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Design, Implementation, and Field Testing of a Portable Fluorescence-Based Vapor Sensor Matthew J. Aernecke,† Jian Guo,‡ Sameer Sonkusale,‡ and David R. Walt†,* Department of Chemistry, Tufts University, 62 Talbot Avenue, Medford, Massachusetts 02155, and Department of Electrical and Computer Engineering, Tufts University, 161 College Avenue, Medford, Massachusetts 02155 The design and implementation of a portable fluorescencebased vapor sensing system are described. The system incorporates previously developed microsensor array technology into a compact, low-power device capable of collecting and delivering ambient vapor samples to the array while monitoring and recording the fluorescent responses of the sensors. The sensors respond differentially when exposed to a sample vapor and, when processed using a support vector machine (SVM) pattern recognition algorithm, are shown to discriminate between three classes of petroleum distillates. The system was characterized using sample vapors prepared under several different conditions in three sensing scenarios. The first scenario demonstrates the basic operational capability of the device in the field by presenting high concentration vapors to the array. The second scenario introduces the potential for a greater degree of variability in both sample vapor concentration and composition in an effort to emulate real-world sensing conditions. The third scenario uses an on-board trained pattern recognition algorithm to identify unknown vapors as their responses are collected. The device demonstrated high classification accuracy throughout the field tests and is capable of improving its classification accuracy when challenged with samples presented under variable ambient conditions by enhancing the signal-to-noise ratio of the array response. Electronic noses are analytical instruments designed to detect and identify vapors using a sensing strategy similar to the mammalian olfactory system.1 Like their biological counterparts, electronic noses employ an array of cross-reactive and modestly selective sensors that collectively generate a response pattern encoding a particular odorant. It is this sensing modality, the idea that one can establish analyte specificity from a collection of nonspecific responses, that distinguishes electronic nose technology from other direct sensing approaches. The collective array responses can be processed with pattern recognition software, stored in a database, and used to identify unknown sensor responses by comparison. The ability of an electronic nose system to classify a wide range of analytes under several conditions is * To whom correspondence should be addressed. E-mail: david.walt@ tufts.edu. † Department of Chemistry. ‡ Department of Electrical and Computer Engineering. (1) Albert, K. J.; Lewis, N. S.; Schauer, C. L.; Sotzing, G. A.; Stitzel, S. E.; Vaid, T. P.; Walt, D. R. Chem. Rev. 2000, 100, 2595–2626. 10.1021/ac900505p CCC: $40.75 2009 American Chemical Society Published on Web 06/10/2009
intimately linked to the stability of the sensor responses over time, the breadth of the stored response database, as well as the ability of the pattern recognition algorithm to effectively discriminate between vapor classes. Electronic noses can be based on a number of different chemical transduction mechanisms including surface acoustic wave (SAW) resonators,2-4 polymer or low volatility small molecule chemiresistors,5-7 cantilevers,8 calorimeters,9 metaloxide semiconductors,10,11 and colorimetric sensors.12,13 Collectively, electronic nose platforms have demonstrated the ability to detect and correctly identify analyte vapors at relatively high concentrations but have struggled with ultratrace detection in complex, highly variable backgrounds. Successfully addressing this limitation is important to move the technology forward and making progress toward achieving this goal lies in the development of systems that are capable of performing measurements in the ambient environment. In our laboratory, we have developed a fluorescence-based artificial nose system that incorporates silica microspheres containing solvatochromic dyes adsorbed onto their surfaces into a high density array.14,15 Several microsphere types, each displaying a different surface functionality, are distributed into microwells chemically etched into one face of an imaging fiber-optic bundle.16 The fluorescent signal measured from each microsphere sensor varies in intensity and wavelength as the vapor is presented to the array. The temporal responses from the sensors are influenced by the polarity of the vapor, its concentration, and the degree to which it partitions into the sensor surface. The array responses have been analyzed collectively using several different pattern (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)
Park, J.; Groves, W. A.; Zellers, E. T. Anal. Chem. 1999, 71, 3877–3886. Grate, J. W. Chem. Rev. 2000, 100, 2627–2648. Alizadeh, T.; Zeynali, S. Sens Actuators, B 2008, 129, 412–423. Lewis, N. S. Acc. Chem. Res. 2004, 37, 663–672. Woodka, M. D.; Brunschwig, B. S.; Lewis, N. S. Langmuir 2007, 23, 13232– 13241. Maldonado, S.; Garcı´a-Berrı´os, E.; Woodka, M. D.; Brunschwig, B. S.; Lewis, N. S. Sens. Actuators, B 2008, 134, 521–531. Vancura, C.; Ruegg, M.; Li, Y.; Hagleitner, C.; Hierlemann, A. Anal. Chem. 2005, 77, 2690–2699. Lerchner, J.; Caspary, D.; Wolf, G. Sens. Actuators, B 2000, 70, 57–66. Srivastava, R.; Dwivedi, R.; Srivastava, S. K. Sens. Actuators, B 1998, 50, 175–180. Raman, B.; Hertz, J. L.; Benkstein, K. D.; Semancik, S. Anal. Chem. 2008, 80, 8364–8371. Rakow, N. A.; Suslick, K. S. Nature 2000, 406, 710. Zhang, C.; Suslick, K. S. J. Agric. Food Chem. 2007, 55, 237–242. Dickinson, T. A.; Michael, K. L.; Kauer, J. S.; Walt, D. R. Anal. Chem. 1999, 71, 2192–2198. Albert, K. J.; Walt, D. R.; Gill, D. S.; Pearce, T. C. Anal. Chem. 2001, 73, 2501–2508. Stitzel, S. E.; Cowen, L. J.; Albert, K. J.; Walt, D. R. Anal. Chem. 2001, 73, 5266–5271.
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recognition programs, and this system has been used to detect explosives,16-18 nerve agent simulants,19,20 volatile organic compounds (VOCs), and VOC mixtures.21 One benchmark of progress for an analytical technique, and those designed for environmental monitoring in particular, is its ability to transition from the laboratory to the field for in situ analysis. Several laboratory-based methods have followed this trendsuchasmassspectrometry,22 gasandliquidchromatography,23-25 and capillary electrophoresis.26 The goals of improved mobility and reduced size for several analytical techniques are underscored by the substantial research effort focused on the development of micro total analysis systems.27 Electronic noses have likewise progressed from a primarily laboratory-based technique to one capable of making in situ vapor measurements.28 Of the several electronic nose platforms reported, only a few have progressed to the stage where field measurements are possible and, in some cases, these devices have been commercialized.29 In general, field applications involving electronic noses can be roughly divided into two classes: novel applications using commercially available hand-held systems and external, site-specific applications of laboratory-based systems. The latter set of applications involves either capturing air samples using an external collection system and analyzing them on a laboratory instrument30,31 or constructing the instrument on site and monitoring a specific sample at regular intervals.32 These devices can be used to address specialized applications where a portable instrument is not required. Hand-held systems, on the other hand, have included a variety of interesting applications such as classifying tumor cell lines,33 identifying bacteria,34 analyzing fire debris,35 identifying plant damage,36 and monitoring air quality on the space shuttle.37 Additionally, the wide availability of different types of metal-oxide gas sensors and their easy integration into compact electronic platforms have led to the construction (17) Albert, K. J.; Walt, D. R. Anal. Chem. 2000, 72, 1947–1955. (18) Albert, K. J.; Myrick, M. L.; Brown, S. B.; James, D. L.; Milanovich, F. P.; Walt, D. R. Environ. Sci. Technol. 2001, 35, 3193–3200. (19) Bencic-Nagale, S.; Sternfeld, T.; Walt, D. R. J. Am. Chem. Soc. 2006, 128, 5041–5048. (20) Bencic-Nagale, S.; Walt, D. R. Anal. Chem. 2005, 77, 6155–6162. (21) Aernecke, M. J.; Walt, D. R. J. Forensic Sci. In press. (22) Gao, L.; Sugiarto, A.; Harper, J. D.; Cooks, R. G.; Ouyang, Z. Anal. Chem. 2008, 80, 7198–7205. (23) Whiting, J. J.; Lu, C.-J.; Zellers, E. T.; Sacks, R. D. Anal. Chem. 2001, 73, 4668–4675. (24) Lu, C.-J.; Whiting, J.; Sacks, R. D.; Zellers, E. T. Anal. Chem. 2003, 75, 1400–1409. (25) Borowsky, J. F.; Giordano, B. C.; Lu, Q.; Terray, A.; Collins, G. E. Anal. Chem. 2008, 80, 8287–8292. (26) Liu, P.; Seo, T. S.; Beyor, N.; Shin, K.-J.; Scherer, J. R.; Mathies, R. A. Anal. Chem. 2007, 79, 1881–1889. (27) West, J.; Becker, M.; Tombrink, S.; Manz, A. Anal. Chem. 2008, 80, 4403– 4419. (28) Yinon, J. Anal. Chem. 2003, 75 (98), 105. (29) Rock, F.; Barsan, N.; Weimar, U. Chem. Rev. 2008, 108, 705–725. (30) Francesco, F. D.; Lazzerini, B.; Marcelloni, F.; Pioggia, G. Atmos. Environ. 2001, 35, 1225–1234. (31) Micone, P. G.; Guy, C. Sens. Actuators, B 2007, 120, 628–637. (32) Pan, L.; Yang, S. Environ. Monit. Assess. 2007, 135, 399–408. (33) Gendron, K. B.; Hockstein, N. G.; Thaler, E. R.; Vachani, A.; Hanson, C. W. Otolaryngol.-Head Neck Surg. 2007, 137, 269–273. (34) Fend, R.; Kolk, A. H. J.; Bessant, C.; Buijtels, P.; Klatser, P. R.; Woodman, A. C. J. Clin. Microbiol. 2006, 44, 2039–2045. (35) Conner, L.; Chin, S.; Furton, K. G. Sens. Actuators, B 2006, 116, 121–129. (36) Laothawornkitkul, J.; Moore, J. P.; Taylor, J. E.; Possell, M.; Gibson, T. D.; Hewitt, C. N.; Paul, N. D. Environ. Sci. Technol. 2008, 42, 8433–8439.
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of several field-portable devices and integrated wireless systems. These devices have been used to identify common petroleum distillates,38 verify the purity of gasolines,39 discriminate diesel fuels,40 and detect ammonia and hydrogen sulfide.41 We have previously reported the development of a field portable version of our fluorescence-based system used exclusively for the detection of explosive vapors.18 The array incorporated a total of seven single-core optical fiber sensors that were either semiselective or broadly reactive. The two distinct types of sensors required different excitation and emission windows, necessitating the use of two different light emitting diode (LED) light sources together in a custom-designed optical block.42 Sensor responses were monitored using a charge-coupled device (CCD) camera. In this paper, we present a second-generation portable system that captures all the essential instrumental components of our laboratory-based system using lower cost, smaller form factor parts. Advances in LED technology coupled with new image sensor platforms have enabled us to reduce the footprint of our portable device to the point where the instrument can be enclosed in a small plastic briefcase. The only extraneous components are a laptop computer used for instrument control and data acquisition and a small external battery pack to power the camera. The device incorporates an optical-fiber vapor sensitive microsphere array together with an ultrabright LED and a complementary-metaloxide-semiconductor (CMOS) image sensor. Since all of the chemical sensors incorporate the same solvatochromic dye (Nile Red), a single LED can be used for excitation, greatly reducing the cost and complexity of the optical block. The use of a highdensity imaging fiber-optic bundle enables us to monitor the responses of several hundred microsphere sensors simultaneously, enhancing the signal-to-noise ratio of the array measurement and improving its performance when sample vapors are present at low concentrations. We examined the ability of this portable device to discriminate between three types of petroleum distillates under ambient outdoor conditions from samples prepared with several different methods. It is beneficial to detect these vapors in the field from a logistical standpoint because it is inconvenient and less informative to collect samples and send them to a laboratory for analysis. All of the discrimination problems presented here are qualitative in nature and serve to characterize the performance of our portable system in an external and uncontrolled environment. Three different sensing scenarios were examined; the first experiment was designed to verify the classification accuracy of the system using sample vapors presented under stable conditions and at high concentrations. The second experiment more closely simulated a real world detection problem by challenging the system with samples possessing greater variability in both vapor concentration and composition. The third experiment evaluated the ability of (37) Ryan, M. A.; Hanying, Z.; Buehler, M. G.; Manatt, K. S.; Mowrey, V. S.; Jackson, S. P.; Kisor, A. K.; Shevade, A. V.; Homer, M. L. IEEE Sens. J. 2004, 4, 337–347. (38) Zhang, S.; Xie, C.; Zeng, D.; Zhang, Q.; Li, H.; Bi, Z. Sens. Actuators, B 2007, 124, 437–443. (39) Sobanski, T.; Szczurek, A.; Nitsch, K.; Licznerski, B. W.; Radwan, W. Sens. Actuators, B 2006, 116, 207–212. (40) Feldhoff, R.; Saby, C.-A.; Bernadet, P. Analyst 1999, 1999, 1167–1173. (41) Cho, J. H.; Kim, Y. W.; Na, K. J.; Jeon, G. J. Sens. Actuators, B 2008, 134, 104–111. (42) Karunamuni, J.; Stitzer, K. E.; DeLyle, E.; Albert, K. J.; Walt, D. R.; Brown, S. B.; Myrick, M. L. Opt. Eng. 2001, 40, 888–895.
Table 1. Saturated Vapor Pressures (20°C) of the Analytes Used in This Study analyte vapor
saturated vapor pressure (mmHg)
regular unleaded gasoline gulflite charcoal starter diesel fuel ethanol
275-475 3 0.4 59.75 (25 °C)
Table 2. Microsphere Sensor Materials sensor name
bead size (µm)
bead surface functionality
SCX
5
Chirex
5
strong cation exchange chiral
Alltech
5
aliphatic (C4)
manufacturer Phenomenex (Torrance CA) Phenomenex (Torrance CA) Alltech Associates (Deerfield, IL)
the device to identify unknown vapors by comparing new responses to responses stored in an onboard training database. EXPERIMENTAL SECTION Materials. Ethanol, Nile Red, and toluene were purchased from Aldrich (St. Louis, MO) and used as received. Regular unleaded gasoline, diesel fuel, and Gulflite odorless charcoal starter were obtained locally and used without further purification. The saturated vapor pressures at 20 °C of each petroleum distillate is listed in Table 1 and are given as estimates of the concentrations measured in this study. Microsphere Sensors and Array Fabrication. Microsphere vapor sensors were fabricated as described previously.16 Briefly, 10 mg of commercially available surface functionalized silica microspheres (Table 2) was stirred in a 1 mg/mL solution of the solvatochromic dye Nile Red in toluene for 1 h. The beads were filtered and dried overnight at 60 °C in an oven. This process was repeated for each of the three sensor types used in this study. Each sensor stock was stored in a desiccator with minimal exposure to light prior to array fabrication. Microsphere sensor arrays were fabricated by chemically etching the polished surface of an imaging optical fiber bundle (Schott, North America). Each bundle is 2 mm in diameter and possesses approximately 42 000 individual 4.5 µm diameter core elements. When exposed to a dilute hydrochloric acid solution, the cores of the optical fiber bundle are preferentially etched producing a microwell array. Microsphere sensors are distributed into the microwell array using a block and load procedure that is repeated one time for each of the three sensor types. This method of array fabrication eliminates the need to positionally register each microsphere sensor within the array while maintaining a high individual sensor count without the need to use multiple fibers.15 This procedure (Figure 1) begins by positioning the etched array vertically on the stage of a stereomicroscope with the microwell array facing the objective and the light source of the microscope transmitting up through the nonetched surface of the fiber. A secured razor blade is brought into contact with the microwell array at an approximately 45° angle to the surface (Figure 1a) and positioned so that it blocks a desired portion of microwells on the fiber surface. An aliquot of a single microsphere sensor
type is placed onto the unblocked fiber surface and gently tapped with a glass coverslip, distributing them across the exposed face of the array (Figure 1b). The razor blade, flush with the microwell surface, prevents individual sensors from crossing over and populating the adjacent wells. Once the microspheres have assembled into the wells, the razor blade is raised from the array surface, the optical-fiber is rotated 180°, and excess beads are removed with a blast of air from a compressed-gas duster. The razor blade is lowered again, this time blocking the wells that have just been filled with microspheres. A gap of 20-30 rows of empty microwells is left between the edge of the first microsphere sensor type and the new border defined by the razor’s edge. A second microsphere sensor type is distributed into the microwells in the same manner as above (Figure 1c). Finally, the microwell array is rotated 90°, and the loading process is repeated one last time to fill a portion of the remaining vacant wells with the final microsphere sensor type. This procedure produces an array with three defined regions containing a single microsphere sensor type (Figure 1e). All three homogeneous regions can be imaged simultaneously on the CMOS image sensor. Finally, the identities of the individual microsphere sensor types are assigned based on their known locations within the partitioned array. This array fabrication procedure partitions the array into homogeneous regions consisting of 400-550 microwells of which 65-85% contain a sensor. Device Configuration. The portable system was constructed using off the shelf optical components and assembled on a breadboard housed in a light tight blow-molded polypropylene briefcase (Figure 2a,b). All components were cushioned with polyurethane foam to prevent mild agitation from disrupting the optical alignment. Two inlets were drilled on either end of the briefcase and fitted with metal through-wall couplers to enable connection of the external vapor delivery lines; one inlet was used for sample vapor collection and the other inlet was used for ambient air collection. Power sources for the LED, micropump, and microsolenoid valve (four 9 V batteries) were contained within the briefcase and a 200 W hour Li-ion battery used to power the CMOS camera was maintained externally. The instrument was controlled using LabView software contained on an HP laptop computer enabling fully automated vapor delivery and data acquisition. Microsphere sensor responses were recorded in the field and subsequently processed in the laboratory. The dimensions of the device, excluding the laptop computer and camera power source, were 38 cm × 28 cm × 11 cm, and it weighed 4.5 kg excluding, and 6.9 kg including, the external camera battery. Vapor Delivery System. The vapor delivery system, presented schematically in Figure 3a, is designed to generate and alternate between ambient air and sample vapor streams. The ambient air stream was composed of unpurified external environmental air collected from the back of the instrument away from the sample collection inlet. The sample stream contained the sample vapor and was drawn in from the front of the device through a sampling nozzle that was manually positioned near the vapor source. Both flows were generated using a single micropump (Virtual Industries, Colorado Springs, CO) and passed through 1/16 in. inner diameter PTFE tubing. A 5 V three-way microsolenoid valve (Humphrey, Kalamazoo, MI) was used to controllably toggle between the two vapor streams. Both streams were directed to Analytical Chemistry, Vol. 81, No. 13, July 1, 2009
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Figure 1. Diagram outlining the block-and-load procedure used for array fabrication in the portable device. (a) A razor blade is positioned flush with the etched microwell array surface blocking a portion of the wells. (b) An aliquot of a single sensor type is placed onto the surface of the exposed array, and the sensors are gently tapped into place with a glass coverslip. The razor blade prevents sensors from crossing over and populating the blocked microwells. (c) The array is rotated 180°, the razor blade is again positioned flush with the microwell array surface, an aliquot of the second sensor type is placed onto the array, and the sensors are gently tapped into the microwells. (d) Excess sensors are removed using a blast of air from a compressed-gas duster and the partitioned sensor array is complete. (e) Fluorescence image collected using the optical system of the portable device showing the final partitioned array. Each sensing region contains a single sensor type.
Figure 2. Portable fluorescence-based vapor sensor: (a) an image of the device hardware configuration inside the blow-molded plastic briefcase and (b) an image of the device outdoors during data collection. The battery used to power the camera is located behind the laptop computer.
the two front inputs of the solenoid valve, and the single output of the valve was directed to the intake of the micropump. The default open position on the three-way solenoid valve was configured to initially present the ambient air stream to the array when the pump is active. Applying a high voltage (12 V) to the valve switches the input line causing the pump to draw vapor on the sample line. Applying a low voltage (0 V) returns the valve to its default setting, and ambient air is once again presented. The output (positive pressure line) of the micropump was connected to a piece of 1/8 in. inner diameter PTFE tubing that housed the sensor array. The sensor array was positioned within the PTFE tubing such that the end containing the sensors was facing the terminus of the vapor delivery line normal to the direction of the vapor flow. Flow rates were fixed at 150 sccm by controlling the voltage on the micropump. This flow rate to the sensor array remained constant regardless of which external channel was being used to collect the vapor. All vapor delivery lines and connections were checked for leaks using a 5284
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soap bubble test and were found to be airtight. A comparison of sensor responses to ambient air passed directly to the sensor array (bypassing all of the vapor delivery lines), and air passed through the vapor delivery system of the portable device confirmed that the vapor delivery components did not elicit any background sensor response other than what was typical for ambient air. Excitation Source, Optics, and Detector. The camera battery voltage was set to 12 V, and the battery provided enough power for all of the experiments described below without recharging. The optical components are presented schematically in Figure 3b. Excitation light was provided by a green Luxeon V Star LED (LED Supply, Randolph, VT) with a peak wavelength of 530 nm. Longer wavelengths in the LED emission spectrum were removed with a 530 nm bandpass filter placed between the LED and the collection lens. A 10× infinity corrected microscope objective was used to collect the LED emission and relay a collimated beam of excitation light onto the dichroic beam splitter where it was
Figure 3. (a) Schematic diagram of the vapor delivery system. A micropump was used to generate and deliver sample and ambient air vapor flows to the microsphere sensor array at a constant flow rate of 150 sccm. A three-way solenoid valve was used to controllably switch between the ambient air intake (located at the back of the portable device) and the sample intake (located at the front). (b) Schematic diagram of the optical system. An ultrabright LED (530 nm peak emission) was passed through a 530 nm excitation filter to remove low-energy emission from the excitation light. A 10× infinity corrected microscope objective was used as a collection lens which relayed the excitation light onto the dichroic mirror. A 20× infinity corrected microscope objective was used to project the excitation light onto the proximal end of the fiber and also to collect the fluorescent signal from the sensors. This signal was passed through a 630 nm emission filter (to remove any residual excitation light) and focused onto the CMOS camera using a low-power collection lens.
reflected to the back aperture of a second infinity corrected microscope objective (20×) and transmitted onto the proximal end of the imaging fiber-optic bundle. The excitation light is transmitted through the individual fiber cores via total internal reflection to the microsphere sensors. The emission light from the microsphere sensors is transmitted back through the fiber core in contact with each sensor and collected through the same 20× objective used to deliver the excitation light. The collimated emission light emerging from the back aperture of the 20× objective passes through the dichroic beam splitter and a 630 nm emission filter and is focused onto the CMOS image sensor with a low-power focusing lens. The CMOS image sensor (Edmund Optics, Barrington, NJ) was positioned at the focal length of the focusing lens producing a resolved, approximately 100× magnified, fluorescence image of the microsphere array. Data Acquisition Sequence. The imaging and vapor sampling systems were completely integrated enabling synchronous control over analyte delivery and data acquisition. Array responses were recorded by monitoring the fluorescence changes of the array on the CMOS image sensor before, during, and after a sample vapor was delivered. Individual microsphere sensor responses to a particular vapor sample were collected using a programmed data acquisition sequence, and this sequence was used throughout the field experiments. The sequence can be divided into two distinct intervals: the data acquisition interval and the purge interval. The data acquisition interval encompasses sample collection and delivery to the array and monitoring the array response with the CMOS image sensor. This interval begins by switching on the image sensor and LED and collecting a series of 15 images of the array prior to vapor exposure. After this initial acquisition period, the pump is switched on and a brief pulse of ambient air is introduced into the vapor delivery lines to establish a vapor baseline during the initial stages of data collection. At a time of 500 ms after the pump is triggered, the solenoid valve is switched, closing off the ambient air line and opening the pump to the sample line. The sample vapor was collected and presented to
the array over a period of 5 s and the responses were recorded. In total, each 9.5 s data collection interval produced a sequence of 72 image frames; the first two images did not contain data due to a brief lag time between hardware and software synchronization and were discarded. Each image sequence consisted of 13 frames without any vapor exposure, 5 frames of ambient air exposure, 37 frames of sample vapor exposure, followed again by 15 frames of ambient air exposure. The purge interval was initiated following a 4 s pause after termination of the data acquisition interval. This pause was incorporated to allow ample time for the sample nozzle to be removed from the sample or disconnected from a sample container. Once removed from the sample, ambient air was drawn through the sample vapor line and presented to the array for 15 s. This purge interval was implemented to remove any residual sample vapor in the vapor delivery lines following the data acquisition interval. The total time required to complete a single data acquisition cycle (data acquisition and purge intervals) was 32 s. Data Processing. Sensor responses collected in the field were processed, formatted, compiled, and classified on a personal computer. Each sequence of 70 image frames (after deleting the first two null images) was compiled into a response video and processed using image processing software (IP Lab version 3.7.1, BD Biosciences, Rockville, MD). The fluorescence intensity of each microsphere sensor as a function of time was extracted from each response movie using two different methods. In the first method, 225 individual microsphere sensors, 75 sensors from each of the three sensor groups, were selected and their responses were monitored. The second method took advantage of the large areas of sensor homogeneity on the surface of the microsphere array. Three large blocks, each encompassing several hundred microsphere sensors, were segmented and monitored from each of the three sensor areas on the array. A single raw fluorescence intensity response from each block was extracted that represented the average of every pixel within each segmented area. This onfiber signal averaging was designed to take advantage of the Analytical Chemistry, Vol. 81, No. 13, July 1, 2009
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enhanced signal-to-noise ratio that is possible when a large number of microsphere sensors are monitored.17 The time traces containing the raw fluorescence intensity of the array response were imported into custom designed software where they were normalized and grouped by sample. The normalization process is designed to reduce sensor-to-sensor variation in raw fluorescence intensity and is accomplished by subtracting the first time point in each microsphere sensor response from every subsequent point. Next, every initial-valuesubtracted point in the sequence is divided by the maximum intensity value (either positive or negative) in the microsphere sensor response series. This process removes sensor-to-sensor variability in initial fluorescence intensity and produces uniform responses that are virtually identical in shape for a given microsphere sensor type and have maximum intensity values between -1 and 1. For data sets that included individual microsphere sensor responses, the responses were averaged according to sensor type. Finally, the responses from the three microsphere sensor types were concatenated, producing an array superresponse for each individual vapor exposure. These superresponses were compiled into a data set and imported into Weka data mining software version 3.4.12 (University of Waikado, New Zealand, freeware available online at http://www.cs.waikato.ac.nz/ ∼ml/weka/). Classification of the super-responses was performed using a support vector machine (SVM) pattern recognition algorithm, and the model was tested using 10-fold cross-validation. Blind samples from experiment 3 were identified by building a classification model using the training data and processing each unknown response individually using the classification model. Each model-assigned vapor identity was then compared to its known identity recorded at the time of exposure. Sample Preparation. Sample vapors were prepared from liquid samples of ethanol, regular unleaded gasoline, Gulfilte odorless charcoal starter, and diesel fuel. Three different sample preparation methods were used during the field tests. The first set of experiments employed clean metal paint cans (1 pint volume, Lynn Peavey, Lenexa, KS) that had two holes punched into either side of the lid and each hole was fitted with a stainless steel through-wall coupler (McMaster-Carr, Atlanta, GA). A rubber gasket was placed in direct contact with the lid on either side of the fastening nut of the through-wall coupler to eliminate any vapor leakage through the seal. Following modification, these containers have been shown to be airtight and do not generate any background vapors that interfere with the sample vapor measurement.21 Sample containers fabricated in this way enable direct connection between either of the through-wall couplers on the lid of the sample container with the sample inlet nozzle of the portable device. Sample vapors were generated by collecting the headspace from a modified paint can that contained 5 mL of liquid. The liquid was placed in the container, the lid was sealed, and the sampling inlets were capped with rubber septa to prevent evaporation during sample preparation and field testing. To collect the headspace within the container, the rubber septa were removed and the sampling nozzle of the portable device was connected to one of the two through-wall couplers. The other through-wall coupler was opened to the ambient environment so that atmospheric pressure was maintained within the sample container. Following 5286
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sample collection, both inlets on the sample container were recapped with rubber septa until the next sampling interval. The second sample preparation method was designed to be more reflective of a real world sampling situation. Aliquots (2 mL) of liquid were placed directly onto a 38 × 15 cm2 swatch of used indoor carpet in discrete locations. Inadvertent mixing of one aliquot into another was prevented by spacing each liquid approximately 12 cm from the other treated areas on the surface of the carpet. Regular unleaded gasoline, Gulflite odorless charcoal starter, and diesel fuel were used for the preparation of vapor samples, and one area of the carpet was left untreated and served as an air (vapor negative) control. Once prepared, the carpet was allowed to sit exposed to the ambient environment throughout data collection; a time period of approximately 30 min. The final sample preparation method utilized a metal paint can; however, the through wall couplers were removed from the lid leaving only the punched holes. A 7.5 × 7.5 cm2 cutting of carpet was treated with 2 mL of liquid and placed in the container. The lid was sealed, and the sampling inlets were capped with adhesive tape. The headspace of the container was sampled by manually placing the inlet nozzle into the hole in the lid and drawing the vapor sample into the portable device. After the array response had been recorded, the lid was resealed with adhesive tape until the next sampling interval. We used the metal paint can in experiment 3 for two reasons. The first reason was to conceal the sample so that the experimenter collecting the response did not know the identity of the vapor presented during data collection. Second, we wanted to maintain relatively uniform vapor concentrations throughout experiment 3 and sealed cans provided a means to limit evaporation between runs. RESULTS AND DISCUSSION Preliminary characterization of the portable device was performed indoors under laboratory conditions while all of the results discussed in experiments 1-3 were obtained from tests performed outdoors. A single vapor-sensitive array was prepared and used for all experiments. The outdoor experiments were performed under clear conditions with temperatures ranging from 25-26 °C and relative humidity ranging from 56-60%. The first two outdoor experiments were performed on the same day, and the third outdoor experiment was performed approximately 1 month later. The entire portable system, including the sensor array, was left under ambient laboratory conditions for the month in-between the 2 days of data collection. Array Preparation. The surface of an etched imaging fiberoptic bundle was partitioned into three homogeneous microsphere sensor regions using the sequential block-and-load procedure outlined in Figure 1. The array was prepared in this manner to eliminate the need to positionally decode each individual microsphere in the array and to easily enable a large number of sensors to be monitored simultaneously with minimal computational overhead.43 The homogeneity of each microsphere sensor region on the partitioned array was verified by examining the responses of several sensors within each section to a known vapor. Little to no cross-contamination between microsphere sensor types was (43) Albert, K. J.; Walt, D. R. Anal. Chem. 2003, 75, 4161–4167.
Figure 4. Graph showing the individual responses of 225 sensors, 75 microsphere sensors each of SCX (black), Chirex (green), and Alltech (red) collected on the portable system during a pulse of ethanol vapor. Each phase of the data collection interval is listed at the bottom of the graph.
observed. For the experiments described here, each sensing region contains a single microsphere sensor type; however, sensor mixtures could also be distributed into the different regions, thereby increasing the total number of sensor types that can be incorporated into the array. System Response. The use of CMOS image sensors provides several advantages compared to conventional CCD image sensors such as their high level system integration, selective high speed pixel readout, and ultralow power operation. Unlike CCD image sensors that use sequential charge transfer readout, individual pixels in CMOS image sensors can be randomly selected via row and column decoders and the charge-to-voltage conversion is directly carried out at the pixel level. This mechanism not only increases the readout speed but also reduces the power consumption since high-frequency readout clocks are not required. CMOS technology makes it possible to embed the digital signal processing unit in the same substrate as the CMOS photodetector, further lowering power consumption and system cost. Nowadays, CMOS image sensors are able to achieve performance comparable to CCD image sensors in terms of fixed-pattern-noise, dynamic range, and optical responsivity.44 These benefits make CMOS technology ideal for portable systems where cost, power consumption, and size are critical. The functionality of the portable device was tested by running the data acquisition sequence with a sample container housing a known vapor connected to the system. The array was mounted in the portable device and brought into focus using a z-direction stage with ambient white light. Once the array was in focus, the lid to the briefcase housing the device was closed and the array was exposed to a pulse of saturated ethanol vapor using the (44) Fossum, E. R. IEEE Trans. Electron Devices 1997, 44, 1689–1698.
preprogrammed data acquisition protocol. Each image in the response sequence was compiled into a video, and 75 microsphere sensors from each of the three partitioned regions were randomly selected and monitored. These 225 normalized microsphere sensor responses are displayed in Figure 4, which also shows the corresponding time intervals of the data acquisition sequence at the bottom of the plot. Two microsphere sensors, SCX and Chirex, exhibited the most uniform responses while the responses of Alltech displayed a considerable amount of noise due in part to the reduced partitioning of the polar vapor into the nonpolar surface of these microsphere sensors. Overall, the microsphere sensor responses collected using the portable device exhibited a greater amount of noise when compared to responses collected using a CCD camera because CMOS image sensors suffer from higher readout noise caused by pixel level transistors and the dark current of the photodiode. This higher noise can be mitigated in our system by averaging the signals from a large collection of microsphere sensors. An example of the signal enhancement that is possible when hundreds of microsphere sensor responses are averaged is detailed below. Despite the higher noise of the portable system, the responses of the microsphere sensors correlate well with those collected on the larger laboratory-based instrument. Saturated Headspace Samples: Experiment 1. This preliminary experiment was designed to test the system with sample vapors prepared at high concentration in an effort to verify that the system was operating properly in an outdoor environment. Vapor samples were prepared from clean metal paint cans that contained 5 mL of liquid and were modified with two metal passthroughs enabling direct connection to the sample inlet of the portable device. Response acquisition was performed over 16 Analytical Chemistry, Vol. 81, No. 13, July 1, 2009
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Table 3. Classification Accuracies and Confusion Matrices for Experiment 1: Saturated Headspace Samples correctly classified instances incorrectly classified instances
16 0
Table 4. Classification Accuracies and Confusion Matrices for Experiment 2 with an Individual Sensor (a) and Block Processing (b)
100% 100%
(a) correctly classified instances incorrectly classified instances
12 4
75% 25%
classified as actual ID
air
gasoline
charcoal starter
diesel fuel
air gasoline charcoal starter diesel fuel
4 0 0 0
0 4 0 0
0 0 4 0
0 0 0 4
randomized vapor exposures: four replicates of each of the three vapors and four air samples. Back in the laboratory, the response images were compiled into a video, individual microsphere sensor responses were extracted, and the data set was processed using a support vector machine (SVM) pattern recognition algorithm. The SVM model was tested using 10-fold cross-validation. The results from experiment 1 are presented in Table 3 and show a classification score of 100% for all three vapors and air, indicating that the array with the portable system performs well when presented with high concentration vapors sampled under optimal conditions. Furthermore, the system was able to correctly discriminate between complex vapor mixtures representing three classes of petroleum distillates. Ambient Headspace Samples: Experiment 2. A more realistic and challenging sensing scenario uses vapor samples that originate from the headspace directly above a substrate containing a volatile liquid without confining the sample within a container. This sampling method introduces sample-to-sample variations in both the vapor composition and concentration due to evaporation, ambient air flow, and nozzle positioning. This variation is expected to translate into less reproducible microsphere sensor responses that can potentially degrade the classification accuracy of the system. Samples were prepared by spotting 2 mL of liquid (gasoline, charcoal starter, and diesel fuel) onto discrete areas of a 15 × 38 cm2 cutting of used nylon indoor carpet. A fourth area of the carpet was left untreated and served as a negative control. The entire cutting was left exposed to the ambient environment throughout data collection, and no attempt was made to prevent sample evaporation. For a complex liquid, such as gasoline, one would expect the composition to change over extended periods of time as the more volatile components evaporate. Headspace vapors were collected and delivered to the array by manually positioning the inlet nozzle directly above the area on the cutting spotted with a particular liquid. After the vapor was sampled and the array response was recorded, the carpeting was removed and the sampling nozzle was directed toward the ambient air. This procedure was used to ensure that the system was exposed to ambient air with minimal vapor contamination during the purge interval. A total of 16 analyte responses were collected in a randomized order from the spiked areas on the carpet cuttings. The microsphere sensor responses were extracted from the response videos using two image processing procedures. In the first procedure, 225 individual microsphere sensors were moni5288
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classified as actual ID
air
gasoline
charcoal starter
diesel fuel
air gasoline charcoal starter diesel fuel
2 0 0 1
0 4 1 0
0 0 3 0
2 0 0 3
(b) correctly classified instances incorrectly classified instances
15 1
93.75% 6.25%
classified as actual ID
air
gasoline
charcoal starter
diesel fuel
air gasoline charcoal starter diesel fuel
4 0 0 0
0 4 0 1
0 0 4 0
0 0 0 3
tored (75 from each sensor class) and these responses were classified using the SVM algorithm. The classification score for this data set with individual microsphere sensor responses is listed in Table 4a. An overall classification score of 75% was obtained with a total of four misclassifications, the majority of which confused diesel fuel and air (three errors). These errors can be ascribed, in part, to the low vapor pressure of diesel fuel together with increased variability in the microsphere sensor response due to the alternative sampling method. It is unlikely that the errors in classification were due to a change in vapor composition due to evaporation because of the short timeframe over which the responses were collected (30 min) and because the magnitude of the sensor responses did not decrease significantly over this time period. Overall, the classification accuracy decreased compared to the results obtained in the first experiment where high concentration vapors were sampled in a very reproducible manner. In an effort to improve the classification accuracy for this data set, we enhanced the signal-to-noise ratio of each array response by increasing the total number of microsphere sensors that were monitored.17 With a greater number of microsphere sensor responses (n) averaged, we can theoretically increase the signalto-noise ratio of the array response by n1/2. We accomplished this enhancement by employing an image processing approach that uses block segmentation to extract and average the signals from large portions of the array during the initial stages of data processing (Figure 5). While this approach does not discriminate between wells containing microsphere sensors and empty wells, the higher number of microsphere sensors that are simultaneously monitored leads to a 2-3-fold improvement in the signal-to-noise
Figure 5. Two alternative methods of image processing employed in these studies: (a) 75 individual sensors from each sensor type are monitored and averaged. The averaged responses, concatenated by sensor type to four exposures of the array to Gulflite charcoal starter are presented in the graph to the right of the image. (b) Image processing using a block segmentation technique. This technique is designed to enhance the signal-to-noise ratio of the array, particularly for sensors that display a considerable amount of noise such as Alltech.
ratio. To determine the best signal output from an array, we compared the averaged responses of 75 individual microsphere sensors (Figure 5a) to the responses obtained after the block processing where the responses of hundreds of microsphere sensors were collected (Figure 5b). The block processing mode was clearly superior, which translated into a higher classification score for this data set (Table 4b). The number of correctly classified vapors increased to 15 with only one misclassification: diesel was mistakenly identified as gasoline. Again, this confusion probably arises due to the low vapor pressure of diesel fuel resulting in a noisy microsphere sensor response as well as the chemical similarity of gasoline and diesel fuel: both vapors contain mixtures of aromatic and aliphatic compounds in contrast to charcoal starter which is composed almost exclusively of aliphatic compounds. Additional experiments are necessary to determine the exact source of this confusion, but overall, the system performs well under less than optimized conditions, particularly when the signal enhancing capability of the array is employed. Experiment 3: Blind Study. Following experiments 1 and 2, the system was allowed to sit with the array under ambient laboratory conditions for over a month before being used again. The final experiment was again performed outdoors and was designed to test if the portable device could maintain its classification ability following a rest period and if it could apply a trained algorithm to identify unknown microsphere sensor responses. This approach differs from the experiments above in that cross-validation was not used to evaluate the quality of the classification model. In this scenario, the system first collects
training data, then builds a classifier using a prescribed classification algorithm, and uses that model to identify new microsphere sensor responses as they are collected. In order to provide the system with the most relevant training data, the same samples were used for both the training and test data sets. Three analyte vapors and air were selected for this experiment, and each vapor was prepared by spiking a swatch of indoor carpeting with 5 mL of liquid. The spiked samples were placed into paint cans and were capped using lids with two holes punched on either side. The container serving as the air sample (negative control) was prepared using an untreated sample of carpet. In contrast to the previous sample containers used in this study, pass-throughs were not inserted into the holes in the lid, and the samples were allowed to vent periodically to the ambient environment. Data collection was divided into two parts. The first 16 responses (four responses to each of the three samples and air) were randomly collected with the system and compiled for use as a training set. The next eight samples (two responses to each of the three samples and air) comprised the test set. Each sample was recorded in the order that it was analyzed. Microsphere sensor responses were collected outside, and the system was brought into the laboratory where the data were processed. Each array response was extracted from the response image sequence using block segmentation, and the results were complied into separate training and test data sets. The training data were used to build an SVM classification model, and each unknown response was individually processed and identified using this model. The identity of each unknown response as assigned by the trained classification algorithm was Analytical Chemistry, Vol. 81, No. 13, July 1, 2009
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Table 5. SVM Classification Results for Experiment 3 identified as unknown run
air
gasoline
charcoal starter
ethanol
actual vapor
run1 run2 run3 run4 run5 run6 run7 run8
0 0 0 0 1 0 1 0
0 1 0 0 0 0 0 1
0 0 1 0 0 1 0 0
1 0 0 1 0 0 0 0
ethanol gasoline charcoal starter ethanol air charcoal starter air gasoline
compared to its known identity recorded at the time of collection. The results are presented in Table 5 and show that the system was able to correctly identify all of the unknowns. CONCLUSION The design and assembly of a portable vapor sensing system incorporating a fluorescence-based microarray are described along with the results of preliminary field tests. The portable system captures all of the essential elements of the larger laboratory based system with a smaller form factor and with considerably lower power consumption. The system can incorporate several fluorescent microsphere sensor types, image the microsphere sensor array, collect and alternate between two separate vapor streams,
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and record microsphere sensor responses as vapors are presented to the array. The differential responses of the microsphere sensors collected with the portable system have been used to build classification models that can correctly identify vapors collected from three petroleum distillates. Three separate sensing scenarios have been explored, and the system is able to maintain its classification accuracy under a variety of sample collection methods. For sensing situations where the exact concentration of sample vapor delivered to the array cannot be controlled, or when the composition of the sample vapor is actively changing due to evaporation, the system is able to enhance the signal-tonoise ratio of each array response which improves the overall classification accuracy. We did not undertake a detailed characterization of the detection limit of the portable system; however, we estimate this limit to be in the mid to low parts-per-million range based on the values listed in Table 1. The concentrations in Table 1 vary over 4 orders of magnitude, but the system is able to discriminate between all the vapors. With continued development, this device should prove useful for routine ambient vapor monitoring and find specific application in areas where the detection of petroleum products is of importance. Received for review March 9, 2009. Accepted May 18, 2009. AC900505P
