Cicely Rathmell

Microplastic particles in aqueous ecosystems constitute a worldwide environmental problem. Raman spectroscopy is uniquely positioned to detect and classify even the smallest, and most abundant, particle sizes, down to micrometers in size. However, the current “filter and scan” method used to concentrate and detect dilute microplastics in water is slow, and prone to contamination from handling and interference from the filter substrate. Here, we present a simpler approach to concentrate the particles in situ, without any sample preparation, in the focus of a Raman probe. The technique uses an ultrasonic resonator with a 2 MHz standing acoustic wave to capture 3.4 µm poly(methyl methacrylate) (PMMA) microspheres in the sample solution for direct detection. When tested with a 90 ppm solution, ultrasonic capture for 5 min enabled a clear PMMA Raman signal to be distinguished from the background. The signal enhancement provided by ultrasonic capture was estimated to be greater than 1500x.

	
	The majority of plastics, like the 78 million tons of plastic packaging manufactured every year, is produced for single-use items, approximately a third of which ends up in the ocean (at the rate of one garbage truck every minute). Engulfed by these vast waters, it gradually breaks apart into progressively smaller particles, and eventually enters the food chain as microplastics, which are defined as plastic particles smaller than 5 mm in size. Slowly, and unbeknownst to the world until recently, microplastic particles have spread to every continent, ocean (1–3), the air (4), and most food and beverages (5–8).

	Studies into the nature and distribution of microplastics across the world have found both bottled and ocean water to contain concentrations on the order of 100 particles per liter of microplastics 5 µm or larger in size, and several 1000s of microplastic particles greater than or equal to 1 µm in size. The most common microplastic particles found are polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET) in drinking water, and PE, PP, and polystyrene (PS) in the ocean.

	Currently, the most common approach to detecting microplastics requires filtering large sample volumes of water to concentrate particles for detection, then scanning the filter substrate for any particles (9), and chemically identifying the particles with Fourier-transform infrared spectroscopy (FT-IR) or Raman spectroscopy (10–11). However, this approach is very time-consuming (12), and can be prone to contamination of the filter and to interference from the filter material itself (13). This approach has shown variable sensitivity to the most abundant, but smallest, particles (14). Furthermore, the lack of a unified protocol for filtering and detection leads to inconsistent results, and thus compromises the collection and comparison of quantitative data for global monitoring (15–19).

	Raman microscopy on filter substrates is currently the only tool capable of detecting and identifying the smallest, and most numerous, particles of 20 µm in size or less, as its short wavelength, tight laser focus, and ability to raster systematically across the sample is sensitive to even small particles at low density. However, Raman microscopy in the present form uses large bench top-sized instruments, scanning slowly across the membrane used for the filtration of a water sample. As one review put it: “There is an urgent need for a fast and easily implementable monitoring tool capable of detecting small microplastics (20).”

	Instead of filtration, a different approach to capture and bring these microparticles to the point of measurement is proposed here as a proof of concept investigation (21). It employs a strong ultrasonic standing wave of about 2 MHz frequency in a resonator composed of a piezo transducer on one end and a Raman probe on the other. Small particles are trapped in the nodes of the standing wave field, where they are held by acoustic radiation forces (22). If one of the nodal planes of the ultrasonic trap is colocated with the laser focus of a Raman immersion probe, the particles can immediately be studied in situ. A trap of this design can be employed directly in the sample stream as collected, bypassing all preparation steps. This reduces the analysis time from hours to minutes, prevents any contamination of the sample from handling, and eliminates the Raman background commonly seen when concentrating samples onto a filter in the currently accepted method.

	The Raman system consisted of a compact Wasatch Photonics WP 785 Raman spectrometer with an integrated 785-nm laser set to 350-mW, equipped with a temperature-regulated CCD detector, and a 50 µm slit leading to a resolution of 10 cm

. The spectrometer was chosen for its f/1.3 input aperture and transmissive optical design, as the application requires a high degree of sensitivity in a portable design to enable eventual deployment in the field. The laser and spectrometer were fiber-coupled to a Raman probe with a 1/2” immersion probe tip terminated by a sapphire ball lens with a very short working distance.

	The ultrasonic resonator used for particle capture, soniccatch by usePAT, has been described in detail elsewhere (23). Briefly, a piezo-electric transducer generates the outgoing ultrasonic wave with an approximate frequency of 2 MHz, while the Raman probe ball lens forms the reflector for the resonator at a distance of about 3 mm from the surface of the transducer. Superposition of the incoming and reflected waves generates a standing wave with a small number of nodes, as shown in Figure 1.

	The radiation forces imposed by the ultrasonic wave are predominantly dependent on the particle size, but also depend on their mass density and compressibility relative to the medium. The forces exerted are due to the scattering of the wave at the particles’ surfaces, and lead to agglomerations in the nodal or anti-nodal regions of the standing wave (23).

	The resonator assembly consisting of the ultrasonic transducer and the Raman probe was lowered directly into a stirred 90-ppm suspension of 3.4 µm poly(methyl methacrylate) (PMMA) microspheres in water, in which the microspheres had been dispersed with a drop of detergent. PMMA is a lightweight, transparent plastic most commonly known as acrylic or plexiglass, and is also used as microbeads in personal care products.

	The integration time of the spectrometer was set to 2.5 s, and 500 individual non-averaged spectra were continuously recorded for each experiment. Five repeat runs were performed, four in which ultrasonic capture was activated at the beginning of each run, and one static reference run (no capture).

	Comparing the Raman spectra with and without ultrasonic capture of the PMMA microspheres, as shown in the bottom panel of Figure 2, we identified three main contributions to the signal arising from the sample and setup. The first contribution comes from a series of bands at ~414 cm

, present in all spectra, which do not change with ultrasonic capture. These were attributed to the sapphire of the ball probe lens, an effect also reported in Raman imaging using sapphire fiber (24). The second contribution, present in all spectra with ultrasonic capture in the steady state, agrees well with a bulk PMMA signal shown for comparison in the top panel of Figure 2, with key peaks at ~596 cm

. The third contribution, a broader peak at roughly ~1640 cm

, was present without ultrasonic capture, but disappeared over time once capture was activated. This more elusive signal can be attributed to the front window of the transducer. As particles are trapped, the Raman laser no longer reaches the surface of the transducer, but instead gets scattered by the trapped particles, leading to the marked reduction in signal and to the enhancement of the signal of the particles of interest. A direct comparison of the Raman spectra with and without ultrasonic capture to bulk PMMA in Figure 2 indicates that trapping leads to a large enhancement of the PMMA signal. The difference is distinct, from no discernible signal without ultrasonic capture to a very large and clean signal after ultrasonic trapping is activated.

	To study the ultrasonic capture process in greater detail, we recorded the Raman signal as a time series after activating the ultrasonic transducer and were able to observe the continuous evolution of the PMMA trapping in time as illustrated in Figure 3. Here, we have bound the region associated with the main PMMA Raman lines using vertical lines just beyond 750 cm

. In this region, we subtracted the baseline to obtain a clearer illustration of the growth of the PMMA signal in time (Figure 4). The asymmetric least squares (ALS) method (25) (26) was used for baseline correction.

	With background subtraction applied, the PMMA signal was clearly seen to rise after an initial induction period with no discernible signal change, during which we saw no indication of any PMMA signal in the baseline to indicate ultrasonic capture. Once initiated, the signal grew and reached a maximum quickly.

	For a quantitative analysis, we separated the signal contributions to the spectra set using multivariate curve resolution (MCR). MCR is an iterative method that can be used to identify a potential separation of the spectra set into fixed component spectra and time-varying contributions for each component. This separation is “ill-defined,” and does not lead to a unique solution, that is, the results depend on the initial guess for the spectra. As we already expected three independent contributions (dip probe, PMMA, and transducer), we limited the decomposition to three components and seeded the analysis with the first three loadings from the principal component analysis (PCA) of the same spectra, setting those three component spectra as our initial guesses for the MCR spectra.

	In addition, we enforced additional non-negative constraints on both the component spectra and the component contributions of the separation, an approach known as the ALS (alternative least squares) variant of MCR (27). This matches the expectations of actual spectra and “concentrations”, each of which would be expected to contribute additively to the observed spectra.

	Figure 5 shows the component spectra resulting from the MCR-ALS analysis. We assigned components 1 and 3 to the Raman signal arising from the dip probe and the transducer, and component 2 to the Raman signal from the PMMA microspheres in solution. Comparison to the Raman spectrum of neat PMMA (Figure 2, top panel) shows very good agreement; the PMMA component spectrum shows little interference from the signal caused by the setup (sapphire ball lens or transducer material).

	The MCR-ALS result for the time-varying contribution of component 2, which we assigned to PMMA, can be interpreted as the time evolution of the PMMA signal, as shown in Figure 6 for four independent repeat runs of the same experiment. The figure presents a similar growth curve of the signal for each run, with an induction period of approximately 250 s, and a full, steady state PMMA signal after about 1000 s. The rapid signal evolution after the initial induction period may be due to a feedback effect in which captured PMMA particles slow down other particles near the trapping nodes of the standing wave.

	To determine the Raman signal enhancement for PMMA attributable to ultrasonic capture in the 90 ppm dispersion, we averaged the MCR-ALS result for the contribution of the component assigned to PMMA with and without ultrasonic trapping activated. The average for the signal with trapping is formed across all four runs for all measurements past the 1000 second mark (steady state capture), leading to an average signal of about 23,100 (arbitrary units).

	For the reference signal, we used a separate experiment with the same sample in the same setup, but without trapping activated, and determined the MCR-ALS signal contributions using the same decomposition as used in the four runs with trapping activated. Averaging over the entire run of 100 spectra, we found a reference signal of 15±20 (arbitrary units) with the uncertainty given by the standard deviation across the 100 spectra. Even at “one sigma,” this signal is not significantly different from zero, limiting the possibilities of standard approaches for calculation of a lower limit for the enhancement factor. Though the true magnitude of the enhancement factor for ultrasonic capture cannot be fully quantified, a minimum enhancement of 1500x can safely be reported using this reference signal value.

	Growing concern over the increasing presence of microplastics in our environment and their potential environmental impact has heightened the need for a simple, robust, and portable measurement method. Capture of PMMA microspheres with an ultrasonic standing wave for direct detection using a compact Raman system is one possible alternative to current filtration-based methods. Here, we demonstrated a more than 1500-fold enhancement of the Raman signal using ultrasonic capture and a dip probe from a 90 ppm suspension in water. The captured microplastic sample amount increased in time up to the capacity of the resonator trap, reaching the full enhancement in about 500 s. While still experimental, the method shows a possible route to microplastic measurements in water requiring little to no sample preparation, and which can be performed using compact, portable instrumentation in the field.

N. P. Ivleva, A.C. Wiesheu, and R. Niessner. 2017, 

L. Van Cauwenberghe, A. Vanreusel, J. Mees, and C.R. Janssen, 

E. Gaston, M. Woo, C. Steele, S. Sukumaran, and S. Anderson, 

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T.M. Karlssona, A.D. Vethaaka, B. Carney Almrothd , F. Ariesee, M. van Velzena, M.Hassellöv, and H.A. Leslie. 

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A.C. Wiesheu, P.M. Anger, T. Baumann, R. Niessner, and N.P. Ivleva, 

G. Sarau, L. Kling, B.E. Ossmann, A-K. Unger, F. Vogler, and S.H. Christiansen, 

R.Lenz, K. Enders, C.A. Stedmon, D. M.A. Mackenzie, and T.G. Nielsen, 

L. Vethaak, G.H. Tinnevelt, J.J. Jansen. 

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B.E. Oßmann, G. Sarau, S.W. Schmitt, H. Holtmannspötter, S.H. Christiansen, and W. Dicke, 

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Microplastics in Aquatic Systems – Monitoring Methods and Biological Consequences

 YOUMARES 8 -Oceans Across Boundaries: Learning from each other Proceedings of the 2017 conference for YOUng MARine RESearchers.

 C.F. Araujo, M.M. Nolasco, A.M.P. Ribeiro, and P.J.A. Ribeiro-Claro, 

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 M. Garrido, F.X. Rius, and M.S. Larrechi, 

are with usePAT GmbH, in Vienna, Austria. 

are with Wasatch Photonics, in Morrisville, North Carolina. Direct correspondence to: 

Articles

Of the 78 million tons of plastic packaging manufactured every year, approximately one-third ends up in the ocean, the air, and most foods and beverages. To monitor the proliferation of these plastics, an ultrasonic capture method is demonstrated that produces a 1500-fold enhancement of Raman signals of microplastics in water.

In Situ Enhancement of Microplastic Raman Signals in Water Using Ultrasonic Capture

Antibiotic resistance rates are rising for many common pathogens, putting added pressure on healthcare providers to rapidly, and accurately, diagnose the cause of bacterial infections. New diagnostic methods must be sensitive, selective, and capable of multiplexed detection, as well as rapid, dependable, and portable enough to operate in a variety of settings. A new application of surface-enhanced Raman spectroscopy (SERS) shows great promise to meet these criteria. In this novel assay, bacteria are captured and isolated using functionalized metal nanoparticles for rapid optical identification via SERS. Initial tests with a portable SERS system validated the ability to identify the presence of 

bacteria, quantifying concentrations down to 10 colony-forming units per mL (cfu/mL), with high reproducibility. The system was also able to discriminate between the bacteria within the same sample matrix at clinically relevant concentrations (10

cfu/mL), demonstrating its potential as a rapid, reliable on-site diagnostic for bacterial pathogens. 

	The proliferation of multidrug-resistant bacteria is a silent but growing crisis, acknowledged by public health authorities and regulatory agencies as an emergent global malady (1). Although resistance to antibiotic drugs may develop naturally via mutation, or by transfer of mobile genetic elements encoded with resistance genes, it is the selective pressure exerted through the widespread use, and misuse, of antimicrobial agents that causes these bacteria to selectively flourish, relative to weaker strains.

	Both the absolute number and proportion of antimicrobial-resistant pathogens have increased this century. Although the prevalence of methicillin-resistant 

(commonly known as MRSA) is stabilizing or decreasing in Europe (2), infections due to antimicrobial-resistant 

) are increasing across the continent. In the United States, 

have been identified as the most urgent threats in a list of 18 drug-resistant pathogens (3). The rise of multi-drug resistance in 

 is particularly concerning, as it is the most common Gram-negative pathogen in humans, causing a variety of maladies, ranging from general digestive upset to urinary tract infections and life-threatening bloodstream infections. Unlike MRSA, which is often acquired in a healthcare setting, most multidrug-resistant strains of 

originate in the general community. In one Icelandic study, increased prevalence of quinolone-resistant 

has even been traced back through the food chain to its presence in chicken feed (4).

	As the costs to our healthcare systems grow, and the points of origin increase, so does the need for rapid, specific, and sensitive pathogen detection. Given that infections due to antimicrobial-resistant pathogens may stem from the food chain, the environment, or the clinic, detection methods must be flexible, and portable enough to be deployed in a wide variety of field conditions and by nonexperts. The vast majority of technologies currently used for the task fail on these counts, due to long analysis times (most approaches require culture prior to further analysis to increase bacteria concentration), the need for specialized equipment or staff, high cost, or additional sample preparation steps; these include staining, culturing and biochemical assays, real-time polymerase chain reaction (RT-PCR), and microarray assays like enzyme-linked immunosorbent assay (ELISA). Lab-on-a-chip technology seeks to streamline sample handling and concentration steps to reduce human error, and improve accuracy in a portable format, but the technology still lacks the simplicity of our technique.

	Surface enhanced Raman spectroscopy (SERS) offers a rapid and portable alternative to the existing methods for bacterial detection. SERS leverages the specificity of Raman signals with their sharp, well-defined peaks to provide a fingerprint unique to each analyte, with enhanced sensitivity due to interaction with silver or gold nanostructures. When in close proximity, surface plasmon resonance in the noble metal generates an amplified electromagnetic field that enhances the Raman signal. If the laser frequency used is tuned to the absorbance maxima of the analyte, additional enhancement can be observed, an effect known as 

surface enhanced resonance Raman scattering

 (SERRS). The combined enhancement can yield up to ~10

in total signal amplification. Using silver-coated gold nanorods, SERRS has been used by Wang and associates to differentiate between strains of carbapenem-resistant 

with almost 100% accuracy, using orthogonal partial least squares (OPLS) discriminant analysis of their spectra (5).

	SERS using functionalized nanoparticles (NPs) tagged with resonant Raman reporter molecules has proven ideal for multiplexed detection of bacteria, offering excellent selectivity, high photostability, and lack of spectral crosstalk. The addition of a "magnetic separation" step to isolate bacteria from the host matrix has been shown to improve detection limits to within clinically relevant levels (10

 colony-forming units [cfu]). This approach enabled Guven and colleagues to report detection limits of eight cfu/mL in water for 

cfu/mL in apple juice (7). Wang and colleagues were able to simultaneously detect 

 in spiked spinach solution and peanut butter at concentrations of 10

cfu/mL, demonstrating the potential of the approach in complex matrices (8). Although these studies meet the threshold for speed and minimum detection limit, they leave opportunities for further improvement in sensitivity and ease of sample preparation.

	To this end, we developed a highly sensitive, efficient, and easy-to-use sandwich SERS assay for the isolation and multiplexed detection of bacteria (Figure 1). Lectin- functionalized magnetic nanoparticles (MNPs) are used to capture and isolate bacteria within the sample, while SERS-active silver nanoparticles (AgNPs), functionalized with bacteria-specific antibodies and Raman reporter molecules, bind to each specific strain for interrogation.


	Figure 1: Schematic illustrating the sandwich SERS assay used for: (a) single-pathogen and (b) multiplexed pathogen detection. (c) Lectin-functionalized silver-coated magnetic nanoparticles facilitate magnetic separation of all bacterial strains from the sample matrix, while SERS active conjugates composed of silver nanoparticles functionalized with strain-specific antibodies and unique Raman reporter molecules facilitate optical detection of each pathogen via SERS.

	Lectins were chosen as the functional group for the magnetic nanoparticles, because of their affinity for the sugars expressed on the surface of bacteria; the MNPs then act as a handle by which magnetic separation can be used to capture bacteria indiscriminately from the sample matrix. This approach allows any unbound material to be washed away, concentrating the sample for increased signal, less background, and elimination of false positives.

	SERS-active conjugates facilitate optical detection of the bacterial pathogens. Each conjugate type consists of silver nanoparticles functionalized with strain-specific bacterial antibodies and a unique Raman reporter molecule, providing each bacterial strain under interrogation with a bespoke optical fingerprint when investigated using SERS. The small size of the AgNPs, relative to the bacteria surface, permits multiple binding sites, allowing the AgNPs to come in close proximity to one another and form SERS hot spots for increased Raman signal. Unbound AgNPs are washed away during the concentration stage, further benefiting signal to noise of the detection technique.

	The use of lectin-functionalized MNPs makes this separation step novel and efficient in capturing and isolating bacteria from a sample matrix, while concurrent addition of bacteria-specific SERS conjugates simplifies and speeds sample preparation. Previous work by our group has shown this detection assay to be effective in isolating and detecting 

 cfu/mL individually, using a benchtop Raman microscope system for SERS detection. We have also demonstrated triplex detection, successfully identifying each bacterial strain within the same sample matrix (9). The present study establishes transferability of the technique to a more compact, cost-effective, and portable system, without compromise to detection limit or multiplexing ability, greatly expanding its potential for practical use.

	Synthesis of silver nanoparticles and silver coated magnetic nanoparticles, and the preparation of the biomolecule-AgNP conjugates that were used in these studies have previously been reported by our group (9). Detailed protocols for the preparation and characterization of these colloidal suspensions are provided in the supporting information (ESI) of this paper, and previously published in the journal 

 (methicillin resistant) ATCC BAA-1766 were used in this study. 

and MRSA were grown on Luria-Bertani (LB) Miller agar in an aerobic atmosphere for 24 h at 37 

C. Harvested cells were suspended in LB broth to obtain an optical (OD

) of 0.6. Cells were then plated onto LB agar plates as triplicates, and incubated for 24 hours, as described previously. All the strains were grown using the same batch of culturing plates to reduce any potential unwanted phenotypic variation.

	Bacterial slurries were prepared by harvesting the biomass from the surface of each plate using sterile inoculating loops, and resuspending in physiological saline solution (1 mL; 0.9% NaCl). The prepared bacterial slurries were washed by centrifugation (

650 g for 5 min; MRSA, 1600 g for 5 min), and the pellets resuspended in 1 mL saline. The wash step was repeated a further twice with the final re-suspension being in deionized water (1 mL; d.H

was recorded for all samples, and bacterial concentrations were obtained by serial dilutions based on plate-counting results. All samples were stored at -80 °C until further analysis.

	The novel method for bacterial detection described previously by Kearns and colleagues (9) was modified slightly for use in this study.

	For each of the bacterial pathogens, the following procedure was carried out: SERS active antibody functionalized silver nanoparticles (200 µL; Ab-AgNP) were added together with lectin functionalized silver coated magnetic nanoparticles (200 µL; Con A-Ag@MNP), and a specific bacterial strain (50 µL, 10

 cfu/mL). Note for the control sample, the bacteria was replaced with d.H

O (50 µL) only. The sample was mixed thoroughly for 30 min before being placed in a magnetic rack for a further 30 min to allow the sample to collect. The clear supernatant was removed and the sample re-suspended in d.H

O (600 µL) ready for analysis. The same procedure was followed for the concentration study of 

except the bacteria concentration was varied from 10

 cfu/mL. It should be noted that each sample was prepared in triplicate.

	The two sets of antibody functionalized silver nanoparticles (100 µL of each conjugate) were added together with lectin-functionalized silver-coated magnetic nanoparticles (200 µL); plus the two bacterial strains (50 µL of each pathogen; 10

O (100 µL) only. The same procedure as described above (for the single pathogen detection) was employed.

	Raman reporters used were malachite green isothiocyanate (MGITC) and 4-(1H-pyrazol-4-yl)-pyridine (PPY) and these were used to detect 

	The samples were analyzed immediately after preparation using a WP 532 compact Raman spectrometer (50 µm slit, temperature-regulated detector set at 15 °C) and fiber optic probe (Wasatch Photonics, Morrisville, North Carolina), with 532 nm laser excitation (InPhotonics, Norwood, Massachusetts). The probe was focused directly into a 1 mL glass vial containing 600 µL of the bacteria–NP conjugate solution. All the measurements had a 1 s acquisition time, and a laser power operating at 7.7 mW. For all Raman spectra, data handling was carried out in Excel software, and no preprocessing was required. The peak intensities were obtained by scanning three replicate samples five times, and in all plots the error bars represent one standard deviation. The peak heights were calculated by averaging the peak intensities acquired from the 15 scans, and then subtracting the maximum intensity at 1620 cm

 (MRSA) from the base of the peak. Following this, the signal enhancements were calculated by dividing the peak heights of the sample by the peak height of the control. Furthermore, the relative standard deviations were calculated (% RSD) by dividing the average standard deviation by the mean peak intensity, and multiplying by 100.

	All multivariate statistical analysis was carried out in MATLAB software (The MathWorks, Natick, Massachusetts). Prior to conducting principal component analysis (PCA), the data was scaled. PCA was performed on three different data sets, two consisting of spectra obtained from single pathogen detection experiments, and one data set obtained for the multiplex detection of the two pathogens (10).

	To validate the SERS assay for simplex detection of each individual bacterial pathogen, we compared spectra of bacterial samples treated with the 

assay (SERS active conjugates + MNPs) to control samples. The total analysis time was ~1 h. The distinct Raman spectrum seen for 10

samples treated with the assay is due to the Raman reporter MGITC (selected for use with 

 antibodies), and thus confirms the presence of 

 in the sample (Figure 2a). In contrast, the control (water + assay) showed only very weak signal attributable to unbound residual SERS conjugates not washed away during the magnetic separation step. Comparing the characteristic MGITC peak at 1620 cm

, a signal enhancement of 14.5 times was observed for the 

 bacteria over the control (Figure 2b), matching the 10 times enhancement observed previously using a Raman microscope for detection (9).


	Figure 2: SERS spectra obtained from single pathogen detection using the sandwich SERS assay. (a) SERS spectrum of MGITC observed when detecting E. coli (green) and the control spectrum representing when no bacteria was present (red); (b) SERS peak intensities at 1620 cm

 for assay and control when detecting E. coli; (c) SERS spectrum of PPY observed when detecting MRSA (blue) and the control spectrum (red); (d) SERS peak intensities at 960 cm

 for assay and control when detecting MRSA. Raman intensity is given in arbitrary units (a.u.).

 cfu/mL MRSA samples treated with the MRSA assay containing the Raman reporter PPY clearly confirmed presence of MRSA in the sample versus the control (Figure 2c). Examining the characteristic PPY peak at 960 cm

, a signal enhancement of 12.5 times was observed for the MRSA bacteria over the control (Figure 2d), matching the 11 times enhancement observed previously using a Raman microscope. Transfer of the method to detection using a portable Raman system did not compromise the level of discrimination which could be achieved, and may have increased it somewhat. We believe the sampling geometry with the portable system may be more conducive to this type of assay, because it accessed a larger optical area and therefore more conjugates. Further work will directly compare the two detection methods using the same sample set.

	Having established the ability of our portable bionanosensor system to clearly identify pathogens within a matrix, we characterized the detection limit for 

 cfu/mL. Spectra for concentrations up to 10

 cfu/mL showed a monotonic increase in signal, with clear discrimination at all concentrations versus the control (Figure 3a). Comparing signal intensity for the characteristic MGITC peak at 1620 cm

 cfu/mL, at which concentration a signal enhancement of 3 times over the control was observed (Figure 3b). Above 10

cfu/mL, the signal decreased; we believe it was dampened due to self-absorbance of Raman light when large numbers of conjugated bacteria are present. If a high concentration of bacteria is present, the sample could be diluted and reanalyzed for accurate signal response.


	Figure 3: Concentration series for E. coli showing: (a) Comparison of SERS spectra for concentrations 10

 cfu/mL versus the control, and (b) peak intensity at 1620 cm-

 as a function of concentration. The dashed line gives visual clarification of the SERS peak intensity of the control. Raman intensity is given in arbitrary units (a.u.).

	Comparing the relative standard deviation (% RSD) of the portable system to results from the earlier microscope system study, the portable system showed much lower variance at all concentrations (Table I). This result indicates improvement in reproducibility, which may be due to the larger sampling area and increased laser power, and should be explored in further work.

	For a bionanosensor to be of practical use in healthcare, food safety, or environmental testing, it must be capable of multiplexed pathogen detection, both Gram-negative and Gram-positive. To this end, we tested the performance of our assay when both 

(Gram-negative) and MRSA (Gram-positive) were present at a clinically relevant concentration, 10

 cfu/mL. SERS conjugates for both pathogens were included in the assay. The resulting duplex sample spectrum shared features of both the 

and MRSA reference spectra, and clear discrimination versus the control (Figure 4a). Given that the peak previously used to identify 

 overlaps a SERS peak for the MRSA Raman reporter (1620 cm

, shown with a green star), we instead used the peak at 1180 cm

. Both peaks were clearly present in the duplex sample. Furthermore, comparison of the duplex sample to the control using the 1620 cm

 peak present in both Raman reporters yielded a signal enhancement of 8.4 times, clearly demonstrating a unique biorecognition interaction between the SERS conjugates and the two bacterial strains (Figure 4b).


	Figure 4: Duplex pathogen detection using the SERS assay for E. coli and MRSA at 10

 cfu/mL. (a) Stacked SERS spectra showing the spectra obtained from the detection of both bacterial pathogens simultaneously (black) versus each pathogen separately and a control sample (red). MGITC spectrum (green) represents E. coli and PPY spectrum (blue) represents MRSA. The black dotted lines show peaks unique to each Raman reporter, used to identify the presence of the bacterial targets. (b) Comparative peak intensities at 1620 cm

 for assay and control. (c) PCA scores plot showing the relationship between the multiplex spectra (black) and the E. coli (green) and MRSA (blue) single pathogen spectra. Raman intensity is given in arbitrary units (a.u.).

	To confirm detection of each pathogen, principal component analysis (PCA) was performed on the multiplex spectrum and two single pathogen spectra (Figure 4c). The tight clustering observed for the fifteen scans taken for each individual pathogen, as well as the duplex sample, shows the excellent reproducibility and discrimination in the identification of pathogens that is possible with a portable system.

	A compact SERS system providing all the requirements for a field-deployable assay (sensitivity, specificity, reproducibility, cost, portability, ease of use, and speed of analysis) has been demonstrated. This novel approach employs a sandwich-type nanoparticle assay in which magnetic separation is used to capture SERS active bacteria complexes for isolation and concentration from the sample matrix. Using this detection assay, cell concentrations as low as 10 cfu/mL were readily detected in single pathogen tests for 

and MRSA using a compact, portable Raman detection system. Duplex detection clearly and simultaneously identified the presence of both pathogens, with discrimination being made using both PCA and SERS analysis.

	The ability of this portable bionanosensor to detect bacterial concentrations below the requirements for clinical diagnosis, and to discriminate between bacterial pathogens within a duplex system in ~1 h with minimal sample preparation, demonstrates its potential as a field-deployable technique for use in healthcare, food, and environmental testing. As sample preparation time and system size are further reduced, its potential to provide rapid, sensitive detection in a field-deployable format will surely play a role in global efforts to curb the spread of antimicrobial-resistant pathogens.

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	(4) T.R. Thorsteinsdottir, G. Haraldsson, V. Fridriksdottir, K.G. Kristinsson, and E. Gunnarsson, 

	(5) J. Li, C. Wang, H. Kang, L. Shao, L. Hu, R. Xiao, S. Wang, and B. Gu, 

	(6) B. Guven, N. Basaran-Akgul, E. Temur, U. Tamer, and I.H. Boyaci, 

	(7) R. Najafi, S. Mukherjee, J. Hudson, A. Sharma, and P. Banerjee, 

	(8) Y. Wang, S. Ravindranath, and J. Irudayaraj, 

	(9) H. Kearns, R. Goodacre, L.E. Jamieson, D. Graham, K. Faulds, 

	(10) D.H. Kim, R.M. Jarvis, Y. Xu, A.W. Oliver, J.W. Allwood, L. Hampson, I. Hampson, and R. Goodacre, 

Hayleigh Kearns, Lauren E. Jamieson, Duncan Graham,

are with the Department of Pure and Applied Chemistry at the University of Strathclyde in Strathclyde, United Kingdom. 

is the Vice President of Marketing at Wasatch Photonics in Morrisville, North Carolina. Direct correspondence to: 

A new application of surface-enhanced Raman spectroscopy (SERS) is described for quantifying low concentrations of pathogens with high reproducibility. In this novel assay, bacteria are captured and isolated using functionalized metal nanoparticles for rapid optical identification via SERS. Initial tests with a portable SERS system validated the ability to identify the presence of Escherichia coli and methicillin-resistant Staphylococcus aureus bacteria.

Rapid, Portable Pathogen Detection with Multiplexed SERS-based Nanosensors

Gas analysis systems used for mud logging in the oil and gas industry provide information that is critical to optimize the drilling process and for ensuring safety on-site. As drilling speeds increase, measurement of hydrocarbons and nonhydrocarbons in real time becomes more challenging for traditional methods like gas chromatography (GC). Raman spectroscopy offers a promising alternative for multicomponent analysis of complex gas mixtures, particularly when high-sensitivity compact Raman instrumentation is combined with advanced data analysis. Here we share a new data-driven Raman spectroscopy (DDRS) method capable of simultaneously measuring 12 hydrocarbon and nonhydrocarbon gases in the presence of matrix interferences. Validation of its performance using both standard gas mixtures and two real mud-logging data sets compared favorably against the GC method, demonstrating the feasibility of this technique for online, high-throughput quantitative analysis of gases in oil and gas exploration and recovery, as well as many other industries.

 details the composition and characteristics of the rock cuttings, mud, and gases brought to the surface by borehole drilling during oil and natural gas exploration. It provides valuable information about the quality and status of a drill site, logging the position and mix of hydrocarbons to facilitate efficient extraction and provide advance warning of dangerous gas levels (1). As rocks are crushed in the drilling process, a variety of gases indicative of the reservoir are released, including hydrocarbons such as methane, ethane, propane, isobutane, normal butane, isopentane, and normal pentane as well as a variety of nonhydrocarbons: CO

. Rapid, continuous analysis of the composition of these complex mixtures is essential to help workers respond quickly to changes during drilling.

	Gas Chromatography Versus Raman Spectroscopy

	Although gas chromatography (GC) is the most widely used technique for the evaluation of multicomponent gases (2), it is limited both in speed (3) and its ability to distinguish between hydrocarbons and nonhydrocarbons simultaneously. GC also carries ongoing costs in the form of consumable columns, associated maintenance, and the need for trained operators.

	Raman spectroscopy, in contrast, can be performed in just seconds, requires no sample preparation, and does not consume the sample. It has very little sensitivity to water vapor, and can probe all the relevant hydrocarbon and nonhydrocarbon gases concurrently. The challenge in making this technique widely deployable lies in achieving the sensitivity and specificity required to quantify each component within the complex, widely varying gas mixtures extracted from boreholes.

	Since the first application of Raman spectroscopy to natural gas detection in 1980 (4), many variations have been proposed to enhance the signal, with successful deployment in some process analysis and onsite applications. Although specialized techniques can go so far as to achieve sub-parts per million detection limits, if Raman is to replace GC in the industry, the instrumentation must also be compact and portable enough for onsite deployment while still retaining sufficient sensitivity for meaningful quantitative analysis. Improvements in detection efficiency are part of the equation, but systems must also be able to quantify both hydrocarbon and nonhydrocarbon gases within the same matrix in a timely manner.

	Analysis Challenges and Proposed Solution

	The Raman spectra of complex gas mixtures of the type seen in mud logging are composed of many overlapping bands, and are also subject to considerable matrix interference. Those challenges demand the development of new chemometric analysis methods that are capable of deconvoluting superimposed spectra and neglecting background and matrix effects. The present body of work introduces a new approach called

) (5). DDRS is an application of partial least squares (PLS) analysis that combines higher-density discrete wavelet transform (HDWT) with variable selection influenced by the data itself, with the goal of isolating the most useful spectral features for target gas quantification in complex multicomponent mixtures. Instead of analyzing Raman spectra based on matching individual peaks at discrete wavelengths, DDRS uses an HDWT representation of the Raman spectrum to extract the most relevant spectral bands for the analysis of each given component.

 breaks down data into its frequency band components, allowing sharp changes in signal to be easily discriminated from noise. This concept has been applied to good effect in JPEG compression of images, which uses two-dimensional (2D) discrete wavelet transform (DWT) to find the local changes in brightness that truly define an image, filtering out noise and allowing the image to be compressed without loss of crucial details. Similarly, DWT has proven useful for background subtraction in Raman spectra (6).

	DWT samples data in both the frequency and time domains. In the case of Raman spectra, the frequency domain correlates to peak width, while the time domain correlates to peak position. Raman spectra contain a rich amount of information in the form of both peak position and width, facilitating analysis in the frequency domain (peak width resolution) to mitigate the effects of background and noise, as well as in the time domain (peak wavelength position) to isolate analyte-specific spectral bands from the presence of other species and matrix interference.

	In developing the DDRS methodology, two key elements of a typical DWT approach were modified to overcome known weaknesses and improve its applicability to the analysis of multicomponent gases. The first improvement related to the transform itself. DWT down-samples data which, in the case of spectra, has the effect of degrading spatial resolution in the time (peak position) domain. To offset this effect, an oversampling technique called 

higher-density discrete wavelet transform

) was used. HDWT was developed to improve both the time and frequency resolution of DWT (7), and to mitigate a known vulnerability of traditional DWT to the alignment of the signal in time (or peak position, in the case of Raman). When applied to overlapping spectral bands in mixtures, the improved spectral resolution of HDWT allowed interfering components to be more easily distinguished. It also has the practical benefit of making the transform less sensitive to wavelength shifts, which is extremely important for Raman instrumentation operating in harsh environments and over a wide range of temperatures.

	The second improvement defining DDRS lies in selection of the variables for PLS analysis-the unique spectral features that will allow each component to be identified and quantified with minimal overlap and greatest accuracy. A random frog algorithm is typically used for this purpose, but because this approach often results in the selection of unimportant variables, a template-oriented frog algorithm (TOFA) was proposed, in which variables are weighted by an estimate of their importance. For a detailed description of the HDWT and TOFA methodology and application, readers are referred to the literature (5).

	Together, these two improvements to traditional DWT define a DDRS analysis method intended to isolate the optimal combination of spectral features for each component gas with minimum overlap, and then construct high-quality calibration models for gas analysis. To test its potential for extracting quantitative information, the method was put through its paces with a series of known multicomponent gas mixtures. It was also compared to GC analysis in continuous monitoring studies at two actual gas logging sites.

	To be viable for use in oil and gas exploration, a Raman spectroscopy system must be compact, robust, and capable of delivering repeatable data over a wide range of conditions, often harsh. The system built to test the DDRS method (Figure 1) was designed with onsite measurement in mind, integrating a 532-nm, 300-mW diode-pumped solid-state laser (WSLS-532-300 m, Wavespectrum Laser Group Limited) with a high numerical aperture Raman spectrometer for optimum throughput (Wasatch Photonics 532 Raman spectrometer, f/1.3). The spectrometer utilized a 1624-lines/mm volume phase holographic (VPH) grating in an aberration-corrected transmissive design with a thermoelectrically cooled Hamamatsu charge coupled device (CCD) detector (1024 × 64 pixels). Free-space coupling to the spectrometer's 25-µm slit was chosen to maximize use of the f/1.3 aperture for maximum sensitivity, thus enabling short integration times while maintaining spectral resolution of ~9 cm


	Figure 1: Design of the Raman gas analysis system. A 532-nm laser was coupled into the gas cell for excitation of Raman scattering, with longpass filtering of signal before detection by a high-sensitivity Raman spectrometer.

	Laser light was delivered to the gas cell via a longpass dichroic beamsplitter designed to reflect the 532-nm laser and pass the Raman scattered light while excluding Rayleigh scattering (Figure 1). A 20-mm focal length lens focused laser light into the gas cell through a sapphire window, also collimating emitted Raman scattering for transmission through the longpass dichroic. The telescope in the laser path expanded the beam to enable a tighter focus of laser light into the gas cell. Rayleigh scattering in the signal path was further suppressed by a longpass filter, after which the Raman scattered light was focused onto the 25-µm entrance slit of the spectrometer by a 30-mm focal length achromatic lens. Throughout the measurements, the gas cell temperature and pressure were monitored, and pressure–flow was precisely controlled using regulator valves.

	Using this gas analysis system, static measurements of 164 standard gas mixtures were performed, in addition to continuous measurements at two mud-logging sites. The standard gas mixtures were divided into three groups, all provided by Messer China Ltd. The first group of 74 samples was composed of hydrocarbons, and included seven alkanes: methane, ethane, and propane at 0.01–20%, isobutane and normal butane at 0.01–6%, and isopentane and normal pentane at 0.01–1.6%. The second group of 52 samples was composed of nonhydrocarbons and included CO

 in concentrations of 0.01–10.5%. In both cases, the distribution of concentrations in the samples were assigned following the principle of uniform design to simulate practical mug-logging gas samples. An additional 38 samples of O

 gas were tested separately because of the risk of explosion. Of the total 164 sample mixtures, 19 were chosen randomly for use as an independent validation set, and the remainder were used for training the models.

	Individual spectra collected for each of the seven hydrocarbon gases exhibit unique signatures (Figure 2), but because of their similar bond structure, many of their Raman bands overlap closely. This overlap is problematic for detecting low concentrations of heavier hydrocarbons like butane and pentane, which are easily swamped by the more abundant, lighter gases. As a result, the spectra of the representative standard gas mixtures show Raman activity in similar bands, but with distinct spectral differences indicative of their differing compositions. The nonhydrocarbon gases, in contrast, were easily discriminated from one another in mixture, because of their well-separated peaks.


	Figure 2: Raman spectra for the seven hydrocarbon gases under study, illustrating the high degree of overlap of the spectral bands key for identification and discrimination of each component. Adapted with permission from reference 5, copyright 2017 Elsevier.

PLS Versus DDRS: Analysis of Standard Mixtures

	To properly assess the success of the newly developed DDRS analysis method, it was compared against plain PLS modeling for the standard gas mixtures. For each of the 12 target gases, PLS modeling was performed and evaluated. The number of PLS factors varied from 2 to 10, and yielded poor prediction precision in general, with root mean square error of prediction (RMSEP) values of up to 0.1113 and R values in the range 0.708–0.996 (Figure 3). Given the high number of PLS factors required and the overall poor quality of fit, PLS alone did not deliver the precision or robustness needed for accurate, reliable detection of multiple gas components in mud logging.

	The same spectra were then reanalyzed using multivariate calibration models under the newly developed DDRS methodology. First, concentrations of the gases in each mixture type (hydrocarbon versus nonhydrocarbon) were assigned according to the principles of uniform design. Then HDWT was performed on each Raman spectrum, setting the decomposition scale to 6. After the sampling probability of each HDWT coefficient for the TOFA was calculated, the parameters of the TOFA were set. After 20 iterations, the TOFA results were collected in a matrix, and the frequency of each variable was calculated. This approach allowed a series of PLS models to be constructed with an increasing number of most frequent variable, from which the PLS model with minimum RMSEP was selected as the final model.

	Looking at the results of modeling each target gas component (Table I), it can be seen that DDRS delivers high prediction precision with a relatively low number of required variables and only two PLS factors. RMSEP values are significantly lower across the board for DDRS versus plain PLS modeling (Figure 3), while 

 values were consistently >0.969, indicating a much better quality of fit. Additionally, an F-test to compare the two approaches confirmed that DDRS is a significantly better calibration model for component analysis in the gas mixtures.


	Figure 3: Comparison of root mean square error of prediction (RMSEP) values for each of the 12 component gases under study as calculated for plain PLS modeling versus DDRS modeling.

	In terms of practical applicability, it is important that DDRS address the particular challenges of applying Raman spectroscopy to mud-logging gas analysis. Firstly, it must be able to successfully isolate a single gas target from within a highly overlapped and unpredictable matrix. This requirement is well evidenced by the results for methane, for which prediction results were improved tremendously by DDRS. Secondly, the model must be capable of quantifying low concentrations of heavier alkanes such as butane and pentane in mixtures dominated by lighter components. DDRS also performed extremely well in this respect, as can be seen by the excellent agreement between predicted and measured values for isobutane, normal butane, isopentane, and normal pentane (Figure 4).


	Figure 4: Measured versus predicted values for (a) isobutane, (b) normal butane, (c) isopentane, and (d) normal pentane as calculated by the DDRS model. Adapted with permission from reference 5, copyright 2017 Elsevier.

	With confidence in DDRS established, the limit of detection (LOD) for each target gas component was calculated according to the International Union of Pure and Applied Chemistry (IUPAC) (8), using pure helium in the system as a measure of the baseline noise. This approach yielded LOD values for the target gases of 0.012–0.086% (Table I), which are sufficiently sensitive to be relevant in the mud-logging industry.

	DDRS Versus GC: A Comparison in the Field

	Having established the capability of DDRS to quantify the target gas components within simulated gas mixtures, the method was benchmarked against the industry standard method of GC at two actual mud-logging sites. For this purpose, the system was connected serially between the extractor and GC system (CPS-KQ-VI, Shanghai China Petroleum Instrument Co., Ltd.) at each site, using a small Raman gas cell volume to minimize gas sample delay between the measurements. The extracted gas was measured for methane, first by the DDRS system (every 6 s), then by the GC system (every 2 min). Measurements were taken for 13 h at the first site and for 6 h at the second (Figure 5). In both cases, DDRS measurements tracked very closely with GC results, providing much faster results and with better time resolution. This speed of analysis is particularly important for the detection of thin or hidden hydrocarbon reservoirs, and represents a significant tangible benefit for use in mud logging.


	Figure 5: Comparison of GC (black curve) and Raman gas analysis system (red curve) results for methane, as measured at two mud-logging sites, (a) for 13 h, and (b) for 6 h. Adapted with permission from reference 5, copyright 2017 Elsevier.

	The use of a high-sensitivity Raman system in combination with a newly developed analysis methodology termed DDRS shows multiple benefits for multicomponent gas analysis in the mud-logging industry. DDRS makes use of higher-density wavelet transform (HDWT) together with template oriented frog algorithm (TOFA) to find an optimal combination of spectral features with minimum overlap, and then constructs high-quality calibration models for gas analysis. Its ability to isolate both hydrocarbon and nonhydrocarbon target gas components within an uncontrolled and variable matrix with high prediction accuracy makes it a promising analysis method at mud-logging sites. When tested under the rigors of two real-world mud-logging sites, DDRS compared favorably with GC. In delivering much faster, better resolved measurements, the system shows excellent potential for high-throughput analysis of multiple gas components in real time, which could be extended to online multicomponent analysis in many other gas and fuel systems.

	(3) S.A. Barclay, R.H. Worden, J. Parnell, D.L. Hall, and S.M. Sterner, 

	(5) X. Han, Z. Huang, X. Chen, Q. Li, K. Xu, and D. Chen, 

	(6) Y. Hu, T. Jiang, A. Shen, W. Li, X. Wang, and J. Hu, 

	(8) M. Safaeian, D. Solomon, and P.E. Castle, 

This new data-driven Raman spectroscopy (DDRS) method is capable of simultaneously measuring 12 hydrocarbon and nonhydrocarbon gases in the presence of matrix interferences.



Cicely Rathmell

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