Show HN: A trainable, modular electronic nose for industrial use

[kwitczak]

Hi HN,

I’m part of the team building Sniphi.

Sniphi is a modular digital nose that uses gas sensors and machine-learning models to convert volatile organic compound (VOC) data into a machine-readable signal that can be integrated into existing QA, monitoring, or automation systems. The system is currently in an R&D phase, but already exists as working hardware and software and is being tested in real environments.

The project grew out of earlier collaborations with university researchers on gas sensors and odor classification. What we kept running into was a gap between promising lab results and systems that could actually be deployed, integrated, and maintained in real production environments.

One of our core goals was to avoid building a single-purpose device. The same hardware and software stack can be trained for different use cases by changing the training data and models, rather than the physical setup. In that sense, we think of it as a “universal” electronic nose: one platform, multiple smell-based tasks.

Some design principles we optimized for:

- Composable architecture: sensor ingestion, ML inference, and analytics are decoupled and exposed via APIs/events

- Deployment-first thinking: designed for rollout in factories and warehouses, not just controlled lab setups

- Cloud-backed operations: model management, monitoring, updates run on Azure, which makes it easier to integrate with existing industrial IT setups

- Trainable across use cases: the same platform can be retrained for different classification or monitoring tasks without redesigning the hardware

One public demo we show is classifying different coffee aromas, but that’s just a convenient example. In practice, we’re exploring use cases such as:

- Quality control and process monitoring

- Early detection of contamination or spoilage

- Continuous monitoring in large storage environments (e.g. detecting parasite-related grain contamination in warehouses)

Because this is a hardware system, there’s no simple way to try it over the internet. To make it concrete, we’ve shared:

- A short end-to-end demo video showing the system in action (YouTube)

- A technical overview of the architecture and deployment model: https://sniphi.com/

At this stage, we’re especially interested in feedback and conversations with people who:

- Have deployed physical sensors at scale

- Have run into problems that smell data might help with

- Are curious about piloting or testing something like this in practice

We’re not fundraising here. We’re mainly trying to learn where this kind of sensing is genuinely useful and where it isn’t.

Happy to answer technical questions.

[skyberrys]

I am definitely curious about this type of a tool, but I am interested in seeing it used in products I would like to buy. For instance, imagine how much better a robot to clean the house would be if it was aware of wow this room smells awful. It could have also prevented the poocalypse from happening (robot ran when not home, dog pooped in house.. robot.. did not realize)

Independent of the sensor smelling, I am recently curious if there are smell libraries where I myself could better learn to classify scents. Recently I came across a laundry detergent scent that was great but I didn't get the brand name and now I can't explain what it smelled like.

[limel]

You nailed it with the ‘poocalypse’. My parents have two cats, and that exact scenario was the reasons they decided not to buy a cleaning robot.

Technically, I’m quite confident that Sniphi could recognize the smell of poo. The bigger challenge would be the environment inside a cleaning robot — dust and particles could interfere with the sensors. I believe this could be addressed with some engineering effort, for example using filtration or protective sensor covers, but it would require additional work.

That’s why we are also looking at some lower-hanging applications. For example, in nursing homes or hospitals. A bedridden patient with a diaper that isn’t changed in time can develop serious complications — especially elderly patients, where infections can become life-threatening. In that context, a “sneaky poo detector” could actually improve care and potentially save lives. Do you know anyone who might be interested in partnering with us to test this idea?

[sovietswag]

Part of my training for doing "engine room checks" on a boat involved checking for any unusual smells, e.g. fuel leak, burning oil (from generator/engine), burning coolant (from generator/engine), or burning rubber (from sea chest raw water impeller). All of the components in there are equipped with sensors[1] that measure levels, temperature, etc. Perhaps there is room for a new olfactory sensor there? Aside from avoiding catostrophic issues like fire and engine or generator failure, it's also important to not pump out[2] any water from the compartment into the ocean if it's contaminated with oil, fuel, or coolant (the laws about this are super strict).

[1] There are digital sensors that are readable directly from the pilothouse by the captain which are rigged to automated alarms, as well as manual sensors (e.g. a pressure dial) that are readable from the engine room itself, for redundancy. So I don't think an olfactory sensor would replace the unusual smell check, but it could maybe augment it.

[2] The "bilge pump" is used to pump out water from the bilge (bottom floor cavity of engine room). To be honest on my vessel the policy is to never turn on the bilge pumps in the engine room at all because the risk of dumping contaminants is too high. But I still thought to mention this just in case there's an idea there.

[limel]

Thanks for sharing these insights and real-life examples, they are very interesting. Yes, I believe there are situations where an olfactory sensor could detect a problem earlier than conventional sensors. For example, the smell of burnt oil might appear before an oil level sensor detects a drop or before temperature sensors trigger an alarm.

The key question, however, is where the biggest value lies - either in cost prevention or hazard reduction - so that the benefit-to-cost ratio justifies the investment in a technology that is not traditionally used for this purpose.

What you mentioned is particularly important: identifying industries where people already rely heavily on the perception of odor. Not just selective measurement of a specific chemical compound, but the overall “human” impression of smell. In many environments an experienced worker with 20 years in the industry can simply smell that something is wrong.

If we can replicate that capability with Sniphi - but in a scalable, continuous way - it could make the value proposition much easier to demonstrate to customers. Thanks for sharing.

[chabes]

I built a prototype “digital nose” almost a decade ago, inspired by this blog post https://web.archive.org/web/20180513090020/http://www.maskau...

I have a friend with Chrons, IBS, and a handful of other gut issues. He wants me to build something like this to help self-diagnose acute issues as they arise. Yes, a fart classifier.

I want to use a smell classifier to identify ripeness levels in agriculture.

I haven’t tested to see if this is even feasible, but I’d like to also use a tool like this for pest scouting in agriculture. If the sensors are sensitive enough to detect small amounts of fungi, arthropod activity, or hormonal shifts, this could be useful for early detection in integrated pest management systems.

[limel]

We conducted research with local universities, and the digital nose was able to detect the presence of pests in oat flakes and beans (two different species).

When we published the white paper ( https://sniphi.com/wp-content/uploads/2025/10/Sniphi_digital... ), we expected a queue of agricultural companies interested in the technology. However, pests apparently aren’t “sexy” enough to capture attention.

We observed the same reaction with bananas — fresh vs. overripe, like in the video. Technically interesting, but no one saw clear business potential.

So now we are looking for use cases that are more obvious and compelling from a business perspective. Any ideas?

[limel]

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).

[murdockq]

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.

[gh5000]

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?

[gavmor]

Can't wait to run smell-to-image GGUF models from HF.

[limel]

lol, that would be fun

[embeddding]

From the pictures, it looks like it's using sensirion VOC sensor. There are plenty of "experimental" VOC detectors in the market, including BME688/690 with their AI SDK, so far I haven't seen a single reliable industry-grade application, only demos that work sterile conditions and fail in the harsh real-world conditions.

[limel]

Yes, many current solutions rely on VOC sensors such as those from Sensirion, and we share the observation that a lot of the existing implementations remain at the demo stage and struggle in real industrial environments.

In our case we are somewhere in between research and real deployment. We recently completed research on detecting food infestation — across different insect species and different food samples, and the results were successful enough that we submitted the paper for publication.

At the same time, we are fully aware that this is not yet an industry-grade solution tested in environments like grain silos. For example, heavy dust is a major factor that can affect sensor performance without proper filtration, so there is still engineering work to be done before large-scale deployments.

However, we believe the core detection capability is already promising, and we are now focusing on solving the practical challenges needed to move from research results to robust industrial applications.

[m0llusk]

Strictly speaking not directly related, but this kind of thing always reminds me of the classic Gogol story: https://www.libraryofshortstories.com/storiespdf/the-nose.pd...

[kamma4434]

The first thing that comes to my mind is that reliance on a external infrastructure (Azure) is a big no no for industrial applications. You would not want your oil refinery plant to stop working because there is a connectivity issue to a server located in a different continent.

[limel]

Azure, together with Power Platform tools such as Power Apps, is primarily used for large-scale training. Since the odor and gas data require efficient labeling to be reliable, Power Apps combined with Data Explorer provides an easy, cost-effective, and scalable way to manage this process. Once trained, the model can be deployed directly on the edge.

[djsedaw]

I wonder if you could use it for truffle hunting?

[limel]

That’s a very good point, and we actually see fermentation batches as one of the most promising early use cases. In many facilities, smell is already used informally as a pass/fail indicator, but it is subjective and difficult to scale.

We measure both humidity and temperature and use them as additional inputs for the ML models. Regarding sensor drift, it is still difficult to fully assess its impact on the business case. At this stage, our main focus is on the accuracy of the classification models rather than very long-term operation — that would be the next step.

For now, the practical approaches we consider are either on-the-fly calibration through a feedback loop based on the actual process output, or simply replacing the sensor when necessary, as the manufacturing cost is relatively low.”