Recent Trends in Exhaled Breath Diagnosis Using an Artificial Olfactory System

Abstract

Artificial olfactory systems are needed in various fields that require real-time monitoring, such as healthcare. This review introduces cases of detection of specific volatile organic compounds (VOCs) in a patient's exhaled breath and discusses trends in disease diagnosis technology development using artificial olfactory technology that analyzes exhaled human breath. We briefly introduce algorithms that classify patterns of odors (VOC profiles) and describe artificial olfactory systems based on nanosensors. On the basis of recently published research results, we describe the development trend of artificial olfactory systems based on the pattern-recognition gas sensor array technology and the prospects of application of this technology to disease diagnostic devices. Medical technologies that enable early monitoring of health conditions and early diagnosis of diseases are crucial in modern healthcare. By regularly monitoring health status, diseases can be prevented or treated at an early stage, thus increasing the human survival rate and reducing the overall treatment costs. This review introduces several promising technical fields with the aim of developing technologies that can monitor health conditions and diagnose diseases early by analyzing exhaled human breath in real time.

Keywords: artificial olfactory system; electronic nose; exhaled breath diagnosis; gas sensor; health monitoring; volatile organic compounds.

Figure 1

Concept of an artificial nose (e-nose) system based on the olfactory perception pathway.

Figure 2

Metal–oxide nanomaterial-based electrochemical sensors. (a) Doped SnO₂ nanomaterial sensor for lung cancer diagnosis. Data reproduced from Ref. [101]. Copyrights ACS 2020. (b) Diabetes diagnosis model using polycrystalline WO₃ hemi-tube for H₂S, selective acetone detecting sensors. Data reproduced from Ref. [116]. Copyrights ACS 2013.

Figure 3

Metal-containing dye sensor array-type e-nose. (a) Suslick used strong chemical reactions such as “Lewis acid/base dyes (i.e., metal ion-containing dyes),” “Brønsted acidic or basic dyes (i.e., pH indicators),” and “dyes with large permanent dipoles (i.e., zwitterionic solvatochromic dyes).” Various types of sensor arrays were fabricated using the base dye (b) Electronic nose model that classifies various types of VOCs using the manufactured sensor array. (c) Exhalation analysis of lung cancer patients using the e-nose technology. The images (a,b) were adapted with permission from [130]. The image (c) was adapted with permission from [103].

Figure 4

Colorimetric sensor system using an M13 bacteriophage as a functional biomaterial [109]. Peptide-based bioreceptor materials can secure various peptide sequences with desired functional groups using the phage display technology. Filamentous bacteriophage material with a scale less than 1 μm has approximately 2700 pairs of functional proteins on its surface. Hence, it can be used as a highly sensitive bioreceptor material. The synthesis process is based on internal DNA genetic information, and high-purity mass production is possible with simple genetic modifications. Phage units produced by bacteria have the same structure of a certain size and, thus have liquid crystal properties. A self-assembled structure can be produced using a bacteriophage as a unit, and it has liquid crystal properties; therefore, a color matrix can be produced by creating light scattering at regular distances formed by the structure. Based on the principle that phage self-assembled structures change the surface structure in response to external stimuli, a highly sensitive colorimetric sensor can be manufactured. This technique is referred to as the phage litmus.

Figure 5

Bioinspired from neuronal pathway: Artificial intelligence algorithms. Deep learning technology based on the neuronal signal process. A structure that draws correct conclusions via iterative learning through activation functions called Sigmoid, Tanh, and ReLU. By applying ReLU to the inner hidden layer and by applying the sigmoid function in the last output layer, the accuracy is significantly increased.