Skip to main content
Log in

A Piezoelectric Microelectrode Arrays System for Real-Time Monitoring of Bacterial Contamination in Fresh Milk

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

Abstract

An accurate, rapid, and simple piezoelectric microelectrode array (PMA) system was developed by integrating piezoelectric sensors and microelectrode arrays modified with conducting polymers for real-time monitoring bacterial contamination in fresh milk. The detection was based on the fact that the selected conducting polymer modified on the microelectrode arrays can react with the volatile metabolic biomarkers identified by GC/MS, which results in the conductance change of the polymer, and then the change can be both sensitively and specifically monitored by the PMA system in real time. The frequency shift-time response profiles were auto-recorded by self-developed software. Both the quantitative detection time (QDT) and relative frequency shift response value (ΔF max) were defined as informative parameters to detect bacterial contamination both quantitatively and qualitatively. The QDTs had a linear relationship with the logarithm values of initial concentration of bacteria in the range of 103~106 cfu/ml. The detection limit is 102 cfu/ml. The ΔF max values combined with multilayer perceptron (MLP)-based artificial neural network (ANN) were used to classify the bacterial species. A total of 50 fresh milk samples were identified. Comparative tests were also carried out by using the microbiological method. The identification time of the proposed method (about 2.0~6.5 h) was quicker than that of the microbiological method (more than 48 h). The results showed that the PMA system is accurate, rapid, simple, and economical. It will be potentially used for monitoring bacterial contamination in fresh milk in real time.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Alexandrakis, D., Brunton, N. P., Downey, G., & Scannell, A. G. M. (2012). Identification of spoilage marker metabolites in Irish chicken breast muscle using HPLC, GC-MS coupled with SPME and traditional chemical techniques. Food Bioprocess Technology, 5(5), 1917–1923.

    Article  CAS  Google Scholar 

  • Ampuero, S., & Bosset, J. O. (2003). The electronic nose applied to dairy products: a review. Sensors and Actuators B, 94, 1–12.

    Article  CAS  Google Scholar 

  • Barbin, D. F., ElMasry, G., Sun, D. W., Allen, P., & Morsy, N. (2013). Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging. Innovation Food Science and Emerging Technologies, 17, 180–191.

    Article  CAS  Google Scholar 

  • Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, 3–31.

    Article  CAS  Google Scholar 

  • Calvo, L., Gregorio, I., García, A., Fernández, M. T., Goñi, P., & Clavel, A. (2013). A new pentaplex-nested PCR to detect five pathogenic bacteria in free living amoebae. Water Research, 47(2), 493–502.

    Article  CAS  Google Scholar 

  • Clifford, R. J., Milillo, M., Prestwood, J., Quintero, R., Zurawski, D. V., & Kwak, Y. I. (2012). Detection of bacterial 16S rRNA and identification of four clinically important bacteria by real-time PCR. PLoS ONE, 7(11), 1–6.

    Article  Google Scholar 

  • Dutta, R., Hines, E. L., Gardner, J. W., & Boilot, P. (2002). Bacteria classification using Cyranose 320 electronic nose. Biomedical Engineering, 1, 1–7.

    Google Scholar 

  • Dutta, R., Das, A., Stocks, N. G., & Morgan, D. (2006). Stochastic resonance-based electronic nose: a novel way to classify bacteria. Sensors Actuators B, 115, 17–27.

    Article  CAS  Google Scholar 

  • Eriksson, A., Waller, K. P., Svennersten-Sjaunja, K., Haugen, J. E., Lundby, F., & Lind, O. (2005). Detection of mastitic milk using a gas-sensor array system (electronic nose). International Dairy Journal, 15, 1193–1201.

    Article  CAS  Google Scholar 

  • Ferguson, C. M., Booth, N. A., & Allan, E. J. (2000). An ELISA for the detection of Bacillus subtilis L-form bacteria confirms their symbiosis in strawberry. Letters in Applied Microbiology, 31(5), 390–394.

    Article  CAS  Google Scholar 

  • Fernández, J. C., Hervás, C., Martínez-Estudillo, F. J., & Gutiérrez, P. A. (2011). Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology. Applied Soft Computing, 11, 534–550.

    Article  Google Scholar 

  • Hassan Amrani, M. E., Payne, P. A., & Persaud, K. C. (1996). Multi-frequency measurements of organic conducting polymers for sensing of gases and vapours. Sensors and Actuators B, 33, 137–141.

    Article  Google Scholar 

  • Hayward, H. R. (1960). Anaerobic degradation of choline. III) Acetaldehyde as an intermediate in the fermentation of choline by extracts Vibrio cholinicus. The Journal of Biological Chemistry, 235, 3592–3596.

    CAS  Google Scholar 

  • He, F. J., Zhang, L. D., Zhao, J. W., Hu, B. L., & Lei, J. T. (2002). A TSM immunosensor for detection of M. tuberculosis with a new membrane material. Sensors and Actuators B, 85, 284–290.

    Article  CAS  Google Scholar 

  • He, F. J., Ren, J. L., & Liu, Z. H. (2007). The study and application of a new IDE-PQC sensor. Sensors and Actuators B, 123(2), 1057–1063.

    Article  CAS  Google Scholar 

  • Hettinga, K. A., Valenberg, H. J. F., Lam, T. J. G. M., & Hooijdonk, A. C. M. (2008). Detection of mastitis pathogens by analysis of volatile bacterial metabolites. Journal of Dairy Science, 91, 3834–3839.

    Article  CAS  Google Scholar 

  • Hettinga, K. A., Valenberg, H. J. F., Lam, T. J. G. M., & Hooijdonk, A. C. M. (2009). The origin of the volatile metabolites found in mastitis milk. Veterinary Microbiology, 137, 384–387.

    Article  CAS  Google Scholar 

  • Iqbal, A., Valous, N. A., Sun, D. W., & Allen, P. (2011). Parsimonious classification of binary lacunarity data computed from food surface images using kernel principal component analysis and artificial neural networks. Meat Science, 87, 107–114.

    Article  Google Scholar 

  • Kamruzzamana, M., ElMasrya, G., Sun, D. W., & Allen, P. (2012). Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Analytica Chimica Acta, 714, 57–67.

    Article  Google Scholar 

  • Karagözlü, N., Karagözlü, C., & Ergönül, B. (2007). Survival characteristics of E. coli O157:H7, S. typhimurium and S. aureus during kefir fermentation. Czech Journal of Food Sciences, 25, 202–207.

    Google Scholar 

  • Mead, P. S., Slutsker, L., Dietz, V., McCaig, L. F., Bresee, J. S., & Shapiro, C. (1999). Food-related illness and death in the United States. Emerging Infectious Diseases Journal, 5(5), 607–625.

    Article  CAS  Google Scholar 

  • Mi, X. W., He, F. J., Xiang, M. Y., Lian, Y., & Yi, S. L. (2012). Novel phage amplified multichannel series piezoelectric quartz crystal sensor for rapid and sensitive detection of Mycobacterium tuberculosis. Analytical Chemistry, 84, 939–946.

    Article  CAS  Google Scholar 

  • Mouwen, D. J. M., Capita, R., Alonso-Calleja, C., Prieto-Gómez, J., & Prieto, M. (2006). Artificial neural network based identification of Campylobacter species by Fourier transform infrared spectroscopy. Journal of Microbiological Methods, 67, 131–140.

    Article  CAS  Google Scholar 

  • Nester, E. W., Anderson, D. G., Roberts, C. E., Pearsall, N. N., & Nester, M. T. (2004). Microbiology: a human perspective (4th ed.). New York: McGraw-Hill.

    Google Scholar 

  • Partridge, A. C., Jansen, M. L., & Arnold, W. M. (2000). Conducting polymer-based sensors. Materials Science and Engineering C, 12, 37–42.

    Article  Google Scholar 

  • Pavlou, A. K., Magan, N., Sharp, D., Brown, J., Barr, H., & Turner, A. P. F. (2000). An intelligent rapid odour recognition model in discrimination of Helicobacter pylori and other gastroesophageal isolates in vitro. Biosensors and Bioelectronics, 15, 333–342.

    Article  CAS  Google Scholar 

  • Peris, M., & Escuder-Gilabert, L. (2009). A 21st century technique for food control: electronic noses. Analytica Chimica Acta, 638, 1–15.

    Article  CAS  Google Scholar 

  • Posudievsky, O. Y., Konoshchuk, N. V., Kukla, A. L., Pavluchenko, A. S., Linyuchev, G. V., & Shirshov, Y. M. (2003). Effect of the nature of the dopant on the response of a sensor array based on polyaniline. Theoretical and Experimental Chemistry, 39(4), 219–224.

    Article  Google Scholar 

  • Posudievsky, O. Y., Konoshchuk, N. V., Kukla, A. L., Pavluchenko, A. S., & Pokhodenko, V. D. (2005). Effect of the dopant nature on the response of sensor arrays based on polypyrrole. Theoretical and Experimental Chemistry, 41(5), 277–283.

    Article  Google Scholar 

  • Posudievsky, O. Y., Konoshchuk, N. V., Kukla, A. L., Pavluchenko, A. S., Linyuchev, G. V., & Pokhodenko, V. D. (2006). Influence of dopant nature on the sensor response of poly (3-methylthiophene) films. Theoretical and Experimental Chemistry, 42(6), 331–338.

    Google Scholar 

  • Ren, J. L., He, F. J., Zhang, L. L., Su, C. W., & Liu, Z. H. (2007). A new B-PAn-P system for the detection of bacteria population. Sensors and Actuators B, 125(2), 510–516.

    Article  CAS  Google Scholar 

  • Ren, J. L., He, F. J., Yi, S. L., & Cui, X. Y. (2008). A new MSPQC for rapid growth and detection of Mycobacterium tuberculosis. Biosensors and Bioelectronics, 24(3), 403–409.

    Article  CAS  Google Scholar 

  • Ren, J. L., Ma, L. N., Li, Z. H., Lin, Q. L., Huang, H. X., & Yi, S. L. (2013). Simultaneous and early detection of Mycobacterium tuberculosis resistance to antituberculosis drugs using an indirect series piezoelectric system. Biosensors and Bioelectronics, 43, 115–119.

    Article  CAS  Google Scholar 

  • Ripley, B. D. (2008). Pattern recognition and neural networks. Cambridge, UK: Cambridge University Press.

  • Ritter, W., & Hanni, H. (1960). The application of gas chromatography in dairying. II. Detection and determination of volatile fatty acids in dairy products and cultures. Milchwissenschaft, 15, 296–302.

    CAS  Google Scholar 

  • Samara, P., Kalbacher, H., Ioannou, K., Radu, D. L., Livaniou, E., & Promponas, V. J. (2013). Development of an ELISA for the quantification of the C-terminal decapeptide prothymosin α (100–109) in sera of mice infected with bacteria. Journal of Immunology Methods, 395(1–2), 54–62.

    Article  CAS  Google Scholar 

  • Severgnini, M., Cremonesi, P., Consolandi, C., De Bellis, G., & Castiglioni, B. (2011). Advances in DNA microarray technology for the detection of foodborne pathogens. Food Bioprocess Technology, 4(6), 936–953.

    Article  Google Scholar 

  • Shen, D. Z., Zhu, W. H., Nie, L. H., & Yao, S. Z. (1993). Behaviour of a series piezoelectric sensor in electrolyte solution: part I. Theory. Analytica Chimica Acta, 276(1), 87–97.

    Article  CAS  Google Scholar 

  • Tait, E., Perry, J. D., Stanforth, S. P., & Dean, J. R. (2014). Use of volatile compounds as a diagnostic tool for the detection of pathogenic bacteria. Trends in Analytical Chemistry, 53, 117–125.

    Article  CAS  Google Scholar 

  • Teena, M., Manickavasagan, A., Mothershaw, A., El Hadi, S., & Jayas, D. S. (2013). Potential of machine vision techniques for detecting fecal and microbial contamination of food products: a review. Food and Bioprocess Technology, 6(7), 1621–1634.

    Article  Google Scholar 

  • Turner, A. P. F., & Magan, N. (2004). Electronic noses and disease diagnostics. Nature Reviews Microbiology, 2, 161–166.

    Article  CAS  Google Scholar 

  • Valous, N. A., Mendoza, F., Sun, D. W., & Allen, P. (2010). Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values. Meat Science, 84, 422–430.

    Article  Google Scholar 

  • Wang, B., Xu, S. Y., & Sun, D. W. (2010). Application of the electronic nose to the identification of different milk flavorings. Food Research International, 43, 255–262.

    Article  CAS  Google Scholar 

  • Wang, C. X., Zhang, B., & Zhuang, X. M. (2013). A biochemical system of rapidly detecting bacteria based on ATP bioluminescence technology. European Food Research and Technology, 236, 41–46.

    Article  CAS  Google Scholar 

  • Wu, D., & Sun, D. W. (2013a). Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta, 111, 39–46.

    Article  CAS  Google Scholar 

  • Wu, D., & Sun, D. W. (2013b). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review—part I: fundamentals. Innovative Food Science and Emerging Technologies, 19, 1–14.

    Article  Google Scholar 

  • Wu, D., & Sun, D. W. (2013c). Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review—part II: applications. Innovative Food Science and Emerging Technologies, 19, 15–28.

    Article  Google Scholar 

  • Zechman, J. M., Aldinger, S., & Labows, J. N., Jr. (1986). Characterization of pathogenic bacteria by automated headspace concentration-gas chromatography. Journal of Chromatography A, 377, 49–57.

    Article  CAS  Google Scholar 

  • Zeisel, S. H., Wishnok, J. S., & Blusztajn, J. K. (1983). Formation of methylamines from ingested choline and leci thin. Journal of Pharmacology and Experimental Therapeutics, 225, 320–324.

  • Zeisel, S. H., Dacosta, K. A., & Fox, J. G. (1985). Endogenous formation of dimethylamine. Biochemical Journal, 232, 403–408.

    CAS  Google Scholar 

Download references

Acknowledgments

We are grateful to the National Natural Science Foundation of China under the grants 31000788 and 31340059, Hunan Provincial Natural Science Foundation of China under the grant 13JJ5028, Outstanding Youth Project of the Education Department of Hunan Province under the grant 12B137, and Project of college student study and innovative experiment of Central South University of Forestry and Technology.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jiali Ren or Qinlu Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, J., Zhou, Y., Zhou, Y. et al. A Piezoelectric Microelectrode Arrays System for Real-Time Monitoring of Bacterial Contamination in Fresh Milk. Food Bioprocess Technol 8, 228–237 (2015). https://doi.org/10.1007/s11947-014-1394-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11947-014-1394-7

Keywords

Navigation