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Comment by limel | Hacker News Reader

The problem is not whether we can digitize the sense of smell, but that no industrial process currently relies on it by default. The real challenge is identifying the first scalable use case that proves measurable business value (sniphi team member here).

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The nearest current use of detection of particles in the air that I can think of is smoke and carbon-monoxide detectors for safety. Could adoption on these smart versions like Nest or Ring by adding your sniphi detector provide other types of early warning systems for safety, air quality or sensing?

Some thoughts are musty odors from mold/mildew, rotten egg smells indicating gas leaks, and fishy/burning plastic odors from electrical issues.

That is actually an interesting direction. Since smoke detectors already exist, the next level could be distinguishing smoke from a cigarette — or even something harmless like burning scrambled eggs — from more dangerous sources such as burning carpet or electrical wire insulation. We will definitely think about it.

A mold detector is also an interesting idea. Our ‘digital nose’ can measure humidity and temperature as well, and these factors are often strongly correlated with mold growth. Combining odor detection with environmental data could therefore be very useful for early mold detection.

The mold detection angle seems especially promising since you already have humidity and temperature sensors on board. Curious whether you've looked at sensor baseline drift over time - that tends to be the main practical barrier for always-on environmental monitoring vs. short-burst QA checks where you can recalibrate between runs.

There are a few industries that use odorants/aromas.

What is the limit of detection on the sensors? Can they reliably pick up compounds in the parts per billion range? Parts per trillion?

That’s true. We even started a PoC with a skincare products factory. The challenge, however, was that the frequent rotation of the product portfolio — and the large number of SKUs — made it difficult to justify the training effort.

On the limits of detection - with Sniphi we follow a different approach than traditional selective sensors. The system is based primarily on non-selective chemical sensors operating at controlled temperature profiles. Each measurement cycle (6 seconds) generates around 60 measurement points per sensor, creating multidimensional signatures of gas mixtures that are then analyzed using classification models.

I’ve seen this approach - so no chromatography? We have a compound that is very trace (parts per trillion) that we need to monitor for. We are always looking for solutions that could be useful.

For the type of application you describe, we are planning to incorporate nanobio detectors, which can be extremely sensitive. In some applications we already combine non-selective and selective sensors, and together they create a multidimensional digital signature that is analyzed by our machine learning models.

If you have a specific problem in mind, please feel free to reach out to us via the Sniphi website. We would be happy to explore whether this is something we could support you with.

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