Classification and calibration of organic matter fluorescence data with multiway analysis methods and artificial neural networks: An operational tool for improved drinking water treatment

Environmetrics

Volumn 22, Issue 3, 2011, Pages 256-270

  Bieroza, Magdalena ^a   Baker, Andy ^b   Bridgeman, John ^c  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Data mining; Fluorescence spectroscopy; Multivariate statistics; Neural networks; Organic matter removal

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; CORRELATION; DATA MINING; DATA SET; DRINKING WATER; FLUORESCENCE SPECTROSCOPY; MULTIVARIATE ANALYSIS; ORGANIC MATTER; SAMPLING; TOTAL ORGANIC CARBON; WATER TREATMENT;

EID: 79954612458     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.1045     Document Type: Article

References (67)

1. Alunkaynak A. 2007. Forecasting surface water level fluctuations of lake Van by artificial neural networks. Water Resources Management 21(2): 399-408.
2. Andersen CM, Bro R. 2003. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data. Journal of Chemometrics 17: 200-215.
3. Andersson CA, Bro R. 2000. The N-way toolbox for MATLAB. Chemometrics and Intelligent Laboratory Systems 52(1): 1-4.
4. Andrews JM, Lieberman SH. 1994. Neural network approach to qualitative identification of fuels and oils from laser induced fluorescence spectra. Analytica Chimica Acta 285(1-2): 237-246.
5. Antunes MCG, Esteves da Silva JCG. 2005. Multivariate curve resolution analysis excitation-emission matrices of fluorescence of humic substances. Analytica Chimica Acta 546: 52-59.
6. Bahram M, Bro R, Stedmon C, Afkhami S. 2006. Handling of Rayleigh and Raman scatter for PARAFAC modeling of fluorescence data using interpolation. Journal of Chemometrics 20(3-4): 99-105.
7. Baker A. 2001. Fluorescence excitation-emission matrix characterization of some sewage-impacted rivers. Environmental Science and Technology 35(5): 948-953.
8. Baker A. 2002. Fluorescence excitation-emission matrix characterization of river waters impacted by a tissue mill effluent. Environmental Science and Technology 36(7): 1377-1382.
9. Basheer IA, Hajmeer M. 2000. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 43(1): 3-31.
10. Battin TJ. 1998. Dissolved organic matter and its optical properties in a blackwater tributary of the upper Orinoco river, Venezuela. Organic Geochemistry 28(9-10): 561-569.
11. Belzile C, Guo L. 2006. Optical properties of low molecular weight and colloidal organic matter: application of the ultrafiltration permeation model to DOM absorption and fluorescence. Marine Chemistry 98(2-4): 183-196.
12. Bengraïne K, Marhaba TF. 2003. Comparison of spectral fluorescent signatures-based models to characterize DOM in treated water samples. Journal of Hazardous Materials B 100(1-3): 117-130.
13. Bieroza M, Baker A, Bridgeman J. 2009. Relating freshwater organic matter fluorescence to organic carbon removal efficiency in drinking water treatment. Science of Total Environment 407: 1765-1774.
14. Boehme J, Coble P, Conmy R, Stovall-Leonard A. 2004. Examining CDOM fluorescence variability using principal component analysis: seasonal and regional modelling of three-dimensional fluorescence in the Gulf of Mexico. Marine Chemistry 89(1-4): 3-14.
15. Bos M, Bos A, van der Linden WE. 1993. Data processing by neural networks in quantitative chemical analysis. The Analyst 118(4): 323-328.
16. Bro R. 1997. PARAFAC. Tutorial and applications. Chemometrics and Intelligent Laboratory Systems 38(2): 149-171.
17. Bro R. 1998. Multi-way analysis in the food industry. Models, algorithms, and applications. PhD Dissertation. Department of Dairy and Food Science, Royal Veterinary and Agricultural University, Denmark.
18. Cammack WKL, Kalff J, Prairie YT, Smith EM. 2004. Fluorescent dissolved organic matter in lakes: relationship with heterotrophic metabolism. Limnology and Oceanography 49(6): 2034-2045.
19. Chen W, Westerhoff P, Leenheer JA, Booksh K. 2003. Fluorescence excitation-emission matrix regional integration to quantify spectra for dissolve organic matter. Environmental Science and Technology 37: 5701-5710.
20. Clark CD, Jimenez-Morais J, Jones G, Zanardi-Lamardo E, Moore CA, Zika RG. 2002. A time-resolved fluorescence study of dissolved organic matter in a riverine to marine transition zone. Marine Chemistry 78(2-3): 121-135.
21. Coble PG. 1996. Characterization of marine and terrestrial DOM in seawater using excitation-emission spectroscopy. Marine Chemistry 51(4): 325-346.
22. Cumberland SA, Baker A. 2007. The freshwater dissolved organic matter fluorescence-total organic carbon relationship. Hydrological Processes 21(16): 2093-2099.
23. Daliakopoulos IN, Coulibaly P, Tsanis IK. 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology 309(1-4): 229-240.
24. Davies DL, Bouldin DW. 1979. Cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2): 224-227.
25. Blough NV, Del Vecchio R. 2002. Photobleaching of chromophoric dissolved organic matter in natural waters: kinetics and modeling. Marine Chemistry 78(4): 231-253.
26. de Souza Sierra MM, Donard OF, Lamotte M. 1997. Spectral identification and behavior of dissolved organic fluorescent material during estuarine mixing processes. Marine Chemistry 58(1-2): 51-58.
27. Despagne F, Massart DL. 1998. Neural networks in multivariate calibration. The Analyst 123(11): 157R-178R.
28. Determann S, Lobbes JM, Reuter R, Rullkötter J. 1998. Ultraviolet fluorescence excitation and emission spectroscopy of marine algae and bacteria. Marine Chemistry 62(1-2): 137-156.
29. Divya O, Mishra AK. 2007. Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study. Analytica Chimica Acta 592(1): 82-90.
30. Garcia JS, da Silva GA, Arruda MA, Poppi RJ. 2007. Application of Kohonen neural network to exploratory analyses of synchroton radiation X-ray fluorescence measurements of sunflower metalloproteins. X-ray Spectrometry 36(2): 122-129.
31. Gontarski CA, Rodrigues PR, Mori M, Prenem LF. 2000. Simulation of an industrial wastewater treatment plant using artificial neural networks. Computers and Chemical Engineering 24(2-7): 1719-1723.
32. Guimet F, Ferré J, Boqué R. 2005. Rapid detection of olive-pomace oil adulteration in extra vrgin olive oils from the protected denomination of origin 'Siurana' using excitation-emission fluorescence spectroscopy and three-way methods of analysis. Analytica Chimica Acta 544(1-2): 143-152.
33. Häck M, Kohne M. 1996. Estimation of wastewater process parameters using neural networks. Water Science and Technology 33(1): 101-115.
34. Hall GJ, Clow KE, Kenny JE. 2005. Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis. Environmental Science and Technology 39(19): 7560-7567.
35. Hammerstrom D. 1993. Working with neural networks. IEEE Spectrum 30(7): 46-53.
36. Henrion R, Henrion G, Böhme M, Behrendt H. 1997. Three-way principal components analysis for fluorescence spectroscopic classification of algae species. Fresenius' Journal of Analytical Chemistry 357(5): 522-526.
37. Hudson NJ, Baker A, Reynolds D. 2007. Fluorescence analysis of dissolved organic matter in natural, waste and polluted waters-a review. River Research and Applications 23(6): 631-649.
38. 5 test in water quality assessment: an example from South West England. Science of the Total Environment 391: 149-158.
39. Jain A, Dubes R. 1998. Algorithms for Clustering Data. Prentice-Hall: New Jersey.
40. Kiers H. 1991. Hierarchical relations among 3-way methods. Psychometrika 56(3): 449-470.
41. Kohonen T. 1998. The self-organizing map. Neurocomputing 21(1): 1-6.
42. Kohonen T. 2001. Self-organizing Maps (3rd edn), Springer: Berlin.
43. Lee KI, Yim YS, Chung SW, Wei J, Rhee JI. 2005. Application of artificial neural networks to the analysis of two-dimensional fluorescence spectra in recombinant E. coli fermentation processes. Journal of Chemical Technology and Biotechnology 80(9): 1036-1045.
44. Li Q, Yao X, Chen X, Liu M, Zhang R, Zhang X, Hu Z. 2000. Application of artificial neural networks for the simultaneous determination of a mixture of fluorescent dyes by synchronous fluorescence. The Analyst 125(11): 2049-2053.
45. Marhaba TF, Bengraine K, Pu Y, Arago J. 2003. Spectral fluorescence signatures and partial least squares regression: model to predict dissolved organic carbon in water. Journal of Hazardous Materials B97: 83-97.
46. Marhaba TF, Van D, Lee Lippincott R. 2000. Rapid identification of dissolved organic matter fractions in water by spectral fluorescent signatures. Water Research 34(14): 3543-3550.
47. Marquardt D. 1963. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics 11(2): 431-441.
48. Martens H, Naes T. 1989. Multivariate Calibration. John Wiley & Sons: Chichester.
49. McAvoy TJ, Su HT, Wang NS, He M, Horwath J, Semerjian H. 1992. A comparison of neural networks and partial least squares for deconvoluting fluorescence spectra. Biotechnology and bioengineering 40: 53-62.
50. McKnight DM, Boyer EW, Westerhoff PK, Doran PT, Kulbe T, Andersen DT. 2001. Spectrofluorometric characterization of dissolved organic matter for indication of precursor organic material and aromaticity. Limnology and Oceanography 46(1): 38-48.
51. Mopper K, Schultz CA. 1993. Fluorescence as a possible tool for studying the nature and water column distribution of DOC components. Marine Chemistry 41(1-3): 229-238.
52. Mounier S, Braucher R, Benaïm JY. 1999. Differentiation of organic matter's properties of the Rio Negro basin by cross-flow ultra-filtration and UV-spectrofluorescence. Water Research 33(10): 2363-2373.
53. Nieke B, Reuter R, Heuermann R, Wang H, Babin M, Therriault JC. 1997. Light absorption and fluorescence properties of chromophoric dissolved organic matter (CDOM), in the St. Lawrence Estuary (Case 2 waters). Continental Shelf Research 17(3): 235-252.
54. Ohno T, Amirbahman A, Bro R. 2008. Parallel factor analysis of excitation-emission matrix fluorescence spectra of water soluble soil organic matter as basis for the determination of conditional metal binding parameters. Environmental Science and Technology 42: 186-192.
55. Park YS, Céréghino R, Compin A, Lek S. 2003. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecological Modelling 160(3): 265-280.
56. Patel-Sorrentino N, Mounier S, Benaim JY. 2002. Excitation-emission fluorescence matrix to study pH influence on organic matter fluorescence in the Amazon basin rivers. Water Research 36(10): 2571-2581.
57. Persson T, Wedborg M. 2001. Multivariate evolution of the fluorescence of aquatic organic matter. Analytica Chimica Acta 434: 179-192.
58. Rhee JI, Lee KI, Kim CK, Yim YS, Chung SW, Wei J, Bellgardt KH. 2005. Classification of two-dimensional fluorescence spectra using self-organizing maps. Biochemical Engineering Journal 22(2): 135-144.
59. Saurina J, Leal C, Compano R, Granados M, Tauler R, Prat MD. 2000. Determination of triphenyltin in sea-water by excitation-emission matrix fluorescence and multivariate curve resolution. Analytica Chimica Acta 409: 237-245.
60. Scott SM, James D, Ali Z, O'Hare WT, Rowell FJ. 2003. Total luminescence spectroscopy with pattern recognition for classification of edible oils. The Analyst 128(7): 966-973.
61. Smits JRM, Melssen WJ, Buydens LMC, Kateman G. 1994. Using artificial neural networks for solving chemical problems. Part I. Multi-layer feed-forward networks. Chemometrics and Intelligent Laboratory Systems 22(2): 165-189.
62. Spencer RGM, Baker A, Ahad JME, Cowie GL, Ganeshram R, Upstill-Goddard RC, Yjer G. 2007. Discriminatory classification of natural and anthropogenic waters in two UK estuaries. Science of the Total Environment 373: 305-323.
63. Stedmon CS, Markager S, Bro R. 2003. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Marine Chemistry 82(3-4): 239-254.
64. Ultsch A. 1993. Self-organizing neural networks for visualization and classification. In Information and Classification, Opitz O, Lausen B, Klar R (eds). Springer-Verlag: Berlin; 307-313.
65. Wolf G, Almeida JS, Pinheiro C, Correia V, Rodrigues C, Reis MA, Crespo JG. 2001. Two-dimensional fluorometry coupled with artificial neural networks: a novel method for on-line monitoring of complex biological processes. Biotechnology and Bioengineering 72(3): 297-306.
66. Wu FC, Evans RD, Dillon PJ. 2003. Separation and characterization of NOM by high-performance liquid chromatography and on-line three-dimensional excitation emission matrix fluorescence detection. Environmental Science and Technology 37(16): 3687-3693.
67. Zupan J, Gasteiger J. 1991. Neural networks: a new method for solving chemical problems or just a passing phase? Analytica Chimica Acta 248(1): 1-30.