4.5 Article

Temperature Modulation of MOS Sensors for Enhanced Detection of Volatile Organic Compounds

Journal

CHEMOSENSORS
Volume 11, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/chemosensors11090501

Keywords

exhaled breath analysis; electronic nose; temperature modulation; metal oxide sensors

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This study explores the potential of temperature modulation in improving the sensitivity of gas sensors in electronic nose devices for exhaled breath analysis. The results demonstrate successful discrimination of different analytes and concentrations, with the square and triangular combination pattern achieving optimal accuracy. The findings highlight the potential for further advancements in the field of disease diagnosis through exhaled breath analysis.
Disease diagnosis through biological fluids, particularly exhaled breath analysis, has gained increasing importance. Volatile organic compounds (VOCs) present in exhaled breath offer diagnostic potential as they reflect altered and disease-specific metabolic pathways. While gas chromatography-mass spectrometry (GC-MS) has been traditionally used for VOCs detection, electronic noses have emerged as a promising alternative for disease screening. Metal oxide semiconductor (MOS) sensors play an essential role in these devices due to their simplicity and cost-effectiveness. However, their limited specificity and sensitivity pose challenges for accurate diagnosis at lower VOCs concentrations, typical of exhaled breath. To address specificity and sensitivity issues, temperature modulation (TM) has been proposed in this paper, introducing a custom-developed electronic nose based on multiple and heterogeneous gas sensors located within an analysis chamber. Four different TM patterns (i.e., square, sine, triangular, and a combination of square and triangular) were applied to the gas sensors to test their response to three different analytes at three distinct concentrations. Data were analyzed by extracting meaningful features from the sensor raw data, and dimensionality reduction using principal component analysis (PCA) was performed. The results demonstrated distinct clusters for each experimental condition, indicating successful discrimination of analytes and concentrations. In addition, an analysis of which set of sensors and modulation pattern yielded the best results was performed. In particular, the most promising TM pattern proved to be the square and triangular combination, with optimal discrimination accuracy between both concentrations and analytes. One specific sensor, namely, TGS2600 from Figaro USA, Inc., provided the best performance. While preliminary results highlighted the potential of TM to improve the sensitivity of gas sensors in electronic nose devices, paving the way for further advancements in the field of exhaled breath analysis.

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