Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening

Bo Mi Lee,Ilias Tagkopoulos,Ameen Eetemadi

doi:10.3390/app112311290

Bo Mi Lee, Ilias Tagkopoulos + Show 1 more

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https://doi.org/10.3390/app112311290

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Journal: Applied Sciences	Publication Date: Nov 29, 2021
Citations: 10	License type: CC BY 4.0

Affiliation: University of California, Davis

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%.

Highlights

Several wrinkles can be found in both thin films, the reduction were uniformly distributed without agglomeration and completely covered the interdigprocess does not affect the topology of the samples
The reduced graphene oxide (RGO) thin films were noncovalently functionalized by metalloporphyrin molecules via π–π stacking (Figure 9a)
We showed the cross-sensing behavior of RGO–metalloporphyrin sensors when exposed to the disease-related volatile organic compounds (VOC) in human breath

Summary

Introduction

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. Exhaled breath contains a number of volatile organic compounds (VOC) as gaseous molecules that are products of physiological and pathophysiological metabolic processes. As many as 872 VOCs can be detected in human breath, including compounds that are based on nitrogen, oxygen, sulfur, and hydrocarbons [1]. The concentrations of VOCs in human breath may be altered due to infectious and metabolic diseases, genetic disorders, and various cancers [2,3,4,5,6,7].

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