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Detection and discrimination of coliform bacteria with gas sensor arrays
Sensors and Actuators B 70 Ž2000. 170–176 www.elsevier.nlrlocatersensorb
Detection and discrimination of coliform bacteria with gas sensor arrays C.M. McEntegart a , W.R. Penrose a , S. Strathmann b,1, J.R. Stetter a,) b
a Chemistry Department, Illinois Institute of Technology, 3101 South Dearborn Street, Chicago, IL 60616, USA Institute of Physical and Theoretical Chemistry, UniÕersity of Tubingen, Auf der Morgenstelle 8, D-72076 Tubingen, Germany ¨ ¨
Received 5 November 1999; received in revised form 16 February 2000; accepted 10 April 2000
Abstract Electronic noses, which are used for characterizing complex vapors and aromas, may be useful for detection of bacterial contamination or diagnosis of infections, if minimal standards of selectivity and sensitivity can be met. A culture of Enterobacter aerogenes is readily discriminated from an Escherichia coli strain using principal components analysis ŽPCA. of data generated by an array of eight quartz microbalance ŽQMB., eight metal oxide semiconductor ŽMOX., and four electrochemical gas sensors. Two strains of E. coli were not discriminated under identical conditions. Retaining headspace air in a sealed vial containing growing bacteria results in an enhancement of sensitivity, so that a concentration of bacteria of about 5 = 10 8rml may be both detected and distinguished from other species. Improvements in sensitivity to levels useful for practical applications will require enhancement of sensors, sampling system, and pattern classification. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Electronic nose; E. coli; Vapor
1. Introduction Typically, a given type of gas sensor will respond to many different gases. An electrochemical carbon monoxide sensor, for example, will generally respond also to alcohols, ethers, aldehydes, and other inorganic gases. In fact, any compound that is electroactive under the conditions of the CO oxidation will produce electrons and respond on the CO sensor w1x. Although this is an interference when the intention is to measure carbon monoxide alone, we have found that an array of different types of gas sensors will produce a set of responses whose relative magnitudes form a unique pattern for each gas w2–5x. A simple histogram of such a set of responses is usually readily distinguished from an analogous histogram made with the same array and a different analyte. In practice, visual comparison of histograms is not used; sensor data are presented to a computerized pattern classifier, which
) Corresponding author. Tel.: q1-312-567-3443; fax: q1-312-5673494. E-mail addresses: [email protected] ŽS. Strathmann., [email protected] ŽJ.R. Stetter.. 1 Tel.: q49-7071-29-78765.
carries out the recognition or identification step under controlled conditions and with greater accuracy. The accuracy of classification can be improved by using branching algorithms which pre-process the data and limit the number of possible choices available to the pattern classifier w6x. The olfactory system of animals similarly consists of a relatively small number of chemical receptors combined with a pattern recognizer Žthe brain. w7x. Early attempts to simulate mammalian olfaction with chemical sensor arrays were used during attempts to understand the process of olfaction. As a result of this similarity, the combination of sensor array and computer has been called an Aelectronic noseB w8x and is now commonly referred to as Ae-nose.B The e-nose approach to analysis is particularly effective for comparing or classifying complex mixtures, such as aromas and flavors, which defy more conventional methods of characterization or chemical analysis. The qualitative discrimination power of the e-nose often has an uncanny resemblance to the subjective discrimination of odors by the human nose. For example, the U.S. Department of Agriculture judges the quality of stored grain using an expert panel that makes its evaluation based mainly on odor. We determined that our e-nose was able to classify grain samples from the USDA with an effectiveness Ž87–93%. that rivaled the rate at which the expert judges agreed with one another Ž93%. w9x.
0925-4005r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 0 0 . 0 0 5 6 1 - X
C.M. McEntegart et al.r Sensors and Actuators B 70 (2000) 170–176
Recently, the electronic nose has been used to detect the volatiles emitted by growing bacteria. By using concentrated samples, the e-nose has consistently been able to discriminate among arbitrarily different types of bacteria, at least at the genus level w10–12x. These studies have generally been done with dense cultures, centrifuged cells, or colonies of bacteria grown on an agar surface. There has recently been a report of an e-nose being used to detect a pulmonary infection w13x. Indeed, the grain odor problem discussed above w9x was a clear case of microorganism detection with the e-nose, since the growth of bacteria and fungi are responsible for the diagnostic odors of low-quality grain. Since conventional bacterial taxonomy is based largely on differing nutritional requirements or metabolic products, it is not surprising that the volatiles in the headspace of a bacterial culture are determined by the metabolism of the specific strains of organisms. In view of these reports, and our early experience, it now appears to us that the key issue with the use of the e-nose in microbiology and medicine is sensitivity rather than selectivity. In this work, we have attempted to assess the sensitivity of a modular electronic nose w14x for the detection and discrimination of microorganisms.
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2. Experimental The instrument used in this work was a MOSES II Electronic Nose built by Lennartz Electronic, Tubingen, ¨ Germany. The MOSES II has an open architecture with interchangeable sensor modules and a sampling module that allows for several alternatives in sample introduction w15x. It is supplied with software for automatic operation as well as for pattern recognition. The MOSES II instrument used in this work was equipped with four modules, including an input module with sampling valves and pump, and sensor modules containing eight metal-oxide semiconductor ŽMOX. sensors and eight quartz microbalance ŽQMB. sensors. In addition, a prototype electrochemical ŽEC. module containing four amperometric gas sensors ŽAGS. was included, for a total of 20 sensors of three different classes. The MOSES II software uses a pattern recognizer based on principal components analysis ŽPCA.; results shown in this paper are expressed as plots of the second principal component against the first. Sample handling was carried out with a headspace sampler ŽHSS 86.50, DANI Strumentazione Analitica spa, Monza, Italy., connected directly to the input of the sensor modules of the MOSES
Fig. 1. Plot of the second principal component against the first for a culture of E. coli ATCC 15490, using the net signal data for the two-sensor array in the MOSES system. The ellipses represent the 0.95 confidence limit for samples of a given class. The class numbers represent the number of hours that the cells were grown in the headspace vials before killing them by brief heating in boiling water. Other classes: AwB s distilled water blanks; AblB s blanks made by heating inoculated vials at zero time.
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II. The modular design allows flexibility in sampling and sensing and the sample could be exposed to any sensor array alone or in any order. The order used here was QMB then MOX, then AGS. All experiments were designed to use the headspace sampler and sealed sample vial system originally designed for use with gas chromatography and GCrMS. An Erlenmeyer flask containing Brain–Heart Infusion ŽBHI. medium was temperature controlled and shaken and then inoculated with culture. The culture was grown for 1 h to establish log phase growth. At time zero in the experiment, 5 ml aliquots of the dilute culture were transferred into standard 20 ml headspace vials and sealed with PTFE-lined butyl rubber diaphragms. The cultures were allowed to grow in the vials at 378C for varying time periods before being killed by 10-min treatment in boiling water. Each time point consisted of five replicates, one of which was used for measuring the optical density of the culture. The remaining four replicates were measured by the MOSES II system. The sealed headspace vial approach effectively retained the volatiles produced by the growing cultures, and gave improved sensitivity and reproducibility over working with open, aerated cultures and the e-nose. Con-
trols confirmed that the vials contained sufficient oxygen to maintain normal growth for at least 5 h. The organisms used in these studies were wild type Escherichia coli ATCC 15490 and 15992, and Enterobacter aerogenes ATCC 13048. Optical density measurements were made at each time period and converted to bacterial cell concentrations using the factor 10 9 cellsrml per O.D. unit at 600 nm, using a Spectronic 20 spectrophotometer ŽBeckman Instruments, Walnut Creek, CA.. The gas sensors in many e-noses, including MOSES II, are sensitive to water vapor. In many experiments herein, we used a simple but effective gas dryer made with Nafion tubing to reduce water vapor in the sample to less than 1% relative humidity. The construction and effectiveness of the gas dryer have been discussed elsewhere w15,16x.
3. Results and discussion Tests were made on five growth media ŽBHI, Nutrient Broth, Tryptone Yeast Broth, Tryptone Soy Broth, and Luria-Bertani, all from Difco. to measure the growth rate of E. coli, as well as the ability to detect growing bacteria
Fig. 2. This experiment was carried out identically to that of Fig. 1, but the flow from the headspace sampler was passed through a 1.5 mm= 4 m Nafion tube to remove water vapor and small hydrophilic molecules. The samples reached 5 = 10 8rml approximately 2 h into the experiment. In this experiment, blanks and early samples Žbefore 2 h. were clustered in the tight zone marked AwB. The dashed box around this part of the plot is expanded in Fig. 3.
C.M. McEntegart et al.r Sensors and Actuators B 70 (2000) 170–176
with the e-nose sensors. The BHI promoted the fastest growth rate, and also gave the lowest backgrounds on the sensor array. In the PCA plots throughout this paper, the zero-time controls, which consisted of BHI medium inoculated with bacteria and immediately killed, always overlapped the plain water blanks. This implied that few volatiles were produced by the uninoculated medium. Therefore, BHI was used for all following experiments. We confirmed that headspace analysis could be used to detect growth of E. coli in the BHI growth medium. Fig. 1 shows an example of the principal components plot obtained from the responses of the MOSES 20-sensor array to samples taken at different periods during bacterial growth. Each ellipse represents a different sample class, labeled by the number of hours of growth. The original medium is indistinguishable from water and from the early cultures Ž0.5, 1 and 2 h.; all these points are grouped under AblanksB. Only at 4 h do the sensor responses become statistically distinguishable from the original medium using this method. Examination of individual sensor responses revealed that the largest component of the response of the QMB and MOX sensors was due to water vapor. Although the amperometric sensors gave little or no response to changes
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in the relative humidity of the sample vapor, for the majority of the sensors in the array, the useful data was found by computing the difference between two large sensor responses. This would inevitably have decreased the signalrnoise ratio of the data. Because of the known interference of water vapor on the responses of the QMB and MOX sensors, the experiment was repeated using a Nafion membrane gas dryer developed specifically for these experiments and inserted between the autosampler and the sensor arrays w16x. Fig. 2 shows that the ellipses separate differently with the water and small hydrophilic compounds removed. The cultures grown for 2 h now separate from the blank and earlier samples. Fig. 3 is a magnification of the cluster of ellipses on the left side of Fig. 2, showing more clearly the separation of the 2-h points from the blank and earlier samples. Detailed analysis of the data from individual sensors showed that the Nafion dryer caused a dramatic change in the sensor responses, reflecting a change in the chemical composition of the sample. One of the electrochemical sensors that typically responds to oxidizable compounds gave oxidizing signals with the untreated Žhigh humidity. samples, and a reducing signal of nearly equal magnitude when the water and some other polar compounds were
Fig. 3. An expansion of the dashed portion of Fig. 2, showing the clear separation of the samples incubated for 2 h from the water Žw., blanks Žbl., 0.5 and 1 h classes.
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Fig. 4. Response of one electrochemical sensor, classified as a Ahydrogen sulfideB sensor, to a bacterial sample incubated for 4 h, with and without the Nafion gas dryer. The dryer appears to remove the oxidizable portion of the sample.
removed by the Nafion treatment ŽFig. 4.. This may be attributed to the removal of electroactive compounds by Nafion treatment, leaving unspecified, but apparently electro-reducible compounds. The net current measured by the electrochemical sensors in the sensor array is the algebraic sum of anodic and cathodic currents at the working elec-
trode w1x and results from exposure to the mixture of oxidizable and reducible compounds. The relative concentration of these species has changed upon passage through the Nafion tube. A similar experiment was repeated, comparing cultures of E. coli and Ent. aerogenes grown in parallel in two sets of headspace vials. Samples were taken at specific time intervals, as before, and measured with the Nafion gas dryer in the flow circuit. The results are shown in Fig. 5, with the trajectories of the error ellipses of the two bacterial types marked with arrows. The behavior of the two species was clearly divergent, reflecting distinct metabolic differences reflected in the different headspace vapor composition and, therefore, different sensor response patterns. A final experiment was carried out to compare the headspace vapors produced by two strains of the same species. The above experiment was repeated using two strains of E. coli, ATCC 15490 and 15922. The Nafion gas dryer was used, as before. In this case, the principal components plot shows the two cultures following the same general trajectory ŽFig. 6.. The 2-, 4-, and 6-h samples overlapped with one another, and followed a common path through the two-dimensional PCA space. Although the 3-h samples did not overlap with one an-
Fig. 5. Discrimination of Ent. aerogenes and E. coli. On the PCA plot, the two cultures follow clearly distinguished trajectories, beginning at 2 h. Samples prefixed AAB are Ent. aerogenes; those prefixed AEB are E. coli. The numbers 2, 3, 4, and 6 represent hours of incubation. The two bacteria grew at almost the same rate.
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Fig. 6. Two strains of E. coli are not discriminated by the e-nose. Although each strain describes a trajectory in PCA-space, these trajectories overlap. Although the 3-h samples do not overlap with one another, both fall on the trajectory.
other, it was nevertheless clear that they lay on the common trajectory indicating compositional similarities.
4. Conclusions Our experiments indicate that it may be possible to identify bacteria based entirely on the volatile materials that are produced during growth and accumulate in the headspace. Larger groups of microorganisms should be compared with one another to demonstrate the universality of the e-nose method for distinguishing bacterial species, but based on our work and that of others, it seems that selectivity of the method will not be the main constraint on its practical application. Rather, the low sensitivities of currently available e-nose methods may prove to be the main limitation. Sensitivity can be improved in several ways. In our experiments, it was increased by eliminating reactive vapors that are common to all samples, and by collecting and retaining volatile compounds generated during growth in a sealed container. Clearly, the choice of sensors, their sensitivities to the types of volatiles produced, the selection of growth media, and the vigor of the organism, play a role in determining how early detection can occur. By choosing a growth medium with a minimum of volatile materials, the background contributed by the
medium does not overwhelm the analytical signal. Using the Nafion membrane to remove water vapor, plus some low-molecular-weight hydrophilic compounds, further improves sensitivity by suppressing the background that is common to all samples. Drying agents that remove water vapor, but that remove fewer hydrophilic metabolites, will be tested in future work and are expected to produce higher sensitivity. Finally, growing the bacteria in sealed vials retains and collects volatiles in the headspace gases so they are not swept away by conventional aeration. As a result, in the method reported here, these bacteria could be detected at an optical density representing about 5 = 10 8 organismsrml, or about 1000 times less than the densities found in mature cultures. The sensitivity to the presence of bacteria is detected by the e-nose as changes in the chemical composition of the headspace vapors. Microbiologists have long been aware that many bacterial species can be identified by their odors. These odors no doubt reflect the metabolism of individual species. However, if we define an AodorB as the subjective mammalian olfactory response, we must be careful not say that the e-nose detects this same AodorB. The biological receptors may be responding to an entirely different part of the complex chemical mixture than the e-nose sensors. So, in some cases, the e-nose may be more sensitive than the mammalian nose, and in others, much less.
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The electronic nose appears to be able to discriminate readily between varieties of bacteria. To be useful for practical applications, however, the e-nose approach must be greatly improved in sensitivity. In these few experiments, we have demonstrated that reasonably simple methods can be used to enhance the sensitivity of the e-nose. Greater increases in sensitivity will involve the development of other selective sampling methods, as well as the introduction of more sensitive vapor sensors and improved data treatment. Aqueous-phase measurements are another possibility, since many potentially diagnostic bacterial metabolites are strongly hydrophilic and do not partition well into the vapor phase. This Aelectronic tongueB approach has been initially explored w17x, but other approaches to examining the solid and liquid phases of bacterial samples must be investigated. In summary: 1. Metabolic products of aqueous bacterial growth could be sensitively detected in the vapor phase using the e-nose and a heterogeneous sensor array, even an array containing water-sensitive sensors. 2. The system as configured by us was able to detect approximately 5 = 10 8 cellsrml. This is about 1000 times more sensitive than estimates of densities used prior reports in the literature Žin most cases, actual cell densities were not reported.. 3. Pretreatment of the vapor sample by passage through a dryer using Nafion tubing changed the sample composition in such a way as to improve the sensitivity of the analysis. 4. Closely related species of bacteria showed clearly different behavior, while different strains from the same species were very similar in this experiment. 5. Because e-nose results are strongly dependent on choice of array and sampling conditions, it is important to carefully describe apparatus and specific method when comparing results from the e-nose.
Acknowledgements Our most sincere gratitude to Professor Dr. Wolfgang Gopel ¨ for sharing equipment and students, but most of all for challenging discussions and personal interactions with our group. Bacterial strains used in this work were provided courtesy of M.L. Tortorello, National Center for Food Safety and TechnologyrU.S. Food and Drug Administration, Argo, IL. Our thanks to Lennartz electronic and Hewlett-Packard for partial support of the equipment used in this work. This work was funded in part by Illinois Institute of Technology and the IIT Research Institute, Chicago, IL.
References w1x Z. Cao, W.J. Buttner, J.R. Stetter, The properties and applications of amperometric gas sensors, Electroanalysis 4 Ž1992. 253–266. w2x J.R. Stetter, Electrochemical sensors, sensor arrays, and computer algorithms, in: D. Schuetzle, R. Hammerle, J. Butler ŽEds.., Fundamentals and Applications of Chemical Sensors, ACS Symposium Series No. 3091986, pp. 299–308. w3x J.R. Stetter, P.C. Jurs, S.L. Rose, Detection of hazardous Gases and vapors: pattern recognition analysis of data from an electrochemical sensor arrays, Anal. Chem. 58 Ž1986. 860–866. w4x J.R. Stetter, S. Zaromb, W.R. Penrose, M.W. Findlay, T. Otagawa, A.J. Sincali, Portable Device for detecting and Identifying Hazardous Vapors, Proceedings of the Hazardous Materials Spills Conference, Paper No. 116, Nashville, Tenn ŽApril 9–12, 1984., John Ludwigson, ed., Government Institutes, Žpub.., Rockville, MD, 20850 Apr. Ž1984.. w5x J.R. Stetter, S. Zaromb, W.R. Penrose, Sensor array for toxic gas detection. U.S. Patent 4,670,405, 1984. w6x S. Zaromb, R. Battin, W.R. Penrose, J.R. Stetter, V.C. Stamoudis, J.O. Stull, Extending the capabilities of the portable chemical parameter spectrometer to the identification of up to 100 compounds, in: J.L. Aucouturier ŽEd.., Proc. of the 2nd International Meeting on Chemical Sensors, Bordeaux, France, 1986, July 7–10 739742. w7x H.A. Schultens, D. Schild, Biophysical properties of olfactory receptor neurones, in Sensors and Sensory Systems for an Electronic Nose, ŽJ.W. Gardner and P.N. Bartlett, Eds.. Proc. NATO Advanced Research Workshop, Reykjavik, Iceland, August 5–8, 1991. w8x G.H. Dodd, P.N. Bartlett, J.W. Gardner, Odours — the stimulus for an electronic nose, in Sensors and Sensory Systems for an Electronic Nose, ŽJ.W. Gardner and P.N. Bartlett, Eds.. Proc. NATO Advanced Research Workshop, Reykjavik, Iceland, August 5–8, 1991. w9x J.R. Stetter, M.W. Findlay Jr., K.M. Schroeder, C. Yue, W.R. Penrose et al., Quality classification of grain using a sensor array and pattern recognition, Anal. Chim. Acta 284 Ž1993. 1–11. w10x J.W. Gardner, M. Craven, C. Dow, E. Hines, The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network, Meas. Sci. Technol. 9 Ž1998. 120– 127. w11x T.D. Gibson, O. Prosser, J.N. Hulbert, R.W. Marshall, P. Corcoran, P. Lowery, E.A. Ruck-Keene, S. Heron, Detection and simultaneous identification of microorganisms from headspace samples using an electronic nose, Sens. Actuators, B 44 Ž1997. 413–422. w12x M. Holmberg, F. Gustafsson, E.G. Hornsten, F. Winquist, L.E. Nilsson, L. Ljung, I. Lundstrom, Bacteria classification based on feature extraction from sensor data, Biotechnol. Tech. 12 Ž1998. 319–324. w13x Pope, K. Technology Improves on the Nose As Science Tries to Imitate Smell, Wall Street Journal, pp. B1–2, 1 1995 March. w14x J. Mitrovics, H. Ulmer, U. Weimar, W. Gopel, Modular sensor ¨ systems for gas sensing and odor monitoring: the MOSES concept, Acc. Chem. Res. 31 Ž1998. 307–315. w15x S. Strathmann, W.R. Penrose, J.R. Stetter, and W. Gopel, Ap¨ proaches to a More Versatile Electronic Nose, Proc. International Symposium on Olfaction and the Electronic Nose ŽISOEN 99., Tuebingen, Germany, September 20–22, 1999. w16x J.R. Stetter, S. Strathmann, C.M. McEntegart, M. DeCastro, W.R. Penrose, New sensor arrays and sampling systems for a modular electronic nose, Sens. Actuators, B Ž1999. submitted for publication. w17x F. Winquist, C. Krantz-Rulcker, P. Wide, I. Lundstrom, Monitoring of freshness of milk by an electronic tongue on the basis of voltammetry, Meas. Sci. Technol. 9 Ž1998. 1937–1946.
Detection and discrimination of coliform bacteria with gas sensor arrays C.M. McEntegart a , W.R. Penrose a , S. Strathmann b,1, J.R. Stetter a,) b
a Chemistry Department, Illinois Institute of Technology, 3101 South Dearborn Street, Chicago, IL 60616, USA Institute of Physical and Theoretical Chemistry, UniÕersity of Tubingen, Auf der Morgenstelle 8, D-72076 Tubingen, Germany ¨ ¨
Received 5 November 1999; received in revised form 16 February 2000; accepted 10 April 2000
Abstract Electronic noses, which are used for characterizing complex vapors and aromas, may be useful for detection of bacterial contamination or diagnosis of infections, if minimal standards of selectivity and sensitivity can be met. A culture of Enterobacter aerogenes is readily discriminated from an Escherichia coli strain using principal components analysis ŽPCA. of data generated by an array of eight quartz microbalance ŽQMB., eight metal oxide semiconductor ŽMOX., and four electrochemical gas sensors. Two strains of E. coli were not discriminated under identical conditions. Retaining headspace air in a sealed vial containing growing bacteria results in an enhancement of sensitivity, so that a concentration of bacteria of about 5 = 10 8rml may be both detected and distinguished from other species. Improvements in sensitivity to levels useful for practical applications will require enhancement of sensors, sampling system, and pattern classification. q 2000 Elsevier Science B.V. All rights reserved. Keywords: Electronic nose; E. coli; Vapor
1. Introduction Typically, a given type of gas sensor will respond to many different gases. An electrochemical carbon monoxide sensor, for example, will generally respond also to alcohols, ethers, aldehydes, and other inorganic gases. In fact, any compound that is electroactive under the conditions of the CO oxidation will produce electrons and respond on the CO sensor w1x. Although this is an interference when the intention is to measure carbon monoxide alone, we have found that an array of different types of gas sensors will produce a set of responses whose relative magnitudes form a unique pattern for each gas w2–5x. A simple histogram of such a set of responses is usually readily distinguished from an analogous histogram made with the same array and a different analyte. In practice, visual comparison of histograms is not used; sensor data are presented to a computerized pattern classifier, which
) Corresponding author. Tel.: q1-312-567-3443; fax: q1-312-5673494. E-mail addresses: [email protected] ŽS. Strathmann., [email protected] ŽJ.R. Stetter.. 1 Tel.: q49-7071-29-78765.
carries out the recognition or identification step under controlled conditions and with greater accuracy. The accuracy of classification can be improved by using branching algorithms which pre-process the data and limit the number of possible choices available to the pattern classifier w6x. The olfactory system of animals similarly consists of a relatively small number of chemical receptors combined with a pattern recognizer Žthe brain. w7x. Early attempts to simulate mammalian olfaction with chemical sensor arrays were used during attempts to understand the process of olfaction. As a result of this similarity, the combination of sensor array and computer has been called an Aelectronic noseB w8x and is now commonly referred to as Ae-nose.B The e-nose approach to analysis is particularly effective for comparing or classifying complex mixtures, such as aromas and flavors, which defy more conventional methods of characterization or chemical analysis. The qualitative discrimination power of the e-nose often has an uncanny resemblance to the subjective discrimination of odors by the human nose. For example, the U.S. Department of Agriculture judges the quality of stored grain using an expert panel that makes its evaluation based mainly on odor. We determined that our e-nose was able to classify grain samples from the USDA with an effectiveness Ž87–93%. that rivaled the rate at which the expert judges agreed with one another Ž93%. w9x.
0925-4005r00r$ - see front matter q 2000 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 0 0 . 0 0 5 6 1 - X
C.M. McEntegart et al.r Sensors and Actuators B 70 (2000) 170–176
Recently, the electronic nose has been used to detect the volatiles emitted by growing bacteria. By using concentrated samples, the e-nose has consistently been able to discriminate among arbitrarily different types of bacteria, at least at the genus level w10–12x. These studies have generally been done with dense cultures, centrifuged cells, or colonies of bacteria grown on an agar surface. There has recently been a report of an e-nose being used to detect a pulmonary infection w13x. Indeed, the grain odor problem discussed above w9x was a clear case of microorganism detection with the e-nose, since the growth of bacteria and fungi are responsible for the diagnostic odors of low-quality grain. Since conventional bacterial taxonomy is based largely on differing nutritional requirements or metabolic products, it is not surprising that the volatiles in the headspace of a bacterial culture are determined by the metabolism of the specific strains of organisms. In view of these reports, and our early experience, it now appears to us that the key issue with the use of the e-nose in microbiology and medicine is sensitivity rather than selectivity. In this work, we have attempted to assess the sensitivity of a modular electronic nose w14x for the detection and discrimination of microorganisms.
171
2. Experimental The instrument used in this work was a MOSES II Electronic Nose built by Lennartz Electronic, Tubingen, ¨ Germany. The MOSES II has an open architecture with interchangeable sensor modules and a sampling module that allows for several alternatives in sample introduction w15x. It is supplied with software for automatic operation as well as for pattern recognition. The MOSES II instrument used in this work was equipped with four modules, including an input module with sampling valves and pump, and sensor modules containing eight metal-oxide semiconductor ŽMOX. sensors and eight quartz microbalance ŽQMB. sensors. In addition, a prototype electrochemical ŽEC. module containing four amperometric gas sensors ŽAGS. was included, for a total of 20 sensors of three different classes. The MOSES II software uses a pattern recognizer based on principal components analysis ŽPCA.; results shown in this paper are expressed as plots of the second principal component against the first. Sample handling was carried out with a headspace sampler ŽHSS 86.50, DANI Strumentazione Analitica spa, Monza, Italy., connected directly to the input of the sensor modules of the MOSES
Fig. 1. Plot of the second principal component against the first for a culture of E. coli ATCC 15490, using the net signal data for the two-sensor array in the MOSES system. The ellipses represent the 0.95 confidence limit for samples of a given class. The class numbers represent the number of hours that the cells were grown in the headspace vials before killing them by brief heating in boiling water. Other classes: AwB s distilled water blanks; AblB s blanks made by heating inoculated vials at zero time.
172
C.M. McEntegart et al.r Sensors and Actuators B 70 (2000) 170–176
II. The modular design allows flexibility in sampling and sensing and the sample could be exposed to any sensor array alone or in any order. The order used here was QMB then MOX, then AGS. All experiments were designed to use the headspace sampler and sealed sample vial system originally designed for use with gas chromatography and GCrMS. An Erlenmeyer flask containing Brain–Heart Infusion ŽBHI. medium was temperature controlled and shaken and then inoculated with culture. The culture was grown for 1 h to establish log phase growth. At time zero in the experiment, 5 ml aliquots of the dilute culture were transferred into standard 20 ml headspace vials and sealed with PTFE-lined butyl rubber diaphragms. The cultures were allowed to grow in the vials at 378C for varying time periods before being killed by 10-min treatment in boiling water. Each time point consisted of five replicates, one of which was used for measuring the optical density of the culture. The remaining four replicates were measured by the MOSES II system. The sealed headspace vial approach effectively retained the volatiles produced by the growing cultures, and gave improved sensitivity and reproducibility over working with open, aerated cultures and the e-nose. Con-
trols confirmed that the vials contained sufficient oxygen to maintain normal growth for at least 5 h. The organisms used in these studies were wild type Escherichia coli ATCC 15490 and 15992, and Enterobacter aerogenes ATCC 13048. Optical density measurements were made at each time period and converted to bacterial cell concentrations using the factor 10 9 cellsrml per O.D. unit at 600 nm, using a Spectronic 20 spectrophotometer ŽBeckman Instruments, Walnut Creek, CA.. The gas sensors in many e-noses, including MOSES II, are sensitive to water vapor. In many experiments herein, we used a simple but effective gas dryer made with Nafion tubing to reduce water vapor in the sample to less than 1% relative humidity. The construction and effectiveness of the gas dryer have been discussed elsewhere w15,16x.
3. Results and discussion Tests were made on five growth media ŽBHI, Nutrient Broth, Tryptone Yeast Broth, Tryptone Soy Broth, and Luria-Bertani, all from Difco. to measure the growth rate of E. coli, as well as the ability to detect growing bacteria
Fig. 2. This experiment was carried out identically to that of Fig. 1, but the flow from the headspace sampler was passed through a 1.5 mm= 4 m Nafion tube to remove water vapor and small hydrophilic molecules. The samples reached 5 = 10 8rml approximately 2 h into the experiment. In this experiment, blanks and early samples Žbefore 2 h. were clustered in the tight zone marked AwB. The dashed box around this part of the plot is expanded in Fig. 3.
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with the e-nose sensors. The BHI promoted the fastest growth rate, and also gave the lowest backgrounds on the sensor array. In the PCA plots throughout this paper, the zero-time controls, which consisted of BHI medium inoculated with bacteria and immediately killed, always overlapped the plain water blanks. This implied that few volatiles were produced by the uninoculated medium. Therefore, BHI was used for all following experiments. We confirmed that headspace analysis could be used to detect growth of E. coli in the BHI growth medium. Fig. 1 shows an example of the principal components plot obtained from the responses of the MOSES 20-sensor array to samples taken at different periods during bacterial growth. Each ellipse represents a different sample class, labeled by the number of hours of growth. The original medium is indistinguishable from water and from the early cultures Ž0.5, 1 and 2 h.; all these points are grouped under AblanksB. Only at 4 h do the sensor responses become statistically distinguishable from the original medium using this method. Examination of individual sensor responses revealed that the largest component of the response of the QMB and MOX sensors was due to water vapor. Although the amperometric sensors gave little or no response to changes
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in the relative humidity of the sample vapor, for the majority of the sensors in the array, the useful data was found by computing the difference between two large sensor responses. This would inevitably have decreased the signalrnoise ratio of the data. Because of the known interference of water vapor on the responses of the QMB and MOX sensors, the experiment was repeated using a Nafion membrane gas dryer developed specifically for these experiments and inserted between the autosampler and the sensor arrays w16x. Fig. 2 shows that the ellipses separate differently with the water and small hydrophilic compounds removed. The cultures grown for 2 h now separate from the blank and earlier samples. Fig. 3 is a magnification of the cluster of ellipses on the left side of Fig. 2, showing more clearly the separation of the 2-h points from the blank and earlier samples. Detailed analysis of the data from individual sensors showed that the Nafion dryer caused a dramatic change in the sensor responses, reflecting a change in the chemical composition of the sample. One of the electrochemical sensors that typically responds to oxidizable compounds gave oxidizing signals with the untreated Žhigh humidity. samples, and a reducing signal of nearly equal magnitude when the water and some other polar compounds were
Fig. 3. An expansion of the dashed portion of Fig. 2, showing the clear separation of the samples incubated for 2 h from the water Žw., blanks Žbl., 0.5 and 1 h classes.
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Fig. 4. Response of one electrochemical sensor, classified as a Ahydrogen sulfideB sensor, to a bacterial sample incubated for 4 h, with and without the Nafion gas dryer. The dryer appears to remove the oxidizable portion of the sample.
removed by the Nafion treatment ŽFig. 4.. This may be attributed to the removal of electroactive compounds by Nafion treatment, leaving unspecified, but apparently electro-reducible compounds. The net current measured by the electrochemical sensors in the sensor array is the algebraic sum of anodic and cathodic currents at the working elec-
trode w1x and results from exposure to the mixture of oxidizable and reducible compounds. The relative concentration of these species has changed upon passage through the Nafion tube. A similar experiment was repeated, comparing cultures of E. coli and Ent. aerogenes grown in parallel in two sets of headspace vials. Samples were taken at specific time intervals, as before, and measured with the Nafion gas dryer in the flow circuit. The results are shown in Fig. 5, with the trajectories of the error ellipses of the two bacterial types marked with arrows. The behavior of the two species was clearly divergent, reflecting distinct metabolic differences reflected in the different headspace vapor composition and, therefore, different sensor response patterns. A final experiment was carried out to compare the headspace vapors produced by two strains of the same species. The above experiment was repeated using two strains of E. coli, ATCC 15490 and 15922. The Nafion gas dryer was used, as before. In this case, the principal components plot shows the two cultures following the same general trajectory ŽFig. 6.. The 2-, 4-, and 6-h samples overlapped with one another, and followed a common path through the two-dimensional PCA space. Although the 3-h samples did not overlap with one an-
Fig. 5. Discrimination of Ent. aerogenes and E. coli. On the PCA plot, the two cultures follow clearly distinguished trajectories, beginning at 2 h. Samples prefixed AAB are Ent. aerogenes; those prefixed AEB are E. coli. The numbers 2, 3, 4, and 6 represent hours of incubation. The two bacteria grew at almost the same rate.
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Fig. 6. Two strains of E. coli are not discriminated by the e-nose. Although each strain describes a trajectory in PCA-space, these trajectories overlap. Although the 3-h samples do not overlap with one another, both fall on the trajectory.
other, it was nevertheless clear that they lay on the common trajectory indicating compositional similarities.
4. Conclusions Our experiments indicate that it may be possible to identify bacteria based entirely on the volatile materials that are produced during growth and accumulate in the headspace. Larger groups of microorganisms should be compared with one another to demonstrate the universality of the e-nose method for distinguishing bacterial species, but based on our work and that of others, it seems that selectivity of the method will not be the main constraint on its practical application. Rather, the low sensitivities of currently available e-nose methods may prove to be the main limitation. Sensitivity can be improved in several ways. In our experiments, it was increased by eliminating reactive vapors that are common to all samples, and by collecting and retaining volatile compounds generated during growth in a sealed container. Clearly, the choice of sensors, their sensitivities to the types of volatiles produced, the selection of growth media, and the vigor of the organism, play a role in determining how early detection can occur. By choosing a growth medium with a minimum of volatile materials, the background contributed by the
medium does not overwhelm the analytical signal. Using the Nafion membrane to remove water vapor, plus some low-molecular-weight hydrophilic compounds, further improves sensitivity by suppressing the background that is common to all samples. Drying agents that remove water vapor, but that remove fewer hydrophilic metabolites, will be tested in future work and are expected to produce higher sensitivity. Finally, growing the bacteria in sealed vials retains and collects volatiles in the headspace gases so they are not swept away by conventional aeration. As a result, in the method reported here, these bacteria could be detected at an optical density representing about 5 = 10 8 organismsrml, or about 1000 times less than the densities found in mature cultures. The sensitivity to the presence of bacteria is detected by the e-nose as changes in the chemical composition of the headspace vapors. Microbiologists have long been aware that many bacterial species can be identified by their odors. These odors no doubt reflect the metabolism of individual species. However, if we define an AodorB as the subjective mammalian olfactory response, we must be careful not say that the e-nose detects this same AodorB. The biological receptors may be responding to an entirely different part of the complex chemical mixture than the e-nose sensors. So, in some cases, the e-nose may be more sensitive than the mammalian nose, and in others, much less.
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The electronic nose appears to be able to discriminate readily between varieties of bacteria. To be useful for practical applications, however, the e-nose approach must be greatly improved in sensitivity. In these few experiments, we have demonstrated that reasonably simple methods can be used to enhance the sensitivity of the e-nose. Greater increases in sensitivity will involve the development of other selective sampling methods, as well as the introduction of more sensitive vapor sensors and improved data treatment. Aqueous-phase measurements are another possibility, since many potentially diagnostic bacterial metabolites are strongly hydrophilic and do not partition well into the vapor phase. This Aelectronic tongueB approach has been initially explored w17x, but other approaches to examining the solid and liquid phases of bacterial samples must be investigated. In summary: 1. Metabolic products of aqueous bacterial growth could be sensitively detected in the vapor phase using the e-nose and a heterogeneous sensor array, even an array containing water-sensitive sensors. 2. The system as configured by us was able to detect approximately 5 = 10 8 cellsrml. This is about 1000 times more sensitive than estimates of densities used prior reports in the literature Žin most cases, actual cell densities were not reported.. 3. Pretreatment of the vapor sample by passage through a dryer using Nafion tubing changed the sample composition in such a way as to improve the sensitivity of the analysis. 4. Closely related species of bacteria showed clearly different behavior, while different strains from the same species were very similar in this experiment. 5. Because e-nose results are strongly dependent on choice of array and sampling conditions, it is important to carefully describe apparatus and specific method when comparing results from the e-nose.
Acknowledgements Our most sincere gratitude to Professor Dr. Wolfgang Gopel ¨ for sharing equipment and students, but most of all for challenging discussions and personal interactions with our group. Bacterial strains used in this work were provided courtesy of M.L. Tortorello, National Center for Food Safety and TechnologyrU.S. Food and Drug Administration, Argo, IL. Our thanks to Lennartz electronic and Hewlett-Packard for partial support of the equipment used in this work. This work was funded in part by Illinois Institute of Technology and the IIT Research Institute, Chicago, IL.
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