Scalable multiplexed machine learning gas sensor chips for food classification
- Carla Bassil
- Kichul Lee
- Xun Liao
- Divya Krishnan
- Yifei Zhan
- Theodorus Jonathan Wijaya
- Edward Hester
- Minhyun Kim
- Il-Doo Kim
- Inkyu Park
- Ali Javey
2026-06-17
Multiplexed gas sensor arrays combined with machine learning have unlocked previously inaccessible applications for scent-based sensing. Current platforms are limited by overlapping sensing materials with similar compositions, leading to highly correlated responses, or multistep deposition processes that hinder scalability. In this work, we developed a 16-element monolithic chip with fully distinct sensing layers, enabling a truly heterogeneous array. The system consists of highly sensitive carbon nanotube field effect transistors that are functionalized through a single-step microdispensing method compatible with automated pipetting systems. The resulting chip produces characteristic signal patterns in response to object-specific scent profiles and, when combined with machine learning algorithms, can perform automated object identification. We demonstrate the classification of 16 different objects, including food spoilage and nut allergens, with a 92.6% overall prediction accuracy.