alt Hacker NewsShow HN: A trainable, modular electronic nose for industrial use
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.
Comments
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.
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).
Interesting direction. My guess is the first strong wedge is narrow pass/fail decisions where people already use smell informally and misses are expensive: fermentation batches, packaging seal leaks, or early spoilage or mold detection in storage. If you can show earlier-than-human detection plus low recalibration burden across facilities and seasons, the ROI story becomes much easier to sell than a broad platform story. How close are you on handling humidity and temperature variation plus sensor drift without site-specific retraining?
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.
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...
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.