Abstract
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 End face, a Hydrophobic-Coated End face, and a Hydrophilic-Coated End face. 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.
Recommended Citation
W. Naku et al., "Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning," ACS Omega, American Chemical Society, Jan 2022.
The definitive version is available at https://doi.org/10.1021/acsomega.2c05451
Department(s)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
2470-1343
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 American Chemical Society, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2022
