Detection of Organic Compounds in Water by an Optical Absorbance Method

Kim, Chi Hoon; Eom, Joo Beom; Jung, Sungmoon; Ji, Taeksoo

doi:10.3390/s16010061

Cited by 41 publications

(24 citation statements)

References 23 publications

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“…Many studies indicated that machine learning has potential for the analysis of single or multi-wavelength spectral data [ 10 , 20 , 21 , 22 , 23 ]. For instance, using UV absorbance spectrometry in the 250–300-nm region, Kim et al [ 24 ] used a multiple linear regression model to detect organic compounds in water. A partial least square (PLS) regression model was developed by Carré et al [ 21 ] to establish a relationship between spectral data and total suspended solids (TSSs), turbidity, and chemical oxygen demand (COD) in reclaimed water.…”

Section: Introductionmentioning

confidence: 99%

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Hossain

Chow

Hewa

et al. 2020

Sensors

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The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.

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Section: Introductionmentioning

confidence: 99%

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Hossain

Chow

Hewa

et al. 2020

Sensors

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“…Among these results, the properties of the organics with unsaturated bonds, such as the aromatic compounds that strongly absorb UV light in the optimum wavelength range of 270-290 nm, showed a clear variation in absorbance for different concentrations. Moreover, saturated molecules such as inorganic compounds which are not detected in the wavelength range of 250-300 nm were also considered [16]. The absorption spectra measured ten times for each sample in the UV-LED spectroscopy system are shown in figures 3(d)-(f).…”

Section: Resultsmentioning

confidence: 99%

“…In figure 4, a best-fit line was drawn through the points of the calculated concentrations. In addition, each adjusted coefficient for the determination of R figure 4(d)), implying that the concentrations of the samples can be determined with accuracies of 99.3%, 99.2%, 95.3%, and 97.8%, respectively [16,18].…”

Section: Resultsmentioning

confidence: 99%

Determination of organic compounds in water using ultraviolet LED

Kim

Eom

2018

Meas. Sci. Technol.

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This paper describes a method of detecting organic compounds in water using an ultraviolet LED (280 nm) spectroscopy system and a photodetector. The LED spectroscopy system showed a high correlation between the concentration of the prepared potassium hydrogen phthalate and that calculated by multiple linear regression, indicating an adjusted coefficient of determination ranging from 0.953-0.993. In addition, a comparison between the performance of the spectroscopy system and the total organic carbon analyzer indicated that the difference in concentration was small. Based on the close correlation between the spectroscopy and photodetector absorbance values, organic measurement with a photodetector could be configured for monitoring.

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“…Nevertheless, because of some drawbacks, such as complex sample pretreatment course, long measurement period, and the requirement of chemical reagents, these systems are not suitable for applications facing sudden pollution accidents. UV-visible spectroscopy has been attracting growing attention in this application field with its advantages of fast response, in-situ multi-parameter analysis, no secondary pollution, and low maintenance costs [3,4,5]. Existing studies on water quality monitoring with UV-visible absorption spectra include building analytical models for certain parameters, such as total organic carbon (TOC) and chemical oxygen demand (COD) [6,7,8], correcting turbidity influences [9], denoising spectral data [10,11], and developing spectral compression approaches [12].…”

Section: Introductionmentioning

confidence: 99%

Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks

Meng

Zhou

et al. 2018

Sensors

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Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.

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Detection of Organic Compounds in Water by an Optical Absorbance Method

Cited by 41 publications

References 23 publications

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Determination of organic compounds in water using ultraviolet LED

Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks

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