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Scientists from Russia invented a "digital nose" to determine the freshness of food

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Scientists from Russia invented a "digital nose" to determine the freshness of food

Scientists from the Enikolopov Institute of Synthetic Polymeric Materials of the Russian Academy of Sciences in Moscow have developed the world's first "electronic nose" sensitive to several toxic gases at once. Such devices will help monitor the environment, regulate the operation of industrial plants, and assess the freshness of perishable food. The results are published in the journal Scientific Reports.

As explained on the website of the Russian Science Foundation (RSF), the resulting sensors do not require high battery power, they are stable in high humidity and can detect the presence of only 30 molecules of nitrogen dioxide or ethylmercaptan among the billion molecules of nitrogen and oxygen, which make up the air.

"Toxic gases such as nitrogen oxides, hydrogen sulfide, mercaptans and ammonia are of great interest to researchers because they are associated with air pollution from industrial plants and internal combustion engines. ...Detected in human exhaled air, these compounds can be markers of a number of dangerous diseases. ...A compact device that detects this gas will alert the sick and allow medication to be taken in time. In addition, food spoilage release ammonia and hydrogen sulfide, sensors to which can be integrated into refrigerators or "smart" packaging. With the help of such "electronic nose", it will be easy to determine which products can still be safely consumed, reducing the amount of discarded fresh products, and what is already time to write off, which will reduce the frequency of food poisoning", - said in the RNF report.

In future, scientists plan to create a compact air quality control station for "smart" cities and test it in Moscow, as well as finalize and test the "electronic nose" as a portable device for early diagnosis of inflammatory lung diseases. Now, the authors of the development optimise manufacturing processes and collect new data for more accurate classification of various gases using machine learning methods.

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    Author: Karina Kamalova

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