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Sensors 2013, 13(1), 736-745; doi:10.3390/s130100736
Published: 7 January 2013
Abstract: A technique for rapid detection of pathogenic microorganisms is essential for the diagnosis of associated infections and for food safety analysis. Aeromonas hydrophila is one such food contaminant. Several methods for rapid detection of this pathogen have been developed; these include multiplex polymerase chain reaction assays and the colony overlay procedure for peptidases. However, these conventional methods can only be used to detect the microorganisms at high accuracy after symptomatic onset of the disease. Therefore, in the future, simple pre-screening methods may be useful for preventing food poisoning and disease. In this paper, we present a novel system for the rapid detection of the microorganism A. hydrophila in cultured media (in <2 h), with the use of an electronic nose (FF-2A). With this electronic nose, we detected the changes of volatile patterns produced by A. hydrophila after 30 min culture. Our calculations revealed that the increased volatiles were similar to the odours of organic acids and esters. In future, distinctive volatile production patterns of microorganisms identified with the electronic nose may have the potential in microorganism detection.
Electronic noses have been used for various purposes , including chemical sensing [2–6], microorganism profiling [7–11], disease diagnosis [12–15], and quality analyses for foods and beverages [16–20]. In microorganism profiling studies, for example, we showed that the electronic nose (FF-2A, Shimadzu Corporation, Kyoto, Japan) could detect the volatile concentration changes depending on the number of yeast between 102 and 107 colony forming units (CFU)/mL in 90 min . Another group demonstrated that the electronic nose system can recognize volatile production patterns of pathogens including Klebsiella pneumoniae, Pseudomonas aeruginosa, and Escherichia coli at the species level . They detected the microorganisms' volatile patterns after 24 h incubation (preincubation concentrations: more than 1.0 × 105 CFU/mL).
Among such bacterial families, Aeromonas species are facultative anaerobic Gram-negative bacteria that belong to the Aeromonadaceae family . These bacteria are found in water, soil, and food items . Some species can grow in chlorinated water . The US Environmental Protection Agency has included Aeromonas hydrophila in the list of potential contaminant organisms that will require regulation in the future, as per the Safe Drinking Water Act . In recent years, Aeromonas species have been isolated from patients with diarrhoea, especially children aged <5 years . Although diarrhoea caused by Aeromonas species is often mild, it can occasionally become serious, accompanied by haematochezia, abdominal pain, and fever . Moreover, although a link between diarrhoea and the consumption of food contaminated with Aeromonas has not been conclusively identified, putative virulence factors have been detected in contaminated food or clinical isolates [25,27,28]. Other diseases associated with these bacteria are wound infections, pneumonia, meningitis, and septicaemia in humans [29,30]. Some patients with Aeromonas infections have developed acute illness and died of septic shock . A. hydrophila and A. sobria are often isolated from patients with diarrhoea and the abovementioned diseases .
In clinical practice, microorganisms in patient samples are mainly identified by isolation from a selective agar medium or by enzyme-substrate reactions. Although these methods enable definitive identification of contaminant microorganisms, they require long periods for the processing and detection. Recently, multiplex polymerase chain reaction (PCR) assays [32,33] and the colony overlay procedure for peptidases (COPP) have been proposed for the rapid detection of Aeromonas species . PCR assays have been used to determine the distribution of virulence factors among Aeromonas species isolated from drinking water utilities  and from a trout farm . It was found that bacterial isolation on agar plates required 24–60 h, while the PCR method was completed in less than 2 h. Further, COPP has been used to identify A. hydrophila on the basis of its lysyl aminopeptidase activity; in this assay, the culture plates were incubated for 16–18 h, after which enzyme activity was detected within 10 min . Both the PCR assays and the COPP are rapid, sensitive, and accurate.
However, these techniques aim at detecting Aeromonas species at high accuracy after disease occurrence. For preventing food or water-borne illness, other quick tests may be useful for determining the presence of Aeromonas species. Thus we propose a novel rapid method based on the combination of an electronic nose and odour descriptors for simple identifying microorganisms, based on the analysis of the odours arising from volatile compounds released from microorganism metabolism. Especially, A. hydrophila is reported to metabolize many types of carbohydrates to organic acids [35,36]. We hypothesized these volatile compounds may be able to be detected with an electronic nose.
In this study, we investigated the possibility of applying the FF-2A electronic nose for the rapid detection of A. hydrophila in liquid culture media. This electronic nose is equipped with 10 metal oxide semiconductor sensors and a preconcentration tube, consisting of carbon-based adsorbents to eliminate water vapour, which affects the sensors. Therefore, this device yields measurements with high sensitivity and reproducibility [37,38]. In order to know the differences of the detected odours, we compared them with known odours of several standard gases.
2. Experimental Section
2.1. Microorganisms and Culture Conditions
A. hydrophila was isolated from a water sample obtained in Japan. The bacteria were cultured in lysogeny broth (LB; Merck, Darmstadt, Germany; containing 1% peptone derived from casein, 0.5% yeast extract, and 1% NaCl) or modified nutrient broth (Becton Dickinson, Franklin Lake, NJ, USA; containing Bacto beef extract and Bacto peptone) supplemented with 1% NaCl. The starter culture was inoculated at a concentration of 9.6 × 102 CFU/mL and incubated at 37 °C for 0–120 min. Samples of the media containing A. hydrophila were collected at specific time points and filtered through a 0.45 μm filter to remove the bacteria.
2.2. Measurement and Calculation of Volatile Compounds and Standard Gases
Aliquots of the liquid samples (2 mL, containing A. hydrophila and the media) were collected into 2 L polyethylene terephthalate (PET) bags filled with dry nitrogen. The samples in the bags were allowed to equilibrate at 25 °C for 30 min. Further, 200 mL of headspace volatiles were collected, transferred to new 2 L PET bags, diluted with dry nitrogen, and equilibrated at 25 °C for 30 min. These diluted samples were introduced into a trap tube of the FF-2A electronic nose (Shimadzu Corporation, Kyoto, Japan) for 60 s and then exposed to a sensor array with pure nitrogen gas. All the samples were measured twice, and the mean data are presented. The first data was obtained within approximately 80 min after the culture started.
Odour similarities were calculated with the FF-2A software in the following manner: the device contains 10 metal oxide semiconductor sensors that vary in their sensitivity and selectivity for different odorous substances [5,11,38]. These sensors were standardized with nine gases (hydrogen sulphide, methyl mercaptan, ammonia, trimethylamine, propionic acid, butyraldehyde, butyrl acetate, toluene, and heptane). These gases were decided as the standard materials from the environmental offensive odours which are targeted to cover general odour problems in Japan. On the basis of the signals yielded by the sensors, standard vectors corresponding to the nine standard gases were calculated in a 10-sensor dimension (Figure 1). The produced volatile samples from the culture media were measured and compared with the standard gases' vectors. Finally, the data were presented in terms of the similarities between the two data sets, calculated using the angles between the sample vectors. This calculation system based on angles is unique and useful to compare the volatile specificities easily, even between different concentrations . Therefore, it is more suitable for gaining specificities from unknown-concentration samples than multivariate statistics applied only on the raw data. For this calculation, the following criteria were used: θ = 0°, 100% similarity; θ > 20°, 0% similarity. These algorithms were based on the following data from developers' measurement results: at θ = 0°, humans perceive the odour similarly (100% similarity), while at θ > 20°, humans perceive the odour differently . The approximated curves and correlation factors (similarity vs. culture time) in the figures were calculated using Microsoft Excel 2003.
3. Results and Discussion
The growth curve of A. hydrophila against time is shown in Figure 2. The starter culture containing 9.6 × 102 CFU/mL of the bacteria was incubated at 37 °C in LB; the bacterial count increased to 1.8 × 103 CFU/mL after 30 minutes, 3.3 × 103 CFU/mL after 60 min, 9.6 × 103 CFU/mL after 90 min, and 6.0 × 104 CFU/mL after 120 min.
Figure 3 shows the similarities in the odours arising from the A. hydrophila derived volatiles against those arising from the nine standard gases. The first data were collected in 80 min from the cultured sample collection. In order to determine the differences in the volatiles released on culture in different media, we used two liquid media—LB (containing yeast extract) and a modified nutrient broth (containing beef extract).
For LB cultures, the similarities between the volatiles from A. hydrophila and organic acid markedly increased with time from 38.2 to 58.4% (Figure 3(a)). Further, the similarities with the ester group also increased drastically from 43.4 to 62.9%. On culture in the modified nutrient broth, the similarities of the volatiles with the organic acids (30.0 to 46.1%) and esters (70.8 to 87.9%) also increased. Both the similarity charts indicated that minor modifications in the culture medium did not affect the tendency for increased production of distinctive odours.
Previous studies have revealed that A. hydrophila strains produce several acids from various types of carbohydrates [35,36]. Abbot et al. investigated fermentation reactions of 17 kinds of carbohydrates and detected acids from 11. Furthermore, Lee et al. showed the time-dependent changes in the concentrations of volatile fatty acids (acetate and formate) from carbohydrate fermentation.
The increase of the similarities observed for organic acids and esters was almost compatible with these previous studies in chemical categories and may become distinguishing markers of the presence of A. hydrophila. Moreover, volatile odours may be described objectively as the combination of distinctive odour descriptors from organic acids and ester. The former was described as pungent, sour, and sweaty , the latter was described as green, pungent, and sweet .
In order to identify the differences in greater detail, we plotted a similarity graph of the odours of the volatiles released on culture in LB against the odour of each standard gas (Figure 4). The graphs were categorized in terms of the correlation factors. As mentioned earlier, the similarities of the volatiles with organic acids (a) and esters (c) increased markedly with time. In addition, the correlation factors for both these standards were high (0.98 and 0.90, respectively). On the other hand, the similarities of the volatile compounds with the aromatic group (b), sulphur (d), and hydrocarbon (e) did not increase considerably, but the correlation factors were high. Other groups (f–h) showed low correlation factors, probably because these standard gases may not be suitable for detecting the metabolic products in this case.
Presence of these distinctive odour groups suggested the possibility of presence of A. hydrophila. Further, since the detected odours were correlated with the concentration of A. hydrophila, this method can be useful for determining the concentration of bacteria in unknown samples. The modified nutrient broth, on the other hand, had relatively low correlation factors (0.0261–0.76, data not shown). These results suggest that LB is more suitable for the detection of A. hydrophila with the electronic nose on the basis of volatiles released. The differences of volatiles between the two media may affect the detection ability of the electronic nose.
The sensitivity, efficiency, and specificity of the electronic nose render it highly advantageous for the detection of microorganisms. First, the sensitivity of the electronic nose used in the present study can be attributed to the fact that it was equipped with the preconcentration tube for eliminating water vapour, which affects the activity of the semiconductor sensors . Preconcentration tubes also enable reproduction of the results under varied humidity conditions.
Second, the electronic nose used was efficient since it took less than 2 h to obtain the first data (incubation time, 30 min; measurement time, 80 min). Moreover, the measurement time included dilution of sample volatiles by two natural equilibration steps to dilute the volatiles to appropriate concentrations (See Section 2) The natural equilibration of the volatiles accounted for 30 min (60 min in total); thus, the process time could be reduced with the development of a more rapid equilibration method.
Finally, to determine the specificity of the system, more volatile data from other microorganisms are needed. Although A. hydrophila was the sole pathogen demonstrated in this study, data on the volatile compounds produced by other microorganisms will enable rapid detection and classification of these microorganisms. Moreover, selection of appropriate standard gases and sensors will be able to carry out more rapid screening.
In this study, we observed the volatile production patterns of A. hydrophila using the FF-2A electronic nose. This device detected the volatile changes after 30-min culture in lysogency broth at a concentration of 1.8 × 103 CFU/mL. Our results imply that A. hydrophila at a starting concentration of 9.6 × 102 CFU/mL can be detected in <2 h, confirming the detection ability of the device. This is the first report on the detection of A. hydrophila on the basis of its volatile production patterns with an electronic nose. In future, combination of selection medium and volatile pattern database from microorganisms will increase the detection specificities.
We thank Hiroshi Yamashita and Masafumi Yamada (Shimadzu Corporation) for assisting with the experiments. We also thank Fumihide Kanaya (National Center for Global Health and Medicine) for supporting manuscript preparation. This research was partially supported by Takeda Science Foundation.
- Rock, F.; Barsan, N.; Weimar, U. Electronic nose: Current status and future trends. Chem Rev. 2008, 108, 705–725.
- Rakow, N.A.; Susllck, K.S. A colorimetric sensor array for odour visualization. Nature 2000, 406, 710–713.
- Susllck, K.S.; Rakow, N.A.; Sen, A. Colorimetric sensor arrays for molecular recognition. Tetrahedron 2004, 60, 11133–11138.
- Shirasu, M.; Fujioka, K.; Kakishima, S.; Tomizawa, Y.; Nagai, S.; Murata, J.; Manome, Y.; Touhara, K. Chemical identity of a rotting animal-like odor emitted from the inflorescence of the Titan Arum (Amorphophallus titanum). Biosci. Biotechnol. Biochem. 2010, 74, 2550–2554.
- Fujioka, K.; Shirasu, M.; Manome, Y.; Ito, N.; Kakishima, S.; Minami, T.; Tominaga, T.; Shimozono, F.; Iwamoto, T.; Ikeda, K.; Yamamoto, K.; Murata, J.; Tomizawa, Y. Objective display and discrimination of floral odors from amorphophallus titanum, bloomed on different dates and at different locations, using an electronic nose. Sensors 2012, 12, 2152–2161.
- Pandey, S.K.; Kim, K.H. Human body-odor components and their determination. Trac-Trends Anal. Chem. 2011, 30, 784–796.
- Holmberg, M.; Gustafsson, F.; Hornsten, E.G.; Winquist, F.; Nilsson, L.E.; Ljung, L.; Lundstorm, I. Bacteria classification based on feature extraction from sensor data. Biotechnol. Tech. 1998, 12, 319–324.
- Gardner, J.W.; Craven, M.; Dow, C.; Hines, E.L. The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perception network. Meas. Sci. Technol. 1998, 9, 120–127.
- Gibson, T.D.; Prosser, O.; Hulbert, J.N.; Marshall, R.W.; Corcoran, P.; Lowery, P.; Ruck-Keene, E.A.; Heron, S. Detection and simultaneous identification of microorganisms from headspace samples using an electronic nose. Sens. Actuators B Chem. 1997, 44, 413–422.
- Casalinuovo, I.A.; Pierro, D.; Bruno, E.; Francesco, P.; Coletta, M. Experimental use of a new surface acoustic wave sensor for the rapid identification of bacteria and yeasts. Lett. Appl. Microbiol. 2006, 42, 24–29.
- Fujioka, K.; Arakawa, E.; Kita, J.A.Y.; Okuda, T.; Manome, Y.; Yamamoto, K. Combination of real-value smell and metaphor exfpression aids yeast detection. PLoS One 2009, 4, e7939.
- Mohamed, E.I.; Linder, R.; Periello, G.; di Daniele, N.; Poppl, S.J.; De Lorenzo, A. Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. Diabetes Nutr. Metab. 2002, 15, 215–221.
- Pavlou, A.K.; Magan, N.; Jones, J.M.; Brown, J.; Klatser, P.; Turner, A.P.F. Detection of Mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system. Biosens. Bioelectron. 2004, 20, 538–544.
- Thaler, E.R.; Hanson, C.W. Use of an electronic nose to diagnose bacterial sinusitis. Am. J. Rhinol. 2006, 20, 170–172.
- Turner, A.P.; Magan, N. Electronic noses and disease diagnostics. Nat. Rev. Microbiol. 2004, 2, 161–166.
- Gutierrez-Mendez, N.; Vallejo-Cordoba, B.; Gonzalez-Cordova, A.F.; Nevarez-Moorillon, G.V.; Rivera-Chavira, B. Evaluation of aroma generation of Lactococcus lactis with an electronic nose and sensory analysis. J. Dairy Sci. 2008, 91, 49–57.
- Keshri, G.; Voysey, P.; Magan, N. Early detection of spoilage moulds in bread using volatile production patterns and quantitative enzyme assays. J. Appl. Microbiol. 2002, 92, 165–172.
- Mamat, M.; Samad, S.A.; Hannan, M.A. An electronic nose for reliable measurement and correct classification of beverages. Sensors 2011, 11, 6435–6453.
- Peris, M.; Escuder-Gilabert, L. A 21st century technique for food control: Electronic noses. Anal. Chim. Acta 2009, 638, 1–15.
- Baldwin, E.A.; Bai, J.; Plotto, A.; Dea, S. Electronic noses and tongues: Applications for the food and pharmaceutical industries. Sensors 2011, 11, 4744–4766.
- Martin-Carnahan, A.; Joseph, S.W.; Family, I. Aeromonadaceae Colwell, MacDonell and De Ley 1986. In Bergey's Manual of Systematic Bacteriology, 2nd ed.; Brenner, D., Krieg, N.R., Staley, J.T., Eds.; Springer: Berlin, Germany, 2005; Volume 2, p. 556.
- Von Graevenitz, A. The role of Aeromonas in Diarrhea: A review. Infection 2007, 35, 59–64.
- Holmes, P.; Niccolls, L.M.; Sartory, D.P. The ecology of mesophilic Aeromonas in the aquatic environment. In The Genus Aeromonas; Austin, B., Altwegg, M., Golling, P.J., Joseph, S.W., Eds.; John Wiley & Sons Ltd.: West Sussex, UK, 1996; pp. 127–150.
- Borchardt, M.A.; Stemper, M.E.; Standridge, J.H. Aeromonas isolates from human diarrheic stool and groundwater compared by pulsed-field gel electrophoresis. Emerg. Infect. Dis. 2003, 9, 224–228.
- Martin-Carnahan, A.; Joseph, S.W.; Genus, I. Aeromonas Stanier 1943. In Bergey's Manual of Systematic Bacteriology, 2nd ed.; Brenner, D., Krieg, N.R., Staley, J.T., Eds.; Springer: Berlin, Germany, 2005; Volume 2, pp. 557–578.
- Nomura, T. Maturation pathway of hemolysin of Aeromonas sobria and the mechanism of action of the hemolysin. Yakugaku Zasshi 2001, 121, 481–485.
- Erova, T.E.; Sha, J.; Horneman, A.J.; Borchardt, M.A.; Khajanchi, B.K.; Fadl, A.A.; Chopra, A.K. Identification of a new hemolysin from diarrheal isolate SSU of Aeromonas hydrophila. FEMS Microbiol. Lett. 2007, 275, 301–311.
- Guimaraes, M.S.; Andrade, J.R.; Freitas-Almeida, A.C.; Ferreira, M.C. Aeromonas hydrophila vacuolating activity in the Caco-2 human enterocyte cell line as a putative virulence factor. FEMS Microbiol. Lett. 2002, 207, 127–131.
- Brink, A.J.; Giannakopoulos, E.; VilJoen, H.G. Fishtank water as a source of a rare case of Aeromonas hydrophila septicaemia. S. Afr. Med. J. 1998, 88, 1011–1012.
- Ender, P.T.; Dolan, M.J.; Dolan, D.; Farmer, J.; Melcher, G.P. Near-drowning-associated Aeromonas pneumonia. J. Emerg. Med. 1996, 14, 737–741.
- Sakata, K.; Ikeda, Y.; Mori, T.; Okamoto, K.; Ideguchi, K.; Nakagawa, K.; Yasumitsu, T. A severe case of aeromonas hydrophila infection after surgery for gastric cancer. Jpn. J. Gastroenterol. Surg. 2003, 36, 470–475.
- Sen, K.; Rodgers, M. Distribution of six virulence factors in Aeromonas species isolated from US drinking water utilities: A PCR identification. J. Appl. Microbiol. 2004, 97, 1077–1086.
- Nam, I.; Joh, K. Rapid detection of virulence factors of Aeromonas isolated from trout farm by hexaplex-PCR. J. Microbiol. 2007, 45, 297–304.
- Richards, G.P.; Watoson, M.A. A simple fluorogenic method to detect Vibrio cholerae and Aeromonas hydrophila in well water for areas impacted by catastrophic disasters. Am. J. Trop. Med. Hyg. 2006, 75, 516–521.
- Lee, S.; Kim, J.; Shin, S.G.; Hwang, S. Biokinetic parameters and behavior of Aeromonas hydrophila during anaerobic growth. Biothechnol. Lett. 2008, 30, 1011–1016.
- Abbot, S.L.; Cheung, W.K.W.; Janda, J.M. The Genus Aeromonas: Biochemical characteristics, atypical reactions, and phenotypic identification schemes. J. Clin. Microbiol. 2003, 41, 2348–2357.
- Kita, J.; Aoyama, Y.; Kinoshita, M.; Akamaru, H.; Okada, M. Role of “FF-2A” electronic nose and its applications—Focusing on odor monitoring. Shimadzu Rev. 2007, 64, 63–73.
- Aoyama, Y. An electronic nose and application to smell evaluation of food and dink by it. J. Integr. Study Dietary Habit. 2006, 17, 266–270.
- Uchida, N.; Takahashi, Y.K.; Tanifuji, M.; Mori, K. Odor maps in the mammalian Olfactory bulb: Domain organization and odorant structural features. Nat. Neurosci. 2000, 3, 1035–1043.
- Kiritsakis, A.K. Flavor components of olive oil—A review. J. Am. Oil Chem. Soc. 1998, 75, 673–681.
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