Please use this identifier to cite or link to this item:
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.
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.
ISSN: 2327-9125
DOI: 10.1364/PRJ.411825
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

Files in This Item:
File Description SizeFormat 
prj-9-2-B38.pdf1.14 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 23, 2022

Download(s) 50

Updated on Jun 23, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.