Deep Learning Denoising for Enhanced Acetone Detection in Cavity Ring-Down Spectroscopy
Abstract
1. Introduction
2. Materials and Methods
2.1. Principle of CRDS-Based Gas Detection and Background Subtraction
2.2. CRDS-Based Gas Detection System
2.3. Dataset Construction and Noise Modeling
2.4. Design and Training of the DUFC Network
3. Results
3.1. Simulation of the Noise Reduction Effect of the Data
3.2. Validation Using Experimental Data
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
| CRDS | Cavity Ring-Down Spectroscopy |
| PMT | Photomultiplier Tube |
| SNR | Signal-to-Noise Ratio |
| DUFC | Decay-Upsampling FC-Net |
| FFT | Fast Fourier Transform |
| WT | Wavelet Transform |
| NLS | Nonlinear Least Squares |
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| Algorithm | Limitations |
|---|---|
| Linear/Nonlinear Least Squares | Easily affected by the initial iteration point, leading to instability |
| Polynomial Fitting | Prone to overfitting and sensitive to noise |
| Fast Fourier Transform (FFT) | Difficulty in distinguishing noise components that share the same frequency as the signal |
| Savitzky–Golay Filter | Relies on manual parameter tuning, with limited generalization capability |
| Wavelet Transform | Effective for high-frequency noise, but struggles with low-frequency noise that is coupled with the ring-down signal |
| CNN-based methods | Local feature extraction limits global decay modeling; high computational cost |
| Transformer-based methods | Heavy resource consumption; lack of prior knowledge of decay function characteristics |
| Substance | Chemical Formula | Concentration in Exhaled Breath | UV Absorption Band (nm) | Absorption at 266 nm | Relative Intensity (Reference: Acetone) |
|---|---|---|---|---|---|
| Acetone | (CH3)2CO | 0.49 ppmv | 225–320 | 2.45 × 10−5 | 1.000 |
| Water | H2O | ~6% | <199 | 0 | 0 |
| Nitrogen | N2 | 78% | 100–150 | 0 | 0 |
| Oxygen | O2 | 16% | 250–300 | 2.13 × 10−5 | 0.869 |
| Carbon dioxide | CO2 | 5% | 105–300 | 5.05 × 10−5 | 2.061 |
| Nitric oxide | NO | 1–20 ppbv | <230 | 0 | 0 |
| Carbon monoxide | CO | 1–10 ppmv | 128–160 | 0 | 0 |
| Ammonia | NH3 | 1–1 ppmv | 111–220 | 0 | 0 |
| Isoprene | C5H8 | 50–200 ppbv | <233 | 1.23 × 10−7 | 0.005 |
| Other trace gases | — | 0–50 ppbv | <233 | <<1.23 × 10−7 | <<0.005 |
| Algorithm | Average Time Cost/(s) | Average SNR/(dB) | Maximum SNR/(dB) |
|---|---|---|---|
| DUFC | 0.000207 | 21.84 | 28.50 |
| POLY | 0.000818 | 22.74 | 30.22 |
| SVM | 0.057068 | 21.95 | 26.83 |
| FFT | 0.000164 | 13.72 | 14.52 |
| S-G | 0.000283 | 20.24 | 24.16 |
| WT | 0.000198 | 18.38 | 20.08 |
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Li, W.; Shi, D.; Wang, F.; Song, Y.; Yang, Y.; Sun, J.; Jiang, C. Deep Learning Denoising for Enhanced Acetone Detection in Cavity Ring-Down Spectroscopy. Chemosensors 2026, 14, 92. https://doi.org/10.3390/chemosensors14040092
Li W, Shi D, Wang F, Song Y, Yang Y, Sun J, Jiang C. Deep Learning Denoising for Enhanced Acetone Detection in Cavity Ring-Down Spectroscopy. Chemosensors. 2026; 14(4):92. https://doi.org/10.3390/chemosensors14040092
Chicago/Turabian StyleLi, Wenxuan, Dongxin Shi, Feifei Wang, Yuxiao Song, Yong Yang, Jing Sun, and Chenyu Jiang. 2026. "Deep Learning Denoising for Enhanced Acetone Detection in Cavity Ring-Down Spectroscopy" Chemosensors 14, no. 4: 92. https://doi.org/10.3390/chemosensors14040092
APA StyleLi, W., Shi, D., Wang, F., Song, Y., Yang, Y., Sun, J., & Jiang, C. (2026). Deep Learning Denoising for Enhanced Acetone Detection in Cavity Ring-Down Spectroscopy. Chemosensors, 14(4), 92. https://doi.org/10.3390/chemosensors14040092

