Post by Carla Bassil

I am excited to wrap up the third year of my PhD with my first lead-author publication in Science Advances titled "Scalable Multiplexed Machine Learning Gas Sensor Chips for Food Classification." When your food starts to spoil, metabolizing bacteria release characteristic gas molecules like sulfides and amines that make up that foul odor we are all familiar with. In this work, we developed a 16-element sensor array that responds to the gas molecules contained in these order profiles. We introduce a highly scalable electronic nose platform to detect and classify food spoilage, nut allergens, and other common household foods. By combining CNT-FET technology, drop-cast compatible gas-sensitive films, and machine learning, we demonstrate that our e-nose chip can be used to discriminate between 16 food objects with over 92% accuracy. Read the full paper: https://lnkd.in/gXeFNZ38 Science Podcast feature: https://lnkd.in/g6udkHnm UC Berkeley News synopsis: https://lnkd.in/gYuMrpMj Co-authors: Kichul Lee, Xun Liao, Divya K., Yifei Zhan, Theodorus Jonathan Wijaya, Edward Hester, Minhyun Kim, Il-Doo Kim, Inkyu Park, Ali Javey Photos credit: Brandon Sánchez-Mejia/UC Berkeley