Bioaerosol detection and classification using dual ...

Bioaerosol detection and classification using dual excitation wavelength laser-induced fluorescence Per Jonsson*a, Pär Wästerbyb, Per-Åke Gradmarkb, Julia Hedborga, Anders Larssonb, Lars Landströmb FOI – Swedish Defence Research Agency, aSensor and EW Systems, PO Box 1165, SE-581 11 Linköping, Sweden, bCBRN Defence and Security, Cementv. 20, SE-901 82 Umeå, Sweden

ABSTRACT We present results obtained by a detection system designed to measure laser-induced fluorescence from individual aerosol particles using dual excitation wavelengths. The aerosol is sampled from ambient air and via a 1 mm diameter nozzle, surrounded by a sheath air flow, confined into a particle beam. A continuous wave blue laser at 404 nm is focused on the aerosol beam and two photomultiplier tubes monitor the presence of individual particles by simultaneous measuring the scattered light and any induced fluorescence. When a particle is present in the detection volume, a laser pulse is triggered from an ultraviolet laser at 263 nm and the corresponding fluorescence spectrum is acquired with a spectrometer based on a diffraction grating and a 32 channel photomultiplier tube array with single-photon sensitivity. The spectrometer measures the fluorescence spectra in the wavelength region from 250 to 800 nm. In the present report, data were measured on different monodisperse reference aerosols, simulants of biological warfare agents, and different interference aerosol particles, e.g. pollen. In the analysis of the experimental data, i.e., the time-resolved scattered and fluorescence signals from 404 nm c.w. light excitation and the fluorescence spectra obtained by a pulsed 263 nm laser source, we use multivariate data analysis methods to classify each individual aerosol particle. Keywords: bioaerosol, laser-induced fluorescence, elastic scattering, fluorescence spectroscopy, multivariate data analysis

1. INTRODUCTION Historically, the development of bioaerosol detection systems has been driven by the biodefence area to protect personnel and citizens from Biological Warfare Agent (BWA). An ideal bioaerosol detection system for this purpose should be able to monitor, in real-time, the actual concentration, down to single organism level, and recognize it with very high specificity. Presently, no single sensor or detection technology is close to achieve this goal. Only traditional molecular biotechnological methods, such as genetic and/or immunological technologies, can provide sufficiently high specificity but the response times span from tens of minutes to several days. (To reach high sensitivity advanced purification and/or amplification steps are commonly needed for these methods.) Therefore, faster and more sensitive, but less specific, methods has been developed to provide timely warning for protective actions and to start automatic aerosol sampling for further, more specific, analysis. Instruments providing this capability are often called detect-to-warn or trigger detectors. A number of different methods based on elastic and inelastic scattering, and emission and mass spectroscopy have been tested (some also commercialized) for this purpose. These methods can give information about, e.g., size, shape, and molecular or elemental composition of the bioaerosol in close to real time [1]. Fluorescence spectroscopy, often called laser-induced fluorescence (LIF), has been one of the most popular methods for detect-to-warn purposes of potentially harmful bioaerosol [2]. Molecules of biological origin often have intrinsic fluorescence (or autofluorescence) when excited by light or ultraviolet (UV) radiation. Therefore it is possible, to some extent, to detect bioaerosol directly without any prior preparation of the aerosol. Fluorescence gives rise to relatively strong signals which enables instant spectral detection of single micron-sized particles. Few other methods have this sensitivity and fast response times. The major drawback of fluorescence is its low specificity. Many harmless naturally occurring particles, including non-biological particles, also fluoresce which may cause false alarms. The most important fluorescent molecules in bioaerosol detection are the aromatic amino acids (e.g., phenylalanine, tyrosine and tryptophan), some molecules related to metabolic processes in microorganisms (e.g., reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD)), and dipicolinic acid (DPA) that is produced during spore formation. Figure 1 shows the excitation-emission spectra for tryptophan, NADH and Bacillus atrophaeus *[email protected]; phone +46-13-378 000; fax +46-13-378 066; www.foi.se

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XVI, edited by Augustus Way Fountain III, Proc. of SPIE Vol. 9455, 945509 · © 2015 SPIE · CCC code: 0277-786X/15/$18 · doi: 10.1117/12.2176744 Proc. of SPIE Vol. 9455 945509-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 05/26/2015 Terms of Use: http://spiedl.org/terms

(BG) spores in water solutions. (Bacillus atrophaeus was previously known as Bacillus globigii and Bacillus subtilis var niger and the acronym BG of the original name is still the most common to use.) To excite tryptophan and NADH, fluorescence excitation wavelengths below about 320 nm and 420 nm, respectively, is required [2]. Although several substances in microorganisms give fluorescence, typical excitation-emission spectra of bioaerosol have a combination of “tryptophan-like” and “NADH-like” fluorescence as can be seen from the BG spores in Figure 1. The fluorescence spectra from bioaerosol particles are usually quite broad and featureless and is one of the reasons to the low specificity of fluorescence based trigger detectors. However, it may be possible to improve the specificity by acquiring additional parameters. In this paper we present a system utilizing dual excitation wavelengths, spectral detection of UV-induced fluorescence, broadband fluorescence detection and static elastic scattering detection on single particles. The system is based on a previous design [3], but with changed excitation sources and other improvements. Tryptophan 1.00 0.40 0.16 0.06 0.03 0.01

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Figure 1. Normalized excitation-emission spectra of substances and bacteria in water solution. Tryptophan is an aromatic amino acid, NADH is the reduced form of intracellular adenine dinucleotide (also known as diphosphopyridine nucleotide or pyridine nucleotide) and is related to metabolic processes, and Bacillus atrophaeus is a sporulating bacteria commonly used as BWA simulant for e.g. anthrax spore.

2. EXPERIMENTAL SETUP 2.1 Bioaerosol detection system The aerosol is sampled from ambient air, where the intake from a MAB (Biological Alarm Monitor, Proengin A. S. France) [4] was used to collect the air. For the results presented in this paper, the virtual impactor (concentrator) was not used, resulting in that only the large particles (> 10 µm) were removed. The filtered aerosol is led into a 1 mm diameter injector nozzle surrounded by a sheath flow to achieve a confined particle beam past the optical system, see Figure 2a. The aerosol flow in the system is sustained by an aerodynamic particle counter (TSI 3321 APS) evacuating air at a flow rate of 5 l/min by the collector nozzle a couple of centimeters below the injector. The flow through the injector nozzle is approximately 1 l/min, resulting in an average speed of about 25 m/s. The schematic of the optical setup is shown in Figure 2b. The particle stream is probed with an 80 mW continuous wave (c.w.) laser operating at 404 nm (Z-Laser, Z80M18H-F-404-PE). The laser is focused on the particle stream with a cylindrical lens, resulting in an elliptical beam about 2 mm wide and 0.1 mm high. A particle passing the laser beam will give rise to elastic scattering and sometimes fluorescence. A portion of this light is collected and collimated by a lens (f=19 mm) after which a dichroic mirror (Semrock, Di-01-R405) reflect wavelengths below 415 nm onto a photomultiplier tube (PMT 1, Hamamatsu, H6780-3) and the wavelengths above 415 nm are transmitted onto another photomultiplier tube (PMT 2, Hamamatsu, H6780-4). (As a small fraction of the elastically scattered light leaks through the dichroic mirror, a notch filter (Edmund Optics, 67-107) is mounted in front of PMT 2 to further reduce this spurious light. Color glass filters are used in front of PMT 1 (WG320) and PMT 2 (WG280) to protect them from scattered radiation from the UV laser.) As a result, PMT 1 is detecting the scattered blue light and PMT 2 the induced fluorescence of the blue trigger laser. The signals of the PMT:s are recorded by an oscilloscope (LeCroy, Waverunner 104 MXi) where PMT 1 and PMT 2 are terminated with 330 Ω and 10 kΩ resistors, respectively.

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Figure 2. Schematics of the experimental setup. a) The generation of the aerosol beam and b) the optical setup.

A pulsed quadrupled Nd:YLF laser at the wavelength 263 nm (Photonic Industries, DC50-263) is used for excitation of UV induced fluorescence. The UV pulse length is about 15 ns, pulse energy about 70 µJ, and the diameter of the beam is about 1 mm at the cross section with the aerosol beam. The resulting fluorescence is collected by an optical probe where the front collimating lens (f=19 mm) is followed by a long-pass edge filter (Semrock LP02-266RU) reflecting wavelengths below 266 nm. The second lens (f=19 mm) is focusing the fluorescence into a fiber (Avantes FC-UV10001) with a core diameter of 1 mm and a numerical aperture of 0.22. The collected fluorescence is analyzed with a spectrometer consisting of a spectrograph (Oriel MS125) and a 32 channel linear PMT array (Hamamatsu H7260-4). The grating in the spectrograph (Oriel 77416, 400 grooves/mm and a blaze wavelength of 350 nm) was set to an angle resulting in a wavelength region from 250 to 800 nm on the detector. The electrical output of the PMT array is recorded with a 32 channel data acquisition system (Vertilon PhotoniQ IQSP480). A data acquisition sequence was triggered at time t0 when the signal on PMT 1 (or alternatively on PMT 2, however, not used in this study) reached a preset threshold level. The oscilloscope saved 502 points for each channel (PMT 1 and PMT 2) and the time base was normally set to 0.1 µs, resulting in a 50 µs long time sequence of data, starting 10 µs before t0. When the particle has passed the blue trigger laser beam this laser is turned off at time t1, typically at t0+6 µs. A pulse from the UV laser was subsequently triggered at t2, typically at t0+18 µs since the center of the blue trigger laser beam was approximately 0.4 mm above the center of the UV laser beam. The integration in the 32 channel data acquisition system started at t2+0.7 µs, to compensate for the internal trigger to pulse delay in the UV laser and was normally set to 0.2 µs. In order to limit the pulse repetition frequency (PRF) of the UV laser at 1 kHz, the blue laser was turned on at time t0+1 ms. The resulting data set for every trigger event, i.e. every detected particle, consisted of the time dependent data of PMT 1 and PMT 2 and the fluorescence spectrum induced by the pulsed UV laser. Examples of the data sets are shown in Figure 3. 2.2 Aerosol generation and monitoring Close to monodisperse aerosols of NaCl, tryptophan, and NADH were generated using a Vibrating Orifice Aerosol Generator (VOAG, TSI Model 3450). The substances were dissolved in a 50:50 mixture of Milli-Q water and 2-propanol at different concentrations resulting in mean particle diameters spanning from 1.0 to 6.5 µm for NaCl and from 1.5 to 4.0 µm for tryptophan and NADH. The aerosols were injected into a 2.5 meter long wind tunnel (30 cm diameter, air flow 0.1 m/s) and monitored by an Aerodynamic Particle Sizer (APS, TSI Model 3321). The geometrical standard deviations of the size distributions were less than 1.2 for NaCl and NADH for all aerosol sizes generated by the VOAG, except for 6.5 µm NaCl. The geometrical standard deviations for tryptophan were larger (1.2-1.4). In addition, a set of biological particles and interferents – with broad size distributions – were generated by an ultrasonic nozzle from liquid slurries inside the wind tunnel. The BWA simulants generated were Bacillus atrophaeus (BG) spores, Bacillus thuringensis (BT) spores, bacteriophage MS2 in different growth media (TSB and BHI) and the interferents used were pine and birch pollen, Bermuda grass smut spores, bentonite, superphosphate suspended in Milli-Q water.

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3. RESULTS AND DISCUSSION 3.1 Reference measurements Figure 3 shows example results from measurements on NaCl, Tryptophan and NADH particles with the geometrical mean diameter of approx. 3 µm. The curves shown are the mean value of 1000 consecutive trigger events. The first peak, starting at t = 0 µs, on the data from PMT 1 and PMT 2 is due to blue laser excitation. PMT 1 measures the elastically scattered light and the particle velocity can be estimated from the peak width, while PMT 2 measures the blue-induced fluorescence. The second peak, starting at t = 18 µs, is due to the pulsed UV laser excitation and is mainly related to the UV induced fluorescence in different wavelengths bands. Since the amplification of the PMT:s and the settings of the oscilloscope was optimized for data from blue laser interactions, these signals were in most cases out of the dynamic range of the measurement system and not used in the analysis. As can be seen, the elastic scattering is similar for the different particles and only NADH particles show blue induced fluorescence, c.f. Figure 1. The UV induced fluorescence spectra are also in good agreement with Figure 1.

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Figure 3. Data from the measurements with NaCl, tryptophan and NADH particles. The left, the middle and the right graphs show the mean value of the signals on PMT 1, PMT 2 and the 32 channel fluorescence spectra, respectively.

In the following analysis, background was subtracted from the raw data. Furthermore, the lowest channels of the APS data (particles < 0.6 µm) were excluded in the calculation of particle statistics, as the bioaerosol detection system does not trigger on such small particles. The variation of particle velocity was analyzed by plotting the time each particle needed to cross through the blue laser focus. Here, the full width at half maximum (FWHM) of the first peak on the PMT 1 data was used as measure and the statistics for NaCl particles are shown in Figure 4a. The particle size data was taken from the APS monitoring the size distribution inside the wind tunnel. Since the vertical focus FWHM was approx. 50 µm, the mean velocities vary from approx. 25 m/s for 1 µm particles to almost 30 m/s for 6.5 µm particles. The variation in velocity is large within one size population as the velocity depends on the position in the particle beam. (The maximum velocity is found in the center of the beam and decreases towards the edge.) As expected, the smaller particles both have lower mean velocity and larger variance than larger particles (except for the largest size where the size distribution was no longer monodisperse), because of the velocity gradient in the particle beam and the higher mobility of smaller particles. The results of the mean velocities are similar for the tryptophan and NADH measurements, but with larger geometric standard deviations, mainly because of larger variation in particle size. The strength of the elastically scattered light of the blue laser may also give information about the particle size. The scattered signal was integrated for the first peak on the PMT 1 data and the results are presented in Figure 4b, Figure 5a and b for NaCl, Tryptophan and NADH, respectively. Similar to the work of Sivaprakasam et al. [5], the data was fitted to a y(x) = axb function, where x is the geometric mean value of the particle diameter, and the exponent, b, was found to be 1.3, 1.5 and 1.7 for NaCl, tryptophan and NADH, respectively. For NaCl the fit is quite poor and it is likely that such a simplistic ‘model’ may not be sufficient for this system. (Examples of this behavior have been shown for a similar geometry in a previous publication [6].) Instead, scattering as function of size parameter (into the solid angle seen by the detector) is most likely better described using Mie theory. Furthermore, the scattering signal also depends on the position of the particle in the beam, both due to intensity inhomogeneity in the laser focus and variations in collection efficiency.

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Figure 4. Scattering from NaCl particles. a) The passage time through blue laser focus as a function of particle diameter. b) scattered signal as a function of particle diameter. The curve represents best non-linear fit resulting in an exponent of 1.3. The black and red symbols correspond to the arithmetic and geometric mean values, respectively.

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Figure 5. Scattered 404 nm signal as a function of the particle size. The black and red symbols correspond to the arithmetic and geometric mean, respectively. The curves represent best non-linear fits, resulting in exponents 1.5 and 1.7 for tryptophan (a) and NADH (b), respectively.

Figure 6 shows the fluorescence dependence of the particle size for NADH and tryptophan. In the case of the blue induced fluorescence detected on PMT 2, the same integration window as for the scattered signal was used. For the UV induced fluorescence detected on the PMT array an integration window from 280 nm to 600 nm was used. The same non-linear expression was fitted as for the scattering signal. As the fluorescence is assumed to be isotropic, the exact position in the particle beam is of less importance compared to the scattered light, however, the UV intensity and collection efficiency is not uniform over the particle beam. The blue induced fluorescence of NADH is proportional to the particle diameter to the power of 2.7 and the UV induced fluorescence for NADH and tryptophan is proportional to the particle diameter to the power of 2.4 and 1.9, respectively. Different exponents for different particles (or composition of the particles) can be expected, see e.g. Hill et al. [7]. Tryptophan has the worst fit to the model which could be explained by saturation effects on the PMT array and readout electronics for the largest (4 µm) particles.

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Figure 6. The detected fluorescence signal as a function of particle diameter. The black and red symbols denote the arithmetic and geometric mean, respectively. The curves represent the best non-linear fits resulting in exponents 2.7 for the blue induced fluorescence for NADH, and 2.4 and 1.9 for NADH and tryptophan for the UV induced fluorescence signals.

3.2 Bioaerosol measurement Bioaerosol measurements were performed on a number of different BWA simulants and some common interferents. Besides the parameters investigated in the previous section, the shape of the UV induced fluorescence is an important parameter for classification purposes. Inspection of average fluorescence spectra (λexc = 263 nm) from the aerosolized substances reveals similarities in shape between pine pollen, MS2, BG and BT, see Figure 7. The average fluorescence spectrum of birch pollen has a quite unique shape and low fluorescence. Remaining aerosols (super phosphate, bentonite and Bermuda grass smut spores) have similar shape and low fluorescence amplitudes. For the evaluation, data from single measurement events were merged into; scattered 404 nm intensity, 404 nm induced fluorescence intensity, sum of spectral 263 nm induced fluorescence, time for particle (FWHM) to cross 404 nm laser beam and spectral data (32 channels) of induced fluorescence from 263 nm laser excitation, resulting in 36 data points for each measurement. Inspection of the data showed that the signal from 404 nm scattered light sometimes reached saturation of the detector and these events were discarded, resulting in 40-70% useful events.

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Figure 7. Average fluorescence spectra of nine different aerosols. The number of spectra averaged was 300 for super phosphate, bentonite, Bermuda grass smut spores, birch and pine pollen, and 750 spectra for the remaining aerosols. The y axis is given in pC and the x axis is the PMT array channel number, where the channel centre wavelength goes from 226 nm to 840 nm. The insets give the geometric mean (d) and geometric standard deviation (std) of the particle diameter during the measurements.

To evaluate the measured data, the classification performance was investigated by means of Partial Least Squares Differential Analysis (PLS-DA) [8]. Here, a training set was constructed based on 1500 randomly selected samples from each of the three classes; interferent, virus and spore. That is, 300 each from the five interferents (super phosphate, bentonite, Bermuda grass smut spores, birch and pine pollen), 750 each from MS2 in the two different media (BHI and TSB), and 750 samples from the two different spore forming simulants (BG and BT). A true prediction matrix (Y) with 4500 rows (equal to the X matrix) and three columns was then constructed to define a class affiliation for each measurement represented by a set of three binary Y-variables (interferent, virus and spore). Because the calibration Y matrix contains binary values, minimizing root mean square error of calibration (RMSEC) is not an appropriate approach for optimization of the PLS-DA model [9]. A better approach is to select a model that best separates true positive from true negative predictions for each class. For this purpose, Receiver Operating Characteristic (ROC) curves were used to analyze the classification performance from predicted values of the training set. Still, each observation results in one predicted value for each variable in Y which can result in ambiguities in the classification. A definite classification for each observation was achieved by setting the predicted variable with largest difference between predicted value and threshold to one (1) and the remaining two variables to zero (0). Different pre-processing methods were investigated to optimize classification performance. Best result was selected by maximizing the area under curve (AUC) in the ROC analysis, and was obtained using the log-power enhancement [10] with different exponents for non-spectral and spectral variables. The number of components to use in the PLS-DA classification model was chosen by inspection of the increase of cumulative explained variance (CEV) of the response variable as a function of the number of components. Nine components were considered enough since the gain of adding one more component, CEV(10)/CEV(9), was less than 1%. Finally, the optimal threshold between 0 and 1 was defined as the value corresponding to the smallest distance to the point (0,1) in the ROC curve, see Figure 8 [11].

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A validation set was then constructed based on a random selection of data not being used in the test set. In the validation set 200 samples were used within each of the five subgroups of interferents (super phosphate, bentonite, Bermuda grass smut spores, birch and pine pollen), and 1000 measurements from virus simulants (MS2) and spores (BG and BT), respectively. Prediction of the validation set was made using the model obtained from the training set and the previously obtained optimal thresholds and pre-processing. The results are presented in Table 1. Table 1. Results from the validation as a confusion matrix broken down on subclasses.

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Among the interferents, superphosphate and bentonite were the best predicted (>99%) while a substantial fraction (14%) of Bermuda grass smut spore was predicted as virus, birch pollen was mainly classified as spores (69%) and pine pollen as both virus (35%) and spores (29%). Measurements from MS2 in different growth media were 86% predicted correctly (on average) and BG spores were better predicted by the model (96%) than BT spores (85%). A very low fraction of virus and spores resulted in false negative predictions.

4. CONCLUSIONS AND FURTHER WORK Depending on the composition of generated particles, different behavior was observed while monitoring scattered light and laser induced fluorescence signals as function of aerodynamic size of the aerosol. Further studies are needed to better understand these changes and how the acquired parameters best can be used in different classification algorithms. Measurements were also performed on bioaerosol simulants and interferents, and in the simplified prediction model presented here (with three classes from nine subclasses), high rates of false positive prediction of virus and spore from

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the two pollen species were found and serves as a good example of the difficulty in separating different aerosols from each other.

ACKNOWLEDGMENTS This work was funded by the Swedish Department of Defence, Project nos. 440-A404114 and 410-A404115.

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