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A Comparison between Bright Field and Phase-Contrast Image Analysis Techniques in Activated Sludge Morphological Characterization

Published online by Cambridge University Press:  26 January 2010

D.P. Mesquita ,
O. Dias ,
A.L. Amaral  and
E.C. Ferreira
D.P. Mesquita
Affiliation:
IBB—Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057, Braga, Portugal
O. Dias
Affiliation:
IBB—Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057, Braga, Portugal
A.L. Amaral
Affiliation:
IBB—Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057, Braga, Portugal Instituto Superior de Engenharia de Coimbra, Instituto Politécnico de Coimbra, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal
E.C. Ferreira*
Affiliation:
IBB—Institute for Biotechnology and Bioengineering, Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057, Braga, Portugal
*
Corresponding author. E-mail: [email protected]
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Abstract

Different approaches using microscopy image analysis procedures were employed for characterization of activated sludge systems. The approaches varied mainly on the type of visualization and acquisition method used for collection of data. In this context, this study focused on the comparison of the two most common acquisition methods: bright field and phase-contrast microscopy. Images were acquired from seven different wastewater treatment plants for a combined period of two years. Advantages and disadvantages of each acquisition technique and the results are discussed. Bright field microscopy proved to be more simple and inexpensive and provided the best overall results.

Keywords

activated sludgeimage analysisbright fieldphase contrastaggregatesfilaments
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 16 , Issue 2 , April 2010 , pp. 166 - 174
Copyright
Copyright © Microscopy Society of America 2010

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References

REFERENCES

Abreu, A.A., Costa, J.C., Araya-Kroff, P., Ferreira, E.C. & Alves, M.M. (2007). Quantitative image analysis as a diagnostic tool for identifying structural changes during a revival process of anaerobic granular sludge. Water Res 41, 1473–1480.CrossRefGoogle ScholarPubMed
Amaral, A.L. (2003). Image analysis in biotechnological processes: Applications to wastewater treatment. Ph.D. Thesis, Braga, Portugal. Available at http://hdl.handle.net/1822/4506.Google Scholar
Amaral, A.L. & Ferreira, E.C. (2005). Activated sludge monitoring of a wastewater treatment plant using image analysis and partial least squares regression. Anal Chim Acta 544, 246–253.CrossRefGoogle Scholar
APHA, AWWA & WPCF. (1989). Standard Methods for the Examination of Water and Wastewater, 17th Ed.Washington, D.C.: American Public Health Association.Google Scholar
Banadda, E.N., Smets, I.Y., Jenné, R. & Van Impe, J.F. (2005). Predicting the onset of filamentous bulking in biological wastewater treatment systems by exploiting image analysis information. Bioproc Biosys Eng 27, 339–348.CrossRefGoogle ScholarPubMed
Cenens, C., Van Beurden, K.P., Jenné, R. & Van Impe, J.F. (2002). On the development of a novel image analysis technique to distinguish between flocs and filaments in activated sludge images. Water Sci Technol 46(1–2), 381–387.CrossRefGoogle ScholarPubMed
Costa, J.C., Abreu, A.A., Ferreira, E.C. & Alves, M.M. (2007). Quantitative image analysis as a diagnostic tool for monitoring structural changes of anaerobic granular sludge during detergent shock loads. Biotechnol Bioeng 98(1), 60–68.CrossRefGoogle ScholarPubMed
da Motta, M., Amaral, A.L., Casellas, M., Pons, M.N., Dagot, C., Roche, N., Ferreira, E.C. & Vivier, H. (2001a). >Characterisation of activated sludge by automated image analysis: Validation on full-scale plants. IFAC Computer Applications in Biotechnology, Québec City, Canada, pp. 427–431.Google Scholar
da Motta, M., Pons, M.N. & Roche, N. (2001b). Automated monitoring of activated sludge in a pilot plant using image analysis. Water Sci Technol 43(7), 91–96.CrossRefGoogle Scholar
Ganczarczyk, J.J. (1994). Microbial aggregates in wastewater treatment. Water Sci Technol 30, 87–95.CrossRefGoogle Scholar
Grijspeerdt, K. & Verstraete, W. (1997). Image analysis to estimate the settleability and concentration of activated sludge. Water Res 31, 1126–1134.CrossRefGoogle Scholar
Jenkins, D., Richard, M.G. & Daigger, G. (2003). Manual on the Causes and Control of Activated Sludge Bulking, Foaming and Other Solids Separation Problems. Boca Raton, FL: Lewis Publishing.CrossRefGoogle Scholar
Jenné, R., Banadda, E.N., Gins, G., Deurinck, J., Smets, I.Y., Geeraerd, A.H. & Van Impe, J.F. (2006). Use of image analysis for sludge characterisation: Studying the relation between floc shape and sludge settleability. Water Sci Technol 54(1), 167–174.CrossRefGoogle ScholarPubMed
Jenné, R., Banadda, E.N., Philips, N. & Van Impe, J.F. (2003). Image analysis as a monitoring tool for activated sludge properties in lab-scale installations. J Environ Sci Health Part A—Toxic/Hazardous Subs & Environ Eng 38(10), 2009–2018.Google ScholarPubMed
Jenné, R., Banadda, E.N., Smets, I.Y., Deurinck, J. & Van Impe, J.F. (2007). Detection of filamentous bulking problems: Developing an image analysis system for sludge composition monitoring. Micros Microanal 13, 36–41.CrossRefGoogle ScholarPubMed
Jenné, R., Banadda, E.N., Smets, I.Y. & Van Impe, J.F. (2004). Monitoring activated sludge settling properties using image analysis. Water Sci Technol 50(7), 281–285.CrossRefGoogle ScholarPubMed
Mesquita, D.P., Dias, O., Amaral, A.L. & Ferreira, E.C. (2009a). Monitoring of activated sludge settling ability through image analysis: Validation on full-scale wastewater treatment plants. Bioprocess Biosyst Eng 32(3), 361–367.CrossRefGoogle ScholarPubMed
Mesquita, D.P., Dias, O., Dias, A.M.A., Amaral, A.L. & Ferreira, E.C. (2009b). Correlation between sludge settleability and image analysis information using partial least squares. Anal Chim Acta 642(1–2), 94–101.CrossRefGoogle Scholar
Pons, M.N. & Vivier, H. (1999). Biomass quantification by image analysis. Adv Biochem Eng/Biotech 66, 133–184.Google Scholar
Schuler, A.J. & Jassby, D. (2007). Filament content threshold for activated sludge bulking: Artifact or reality? Water Res 41, 4349–4356.CrossRefGoogle ScholarPubMed
Walsby, A.E. & Avery, A. (1996). Measurement of filamentous cyanobacteria by image analysis. J Microbiol Methods 26, 11–20.CrossRefGoogle Scholar