Smart ring resonator–based sensor for multicomponent chemical analysis via machine learning

• Main 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
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics; Forecasting; Machine Learning
• Online Access: https://hdl.handle.net/10356/151459
• Institution: Nanyang Technological University

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.