Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-23T18:14:23.772Z Has data issue: false hasContentIssue false

Dilution and Magnification Effects on Image Analysis Applications in Activated Sludge Characterization

Published online by Cambridge University Press:  31 August 2010

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
R.A.V. Elias
Affiliation:
Escola Superior de Tecnologia e de Gestão, Instituto Politécnico de Bragança, Campus de Santa Apolónia, Apartado 134, 5301-857 Bragança, 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]
Get access

Abstract

The properties of activated sludge systems can be characterized using image analysis procedures. When these systems operate with high biomass content, accurate sludge characterization requires samples to be diluted. Selection of the best image acquisition magnification is directly related to the amount of biomass screened. The aim of the present study was to survey the effects of dilution and magnification on the assessment of aggregated and filamentous bacterial content and structure using image analysis procedures. Assessments of biomass content and structure were affected by dilutions. Therefore, the correct operating dilution requires careful consideration. Moreover, the acquisition methodology comprising a 100× magnification allowed data on aggregated and filamentous biomass to be determined and smaller aggregates to be identified and characterized, without affecting the accuracy of lower magnifications regarding biomass representativeness.

Type
Biological Applications
Copyright
Copyright © Microscopy Society of America 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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, 14731480.CrossRefGoogle ScholarPubMed
Amaral, A.L. (2003). Image analysis in biotechnological processes: Applications to wastewater treatment. PhD. Thesis. Braga, Portugal: University of Minho. 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, 246253.CrossRefGoogle Scholar
Amaral, A.L., Ginoris, Y.P., Nicolau, A., Coelho, M.A.Z. & Ferreira, E.C. (2008). Stalked protozoa identification by image analysis and multivariable statistical techniques. Anal Bioanal Chem 391, 13211325.CrossRefGoogle ScholarPubMed
Banadda, E.N., Smets, I.Y., Jenné, R. & Van Impe, J.F. (2005). Predicting the onset filamentous bulking in biological wastewater treatment systems exploiting image analysis information. Bioproc Biosys Eng 27, 339348.CrossRefGoogle ScholarPubMed
Bradley, J.V. (1968). Distribution-Free Statistical Tests. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
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), 381387.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), 6068.CrossRefGoogle ScholarPubMed
da Motta, M., Amaral, A.L., Neves, L., Araya-Koff, P., Ferreira, E.C., Alves, M.M., Mota, M., Roche, N., Vivier, H. & Pons, M.N. (2002). Dilution effects on biomass characterization by image analysis. In Proceedings of the 14th Brazilian Congress on Chemical Engineering, Natal, Brazil, p. 9 (CD-ROM).Google Scholar
da Motta, M., Pons, M.N. & Roche, N. (2001). Automated monitoring of activated sludge in a pilot plant using image analysis. Wat Sci Technol 43(7), 9196.CrossRefGoogle Scholar
Ginoris, Y.P., Amaral, A.L., Nicolau, A., Coelho, M.A.Z., Ferreira, E.C. (2007a). Recognition of protozoa and metazoa using image analysis tools, discriminant analysis, neural networks and decision trees. Anal Chim Acta 595, 160169.CrossRefGoogle ScholarPubMed
Ginoris, Y.P., Amaral, A.L., Nicolau, A., Coelho, M.A.Z. & Ferreira, E.C. (2007b). Development of an image analysis procedure for identifying protozoa and metazoa typical of activated sludge system. Water Res 41, 25812589.CrossRefGoogle ScholarPubMed
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), 167174.CrossRefGoogle 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, 3641.CrossRefGoogle ScholarPubMed
Jin, B., Wilén, B.M. & Lant, P. (2003). A comprehensive insight into floc characteristics and their impact on compressibility and settleability of activated sludge. Chem Eng J 95, 221234.CrossRefGoogle Scholar
Mesquita, D.P., Dias, O., Amaral, A.L. & Ferreira, E.C. (2009b). Monitoring of activated sludge settling ability through image analysis: Validation on full-scale wastewater treatment plants. Bioprocess Biosyst Eng 32(3), 361367.CrossRefGoogle ScholarPubMed
Mesquita, D.P., Dias, O., Amaral, A.L. & Ferreira, E.C. (2010). A Comparison between bright field and phase-contrast image analysis techniques in activated sludge morphological characterization. Micros Microanal 16(2), 166174.CrossRefGoogle ScholarPubMed
Mesquita, D.P., Dias, O., Dias, A.M.A., Amaral, A.L. & Ferreira, E.C. (2009a). Correlation between sludge settling ability and image analysis information using partial least squares. Anal Chim Acta 642(1–2), 94101.CrossRefGoogle ScholarPubMed
Wilén, B.M., Lumley, D., Mattsson, A. & Mino, T. (2008). Relationship between floc composition and flocculation and settling properties studied at a full scale activated sludge plant. Water Res 42, 44044418.CrossRefGoogle Scholar