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

Li, Zhenyu; Zhang, Hui; Nguyễn, Bình Thị Thanh; Luo, Shaobo; Liu, Patricia Yang; Zou, Jun; Shi, Yuzhi; Cai, Hong; Yang, Zhiqing; Jin, Yufeng; Hao, Yue; Zhang, Yi; Liu, Ai Qun

doi:10.1364/prj.411825

Cited by 33 publications

(18 citation statements)

References 38 publications

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“…The training set accounts for 70%, and the test set accounts for 30%. 8 , 9 , 19 , 23 The 70% of the data is randomly selected as the training set each time and the remaining 30% is retained as the validation set during clinical sample training process. The training process performs iterations of twenty large cycles.…”

Section: Methodsmentioning

confidence: 99%

“…Currently, a series of good studies about optofluidic imaging systems combined artificial intelligence (AI) for smart disease diagnosing are gradually emerging, 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 which implies a potential possibility of combining optical imaging and AI technologies for convenient and accurate smart blood coagulation function diagnoses. Blood clotting is a continuous process that includes clotting activation, formation, and densification, and the patients differ in the rate and intensity of each process.…”

Section: Introductionmentioning

confidence: 99%

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Space-time-regulated imaging analyzer for smart coagulation diagnosis

Liu

et al. 2022

Cell Reports Medicine

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space-time-regulated imaging analyzer for smart coagulation diagnosis

Liu

et al. 2022

Cell Reports Medicine

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“…The sensing of chemical compounds for monitoring or detection is necessary for various fields and industries 12 . 3 Integrated optical sensors offer a cost-effective and scalable solution, 4 as well as the opportunity to integrate with electronics to achieve an effective lab-on-a-chip that is compact and portable. 5 Integrated optical sensing falls into two categories, either detecting the real component n of the medium in refractive index sensing, or determining the imaginary component k through absorption sensing.…”

Section: Introductionmentioning

confidence: 99%

Prediction of medium chemical concentration with micro-ring resonators and deep learning

Mikhail

Shamy

Swillam

et al. 2023

Smart Photonic and Optoelectronic Integrated Circuits 2023

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A new approach for determining the concentration composition of a multi-element media using a micro-ring resonator (MRR) is proposed which allows for noise removal as well as moderately higher average accuracy. This method uses two neural networks, namely a convolutional neural network (CNN) and a deep neural network (DNN). The CNN differentiates the transmission spectrum from the noise. This spectrum is used to obtain selected features before being fed into the DNN, which determines the concentration of each chemical in the analyte. Both models are trained to work with a silicon on-insulator ring resonator operating between the infrared wavelengths of λ=1.46 µm to λ=1.6µm on mixtures of water,ethanol,methanol, and propanol by using simulation data obtained from finite difference eigenmode, although the same approach can be used with other designs and chemical combinations. The CNN was trained using the MRR transmission spectra superimposed with white Gaussian noise as well as Poisson noise to mimic various noise sources, while the DNN underwent training on the extracted features. Average Root-Mean-Square Error was for a range of concentrations from 0.0357-75% is 5.531%.

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“…Fortunately, the multilayer perceptron (MLP) creates the opportunity to solve these two problems. Recently, Li et al demonstrated an MLP regressor-enabled NIR ring sensor 76 for decomposing the resonant spectrum that records the resonant peak shift over time. Compared with the resonant spectrum, the absorption spectrum contains more distinctive information about the molecule and is the main characteristic leveraged in the MIR region.…”

mentioning

confidence: 99%

Midinfrared Spectroscopic Analysis of Aqueous Mixtures Using Artificial-Intelligence-Enhanced Metamaterial Waveguide Sensing Platform

et al. 2022

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As miniaturized solutions, mid-infrared (MIR) waveguide sensors are promising for label-free compositional detection of mixtures leveraging plentiful absorption fingerprints. However, the quantitative analysis of liquid mixtures is still challenging using MIR waveguide sensors, as the absorption spectrum overlaps for multiple organic components accompanied by strong water absorption background. Here, we present an artificial-intelligence-enhanced metamaterial waveguide sensing platform (AIMWSP) for aqueous mixture analysis in the MIR. With the sensitivity-improved metamaterial waveguide and assistance of machine learning, the MIR absorption spectra of a ternary mixture in water can be successfully distinguished and decomposed to single-component spectra for predicting concentration. A classification accuracy of 98.88% for 64 mixing ratios and 92.86% for four concentrations below the limit of detection (972 ppm, based on 3σ) with steps of 300 ppm are realized. Besides, the mixture concentration prediction with root-mean-squared error varying from 0.107 vol % to 1.436 vol % is also achieved. Our work indicates the potential of further extending this sensing platform to MIR spectrometer-on-chip aiming for the data analytics of multiple organic components in aqueous environments.

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

Abstract: A. Q. (2021). Smart ring resonator-based sensor for multicomponent chemical analysis via machine learning. Photonics Research, 9(2),

Cited by 33 publications

References 38 publications

Space-time-regulated imaging analyzer for smart coagulation diagnosis

Space-time-regulated imaging analyzer for smart coagulation diagnosis

Prediction of medium chemical concentration with micro-ring resonators and deep learning

Midinfrared Spectroscopic Analysis of Aqueous Mixtures Using Artificial-Intelligence-Enhanced Metamaterial Waveguide Sensing Platform

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