Monitoring of Total Phosphorus in Urban Water Bodies Using Silicon Crystal-Based FTIR-ATR Coupled with Different Machine Learning Approaches
Abstract
:1. Introduction
2. Materials and Methods
2.1. Water Sample Collection and Determination of Physical and Chemical Properties
2.2. Spectra Measurements
2.3. Preprocessing of Spectra
2.4. Prediction Models
2.5. Model Evaluation
3. Results and Discussion
3.1. Properties and Characteristics of Natural Water Samples
3.2. FTIR-ATR Spectra of Natural Water Samples
3.3. PCA Analysis
3.4. Prediction of TP in Water Based on Si-ATR and ZnSe-ATR
3.5. Effect of Season Variation on TP Prediction
3.6. Application of SA-PLS Models
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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ATR Accessory | Pretreatment | RV2 | RMSEV (mg L−1) | RPDV |
---|---|---|---|---|
Si | Original data | 0.423 | 0.052 | 1.316 |
Base | 0.681 | 0.050 | 1.761 | |
Base + MC | 0.664 | 0.053 | 1.721 | |
Base + FD | 0.662 | 0.055 | 1.696 | |
Base + SD | 0.641 | 0.053 | 1.669 | |
ZnSe | Original data | 0.267 | 0.049 | 1.127 |
Base | 0.631 | 0.046 | 1.627 | |
Base + MC | 0.566 | 0.051 | 1.505 | |
Base + FD | 0.683 | 0.053 | 1.744 | |
Base + SD | 0.618 | 0.044 | 1.611 |
ATR | Seasons | LV | Calibration Set (75 Samples) | Validation Set (25 Samples) | Bias | |||||
---|---|---|---|---|---|---|---|---|---|---|
RMSECV | RC2 | RMSEC | RPDC | RV2 | RMSEV | RPDV | ||||
Si | Spring | 6 | 0.052 | 0.897 | 0.044 | 3.116 | 0.863 | 0.049 | 2.401 | 0.007 |
Summer | 8 | 0.010 | 0.707 | 0.008 | 1.847 | 0.745 | 0.008 | 1.904 | 0.000 | |
Autumn | 9 | 0.020 | 0.774 | 0.017 | 2.102 | 0.728 | 0.022 | 1.836 | 0.002 | |
Winter | 10 | 0.008 | 0.791 | 0.006 | 2.186 | 0.809 | 0.006 | 2.190 | 0.000 | |
ZnSe | Spring | 6 | 0.059 | 0.810 | 0.052 | 2.297 | 0.763 | 0.078 | 2.053 | 0.000 |
Summer | 13 | 0.009 | 0.761 | 0.007 | 2.047 | 0.743 | 0.008 | 1.918 | 0.000 | |
Autumn | 9 | 0.026 | 0.693 | 0.022 | 1.804 | 0.707 | 0.017 | 1.838 | 0.000 | |
Winter | 9 | 0.008 | 0.763 | 0.007 | 2.052 | 0.744 | 0.007 | 1.902 | 0.000 |
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Zheng, S.; Ma, F.; Zhou, J.; Du, C. Monitoring of Total Phosphorus in Urban Water Bodies Using Silicon Crystal-Based FTIR-ATR Coupled with Different Machine Learning Approaches. Water 2024, 16, 2479. https://doi.org/10.3390/w16172479
Zheng S, Ma F, Zhou J, Du C. Monitoring of Total Phosphorus in Urban Water Bodies Using Silicon Crystal-Based FTIR-ATR Coupled with Different Machine Learning Approaches. Water. 2024; 16(17):2479. https://doi.org/10.3390/w16172479
Chicago/Turabian StyleZheng, Shuailin, Fei Ma, Jianmin Zhou, and Changwen Du. 2024. "Monitoring of Total Phosphorus in Urban Water Bodies Using Silicon Crystal-Based FTIR-ATR Coupled with Different Machine Learning Approaches" Water 16, no. 17: 2479. https://doi.org/10.3390/w16172479