Electronic Nose Detects Food Spoilage and Allergens Using Artificial Intelligence

Researchers at the University of California, Berkeley, have developed a sensor chip known as an “electronic nose” that identifies various types of food, monitors spoilage, and detects small amounts of certain food allergens.
This technology includes 16 gas sensors combined with a machine learning model that recognizes the chemical signatures of different odors, classifying foods with an accuracy of 92.6 percent.
The device consists of gas-sensitive materials reacting to different mixtures of molecules, translating chemical reactions into electrical signals that the machine learning model uses to recognize distinct odor signatures.
The sensors are designed to recognize specific foods and can detect single food allergens such as walnuts, though these tests have been conducted under controlled conditions.
The creation process utilizes carbon nanotubes for transistors which allows the sensor to operate at room temperature, eliminating the need for heating and making it more versatile.
Potential future applications of this technology include integration into smart refrigerators to alert users about food spoilage and a portable version accessible via an iPhone app.
Despite these promising results, the technology needs more testing in diverse environments and further refinement for potential commercial use in home appliances or food safety monitoring.

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