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Keywords:

  • bacteria;
  • microbial odours;
  • VaporPrintTM images;
  • yeasts;
  • zNoseTM

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Aims:  Use of an electronic nose (zNoseTM) to discriminate between volatile organic molecules delivered during bacterial/fungal growth on agar and in broth media.

Methods and Results:  Cultures of bacteria (Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli) and yeasts (two Candida albicans strains) were grown on agar and in broth media and incubated for 24 h at 37°C. Headspace samples from microbial cultures were analysed by the zNoseTM, a fast gas chromatography-surface acoustic wave detector. Olfactory images of volatile production patterns were observed to be different for the various species tested after 24 h. Moreover, some strains (two K. pneumoniae, two C. albicans) did not show changes in volatile production patterns within our species.

Conclusions:  Our experiments demonstrate that the electronic nose system can recognize volatile production patterns of pathogens at species level.

Significance and Impact of the Study:  Our results, although preliminary, promise exciting challenges for microbial diagnostics.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The electronic nose (e-nose) is a device designed to identify many volatile compounds, including toxic odours not perceptible by humans, i.e. carbon monoxide and nitric oxide (Craven et al. 1996). Individual sensors with partial specificity (sensor arrays), each interacting with various odours are able to detect a gas and/or odour and/or vapour; the resulting reaction induces changes in some physical parameters, which are transduced into electrical pulses.

Sensor responses provide a pattern which is typical of the volatile compound, and can be compared with learnt and stored patterns by computer (Gardner and Bartlett 1999).

Data sets are obtained using conventional techniques based on mathematical analysis, such as principal component analysis (PCA) or data processing systems that do not employ conventional mathematical models, e.g. artificial neural networks and fuzzy-inference systems (Gardner 1991; Hines et al. 1999).

The first sensor array, a metal oxide semiconductor, detected 20 odours (Persaud and Dodd 1982). Modern e-noses can include more than one type of sensor (Gardner and Bartlett 1994; Hartmann et al. 1994; Roh et al. 1998; Hines et al. 1999).

The e-nose has been used to control the quality of industrial products such as beer, coffee, red wine, olive oil and others (Pearce et al. 1993; Frank et al. 2001; Biswas et al. 2004). Moreover, detection of spoilage bacteria, yeasts and mould in food-stuffs and volatile production patterns by micro-organisms in milk, water, poultry, dairy and bakery products have also been achieved (Arnold and Senter 1998; Magan et al. 2001; Keshri et al. 2002; Ampuero and Bosset 2003; Canhoto and Magan 2003).

Specific publications have described the use of the e-nose in the clinical diagnosis of several diseases such as lung cancer, uraemia and diabetes by detecting alterations in volatile components in the breath (Wang et al. 1997; Yuh-Jiuan et al. 2001; Di Natale et al. 2003).

Finally, e-nose technology has also been applied to the detection of volatile molecules in urine, sputum and bacteria cultures (McEntegart et al. 2000).

The development of gas chromatographic (GC) detectors together with direct column heating has recently produced a new type of ultrafast GC, called zNoseTM (Electronic Sensor Technology, Newbury Park, CA, USA) (Staples and Watson 1998; Staples 1999, 2000).

This new e-nose is based on gas chromatography and surface acoustic wave (GC-SAW) technology and can detect up to picograms (pg) of volatile compounds in 10 s. The zNoseTM consists of a heated inlet, vapour preconcentrator, temperature-programmed GC column and a solid-state SAW detector. The SAW sensor is a temperature-controlled quartz crystal, which absorbs vapours as they exit the GC capillary column. The fundamental acoustic frequency of the crystal changes, according to the mass of each condensed analyte. Any chemical within an odour can be calibrated according to the retention times of a standard odour mixture of linear chain n-alkanes. Finally, a chromatogram showing retention times and total counts per second provides a qualitative and quantitative analysis of each specific chemical in the odour. Polar olfactory images of specific vapour mixtures can be obtained (VaporPrintTM images, Electronic Sensor Technology, Newbury Park, CA, USA) plotting the sensor frequency change (radial) vs elution time (angle); such images can be viewed and recognized as part of a previously learnt image set (Staples 2000).

The GCs employed in scientific laboratories normally take 30 min to 1 h to provide results. The zNoseTM is based upon ultrafast chromatography performing analytical measurements in seconds. This instrument differs from conventional e-noses in that it is designed to specifically separate and identify each of the volatile components of a sample.

Electronic noses, called eNose, utilize non- or weakly specific arrays of physical sensors to produce an N-dimensional response (where N equals the number of sensors) of specific vapour mixtures and this response can be analysed by PCA (Staples 2000). Moreover, the sensitivity of eNose is in ppm levels whereas the sensitivity of zNoseTM is in ppb.

The performance of this new e-nose encourages its possible use in diagnostic microbiology. The purposes of our work were to: (i) detect volatile compounds by the zNoseTM in order to identify bacteria and yeasts up to species level; and (ii) evaluate the influence of different cultural conditions on the zNoseTM sensitivity.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Micro-organisms

Bacterial and yeast species were used in this study. The strains were obtained from the American Type Culture Collection (ATCC, Rockville, MD, USA), i.e. Klebsiella pneumoniae ATCC 10031, Candida albicans ATCC 2091 and from the Clinical Microbiology Laboratory of the Policlinico Tor Vergata (PTV, Rome, Italy), i.e. K. pneumoniae, C. albicans, Pseudomonas aeruginosa and Escherichia coli.

Culture media

The following culture media (Difco Laboratories, Detroit, MI, USA) were used in this study: Sabouraud Dextrose Broth, Sabouraud Dextrose Agar (SDA), Brain Heart Infusion Broth (BHI), Brain Heart Infusion Agar (BHIA). Sterile Sabouraud and BHI media, used for yeasts and bacteria, respectively, were dispensed (12 ml) into vials sealed with a Hole cap PTFE/Silicone septa (40 ml Clear Screw Top Vial, 28 × 28; Supelco, Bellefonte, PA, USA). The vials containing agar media were cooled in a slanting position.

Experimental model

Broth cultures.  At time zero, the micro-organisms were transferred using an inoculating loop into two sets of vials. The optical density of broth cultures ranged from 0·024 to 0·080 for bacteria (OD, 600 nm) and from 0·315 to 0·393 for yeasts (OD, 560 nm). Suitable dilutions were plated twice on SDA and BHIA plates to determine the viable cell number: C. albicans (PTV) 2·85 × 106 CFU ml−1, C. albicans (ATCC 2091) 5·00 × 106 CFU ml−1, K. pneumoniae (ATCC 10031) 2·5 × 105 CFU ml−1, K. pneumoniae (PTV) 6·4 × 106 CFU ml−1, P. aeruginosa (PTV) 1·00 × 105 CFU ml−1, E. coli (PTV) 6·1 × 106 CFU ml−1. Cultures and controls containing only sterile media were incubated at 37°C for 24 h. Cultures of each organism were performed in duplicate in three separate experiments.

Agar cultures.  Each strain of bacteria and yeasts was streaked on agar media slants, prepared as above. Cultures and controls performed in duplicate were grown in an incubator at 37°C for 24 h. Three experiments for both bacteria and yeasts were performed.

Odour detection

The zNoseTM (Electronic Sensor Technology) directly measured odour intensity of sample preparations vs elution time from a GC column. The instrument was equipped with a DB 624 column (Electronic Sensor Technology), which was temperature programmed from 40 to 120°C at 10°C s−1. Sensitivity was controlled by the temperature of the detector (30°C) and the volume of the vapour sampled was 15 ml. After each sampling, the detector was baked at 150°C for 60 s. Vapour samples were collected by piercing the hole cap of the above-described vials.

Statistical analysis

The retention times of nine-replicate measurements were analysed to compute mean values and standard deviations. The significance of data was evaluated by the Student's t-test. Differences were considered statistically significant when the P-value was <0·01.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Sensor-response curves of volatile compounds in the headspace of cultures were obtained for each bacterial and yeast species tested. Volatile profiles of the agar culture media were well-differentiated among the bacterial species and the replicates showed good reproducibility (Fig. 1). On the contrary, volatile profiles of bacterial broth cultures were scarcely perceptible.

image

Figure 1. Volatile profiles and VaporPrintTM images (on the right) of different infectious bacteria (Pseudomonas aeruginosa, red; Escherichia coli, black). The microbial volatile organic compounds were detected after 24 h at 37°C from the headspaces of agar cultures. Data are from one representative experiment. Statistical analysis (Student's t-test) was performed comparing replicate measurements (n = 9). In all cases the standard deviation of nine-replicate measurements was <5–10%; P < 0·01. Mean retention time (s) ± SD (s) of the peaks: A = 0·681 ± 0·045, B = 1·526 ± 0·071, C = 2·293 ± 0·016, D = 3·054 ± 0·170, E = 6·488 ± 0·304, F = 6·793 ± 0·351, 1 = 7·099 ± 0·426, 2 = 7·556 ± 0·652, G = 7·908 ± 0·510, H = 10·452 ± 0·592, I = 11·453 ± 0·581, J = 11·764 ± 0·652

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In our experimental model we also detected the odours generated by agar cultures of the strains: C. albicans (PTV) and C. albicans (ATCC 2091) (Fig. 2), K. pneumoniae (PTV) and K. pneumoniae (ATCC 10031) (Fig. 3).

image

Figure 2. Olfactory images showing the similarity between strains of identical species (Candida albicans) of micro-organisms; the peaks of the strains overlap. Black, C. albicans clinical isolate; red, C. albicans ATCC 2091. Statistical analysis (Student's t-test) was performed comparing replicate measurements (n = 9). In all cases the standard deviation of nine-replicate measurements was <5–10%. P < 0·01. Mean retention time (s) ± SD (s) of the peaks: A = 0·589 ± 0·041, B = 0·964 ± 0·057, C = 1·446 ± 0·086, D = 1·842 ± 0·128, E = 2·143 ± 0·107, F = 3·010 ± 0·151, G = 3·803 ± 0·232, H = 4·446 ± 0·240, I = 6·160 ± 0·374, J = 13·393 ± 0·803

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image

Figure 3. Comparison of the volatile production profiles of two strains of Klebsiella pneumoniae. The samples, although differing in some peaks, followed the same general trajectory. Black, K. pneumoniae clinical isolate; red, K. pneumoniae ATCC 10031. Statistical analysis (Student's t-test) was performed comparing replicate measurements (n = 9). In all cases the standard deviation of nine-replicate measurements was <5–10%. P-value <0·01. Mean retention time (s) ± SD (s) of the peaks: A = 0·621 ± 0·043, B = 3·020 ± 0·181, C = 3·550 ± 0·213, D = 5·862 ± 0·322, E = 6·413 ± 0·385, F = 6·792 ± 0:339, G = 7·896 ± 0·395

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As shown, each strain generated identical or nearly identical, volatile profiles when compared with its own species. Similar results were obtained from the headspaces of broth cultures of yeasts.

Because the zNoseTM is based upon fast GC, vapours can be detected using a chromatogram. Each chemical can be quantified and visually recognized. Analysis by algorithmic methods is not required and an olfactory image can be obtained with a virtual sensor array recognizing subtle visual changes (VaporPrintTM).

In this way, we can discriminate between different volatiles and their concentrations without interference. Examination of the volatile profiles revealed distinct metabolic differences between microbial species and each micro-organism was recognized by its VaporPrintTM image. Moreover, strains of the same species gave identical VaporPrintTM images. Further studies on the detailed evaluation of the individual analytes are currently in progress.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Our work has examined the possible use of the zNoseTM for medical diagnostics with regard to bacteria and yeasts species.

These experiments have evaluated the capability of the zNoseTM to detect vapours generated during the growth of micro-organisms. Cultures were tested at 24 h of growth to obtain greater concentrations of volatile compounds produced by metabolic processes. However, no detectable volatile production pattern was produced by bacterial broth cultures. Our results have shown that a technical sensor device is able to discriminate between bacteria (agar cultures) or yeasts (agar and broth cultures) at species level.

According to other studies, the above results suggest the possible use of the zNoseTM in medical diagnostics (Gardner et al. 2000; Pavlou et al. 2000, 2002; Dutta et al. 2002). Nevertheless, a larger number of experimental data are still necessary. Moreover, we do not exclude that similar to yeast broth culture, metabolic products of bacterial broth cultures could be detected in the vapour phase depending on the number of micro-organisms and time of growth. In addition, other aspects may be evaluated to improve the performance of this technology. For instance, the chemical profiles of the volatile characteristics for each pathogen could be achieved by means of specific media and standard reference curves. In these experiments different species showed clearly different VaporPrint images, while different strains from the same species were very similar.

Moreover, it could be useful to set the sensitivity of the zNoseTM to distinguish strains of identical species as well as to discriminate the drug-resistant pathogens. The latter should facilitate molecular studies on antimicrobial resistance (Casalinuovo et al. 2004).

However, it is necessary to establish minimal concentrations of micro-organisms leading to early volatile production so as to obtain the lowest threshold level. Thus, e-nose technology offers a new and useful tool in the early detection of micro-organisms in various fields, including nutritional sciences, health care and environmental analysis (Schnürer et al. 1999; Mohamed et al. 2002; Bourgeois et al. 2003).

Currently, experiments aimed at monitoring the sensitivity of the zNoseTM are in progress. Existing GC methods detect pg levels from 30 min to 1 h; the zNose was able to perform headspace air sampling (pg levels) within seconds. The comparison of this method with other microbiological methods results in increased sensitivity yielding a good classification rate at species level. However, other approaches to examine different micro-organisms and false-positive and -negative results should be investigated. As a consequence of the fast identification of micro-organisms, rapid diagnosis can be obtained.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The authors thank Prof. C. Favalli and Dr C. Fontana (Microbiology Laboratory, Policlinico Tor Vergata, University of Rome ‘Tor Vergata’, Italy) for the supply of clinical strains used in this study, to the ‘Comunità Montana Destra Crati’, Acri, Cosenza, Italy and progetto FIRB no. RBNE01P4B5-006, and MINSAN, ICS 120·5/RF00.121 and Progetto Finalizzato BS1 Regione Campania for the financial support. We thank A. Inglis B.A. for her useful linguistic revision of the manuscript and E. Rodola for his excellent technical assistance.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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