Skip to main content
SpringerLink
Log in

Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network

  • Original Paper
  • Published:
Food and Bioprocess Technology Aims and scope Submit manuscript

Abstract

A rapid and cost-effective technique for identification of microorganisms was explored using fluorescence microscopy and image analysis, and classification was done with trained neural network. The microorganisms used in this study are Bacillus thuringiensis (C399), Escherichia coli K12 (ATCC 10798), Lactobacillus brevis (LJH240), Listeria innocua (C366), and Staphylococcus epidermis (LJH343). After staining the microorganisms with fluorescent dyes [diamidino-2-phenyl-indole and acridine orange (AO)], images of the microorganisms were captured using a digital camera attached to a light microscope. Geometrical, optical, and textural features were extracted from the images using image analysis. Parameters extracted from images of microorganisms stained with AO gave better results for classification of the microorganisms. From these parameters, the best identification parameters that could classify the microorganisms with higher accuracy were selected using a probabilistic neural network (PNN). PNN was then used to classify the microorganisms with a 100% accuracy using nine identification parameters. These parameters are: 45° run length non-uniformity, width, shape factor, horizontal run length non-uniformity, mean gray level intensity, ten percentile values of the gray level histogram, 99 percentile values of the gray level histogram, sum entropy, and entropy. When the five microorganisms were mixed together then, also the PNN could classify the microorganisms with 100% accuracy using these nine parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abramowitz, M., & Davidson, M. W. (2000). Olympus America Inc., Melville, New York. http://www.olympusmicro.com/primer/techniques/fluorescence/fluorotable1.html.

  • Arhaliass, A., Legrand, J., Vauchel, P., Fodil-Pacha, F., Lamer, T., & Bouvier, J. M. (2008). The effect of wheat and maize flours properties on the expansion mechanism during extrusion cooking. Food and Bioprocess Technology, doi:10.1007/s11947-007-0038-6.

  • Bovaci, I. H., Sumnu, G., & Sakiyan, O. (2008). Estimation of dielectric properties of cakes based on porosity, moisture content, and formulations using statistical methods and artificial neural networks. Food and Bioprocess Technology, doi:10.1007/s11947-008-0064-z.

  • Cram, L. S. (2002). Flow cytometry, an overview. Methods in Cell Science, 24, 1–9. doi:10.1023/A:1024198904819.

    Article  CAS  Google Scholar 

  • Du, C. J., & Sun, D. W. (2008). Retrospective shading correction of confocal laser scanning microscopy beef images for three-dimensional visualization. Food and Bioprocess Technology, doi:10.1007/s11947-007-0032-z.

  • Ellis, D. I., Broadhurst, D., Kell, D. B., Rowland, J. J., & Goodacre, R. (2002). Rapid and quantitative detection of the microbial spoilage of meat using FT-IR spectroscopy and machine learning. Applied and Environmental Microbiology, 68, 2822–2828. doi:10.1128/AEM.68.6.2822-2828.2002.

    Article  CAS  Google Scholar 

  • Fanatsu, T., Taniyama, T., Tajima, T., Tadakuma, H., & Namiki, H. (2002). Rapid and sensitive detection method of a bacterium by using GFP reporter phage. Microbiology and Immunology, 46(6), 365–369.

    Google Scholar 

  • Haralick, R., Shanmugam, B., & Dinstein, I. (1973). Texture features in image classification. IEEE Transaction on Systems, Man, and Cybernetics, 3(6), 610–621. doi:10.1109/TSMC.1973.4309314.

    Article  Google Scholar 

  • Huang, J. (1999). Identification and enumeration of Salmonella on sample slides of poultry carcass wash water using image analysis with fluorescent microscopy. Transactions of the American Society of Agricultural Engineers, 42, 267–273.

    Google Scholar 

  • Kramer, M. F., & Lim, D. V. (2004). A rapid and automated fiber optic-based biosensor assay for the detection of Salmonella in spent irrigation water used in the sprouting of sprout seeds. Journal of Food Protection, 67, 46–52.

    Google Scholar 

  • Krishnamurthy, K., Tewari, J. C., Irudayaraj, J., & Demirci, A. (2008). Microscopic and spectroscopic evaluation of inactivation of Staphylococcus aureus by pulsed UV light and infrared heating. Food and Bioprocess Technology, doi:10.1007/s11947-008-0084-8.

  • Kumar, S., & Mittal, G. S. (2008a). Geometric and optical characteristics of five microorganisms for rapid detection using image processing. Biosystems Engineering, 99, 1–8. doi:10.1016/j.biosystemseng.2007.10.009.

    Article  Google Scholar 

  • Kumar, S., & Mittal, G. S. (2008b). Textural characteristics of five microorganisms for rapid detection using image processing. Journal of Food Process Engineering (in press).

  • Materka, A., & Strzelecki, M. (1998). Texture analysis methods—A review. COST B11 report presented and distributed at MC meeting and workshop in Brussels, Technical Univ. of Lodz, Poland.

  • Perkins, E. A., & Squirrell, D. J. (2002). Development of instrumentation to allow the detection of microorganisms using light scattering in combination with surface plasmon resonance. Biosensors and Bioelectronics, 14, 853–859. doi:10.1016/S0956-5663(99)00069-X.

    Article  Google Scholar 

  • Rand, G. A., Ye, J., Brown, C. W., & Letcher, S. V. (2002). Optical biosensors for food pathogen detection. Food Technology, 56(3), 32–37.

    CAS  Google Scholar 

  • Schonholzer, F., Hahn, D., Zarda, B., & Zeyer, J. (2002). Automated image analysis and in situ hybridization as tools to study bacterial population in food resources, gut and cast of Lumricus terrestris. Journal of Microbiology Methods, 48, 53–68. doi:10.1016/S0167-7012(01)00345-1.

    Article  CAS  Google Scholar 

  • Singh, C. B., Choudhary, R., Jayas, D. S., & Paliwal, J. (2008). Wavelet analysis of signals in agriculture and food quality inspection. Food and Bioprocess Technology, doi:10.1007/s11947-008-0093-7.

  • Takeuchi, K., & Frank, J. F. (2001). Confocal microscopy and microbial viability detection for food research. Journal of Food Protection, 64, 2088–2102.

    CAS  Google Scholar 

  • Trujillo, O., Griffis, C., Li, Y., & Slavik, M. (2001). A machine vision system using immuno-fluorescence microscopy for rapid recognition of Salmonella typhimurium. Journal of Rapid Methods in Automation of Microbiology, 9, 63–134. doi:10.1111/j.1745-4581.2001.tb00234.x.

    Article  Google Scholar 

  • Veal, D. A., Deere, D., Ferrari, B., Piper, J., & Attfield, P. V. (2000). Fluorescence staining and flow cytometry for monitoring microbial cells. Journal of Immunological Methods, 243, 191–210. doi:10.1016/S0022-1759(00)00234-9.

    Article  CAS  Google Scholar 

  • Woodcock, T., Fagan, C. C., O’Donnell, C. P., & Downey, G. (2008). Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity. Food Bioprocess Technology, 1, 117–129. doi:10.1007/s11947-007-0033-y.

    Article  Google Scholar 

  • Yu, Q., Moloney, C., & Williams, F. M. (2002). SAR Sea-ice texture classification using discrete wavelet transform based methods. Geoscience and Remote Sensing Symposium, IGARSS ‘02, IEEE International, 5, 3041–3043.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering, University of Guelph, Guelph, Ontario, Canada, N1G 2W1

    Saurabh Kumar & Gauri S. Mittal

Authors
  1. Saurabh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gauri S. Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gauri S. Mittal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kumar, S., Mittal, G.S. Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network. Food Bioprocess Technol 3, 741–751 (2010). https://doi.org/10.1007/s11947-008-0122-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11947-008-0122-6

Keywords

Search

Navigation