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The Digital Nose That Can Detect Bad Smells

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The Digital Nose That Can Detect Bad Smells

This smart, robotic nose detects bad-smelling food to help manufacturers detect pathogens in food and therefore reduce the spread of food-borne illnesses.

· AI Zone ·
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A few years ago, I wrote about a fascinating project called the SNIFFPHONE, which aimed to analyze breath for signs of disease. The Israeli-led project works by using an array of micro- and nano- sensors that can analyze the breath as its exhaled and then communicate that information back to the smartphone for interpretation.

This "digital nose" technology was developed by Professor Hossam Haick, and it has recently been taken to market by marine biologist Pierre Salameh via OlfaGuard. The startup aims to help manufacturers detect pathogens in food and therefore reduce the spread of food-borne illnesses.

Rapid Detection

The company believes that their solution offers a marked improvement on the competition, as existing methods of testing typically require around three days and expensive lab testing. By contrast, the OlfaGuard sensor unit is capable of providing results in around six hours, with an accuracy of around 94%, and the team is hoping to get this time down even more.

The company recently raised $400,000 and is working with the Strauss Group, the largest food manufacturer in Israel, on a research project that is hoping to start this autumn.

Along similar lines are Grenoble-based startup Aryballe Technologies. They've developed their own digital nose, called NeOse, and they're also targeting industrial users to allow for the rapid detection of unwelcome odors, such as gas leaks.

The device uses optical sensor technology to identify specific molecules, while they share the kind of surface plasmon resonance techniques found on many lab-on-a-chip sensors. Collectively, they're capable of identifying hundreds of different smells.

The technology was the brainchild of Tristan Rousselle, a veteran of the biotech startup scene. The company has already raised over $3 million and has the support of the Grenoble Ecole de Management to bring the product to market and develop a business model that will allow them to commercialize the technology successfully.

As with Olfaguard, the technology is still at a very early stage, with plans to launch their first product onto the market in early 2018. This is likely to be an industrial solution, although the team hopes to expand into consumer devices in time.

Despite both ventures being at an early stage in their lifecycle, they offer an interesting insight into the growing capabilities of digital devices to detect and understand smell. The potential applications for such capabilities are enormous, so it will be fascinating to track their progress.

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Topics:
ai ,predictive modeling ,sensor ,robotics

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