Machine learning techniques for real-time UV-Vis spectral analysis to monitor dissolved nutrients in surface water

Fei, Chuhong; Cao, Xiang Sheng; Zang, Devin; Hu, Cindy; Wu, Claudia; Morris, Eric; Tao, Juan; Liu, Ting; Λαμπρόπουλος, Γεώργιος

doi:10.1117/12.2577050

Cited by 3 publications

(4 citation statements)

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“…SVM is a machine learning algorithm that can be used for classification, regression and outlier detection. SVM was used to determine the concentration of dissolved nutrients in surface water using the full spectral wavelengths and laboratory values and demonstrated the effectiveness of the approach [58]. SVR is similar machine learning method as SVM, but it works with continuous values instead of classification, as in SVM.…”

Section: User-developed Algorithms For Spectral Absorbance Measurementsmentioning

confidence: 99%

“…UV-Vis instruments generally work well for real-time monitoring for treated drinking water, as fewer interferences exist [58]. However, they have experienced measurement issues in field applications for source water quality monitoring, particularly for surface water that has complex chemical compositions.…”

Section: Challenges and Solutions Of Using Online Uv-vis Spectrophoto...mentioning

confidence: 99%

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Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Shi

Chow

Fabris

et al. 2022

Sensors

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Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system.

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Section: User-developed Algorithms For Spectral Absorbance Measurementsmentioning

confidence: 99%

Section: Challenges and Solutions Of Using Online Uv-vis Spectrophoto...mentioning

confidence: 99%

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review

Shi

Chow

Fabris

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…These remote sensing methods have been used to monitor contaminants in water, air quality trends, dissolved nutrients in surface water, and many other advanced techniques. 33–35 For example, Spangenberg et al demonstrated how quantum materials could be combined to detect relative concentrations of mixtures within water in real-time. 34 Fei et al demonstrated that the right combination of machine learning algorithms and SPS material, the monitoring of groundwater contamination could be achieved.…”

Section: Introductionmentioning

confidence: 99%

“…34 Fei et al demonstrated that the right combination of machine learning algorithms and SPS material, the monitoring of groundwater contamination could be achieved. 35 However, the limited dataset of 1665 materials used in Fei et al 's work highlights the need for much larger datasets. Moreover, Mamede et al further demonstrated the potential of machine learning to be applied with quantum materials by focusing on finding the UV/vis absorption spectrum of organic molecules using fingerprints generated from 2D chemical structures.…”

Section: Introductionmentioning

confidence: 99%