Abstract

The issue of distribution water quality security ensuring is recently attracting global attention due to the potential threat from harmful contaminants. The real-time monitoring based on ultraviolet optical sensors is a promising technique. This method is of reagent-free, low maintenance cost, rapid analysis and wide cover range. However, the ultraviolet absorption spectra are of large size and easily interfered. While within the on-site application, there is almost no prior knowledge like spectral characteristics of potential contaminants before determined. Meanwhile, the concept of normal water quality is also varying due to the operating condition. In this paper, a procedure based on multivariate statistical analysis is proposed to detect distribution water quality anomaly based on ultraviolet optical sensors. Firstly, the principal component analysis is employed to capture the main variety features from the spectral matrix and reduce the dimensionality. A new statistical variable is then constructed and used for evaluating the local outlying degree according to the chi-square distribution in the principal component subspace. The possibility of anomaly of the latest observation is calculated by the accumulation of the outlying degrees from the adjacent previous observations. To develop a more reliable anomaly detection procedure, several key parameters are discussed. By utilizing the proposed methods, the distribution water quality anomalies and the optical abnormal changes can be detected. The contaminants intrusion experiment is conducted in a pilot-scale distribution system by injecting phenol solution. The effectiveness of the proposed procedure is finally testified using the experimental spectral data.

© 2015 Optical Society of America

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References

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  1. L. Perelman, J. Arad, M. Housh, and A. Ostfeld, “Event detection in water distribution systems from multivariate water quality time series,” Environ. Sci. Technol. 46(15), 8212–8219 (2012).
    [Crossref] [PubMed]
  2. K. A. Klise and S. A. McKenna, “Water quality change detection: multivariate algorithms,” in Conference on Optics and Photonics in Global Homeland Security II, T. T. Saito, and D. Lehrfeld, ed. (SPIE, 2006), J2030.
    [Crossref]
  3. Y. Jeffrey Yang, R. C. Haught, and J. A. Goodrich, “Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results,” J. Environ. Manage. 90(8), 2494–2506 (2009).
    [Crossref] [PubMed]
  4. G. Langergraber, A. Weingartner, and N. Fleischmann, “Time-resolved delta spectrometry: a method to define alarm parameters from spectral data,” in International Conference on Automation in Water Quality Monitoring, (IWA, 2006), pp. 13–20.
  5. J. Broeke, G. Langergraber, and A. Weingartner, “On-line and in-situ UV/vis spectroscopy for multi-parameter measurements: a brief review,” Spectrosc. Eur. 18(4), 15–18 (2006).
  6. V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. 41(3), 1 (2009).
    [Crossref]
  7. J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).
  8. M. V. Storey, B. van der Gaag, and B. P. Burns, “Advances in on-line drinking water quality monitoring and early warning systems,” Water Res. 45(2), 741–747 (2011).
    [Crossref] [PubMed]
  9. Y. Jeffrey Yang, J. A. Goodrich, R. M. Clark, and S. Y. Li, “Modeling and testing of reactive contaminant transport in drinking water pipes: Chlorine response and implications for online contaminant detection,” Water Res. 42(6-7), 1397–1412 (2008).
    [Crossref] [PubMed]
  10. N. D. Lourenço, J. C. Menezes, H. M. Pinheiro, and D. Diniz, “Development of PLS calibration models from UV-Vis spectra for TOC estimation at the outlet of a fuel park wastewater treatment plant,” Environ. Technol. 29(8), 891–898 (2008).
    [Crossref] [PubMed]
  11. R. Aryal, S. Vigneswaran, and J. Kandasamy, “Application of ultraviolet (UV) spectrophotometry in the assessment of membrane bioreactor performance for monitoring water and wastewater treatment,” Appl. Spectrosc. 65(2), 227–232 (2011).
    [Crossref]
  12. L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
    [Crossref]
  13. F. J. Acevedo, J. Jiménez, S. Maldonado, E. Domínguez, and A. Narváez, “Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines,” J. Agric. Food Chem. 55(17), 6842–6849 (2007).
    [Crossref] [PubMed]
  14. O. Barbosa-García, G. Ramos-Ortiz, J. L. Maldonado-Molina, M. A. Meneses-Nava, and J. E. Landgrave, “UV–vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila,” Spectrochim. Acta PT. A- Mol, Bio. 66(1), 129–134 (2007).
  15. U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
    [Crossref]
  16. S. Fogelman, M. Blumenstein, and H. Zhao, “Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks,” Neural Comput. Appl. 15(3-4), 197–203 (2006).
    [Crossref]
  17. S. Fogelman, H. Zhao, and M. Blumenstein, “A rapid analytical method for predicting the oxygen demand of wastewater,” Anal. Bioanal. Chem. 386(6), 1773–1779 (2006).
    [Crossref] [PubMed]
  18. R. Guercio and E. Ruzza, “An early warning monitoring system for quality control in a water distribution network,” in International Conference on Sustainable Water Resources Management, C. A. Guercio, E. Di Ruzza, ed. (Wessex Inst Technol, 2007), pp. 143–152.
    [Crossref]
  19. J. Broeke, A. Brandt, A. Weingartner, and F. Hofstadter, “Monitoring of organic micro contaminants in drinking water using a submersible UV/vis spectrophotometer” in NATO Advanced Research Workshop on Security of Water Supply Systems, J. Pollert, B. Dedus, ed. (NATO, 2005), pp. 27–31.
  20. G. Langergraber, J. Broeke, W. Lettl, and A. Weingartner, “Real-time detection of possible harmful events using UV/vis spectrometry,” Spectrosc. Eur. 18(4), 19–22 (2006).
  21. D. J. Dürrenmatt and W. Gujer, “Identification of industrial wastewater by clustering wastewater treatment plant influent ultraviolet visible spectra,” Water Sci. Technol. 63(6), 1153–1159 (2011).
    [Crossref] [PubMed]
  22. N. D. Lourenço, F. Paixão, H. M. Pinheiro, and A. Sousa, “Use of spectra in the visible and near-mid-ultraviolet range with Principal Component Analysis and Partial Least Squares Processing for monitoring of suspended solids in municipal wastewater treatment plants,” Appl. Spectrosc. 64(9), 1061–1067 (2010).
    [Crossref] [PubMed]
  23. S. M. S. Nagendra and M. Khare, “Principal component analysis of urban traffic characteristics and meteorological data,” Transp. Res. Pt D-Transp, Enviro. 8(4), 285–297 (2003).
  24. P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, and M. Alpert, “Fraud classification using principal component analysis of RIDITs,” J. Risk Insur. 69(3), 341–371 (2002).
    [Crossref]
  25. W. Sun, J. Chen, and J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mech. Syst. Signal Process. 21(3), 1300–1317 (2007).
    [Crossref]
  26. W. Zhao, R. Chellappa, and A. Krishnaswamy, “Discriminant analysis of principal components for face recognition,” in IEEE International Conference on Automatic Face and Gesture Recognition, (FG, 1998), pp. 336–341.
    [Crossref]
  27. A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
    [Crossref] [PubMed]
  28. W. Li, H. Yue, S. Valle-Cervantes, and S. Qin, “Recursive PCA for adaptive process monitoring,” J. Process Contr. 10(5), 471–486 (2000).
    [Crossref]
  29. Standard for drinking water, GB 5749–2006 of China, 2006.
  30. H. Kenzo, Handbook of Ultraviolet and Visible Absorption Spectra of Organic Compounds (Plenum Press Data Division, New York 1967)

2012 (1)

L. Perelman, J. Arad, M. Housh, and A. Ostfeld, “Event detection in water distribution systems from multivariate water quality time series,” Environ. Sci. Technol. 46(15), 8212–8219 (2012).
[Crossref] [PubMed]

2011 (3)

R. Aryal, S. Vigneswaran, and J. Kandasamy, “Application of ultraviolet (UV) spectrophotometry in the assessment of membrane bioreactor performance for monitoring water and wastewater treatment,” Appl. Spectrosc. 65(2), 227–232 (2011).
[Crossref]

D. J. Dürrenmatt and W. Gujer, “Identification of industrial wastewater by clustering wastewater treatment plant influent ultraviolet visible spectra,” Water Sci. Technol. 63(6), 1153–1159 (2011).
[Crossref] [PubMed]

M. V. Storey, B. van der Gaag, and B. P. Burns, “Advances in on-line drinking water quality monitoring and early warning systems,” Water Res. 45(2), 741–747 (2011).
[Crossref] [PubMed]

2010 (2)

N. D. Lourenço, F. Paixão, H. M. Pinheiro, and A. Sousa, “Use of spectra in the visible and near-mid-ultraviolet range with Principal Component Analysis and Partial Least Squares Processing for monitoring of suspended solids in municipal wastewater treatment plants,” Appl. Spectrosc. 64(9), 1061–1067 (2010).
[Crossref] [PubMed]

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

2009 (3)

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Y. Jeffrey Yang, R. C. Haught, and J. A. Goodrich, “Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results,” J. Environ. Manage. 90(8), 2494–2506 (2009).
[Crossref] [PubMed]

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. 41(3), 1 (2009).
[Crossref]

2008 (3)

Y. Jeffrey Yang, J. A. Goodrich, R. M. Clark, and S. Y. Li, “Modeling and testing of reactive contaminant transport in drinking water pipes: Chlorine response and implications for online contaminant detection,” Water Res. 42(6-7), 1397–1412 (2008).
[Crossref] [PubMed]

N. D. Lourenço, J. C. Menezes, H. M. Pinheiro, and D. Diniz, “Development of PLS calibration models from UV-Vis spectra for TOC estimation at the outlet of a fuel park wastewater treatment plant,” Environ. Technol. 29(8), 891–898 (2008).
[Crossref] [PubMed]

A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
[Crossref] [PubMed]

2007 (4)

W. Sun, J. Chen, and J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mech. Syst. Signal Process. 21(3), 1300–1317 (2007).
[Crossref]

J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).

F. J. Acevedo, J. Jiménez, S. Maldonado, E. Domínguez, and A. Narváez, “Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines,” J. Agric. Food Chem. 55(17), 6842–6849 (2007).
[Crossref] [PubMed]

O. Barbosa-García, G. Ramos-Ortiz, J. L. Maldonado-Molina, M. A. Meneses-Nava, and J. E. Landgrave, “UV–vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila,” Spectrochim. Acta PT. A- Mol, Bio. 66(1), 129–134 (2007).

2006 (4)

J. Broeke, G. Langergraber, and A. Weingartner, “On-line and in-situ UV/vis spectroscopy for multi-parameter measurements: a brief review,” Spectrosc. Eur. 18(4), 15–18 (2006).

S. Fogelman, M. Blumenstein, and H. Zhao, “Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks,” Neural Comput. Appl. 15(3-4), 197–203 (2006).
[Crossref]

S. Fogelman, H. Zhao, and M. Blumenstein, “A rapid analytical method for predicting the oxygen demand of wastewater,” Anal. Bioanal. Chem. 386(6), 1773–1779 (2006).
[Crossref] [PubMed]

G. Langergraber, J. Broeke, W. Lettl, and A. Weingartner, “Real-time detection of possible harmful events using UV/vis spectrometry,” Spectrosc. Eur. 18(4), 19–22 (2006).

2003 (1)

S. M. S. Nagendra and M. Khare, “Principal component analysis of urban traffic characteristics and meteorological data,” Transp. Res. Pt D-Transp, Enviro. 8(4), 285–297 (2003).

2002 (1)

P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, and M. Alpert, “Fraud classification using principal component analysis of RIDITs,” J. Risk Insur. 69(3), 341–371 (2002).
[Crossref]

2000 (1)

W. Li, H. Yue, S. Valle-Cervantes, and S. Qin, “Recursive PCA for adaptive process monitoring,” J. Process Contr. 10(5), 471–486 (2000).
[Crossref]

Acevedo, F. J.

F. J. Acevedo, J. Jiménez, S. Maldonado, E. Domínguez, and A. Narváez, “Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines,” J. Agric. Food Chem. 55(17), 6842–6849 (2007).
[Crossref] [PubMed]

Alpert, M.

P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, and M. Alpert, “Fraud classification using principal component analysis of RIDITs,” J. Risk Insur. 69(3), 341–371 (2002).
[Crossref]

Alves, M. M.

A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
[Crossref] [PubMed]

Arad, J.

L. Perelman, J. Arad, M. Housh, and A. Ostfeld, “Event detection in water distribution systems from multivariate water quality time series,” Environ. Sci. Technol. 46(15), 8212–8219 (2012).
[Crossref] [PubMed]

Araujo, M. C. U.

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

Aryal, R.

Banerjee, A.

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. 41(3), 1 (2009).
[Crossref]

Barbosa-García, O.

O. Barbosa-García, G. Ramos-Ortiz, J. L. Maldonado-Molina, M. A. Meneses-Nava, and J. E. Landgrave, “UV–vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila,” Spectrochim. Acta PT. A- Mol, Bio. 66(1), 129–134 (2007).

Blumenstein, M.

S. Fogelman, M. Blumenstein, and H. Zhao, “Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks,” Neural Comput. Appl. 15(3-4), 197–203 (2006).
[Crossref]

S. Fogelman, H. Zhao, and M. Blumenstein, “A rapid analytical method for predicting the oxygen demand of wastewater,” Anal. Bioanal. Chem. 386(6), 1773–1779 (2006).
[Crossref] [PubMed]

Brockett, P. L.

P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, and M. Alpert, “Fraud classification using principal component analysis of RIDITs,” J. Risk Insur. 69(3), 341–371 (2002).
[Crossref]

Broeke, J.

G. Langergraber, J. Broeke, W. Lettl, and A. Weingartner, “Real-time detection of possible harmful events using UV/vis spectrometry,” Spectrosc. Eur. 18(4), 19–22 (2006).

J. Broeke, G. Langergraber, and A. Weingartner, “On-line and in-situ UV/vis spectroscopy for multi-parameter measurements: a brief review,” Spectrosc. Eur. 18(4), 15–18 (2006).

Burns, B. P.

M. V. Storey, B. van der Gaag, and B. P. Burns, “Advances in on-line drinking water quality monitoring and early warning systems,” Water Res. 45(2), 741–747 (2011).
[Crossref] [PubMed]

Chandola, V.

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. 41(3), 1 (2009).
[Crossref]

Chellappa, R.

W. Zhao, R. Chellappa, and A. Krishnaswamy, “Discriminant analysis of principal components for face recognition,” in IEEE International Conference on Automatic Face and Gesture Recognition, (FG, 1998), pp. 336–341.
[Crossref]

Chen, J.

W. Sun, J. Chen, and J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mech. Syst. Signal Process. 21(3), 1300–1317 (2007).
[Crossref]

Clark, R. M.

Y. Jeffrey Yang, J. A. Goodrich, R. M. Clark, and S. Y. Li, “Modeling and testing of reactive contaminant transport in drinking water pipes: Chlorine response and implications for online contaminant detection,” Water Res. 42(6-7), 1397–1412 (2008).
[Crossref] [PubMed]

Correia, D. M.

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Cunha, F. A. S.

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

Derrig, R. A.

P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, and M. Alpert, “Fraud classification using principal component analysis of RIDITs,” J. Risk Insur. 69(3), 341–371 (2002).
[Crossref]

Dias, A. M. A.

A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
[Crossref] [PubMed]

Dias, L. G.

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Diniz, D.

N. D. Lourenço, J. C. Menezes, H. M. Pinheiro, and D. Diniz, “Development of PLS calibration models from UV-Vis spectra for TOC estimation at the outlet of a fuel park wastewater treatment plant,” Environ. Technol. 29(8), 891–898 (2008).
[Crossref] [PubMed]

Domínguez, E.

F. J. Acevedo, J. Jiménez, S. Maldonado, E. Domínguez, and A. Narváez, “Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines,” J. Agric. Food Chem. 55(17), 6842–6849 (2007).
[Crossref] [PubMed]

Dürrenmatt, D. J.

D. J. Dürrenmatt and W. Gujer, “Identification of industrial wastewater by clustering wastewater treatment plant influent ultraviolet visible spectra,” Water Sci. Technol. 63(6), 1153–1159 (2011).
[Crossref] [PubMed]

Ferreira, E. C.

A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
[Crossref] [PubMed]

Fleischmann, N.

G. Langergraber, A. Weingartner, and N. Fleischmann, “Time-resolved delta spectrometry: a method to define alarm parameters from spectral data,” in International Conference on Automation in Water Quality Monitoring, (IWA, 2006), pp. 13–20.

Fogelman, S.

S. Fogelman, M. Blumenstein, and H. Zhao, “Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks,” Neural Comput. Appl. 15(3-4), 197–203 (2006).
[Crossref]

S. Fogelman, H. Zhao, and M. Blumenstein, “A rapid analytical method for predicting the oxygen demand of wastewater,” Anal. Bioanal. Chem. 386(6), 1773–1779 (2006).
[Crossref] [PubMed]

Galvão, R. K. H.

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

Golden, L. L.

P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, and M. Alpert, “Fraud classification using principal component analysis of RIDITs,” J. Risk Insur. 69(3), 341–371 (2002).
[Crossref]

Goodrich, J. A.

Y. Jeffrey Yang, R. C. Haught, and J. A. Goodrich, “Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results,” J. Environ. Manage. 90(8), 2494–2506 (2009).
[Crossref] [PubMed]

Y. Jeffrey Yang, J. A. Goodrich, R. M. Clark, and S. Y. Li, “Modeling and testing of reactive contaminant transport in drinking water pipes: Chlorine response and implications for online contaminant detection,” Water Res. 42(6-7), 1397–1412 (2008).
[Crossref] [PubMed]

Gujer, W.

D. J. Dürrenmatt and W. Gujer, “Identification of industrial wastewater by clustering wastewater treatment plant influent ultraviolet visible spectra,” Water Sci. Technol. 63(6), 1153–1159 (2011).
[Crossref] [PubMed]

Hall, J.

J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).

Haught, R. C.

Y. Jeffrey Yang, R. C. Haught, and J. A. Goodrich, “Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results,” J. Environ. Manage. 90(8), 2494–2506 (2009).
[Crossref] [PubMed]

Herrmann, J. G.

J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).

Housh, M.

L. Perelman, J. Arad, M. Housh, and A. Ostfeld, “Event detection in water distribution systems from multivariate water quality time series,” Environ. Sci. Technol. 46(15), 8212–8219 (2012).
[Crossref] [PubMed]

Jeffrey Yang, Y.

Y. Jeffrey Yang, R. C. Haught, and J. A. Goodrich, “Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results,” J. Environ. Manage. 90(8), 2494–2506 (2009).
[Crossref] [PubMed]

Y. Jeffrey Yang, J. A. Goodrich, R. M. Clark, and S. Y. Li, “Modeling and testing of reactive contaminant transport in drinking water pipes: Chlorine response and implications for online contaminant detection,” Water Res. 42(6-7), 1397–1412 (2008).
[Crossref] [PubMed]

Jiménez, J.

F. J. Acevedo, J. Jiménez, S. Maldonado, E. Domínguez, and A. Narváez, “Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines,” J. Agric. Food Chem. 55(17), 6842–6849 (2007).
[Crossref] [PubMed]

Kandasamy, J.

Kefauver, P. C.

J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).

Khare, M.

S. M. S. Nagendra and M. Khare, “Principal component analysis of urban traffic characteristics and meteorological data,” Transp. Res. Pt D-Transp, Enviro. 8(4), 285–297 (2003).

Krishnan, E. R.

J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).

Krishnaswamy, A.

W. Zhao, R. Chellappa, and A. Krishnaswamy, “Discriminant analysis of principal components for face recognition,” in IEEE International Conference on Automatic Face and Gesture Recognition, (FG, 1998), pp. 336–341.
[Crossref]

Kumar, V.

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. 41(3), 1 (2009).
[Crossref]

Landgrave, J. E.

O. Barbosa-García, G. Ramos-Ortiz, J. L. Maldonado-Molina, M. A. Meneses-Nava, and J. E. Landgrave, “UV–vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila,” Spectrochim. Acta PT. A- Mol, Bio. 66(1), 129–134 (2007).

Langergraber, G.

G. Langergraber, J. Broeke, W. Lettl, and A. Weingartner, “Real-time detection of possible harmful events using UV/vis spectrometry,” Spectrosc. Eur. 18(4), 19–22 (2006).

J. Broeke, G. Langergraber, and A. Weingartner, “On-line and in-situ UV/vis spectroscopy for multi-parameter measurements: a brief review,” Spectrosc. Eur. 18(4), 15–18 (2006).

G. Langergraber, A. Weingartner, and N. Fleischmann, “Time-resolved delta spectrometry: a method to define alarm parameters from spectral data,” in International Conference on Automation in Water Quality Monitoring, (IWA, 2006), pp. 13–20.

Lettl, W.

G. Langergraber, J. Broeke, W. Lettl, and A. Weingartner, “Real-time detection of possible harmful events using UV/vis spectrometry,” Spectrosc. Eur. 18(4), 19–22 (2006).

Levine, A.

P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, and M. Alpert, “Fraud classification using principal component analysis of RIDITs,” J. Risk Insur. 69(3), 341–371 (2002).
[Crossref]

Li, J.

W. Sun, J. Chen, and J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mech. Syst. Signal Process. 21(3), 1300–1317 (2007).
[Crossref]

Li, S. Y.

Y. Jeffrey Yang, J. A. Goodrich, R. M. Clark, and S. Y. Li, “Modeling and testing of reactive contaminant transport in drinking water pipes: Chlorine response and implications for online contaminant detection,” Water Res. 42(6-7), 1397–1412 (2008).
[Crossref] [PubMed]

Li, W.

W. Li, H. Yue, S. Valle-Cervantes, and S. Qin, “Recursive PCA for adaptive process monitoring,” J. Process Contr. 10(5), 471–486 (2000).
[Crossref]

Lopes, J. A.

A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
[Crossref] [PubMed]

Lourenço, N. D.

N. D. Lourenço, F. Paixão, H. M. Pinheiro, and A. Sousa, “Use of spectra in the visible and near-mid-ultraviolet range with Principal Component Analysis and Partial Least Squares Processing for monitoring of suspended solids in municipal wastewater treatment plants,” Appl. Spectrosc. 64(9), 1061–1067 (2010).
[Crossref] [PubMed]

N. D. Lourenço, J. C. Menezes, H. M. Pinheiro, and D. Diniz, “Development of PLS calibration models from UV-Vis spectra for TOC estimation at the outlet of a fuel park wastewater treatment plant,” Environ. Technol. 29(8), 891–898 (2008).
[Crossref] [PubMed]

Maldonado, S.

F. J. Acevedo, J. Jiménez, S. Maldonado, E. Domínguez, and A. Narváez, “Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines,” J. Agric. Food Chem. 55(17), 6842–6849 (2007).
[Crossref] [PubMed]

Maldonado-Molina, J. L.

O. Barbosa-García, G. Ramos-Ortiz, J. L. Maldonado-Molina, M. A. Meneses-Nava, and J. E. Landgrave, “UV–vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila,” Spectrochim. Acta PT. A- Mol, Bio. 66(1), 129–134 (2007).

Marx, R. B.

J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).

Meneses-Nava, M. A.

O. Barbosa-García, G. Ramos-Ortiz, J. L. Maldonado-Molina, M. A. Meneses-Nava, and J. E. Landgrave, “UV–vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila,” Spectrochim. Acta PT. A- Mol, Bio. 66(1), 129–134 (2007).

Menezes, J. C.

N. D. Lourenço, J. C. Menezes, H. M. Pinheiro, and D. Diniz, “Development of PLS calibration models from UV-Vis spectra for TOC estimation at the outlet of a fuel park wastewater treatment plant,” Environ. Technol. 29(8), 891–898 (2008).
[Crossref] [PubMed]

Moita, I.

A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
[Crossref] [PubMed]

Nagendra, S. M. S.

S. M. S. Nagendra and M. Khare, “Principal component analysis of urban traffic characteristics and meteorological data,” Transp. Res. Pt D-Transp, Enviro. 8(4), 285–297 (2003).

Narváez, A.

F. J. Acevedo, J. Jiménez, S. Maldonado, E. Domínguez, and A. Narváez, “Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines,” J. Agric. Food Chem. 55(17), 6842–6849 (2007).
[Crossref] [PubMed]

Oliveira, M. S. R.

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

Ostfeld, A.

L. Perelman, J. Arad, M. Housh, and A. Ostfeld, “Event detection in water distribution systems from multivariate water quality time series,” Environ. Sci. Technol. 46(15), 8212–8219 (2012).
[Crossref] [PubMed]

Paixão, F.

Páscoa, R.

A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
[Crossref] [PubMed]

Perelman, L.

L. Perelman, J. Arad, M. Housh, and A. Ostfeld, “Event detection in water distribution systems from multivariate water quality time series,” Environ. Sci. Technol. 46(15), 8212–8219 (2012).
[Crossref] [PubMed]

Peres, A. M.

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Pinheiro, H. M.

N. D. Lourenço, F. Paixão, H. M. Pinheiro, and A. Sousa, “Use of spectra in the visible and near-mid-ultraviolet range with Principal Component Analysis and Partial Least Squares Processing for monitoring of suspended solids in municipal wastewater treatment plants,” Appl. Spectrosc. 64(9), 1061–1067 (2010).
[Crossref] [PubMed]

N. D. Lourenço, J. C. Menezes, H. M. Pinheiro, and D. Diniz, “Development of PLS calibration models from UV-Vis spectra for TOC estimation at the outlet of a fuel park wastewater treatment plant,” Environ. Technol. 29(8), 891–898 (2008).
[Crossref] [PubMed]

Pontes, M. J. C.

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

Qin, S.

W. Li, H. Yue, S. Valle-Cervantes, and S. Qin, “Recursive PCA for adaptive process monitoring,” J. Process Contr. 10(5), 471–486 (2000).
[Crossref]

Ramos-Ortiz, G.

O. Barbosa-García, G. Ramos-Ortiz, J. L. Maldonado-Molina, M. A. Meneses-Nava, and J. E. Landgrave, “UV–vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila,” Spectrochim. Acta PT. A- Mol, Bio. 66(1), 129–134 (2007).

Rocha, I.

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Rocha, O.

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Rodrigues, L. R.

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Sanches, F. A. C.

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

Silva, E. C.

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

Sousa, A.

Souto, U. T. C. P.

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

Storey, M. V.

M. V. Storey, B. van der Gaag, and B. P. Burns, “Advances in on-line drinking water quality monitoring and early warning systems,” Water Res. 45(2), 741–747 (2011).
[Crossref] [PubMed]

Sun, W.

W. Sun, J. Chen, and J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mech. Syst. Signal Process. 21(3), 1300–1317 (2007).
[Crossref]

Torres, D.

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Valle-Cervantes, S.

W. Li, H. Yue, S. Valle-Cervantes, and S. Qin, “Recursive PCA for adaptive process monitoring,” J. Process Contr. 10(5), 471–486 (2000).
[Crossref]

van der Gaag, B.

M. V. Storey, B. van der Gaag, and B. P. Burns, “Advances in on-line drinking water quality monitoring and early warning systems,” Water Res. 45(2), 741–747 (2011).
[Crossref] [PubMed]

Veloso, A. C.

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

Vigneswaran, S.

Weingartner, A.

J. Broeke, G. Langergraber, and A. Weingartner, “On-line and in-situ UV/vis spectroscopy for multi-parameter measurements: a brief review,” Spectrosc. Eur. 18(4), 15–18 (2006).

G. Langergraber, J. Broeke, W. Lettl, and A. Weingartner, “Real-time detection of possible harmful events using UV/vis spectrometry,” Spectrosc. Eur. 18(4), 19–22 (2006).

G. Langergraber, A. Weingartner, and N. Fleischmann, “Time-resolved delta spectrometry: a method to define alarm parameters from spectral data,” in International Conference on Automation in Water Quality Monitoring, (IWA, 2006), pp. 13–20.

Yue, H.

W. Li, H. Yue, S. Valle-Cervantes, and S. Qin, “Recursive PCA for adaptive process monitoring,” J. Process Contr. 10(5), 471–486 (2000).
[Crossref]

Zaffiro, A. D.

J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).

Zhao, H.

S. Fogelman, H. Zhao, and M. Blumenstein, “A rapid analytical method for predicting the oxygen demand of wastewater,” Anal. Bioanal. Chem. 386(6), 1773–1779 (2006).
[Crossref] [PubMed]

S. Fogelman, M. Blumenstein, and H. Zhao, “Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks,” Neural Comput. Appl. 15(3-4), 197–203 (2006).
[Crossref]

Zhao, W.

W. Zhao, R. Chellappa, and A. Krishnaswamy, “Discriminant analysis of principal components for face recognition,” in IEEE International Conference on Automatic Face and Gesture Recognition, (FG, 1998), pp. 336–341.
[Crossref]

ACM Comput. Surv. (1)

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. 41(3), 1 (2009).
[Crossref]

Anal. Bioanal. Chem. (1)

S. Fogelman, H. Zhao, and M. Blumenstein, “A rapid analytical method for predicting the oxygen demand of wastewater,” Anal. Bioanal. Chem. 386(6), 1773–1779 (2006).
[Crossref] [PubMed]

Appl. Spectrosc. (2)

Environ. Sci. Technol. (1)

L. Perelman, J. Arad, M. Housh, and A. Ostfeld, “Event detection in water distribution systems from multivariate water quality time series,” Environ. Sci. Technol. 46(15), 8212–8219 (2012).
[Crossref] [PubMed]

Environ. Technol. (1)

N. D. Lourenço, J. C. Menezes, H. M. Pinheiro, and D. Diniz, “Development of PLS calibration models from UV-Vis spectra for TOC estimation at the outlet of a fuel park wastewater treatment plant,” Environ. Technol. 29(8), 891–898 (2008).
[Crossref] [PubMed]

Food Chem. (2)

U. T. C. P. Souto, M. J. C. Pontes, E. C. Silva, R. K. H. Galvão, M. C. U. Araujo, F. A. C. Sanches, F. A. S. Cunha, and M. S. R. Oliveira, “UV–Vis spectrometric classification of coffees by SPA–LDA,” Food Chem. 119(1), 368–371 (2010).
[Crossref]

L. G. Dias, A. C. Veloso, D. M. Correia, O. Rocha, D. Torres, I. Rocha, L. R. Rodrigues, and A. M. Peres, “UV spectrophotometry method for the monitoring of galacto-oligosaccharides production,” Food Chem. 113(1), 246–252 (2009).
[Crossref]

J. Agric. Food Chem. (1)

F. J. Acevedo, J. Jiménez, S. Maldonado, E. Domínguez, and A. Narváez, “Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines,” J. Agric. Food Chem. 55(17), 6842–6849 (2007).
[Crossref] [PubMed]

J. Amer. Water Work, Assn. (1)

J. Hall, A. D. Zaffiro, R. B. Marx, P. C. Kefauver, E. R. Krishnan, and J. G. Herrmann, “On-line water quality parameters as indicators of distribution system contamination,” J. Amer. Water Work, Assn. 99(1), 66–77 (2007).

J. Environ. Manage. (1)

Y. Jeffrey Yang, R. C. Haught, and J. A. Goodrich, “Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results,” J. Environ. Manage. 90(8), 2494–2506 (2009).
[Crossref] [PubMed]

J. Process Contr. (1)

W. Li, H. Yue, S. Valle-Cervantes, and S. Qin, “Recursive PCA for adaptive process monitoring,” J. Process Contr. 10(5), 471–486 (2000).
[Crossref]

J. Risk Insur. (1)

P. L. Brockett, R. A. Derrig, L. L. Golden, A. Levine, and M. Alpert, “Fraud classification using principal component analysis of RIDITs,” J. Risk Insur. 69(3), 341–371 (2002).
[Crossref]

Mech. Syst. Signal Process. (1)

W. Sun, J. Chen, and J. Li, “Decision tree and PCA-based fault diagnosis of rotating machinery,” Mech. Syst. Signal Process. 21(3), 1300–1317 (2007).
[Crossref]

Neural Comput. Appl. (1)

S. Fogelman, M. Blumenstein, and H. Zhao, “Estimation of chemical oxygen demand by ultraviolet spectroscopic profiling and artificial neural networks,” Neural Comput. Appl. 15(3-4), 197–203 (2006).
[Crossref]

Spectrochim. Acta PT. A- Mol, Bio. (1)

O. Barbosa-García, G. Ramos-Ortiz, J. L. Maldonado-Molina, M. A. Meneses-Nava, and J. E. Landgrave, “UV–vis absorption spectroscopy and multivariate analysis as a method to discriminate tequila,” Spectrochim. Acta PT. A- Mol, Bio. 66(1), 129–134 (2007).

Spectrosc. Eur. (2)

J. Broeke, G. Langergraber, and A. Weingartner, “On-line and in-situ UV/vis spectroscopy for multi-parameter measurements: a brief review,” Spectrosc. Eur. 18(4), 15–18 (2006).

G. Langergraber, J. Broeke, W. Lettl, and A. Weingartner, “Real-time detection of possible harmful events using UV/vis spectrometry,” Spectrosc. Eur. 18(4), 19–22 (2006).

Transp. Res. Pt D-Transp, Enviro. (1)

S. M. S. Nagendra and M. Khare, “Principal component analysis of urban traffic characteristics and meteorological data,” Transp. Res. Pt D-Transp, Enviro. 8(4), 285–297 (2003).

Water Res. (2)

M. V. Storey, B. van der Gaag, and B. P. Burns, “Advances in on-line drinking water quality monitoring and early warning systems,” Water Res. 45(2), 741–747 (2011).
[Crossref] [PubMed]

Y. Jeffrey Yang, J. A. Goodrich, R. M. Clark, and S. Y. Li, “Modeling and testing of reactive contaminant transport in drinking water pipes: Chlorine response and implications for online contaminant detection,” Water Res. 42(6-7), 1397–1412 (2008).
[Crossref] [PubMed]

Water Sci. Technol. (2)

D. J. Dürrenmatt and W. Gujer, “Identification of industrial wastewater by clustering wastewater treatment plant influent ultraviolet visible spectra,” Water Sci. Technol. 63(6), 1153–1159 (2011).
[Crossref] [PubMed]

A. M. A. Dias, I. Moita, M. M. Alves, E. C. Ferreira, R. Páscoa, and J. A. Lopes, “Activated sludge process monitoring through in situ near-infrared spectral analysis,” Water Sci. Technol. 57(10), 1643–1650 (2008).
[Crossref] [PubMed]

Other (7)

Standard for drinking water, GB 5749–2006 of China, 2006.

H. Kenzo, Handbook of Ultraviolet and Visible Absorption Spectra of Organic Compounds (Plenum Press Data Division, New York 1967)

R. Guercio and E. Ruzza, “An early warning monitoring system for quality control in a water distribution network,” in International Conference on Sustainable Water Resources Management, C. A. Guercio, E. Di Ruzza, ed. (Wessex Inst Technol, 2007), pp. 143–152.
[Crossref]

J. Broeke, A. Brandt, A. Weingartner, and F. Hofstadter, “Monitoring of organic micro contaminants in drinking water using a submersible UV/vis spectrophotometer” in NATO Advanced Research Workshop on Security of Water Supply Systems, J. Pollert, B. Dedus, ed. (NATO, 2005), pp. 27–31.

W. Zhao, R. Chellappa, and A. Krishnaswamy, “Discriminant analysis of principal components for face recognition,” in IEEE International Conference on Automatic Face and Gesture Recognition, (FG, 1998), pp. 336–341.
[Crossref]

G. Langergraber, A. Weingartner, and N. Fleischmann, “Time-resolved delta spectrometry: a method to define alarm parameters from spectral data,” in International Conference on Automation in Water Quality Monitoring, (IWA, 2006), pp. 13–20.

K. A. Klise and S. A. McKenna, “Water quality change detection: multivariate algorithms,” in Conference on Optics and Photonics in Global Homeland Security II, T. T. Saito, and D. Lehrfeld, ed. (SPIE, 2006), J2030.
[Crossref]

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Figures (25)

Fig. 1
Fig. 1 Main stages of the proposed procedure.
Fig. 2
Fig. 2 Main stages of the outlying degrees calculation.
Fig. 3
Fig. 3 a)Simulating intruded concentration, absorbance at b)270nm, c)370nm for each step.
Fig. 4
Fig. 4 a) simulating intensity, b) anomaly probability, c) alarm signal d) alarm signal assessment.
Fig. 5
Fig. 5 The pilot scale distribution pipe system setup for the testing of the real contaminant intrusion.
Fig. 6
Fig. 6 Spectra of phenol and peak amplitudes with back ground removed at different concentrations.
Fig. 7
Fig. 7 Signal to noise ratio against concentrations for both the whole spectrum (Left) and the absorbance at 270nm (Right).
Fig. 8
Fig. 8 a) Actual intruded concentration, absorbance at b) 260nm, c) 270nm, d) 280nm for each step.
Fig. 9
Fig. 9 Absorbance curves plot against wavelength 255-280nm and time.
Fig. 10
Fig. 10 Eigen values (Left) and the cumulative percent variance (Right) for all spectral data analyzed using the Principal Components Analysis.
Fig. 11
Fig. 11 True intruded concentration and detected event probability for each step.
Fig. 12
Fig. 12 ROC curve of the proposed procedure.
Fig. 13
Fig. 13 a) intruded concentration, b) anomaly probability, c) alarm signal d) alarm signal assessment when spectral range 220nm-400nm selected in analysis.
Fig. 14
Fig. 14 a) intruded concentration, b) anomaly probability, c) alarm signal d) alarm signal assessment when spectral range 230nm-500nm selected in analysis.
Fig. 15
Fig. 15 a) intruded concentration, b)anomaly probability, c)alarm signal d)alarm signal assessment using moving window size 100.
Fig. 16
Fig. 16 a) intruded concentration, b) anomaly probability, c) alarm signal d) alarm signal assessment using moving window size 500.
Fig. 17
Fig. 17 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using cut-off threshold 0.90.
Fig. 18
Fig. 18 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using cut-off threshold 0.80.
Fig. 19
Fig. 19 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using cut-off threshold 0.70.
Fig. 20
Fig. 20 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using cut-off threshold 0.60.
Fig. 21
Fig. 21 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using cumulating scale 3.
Fig. 22
Fig. 22 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using cumulating scale 6.
Fig. 23
Fig. 23 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using cumulating scale 9.
Fig. 24
Fig. 24 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using threshold method at 270nm.
Fig. 25
Fig. 25 a) intruded concentration, b) anomaly probability, c) alarm signal d)alarm signal assessment using threshold method at whole wavelengths from 230nm to 400nm.

Tables (8)

Tables Icon

Table 1 Calculation of FAR and PD

Tables Icon

Table 2 Procedure parameters configures

Tables Icon

Table 3 Approach parameters need to be set

Tables Icon

Table 4 AUC using different wavelength range

Tables Icon

Table 5 AUC using different moving window size

Tables Icon

Table 6 AUC using different cut-off threshold

Tables Icon

Table 7 AUC using different cumulating scale

Tables Icon

Table 8 AUC using 3σ threshold method

Equations (11)

Equations on this page are rendered with MathJax. Learn more.

X= t 1 p 1 T + t 2 p 2 T + t 3 p 3 T ++ t m p m T
X= t 1 p 1 T + t 2 p 2 T + t 3 p 3 T ++ t f p f T +E
CPV(f)= j=1 f λ j j=1 m λ i Threshol d cpv
D M 2 ( x i )= t i1 2 λ 1 + t i2 2 λ 2 ++ t if 2 λ f
χ α 2 (f)= D M 2 ( x i )
P o ( x i )=1α
P A ( x i )= a i3σ P o ( x i3σ )+ a i3σ+1 P o ( x i3σ+1 )++ a i P o ( x i )
a j = e ( ji ) 2 2 σ 2 k=i3σ i e ( ki ) 2 2 σ 2
j=i3σ i a j =1
a=5 σ 270 e ( λ270 ) 2 2× 20 2
b=5 σ 370 e ( λ370 ) 2 2× 30 2

Metrics