An Innovative Modular eNose System Based on a Unique Combination of Analog and Digital Metal Oxide Sensors

Jaeschke, Carsten; Glöckler, Johannes; Azizi, Oussama El; González, Oriol Ríos; Padilla, Marta; Mitrovics, Jan; Mizaikoff, Boris

doi:10.1021/acssensors.9b01244

Cited by 23 publications

(23 citation statements)

References 34 publications

(39 reference statements)

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“…Some researchers have used a selection of these sensors in devices for air quality monitoring purposes [22,23], but applications in breath research are limited. The breath E-nose developed by Jaeschke et al [24] uses many of the most relevant commercial gas sensors currently available. However, this unit focuses on a modular approach with three exchangeable sensing compartments and has not been tested using exhaled breath samples.…”

Section: Introductionmentioning

confidence: 99%

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

et al. 2020

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In this paper, we report on an in-house developed electronic nose (E-nose) for use with breath analysis. The unit consists of an array of 10 micro-electro-mechanical systems (MEMS) metal oxide (MOX) gas sensors produced by seven manufacturers. Breath sampling of end-tidal breath is achieved using a heated sample tube, capable of monitoring sampling-related parameters, such as carbon dioxide (CO2), humidity, and temperature. A simple mobile app was developed to receive real-time data from the device, using Wi-Fi communication. The system has been tested using chemical standards and exhaled breath samples from healthy volunteers, before and after taking a peppermint capsule. Results from chemical testing indicate that we can separate chemical standards (acetone, isopropanol and 1-propanol) and different concentrations of isobutylene. The analysis of exhaled breath samples demonstrate that we can distinguish between pre- and post-consumption of peppermint capsules; area under the curve (AUC): 0.81, sensitivity: 0.83 (0.59–0.96), specificity: 0.72 (0.47–0.90), p-value: <0.001. The functionality of the developed device has been demonstrated with the testing of chemical standards and a simplified breath study using peppermint capsules. It is our intention to deploy this system in a UK hospital in an upcoming breath research study.

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Section: Introductionmentioning

confidence: 99%

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

et al. 2020

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“…The hold-out CV method was used to train the LDA model. The maximum 76.4% LDA accuracy was recorded for the classification of different VOC concentrations [132] (see Figure 14A). The lower accuracy rate might have been due to the inappropriate selection of the CV method since the hold-out method randomly shuffled the entire dataset for training and testing.…”

Section: Chemiresistive Type Smart Gas Sensors Using Machine Learningmentioning

confidence: 99%

“…Jaeschke et al [132] demonstrated an innovative e-nose system using a unique combination of analog and digital MOx sensors for ethanol and acetone detection in dry and humid environments. The sensing array consisted of 8 analog and 10 digital sensors.…”

Section: Chemiresistive Type Smart Gas Sensors Using Machine Learningmentioning

confidence: 99%

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Yaqoob

Younis

2021

Sensors

127

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Nowadays, there is increasing interest in fast, accurate, and highly sensitive smart gas sensors with excellent selectivity boosted by the high demand for environmental safety and healthcare applications. Significant research has been conducted to develop sensors based on novel highly sensitive and selective materials. Computational and experimental studies have been explored in order to identify the key factors in providing the maximum active location for gas molecule adsorption including bandgap tuning through nanostructures, metal/metal oxide catalytic reactions, and nano junction formations. However, there are still great challenges, specifically in terms of selectivity, which raises the need for combining interdisciplinary fields to build smarter and high-performance gas/chemical sensing devices. This review discusses current major gas sensing performance-enhancing methods, their advantages, and limitations, especially in terms of selectivity and long-term stability. The discussion then establishes a case for the use of smart machine learning techniques, which offer effective data processing approaches, for the development of highly selective smart gas sensors. We highlight the effectiveness of static, dynamic, and frequency domain feature extraction techniques. Additionally, cross-validation methods are also covered; in particular, the manipulation of the k-fold cross-validation is discussed to accurately train a model according to the available datasets. We summarize different chemresistive and FET gas sensors and highlight their shortcomings, and then propose the potential of machine learning as a possible and feasible option. The review concludes that machine learning can be very promising in terms of building the future generation of smart, sensitive, and selective sensors.

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“…In this work, we explore the performance of several CT techniques in a real-life breath analysis study using our recently published [ 80 , 81 ] sensing array and three instruments. The experiment consists of the discrimination of breath samples from people before and after eating a specified meal.…”

Section: Introductionmentioning

confidence: 99%

“…This study is mainly motivated by the difficulty of calibration of eNoses for breath analysis applications, the differences between instruments, the frequent recalibrations needed due to aging and drift and the environmental and other different conditions present in different hospitals which prevents obtaining a unified dataset for deep statistical studies. Another goal in this work, is to present our modular breath analyzer (MBA) platform (shown in Figure 1 ), a new updated version of our modular eNose [ 80 , 81 , 82 ] specifically designed for breath analysis. The previous version of the modular eNose concept was recently presented [ 80 , 81 , 82 ].…”

Section: Introductionmentioning

confidence: 99%

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Jaeschke

Padilla²,

Glöckler

et al. 2021

Molecules

Self Cite

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Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

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An Innovative Modular eNose System Based on a Unique Combination of Analog and Digital Metal Oxide Sensors

Cited by 23 publications

References 34 publications

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

Chemical Gas Sensors: Recent Developments, Challenges, and the Potential of Machine Learning—A Review

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

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