New article in Applied Sciences

A new publication from Dr. Bo Mi Lee, Dr. Ameen Eetemadi, and Dr. Ilias Tagkopoulos, about Reduced graphene oxide-metalloporphyrin sensors for human breath screening.

Abstract: The objective of this study is to validate reduced graphene oxide (RGO)-based volatile organic compounds (VOC) sensors, assembled by simple and low-cost manufacturing, for the detection of disease-related VOCs in human breath using machine learning (ML) algorithms. RGO films were functionalized by four different metalloporphryins to assemble cross-sensitive chemiresistive sensors with different sensing properties. This work demonstrated how different ML algorithms affect the discrimination capabilities of RGO–based VOC sensors. In addition, an ML-based disease classifier was derived to discriminate healthy vs. unhealthy individuals based on breath sample data. The results show that our ML models could predict the presence of disease-related VOC compounds of interest with a minimum accuracy and F1-score of 91.7% and 83.3%, respectively, and discriminate chronic kidney disease breath with a high accuracy, 91.7%.

Reference: Lee, Bo Mi, Ameen Eetemadi, and Ilias Tagkopoulos. “Reduced graphene oxide-metalloporphyrin sensors for human breath screening.” Applied Sciences (2021). doi: 10.3390/app112311290 (link)