Measuring the Scoville Heat Units of Mike’s Hot Honey: A Repeatability and Data‑Tracking Demonstration
- martinpeacock13
- 1 day ago
- 3 min read
In this blog, I walk through a complete demonstration of measuring the Scoville Heat Units (SHU) of Mike’s Hot Honey using the FoodSense Generation 4 platform. The goal is not only to measure the heat level once, but to perform multiple repeat tests to understand test‑to‑test variability, and to show how results are automatically uploaded and stored in the Djuli cloud system for long‑term tracking and quality management.
If you are a FoodSense Gen 4 user and you cannot access the Premier‑level cloud features, reach out with the email linked to your device purchase and the team can activate your Premier account.
1. Sample Preparation
For this demonstration, I prepared the honey using the newly introduced 1-in-5 dilution method, which is recommended for milder samples such as hot honey.
Steps:
Weigh the honeyI weighed out 100 mg (0.1 g) of Mike’s Hot Honey. The balance read 98 mg — only 2 mg off target, which is insignificant for this application.
Add bufferI added 400 µL of buffer to achieve a 1:5 dilution.(This 1:5 dilution option was added to the system about 4–6 weeks ago.)
Mixing a viscous sampleHot honeys dissolve surprisingly well in buffer. For viscous materials:
I sometimes trim the pipette tip to widen it.
I mix by pipetting up and down.
After mixing, I transfer the solution into a small vial.
VortexingI vortexed the sample for 20 seconds to ensure complete homogenization.
At this point, the sample is ready for measurement.
2. Running the First Measurement
With the FoodSense Gen 4 device connected via Bluetooth to the app:
I named the sample “Mike’s Honey 5A1” so I could easily locate it later.
Inserted a new chili sensor.
Pipetted 50 µL of the 1:5 diluted sample onto the sensor.
Very important: I selected “1 in 5” from the dilution options.
Started the measurement.
The system displayed small but distinct capsaicin peaks (expected for mild samples).The data then uploaded automatically to the Djuli cloud.
Result #1: 180 SHU
3. Viewing the Results in Djuli Cloud
Once uploaded, the result appears in the cloud under the selected cluster (in this case, demo).Each measurement includes:
Time
Date
Sample name
Operator
Raw signal (peaks 1, 2, and 3)
Calculated SHU
The first measurement stored as Mike’s Honey 5A1 showed:
Displayed in app: 180 SHU
Cloud value: 179.99999 SHU
(These are effectively identical.)
4. Second Measurement (Repeatability Study)
To perform a proper repeatability test:
Removed the previous sensor.
Inserted a fresh sensor.
Named the new test “Mike’s Honey 5A2”.
Quickly vortexed the sample again (5 seconds).
Pipetted 50 µL onto the sensor.
Selected 1‑in‑5 dilution.
Ran the measurement.
The app noted that the sample was very low, but this is expected for mild products.
Result #2: 152 SHU
This result uploaded to the cloud as Mike’s Honey 5A2.
5. Third Measurement
I performed a third repeat:
Fresh sensor
Sample re‑vortexed
Named “Mike’s Honey 5A3”
50 µL applied
1‑in‑5 dilution selected
Start measurement
Result #3: 181 SHU
Again, small capsaicin peaks consistent with mild honey products.
6. Understanding the Variability
Across the three tests:
Test | Result (SHU) |
5A1 | 180 |
5A2 | 152 |
5A3 | 181 |
To quantify the variability, I calculated:
Average: 171 SHU
Standard deviation: ~15 SHU
Relative standard deviation (RSD): ~9%
Why this variation is acceptable
Remember:
The Scoville scale spans 0 to 16,000,000 SHU.
Practical food products generally fall between 0 and ~2.6 million SHU.
A difference between 152 and 180 on that enormous scale is trivial.
Natural products — like chilies and honey — inherently vary.
The repeatability demonstrated here is entirely reasonable for a real‑world mild product.
7. Using Djuli’s Comparison Tools
Within the cloud system, I selected:
5A1
5A2
5A3
Djuli generated a comparison report showing:
Overlaid capsaicin peaks
SHU values
Metadata for each run
This makes Djuli a strong tool for:
QA/QC
Batch‑to‑batch comparison
Operator tracking
Supporting traceability for audits
You also have the option to export a PDF for long‑term archiving, though everything is already permanently stored in Djuli.
8. Final Thoughts
This demonstration shows how to:
Prepare a hot honey sample using the 1‑in‑5 dilution
Perform multiple repeat SHU tests
Retrieve and compare results using the Djuli cloud system
Interpret mild‑sample variability in a scientifically sound way
Final SHU results for this Mike’s Hot Honey sample:180, 152, and 181 SHU, giving an average of 171 SHU.
If your device isn’t showing cloud upload functionality or the 1‑in‑5 dilution option, you may not yet be activated on the Premier tier — simply contact the team so they can enable it.
If you have any questions about FoodSense Generation 4, SHU testing, or working with challenging sample types like honey, feel free to reach out.
If you’d like, I can also create:
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