{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T13:01:05Z","timestamp":1778936465137,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T00:00:00Z","timestamp":1605916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012480","name":"Water Research Australia","doi-asserted-by":"publisher","award":["4535-17"],"award-info":[{"award-number":["4535-17"]}],"id":[{"id":"10.13039\/100012480","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001787","name":"University of South Australia","doi-asserted-by":"publisher","award":["Postgraduate scholarship"],"award-info":[{"award-number":["Postgraduate scholarship"]}],"id":[{"id":"10.13039\/501100001787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u00b1 0.1 mg L\u22121.<\/jats:p>","DOI":"10.3390\/s20226671","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T01:28:48Z","timestamp":1606094928000},"page":"6671","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2794-3431","authenticated-orcid":false,"given":"Sharif","family":"Hossain","sequence":"first","affiliation":[{"name":"Scarce Resources and Circular Economy (ScaRCE), UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-8944","authenticated-orcid":false,"given":"Christopher W.K.","family":"Chow","sequence":"additional","affiliation":[{"name":"Scarce Resources and Circular Economy (ScaRCE), UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"},{"name":"Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guna A.","family":"Hewa","sequence":"additional","affiliation":[{"name":"Scarce Resources and Circular Economy (ScaRCE), UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Cook","sequence":"additional","affiliation":[{"name":"Water Science Laboratory, South Australian Water Corporation, Adelaide, SA 5000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Harris","sequence":"additional","affiliation":[{"name":"Operations &amp; Water Quality, TRILITY, Adelaide, SA 5000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,21]]},"reference":[{"key":"ref_1","unstructured":"Percival, S.L., Yates, M.V., Williams, D.W., Chalmers, R.M., and Gray, N.F. (2014). Chapter Thirty-One-Free and Combined Chlorine. Microbiology of Waterborne Diseases, Academic Press. [2nd ed.]."},{"key":"ref_2","unstructured":"Kirmeyer, G.J., Martel, K., Thompson, G., Radder, L., Klement, W., LeChevallier, M., Baribeau, H., and Flores, A. (2004). Optimizing Chloramine Treatment, American Water Works Association."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ratnayaka, D.D., Brandt, M.J., and Johnson, K.M. (2009). CHAPTER 11-Disinfection of Water. Water Supply, Butterworth-Heinemann. [6th ed.].","DOI":"10.1016\/B978-0-7506-6843-9.00019-6"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1002\/j.1551-8833.1984.tb05337.x","article-title":"Inorganic Chloramines as Drinking Water Disinfectants: A Review","volume":"76","author":"Wolfe","year":"1984","journal-title":"J. Am. Water Works Assoc."},{"key":"ref_5","unstructured":"NHMRC, and NRMMC (2011). Australian Drinking Water Guidelines 6: National Water Quality Management Strategy."},{"key":"ref_6","unstructured":"APHA, AWWA, and WEF (2017). Standard Methods for the Examination of Water and Wastewater, Water Environment Federation. [23rd ed.]."},{"key":"ref_7","unstructured":"Malcov, V.B., Zachman, B., and Scribner, T. (2009). Comparison of On-Line Chlorine Analysis Methods and Instrumentation on Amperometric and Colorimetric Technologies, American Water Works Association."},{"key":"ref_8","first-page":"1","article-title":"Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy","volume":"2014","author":"Dibo","year":"2014","journal-title":"J. Spectrosc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.watres.2016.03.001","article-title":"UV254 absorbance as real-time monitoring and control parameter for micropollutant removal in advanced wastewater treatment with powdered activated carbon","volume":"94","author":"Altmann","year":"2016","journal-title":"Water Res."},{"key":"ref_10","first-page":"1227","article-title":"Support vector machine for ultraviolet spectroscopic water quality analyzers","volume":"32","author":"Du","year":"2004","journal-title":"Chin. J. Anal. Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"143","DOI":"10.2166\/wst.2004.0682","article-title":"Spectral in-situ analysis of NO2, NO3, COD, DOC and TSS in the effluent of a WWTP","volume":"50","author":"Rieger","year":"2004","journal-title":"Water Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.watres.2015.07.029","article-title":"Use of log-transformed absorbance spectra for online monitoring of the reactivity of natural organic matter","volume":"84","author":"Roccaro","year":"2015","journal-title":"Water Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.scitotenv.2016.04.164","article-title":"Development and evaluation of a novel monitor for online measurement of iron, manganese, and chromium in ambient particulate matter (PM)","volume":"565","author":"Wang","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Carreres-Prieto, D., Garc\u00eda, J.T., Cerd\u00e1n-Cartagena, F., and Suardiaz-Muro, J. (2020). Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models. Sensors, 20.","DOI":"10.3390\/s20195631"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.electacta.2011.12.092","article-title":"Revealing the mechanism of indirect ammonia electrooxidation","volume":"63","author":"Gendel","year":"2012","journal-title":"Electrochim. Acta"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1021\/es8016304","article-title":"UV Photodegradation of Inorganic Chloramines","volume":"43","author":"Li","year":"2009","journal-title":"Environ. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7423","DOI":"10.1021\/jp030198k","article-title":"Radiolytic Reactions of Monochloramine in Aqueous Solutions","volume":"107","author":"Poskrebyshev","year":"2003","journal-title":"J. Phys. Chem. A"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/S0003-2670(00)86414-4","article-title":"Ultraviolet absorption spectra of some alkylchloramines","volume":"231","author":"Ferriol","year":"1990","journal-title":"Anal. Chim. Acta"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/S0167-9244(07)80004-7","article-title":"Chapter 2 From spectra to qualitative and quantitative results","volume":"Volume 27","author":"Thomas","year":"2007","journal-title":"UV-Visible Spectrophotometry of Water and Wastewater"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s10661-018-6702-7","article-title":"Use of ultraviolet\u2013visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index","volume":"190","author":"Alves","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"633","DOI":"10.2166\/wst.2017.096","article-title":"Estimation of water quality by UV\/Vis spectrometry in the framework of treated wastewater reuse","volume":"76","author":"Jauzein","year":"2017","journal-title":"Water Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"136765","DOI":"10.1016\/j.scitotenv.2020.136765","article-title":"Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy","volume":"714","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1177\/0142331211403797","article-title":"Predicting organic acid concentration from UV\/vis spectrometry measurements\u2013a comparison of machine learning techniques","volume":"35","author":"Wolf","year":"2011","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kim, C., Eom, J.B., Jung, S., and Ji, T. (2016). Detection of Organic Compounds in Water by an Optical Absorbance Method. Sensors, 16.","DOI":"10.3390\/s16010061"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1080\/10643389.2017.1309186","article-title":"Utilization of UV-Vis spectroscopy and related data analyses for dissolved organic matter (DOM) studies: A review","volume":"47","author":"Li","year":"2017","journal-title":"Crit. Rev. Environ. Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/03067310108044385","article-title":"Determination of Nitrate in Water Containing Dissolved Organic Carbon by Ultraviolet Spectroscopy","volume":"80","author":"Edwards","year":"2001","journal-title":"Int. J. Environ. Anal. Chem."},{"key":"ref_27","first-page":"87","article-title":"Light scattering by small particles","volume":"47","author":"Huber","year":"1998","journal-title":"Aqua"},{"key":"ref_28","first-page":"3020","article-title":"Experimental research of turbidity influence on water quality monitoring of COD in UV-visible spectroscopy","volume":"34","author":"Tang","year":"2014","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, X., Tong, R., Wang, Y., Mei, C., and Li, Q. (2019). Study on an online detection method for ground water quality and instrument design. Sensors, 19.","DOI":"10.3390\/s19092153"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.snb.2015.12.078","article-title":"Novel method of turbidity compensation for chemical oxygen demand measurements by using UV\u2013vis spectrometry","volume":"227","author":"Hu","year":"2016","journal-title":"Sens. Actuators B Chem."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., and Vapnik, V.N. (1992, January 27\u201329). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_33","first-page":"281","article-title":"Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing","volume":"9","author":"Vapnik","year":"1996","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","first-page":"203","article-title":"Support Vector Regression","volume":"11","author":"Basak","year":"2007","journal-title":"Neural Inf. Process. Lett. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.proenv.2011.09.074","article-title":"A Method of Water Quality Assessment Based on Biomonitoring and Multiclass Support Vector Machine","volume":"10","author":"Liao","year":"2011","journal-title":"Procedia Environ. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.asoc.2014.02.002","article-title":"Support vector machine applications in the field of hydrology: A review","volume":"19","author":"Raghavendra","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Nanda, M.A., Seminar, K.B., Nandika, D., and Maddu, A. (2018). A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection. Information, 9.","DOI":"10.3390\/info9010005"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Granata, F., Gargano, R., and De Marinis, G. (2016). Support Vector Regression for Rainfall-Runoff Modeling in Urban Drainage: A Comparison with the EPA\u2019s Storm Water Management Model. Water, 8.","DOI":"10.3390\/w8030069"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_40","unstructured":"Karush, W. (1939). Minima of Functions of Several Variables with Inequalitiesas Side Constraints. [Master\u2019s Thesis, Dept. of Mathematics]."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, J., Liu, L., Zhang, L., Li, G., Sun, Z., and Song, H. (2019). Prediction of Marine Pycnocline Based on Kernel Support Vector Machine and Convex Optimization Technology. Sensors, 19.","DOI":"10.3390\/s19071562"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bae, I., and Ji, U. (2019). Outlier Detection and Smoothing Process for Water Level Data Measured by Ultrasonic Sensor in Stream Flows. Water, 11.","DOI":"10.3390\/w11050951"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Henrie, M., Carpenter, P., and Nicholas, R.E. (2016). Chapter 5-Statistical Processing and Leak Detection. Pipeline Leak Detection Handbook, Gulf Professional Publishing.","DOI":"10.1016\/B978-0-12-802240-5.00005-4"},{"key":"ref_44","unstructured":"Iglewicz, B., and Hoaglin, D.C. (1993). How to Detect and Handle Outliers, ASQC Quality Press."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yoo, C., and Cho, E. (2019). Effect of Multicollinearity on the Bivariate Frequency Analysis of Annual Maximum Rainfall Events. Water, 11.","DOI":"10.3390\/w11050905"},{"key":"ref_46","unstructured":"Sch\u00f6lkopf, B., Burges, C.J.C., and Smola, A.J. (1999). Support Vector Machines for Dynamic Reconstruction of a Chaotic System, MIT Press."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hossain, S., Hewa, G., and Wella-Hewage, S. (2019). A Comparison of Continuous and Event-Based Rainfall\u2013Runoff (RR) Modelling Using EPA-SWMM. Water, 11.","DOI":"10.3390\/w11030611"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1016\/j.watres.2007.11.013","article-title":"Differential absorbance study of effects of temperature on chlorine consumption and formation of disinfection by-products in chlorinated water","volume":"42","author":"Roccaro","year":"2008","journal-title":"Water Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6671\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:35:26Z","timestamp":1760178926000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,21]]},"references-count":48,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20226671"],"URL":"https:\/\/doi.org\/10.3390\/s20226671","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,21]]}}}