iTWire – UTS e-nose identifies whiskeys


Researchers at the University of Technology Sydney have developed an electronic nose that can distinguish different types of whiskey – and may have applications in other areas as well.

Led by Associate Professor Steven Su, the VAT Researchers have developed an E-nose that can distinguish between different brands, origins and styles of whiskey.

It achieved 100% accuracy for region detection, 96.15% accuracy for brand name, and 92.31% accuracy for style.

The importance of whiskey is that its popularity and value make it a target for fraud, so a quick, easy-to-use whiskey evaluation to identify quality and detect adulteration or fraud for both high-end wholesalers and retailers could be very beneficial for buyers, the researchers suggest.

“Until now, spotting the differences between whiskeys has required either a trained whiskey connoisseur, who could still be wrong, or complex and time-consuming chemical analysis by scientists in a lab,” Su said.

“So a fast, user-friendly, real-time evaluation of whiskey to identify quality and detect adulteration or fraud could be very beneficial for both high-end wholesalers and buyers.”

The prototype – dubbed NOS.E – was able to identify the differences between six whiskeys (including Johnnie Walker red and black, Ardberg, Chivas Regal and a 12-year-old Macallan’s) based on their brand names, regions and styles in fewer than four minutes.

The device uses eight gas sensors to detect odors and feeds the data to a machine learning algorithm trained to recognize whiskey characteristics.

NOS.E provided results similar to those from state-of-the-art laboratory tests (time-of-flight mass spectrometry and two-dimensional gas chromatography).

It could also be applied to other alcoholic beverages such as wine and cognac, as well as other counterfeit-prone products such as high-end perfumes.

The underlying technology has also been used to detect illegal animal parts (e.g. black rhino horn) and has potential health applications, including disease detection.


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