Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/151459
Title: | Smart ring resonator–based sensor for multicomponent chemical analysis via machine learning | Authors: | Li, Zhenyu Zhang, Hui Nguyen, Binh Thi Thanh Luo, Shaobo Liu, Patricia Yang Zou, Jun Shi, Yuzhi Cai, Hong Yang, Zhenchuan Jin, Yufeng Hao, Yilong Zhang, Yi Liu, Ai Qun |
Keywords: | Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics | Issue Date: | 2021 | Source: | Li, Z., Zhang, H., Nguyen, B. T. T., Luo, S., Liu, P. Y., Zou, J., Shi, Y., Cai, H., Yang, Z., Jin, Y., Hao, Y., Zhang, Y. & Liu, A. Q. (2021). Smart ring resonator–based sensor for multicomponent chemical analysis via machine learning. Photonics Research, 9(2), B38-B44. https://dx.doi.org/10.1364/PRJ.411825 | Journal: | Photonics Research | Abstract: | We demonstrate a smart sensor for label-free multicomponent chemical analysis using a single label-free ring resonator to acquire the entire resonant spectrum of the mixture and a neural network model to predict the composition for multicomponent analysis. The smart sensor shows a high prediction accuracy with a low root-mean-squared error ranging only from 0.13 to 2.28 mg/mL. The predicted concentrations of each component in the testing dataset almost all fall within the 95% prediction bands. With its simple label-free detection strategy and high accuracy, the smart sensor promises great potential for multicomponent analysis applications in many fields. | URI: | https://hdl.handle.net/10356/151459 | ISSN: | 2327-9125 | DOI: | 10.1364/PRJ.411825 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2021 Chinese Laser Press. This is an open-access article distributed under the terms of the Creative Commons Attribution License. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | EEE Journal Articles |
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prj-9-2-B38.pdf | 1.14 MB | Adobe PDF | View/Open |
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