lab samples can differ by up to ~22× in either direction. Categorical accuracy is 0.66 (Cohen’s κ = 0.40), i.e. “fair” agreement. This inter-method disagreement sets the ceiling for what any sensor can be expected to achieve against either reference.
Column 2 · Lume vs. Colilert (n = 209, Colilert-trained)
The Colilert-trained Lume regression is evaluated against Colilert across all bench (n = 176) and field (n = 33) observations. The sensor achieves R² = 0.881, a bias of 0.00 log10, and tight limits of agreement [−0.42, +0.42] — Lume predictions stay within ~2.6× of the reference. Categorical accuracy is 0.89 with κ = 0.88, which is “almost perfect” agreement. Against its training reference, the Lume performs as well as or better than the two EPA methods perform against each other.
Column 3 · Lume vs. MF (n = 173, Colilert-trained)
The same Colilert-trained Lume model is now evaluated against membrane filtration — a reference method it was never trained on. Performance drops to R² = 0.514 with LoA [−0.80, +0.83] and categorical accuracy 0.84 (κ = 0.65). Critically, the ~0.37 drop in R² from column 2 to column 3 is of the same order as the inter-method disagreement between Colilert and MF themselves (column 1, R² = 0.572). Most of the apparent loss is attributable to reference-method disagreement, not sensor limitations.
Column 4 · Lume vs. MF (n = 303, MF-trained)
To isolate the effect of reference-method choice, the Lume regression is refit using MF as the training target, over the full bucket dataset. Performance jumps back to R² = 0.872 — essentially matching the Colilert-trained model against Colilert. Bias is 0.00 with LoA [−0.93, +0.93]; the slightly wider LoA reflects the higher within-method variability of MF replicates (57.9% RPD vs. 43.5% for Colilert), not a sensor deficiency. Categorical accuracy is 0.81 (κ = 0.66).
Headline Finding
Sensor-to-reference agreement is bounded by reference-method reproducibility, not by Lume hardware. Whichever culture method is adopted as truth, the Lume fits it at R² ≈ 0.87–0.88. The gap between columns 2 and 3 is almost exactly the disagreement between the two lab methods themselves (column 1). The Lume is method-agnostic; its ceiling is set by the reference it is trained against, and it already achieves quantitative performance at or above the inter-method agreement ceiling between the two accepted laboratory techniques — while providing continuous temporal coverage that grab-sample laboratory methods cannot.
