That is conceivable, not a top priority as we want to focus on this being a great experience for every backend, but certainly something we are thinking of
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.
But a banana is yellow for a very different reason (and a much easier to explain reason) than why the sky is blue. And air isn’t blue, because it’s red at the end of the day?
