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Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

[Image: see text] In this paper, we report an array of fiber-optic sensors based on the Fabry–Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOLs). Three optical fiber tip sensors with different surfaces were included in the array of sensor...

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Autores principales: Naku, Wassana, Nambisan, Anand K., Roman, Muhammad, Zhu, Chen, Gerald, Rex E., Huang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909791/
https://www.ncbi.nlm.nih.gov/pubmed/36777572
http://dx.doi.org/10.1021/acsomega.2c05451
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author Naku, Wassana
Nambisan, Anand K.
Roman, Muhammad
Zhu, Chen
Gerald, Rex E.
Huang, Jie
author_facet Naku, Wassana
Nambisan, Anand K.
Roman, Muhammad
Zhu, Chen
Gerald, Rex E.
Huang, Jie
author_sort Naku, Wassana
collection PubMed
description [Image: see text] In this paper, we report an array of fiber-optic sensors based on the Fabry–Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOLs). Three optical fiber tip sensors with different surfaces were included in the array of sensors to improve the accuracy for identifying liquids: an intrinsic (unmodified) flat cleaved endface, a hydrophobic-coated endface, and a hydrophilic-coated endface. The time-transient responses of evaporating droplets from the optical fiber tip sensors were monitored and collected following the controlled immersion tests of 11 different organic liquids. A continuous wavelet transform was used to convert the time-transient response signal into images. These images were then utilized to train convolution neural networks for classification (identification of VOLs). We show that diversity in the information collected using the array of three sensors helps machine learning-based methods perform significantly better. We explore different pipelines for combining the information from the array of sensors within a machine learning framework and their effect on the robustness of models. The results showed that the machine learning-based methods achieved high accuracy in their classification of different liquids based on their droplet evaporation time-transient events.
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spelling pubmed-99097912023-02-10 Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning Naku, Wassana Nambisan, Anand K. Roman, Muhammad Zhu, Chen Gerald, Rex E. Huang, Jie ACS Omega [Image: see text] In this paper, we report an array of fiber-optic sensors based on the Fabry–Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOLs). Three optical fiber tip sensors with different surfaces were included in the array of sensors to improve the accuracy for identifying liquids: an intrinsic (unmodified) flat cleaved endface, a hydrophobic-coated endface, and a hydrophilic-coated endface. The time-transient responses of evaporating droplets from the optical fiber tip sensors were monitored and collected following the controlled immersion tests of 11 different organic liquids. A continuous wavelet transform was used to convert the time-transient response signal into images. These images were then utilized to train convolution neural networks for classification (identification of VOLs). We show that diversity in the information collected using the array of three sensors helps machine learning-based methods perform significantly better. We explore different pipelines for combining the information from the array of sensors within a machine learning framework and their effect on the robustness of models. The results showed that the machine learning-based methods achieved high accuracy in their classification of different liquids based on their droplet evaporation time-transient events. American Chemical Society 2023-01-24 /pmc/articles/PMC9909791/ /pubmed/36777572 http://dx.doi.org/10.1021/acsomega.2c05451 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Naku, Wassana
Nambisan, Anand K.
Roman, Muhammad
Zhu, Chen
Gerald, Rex E.
Huang, Jie
Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning
title Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning
title_full Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning
title_fullStr Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning
title_full_unstemmed Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning
title_short Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning
title_sort identification of volatile organic liquids by combining an array of fiber-optic sensors and machine learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9909791/
https://www.ncbi.nlm.nih.gov/pubmed/36777572
http://dx.doi.org/10.1021/acsomega.2c05451
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