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Vol 34 No s6 Raman Technology for Today's Spectroscopists

In recent years, confocal Raman imaging and related techniques have become more and more popular in many fields of application such as materials science, pharmaceutics, life sciences, geoscience, food technology, and many others. The available Raman microscopes are focused on user-friendly and intuitive operation. Additionally, several key qualitative factors of confocal Raman imaging microscopes should be considered to guarantee clear and substantial results. In this article, the following five criteria–speed, sensitivity, resolution, modularity and upgradeability, and combinability–are explained, and factors that influence them positively or negatively are examined. On the basis of application examples, modern Raman imaging and correlative imaging techniques are introduced, and state-of-the-art practice examples from polymer research, pharmaceutics, low-dimensional materials research, and life sciences are presented.

	It was 90 years ago that Chandrasekhara Venkata Raman and Kariamanickam Srinivasa Krishnan first documented "A New Type of Secondary Radiation," which then became known as the 

(1,2). Raman spectroscopy is based on this effect, and it is used for qualitative and quantitative analysis of the chemical components and molecules of a sample. It is a nondestructive method that requires little, if any, sample preparation.

	Nevertheless, Raman spectroscopy long remained a technique that was only performed in specialized laboratories. In recent years, however, it has been increasingly losing its outsider status. One reason for this is the development of the confocal Raman microscope, with which not only individual Raman spectra, but also complete images generated from thousands of spectra, can be acquired. Through continuous development, commercially available Raman microscopes are also becoming more user-friendly. For example, modern software interfaces guide the user through the Raman measurement and the subsequent data analysis.

	There are several key factors that can be used as criteria for determining the quality of confocal Raman microscopes, which are explained as follows:

	In the past, exposure times of minutes to hours were common for acquiring single Raman spectra, but today the process generally takes fractions of a second to less than one millisecond. In one second, more than 1000 Raman spectra can be recorded. Thus, a Raman image can be generated within a few minutes. To achieve this acquisition speed, the Raman imaging system should be equipped with optimized optics and an electron multiplying charge coupled device (EMCCD) camera.

	High acquisition speeds are particularly important for measurements on sensitive and valuable samples in which the excitation energy must be as low as possible. Time-resolved investigations of dynamic processes can also benefit from rapid Raman spectral acquisition. Operating costs can also be reduced by shorter analysis times concurrent with increased data rates. Having a high system speed is also advantageous for time-critical work.

	The signal sensitivity of a system is critical for the quality of the results, and is especially important when weak Raman signals are to be detected.

	To achieve the best possible sensitivity, a confocal beam path, such as using a diaphragm aperture, must be employed to eliminate light from outside the focal plane to increase the signal-to-noise ratio. The entire Raman imaging system should also be optimized for high light throughput. This includes a spectrometer that ensures throughput of over 70%, and is designed for measurements with low light and signal intensity. Charge-coupled device (CCD) cameras optimized for spectroscopy, which exhibit more than 90% quantum efficiency in the visible range, are most commonly used as detectors. Finally, the use of almost lossless photonic fibers ensures efficient light and signal transmission.

	The resolution of a Raman system comprises both spatial and spectral resolution. The spatial resolution includes the lateral resolution (

-direction). The spatial resolution is determined by the numerical aperture (NA) of the objective used and the excitation wavelength. In addition, a confocal microscope produces images with a higher contrast, because the background signal is reduced. The smaller the aperture of a confocal microscope, the higher its resolution. In a confocal Raman microscope, the lateral resolution is about 200–300 nm, and the depth resolution below 1 micrometer. A confocal microscope can also create optical sections from different focal planes, which can be used with transparent samples for depth profiles and 3D images.

	Spectral resolution defines the ability of a spectroscopic system to separate Raman lines near one another. Symmetric peaks in the spectrum are ensured by a spectrometer design that operates free of coma and astigmatism. The grating used, the focal length of the spectrometer, the pixel size of the CCD camera, and the size of the aperture also affect the spectral resolution.

	At room temperature, the width of the Raman lines is typically greater than 3 cm

, but some applications (gases, low temperature, or stress analysis) may require significantly higher resolution (Figure 1).


	Figure 1: Speed, sensitivity, and resolution are some of the characteristics that can be used to identify a high quality Raman microscope. These three characteristics should not be mutually exclusive. Ideally, the Raman imaging system should be configured in such a way that high-resolution images with a high signal-to-noise ratio can be acquired in a short period of time.

	The introduction of Raman microscopy into laboratories puts new demands on commercially available systems. These requirements can sometimes appear contradictory: easy operation with diverse functionality, a wide range of applications with optimized sensitivity, low cost, and high performance. To offer users a Raman system tailored to their individual requirements, it is particularly important that systems have a modular design that can be adapted to new conditions through being reconfigured or upgraded. A system can be optimized for specific requirements by individually combining suitable lasers, filters, lenses, spectrometers, and detectors. With such a customized Raman imaging system, the user is able to obtain meaningful Raman images, perform 3D volume scans, and create depth profiles.

	Confocal Raman microscopy can be combined with other microscopy techniques. By using different methods and correlating the data, the user attains a more comprehensive understanding of the sample. Common examples of correlative microscopy techniques are Raman-atomic force microscopy (AFM), Raman–scanning near-field optical microscopy (SNOM), and Raman–scanning electron microscopy (SEM). To correlate the data of these disparate technologies, the exact same sample location must be examined by each approach. If different instruments are to be used, finding this sample location can be very difficult and time-consuming. This is made much easier with a hybrid system that combines the different analysis methods in one instrument so that the sample can remain in place during all measurements.

	Some applications of correlative Raman microscopy are:

	For Raman microscopy, most samples do not need to be treated, stained, or otherwise prepared prior to measurement. The combination of a confocal Raman microscope with a profilometer module for focus stabilization allows rough or inclined surfaces to be examined (3,4). During Raman analysis, the examination area is kept constantly in focus by the simultaneously acquired profilometry data. This also compensates for thermal shifts and enables long-term measurements. The application example in Figure 2 shows the analysis of a microstructured silicon sample. The chemical image of the Raman measurement was overlaid onto the topographic profile measurement.


	Figure 2: Topographic Raman image of a silicon microstructure.

	Fluorescence microscopy has been a widespread imaging method for the analysis of biological cells and organisms for decades. Samples are stained with fluorescent dyes or organisms are genetically engineered to express fluorescent proteins. The fluorescence signal is usually much stronger than the Raman signal. Nevertheless, correlative Raman fluorescence measurements are possible with an appropriate system. Figure 3 shows a Raman fluorescence image of a live cell culture of eukaryotic cells. An inverted confocal Raman microscope was used to examine the cells in their aqueous cell culture medium in the Petri dish. The cell nuclei were stained with the fluorescent dye 4',6-diamidino-2-phenylindole (DAPI). An excitation wavelength of 532 nm was used for the Raman measurement. An image with 50 x 40 µm² and 150 x 120 pixels was acquired. A Raman spectrum was recorded at each pixel. The recording time was 0.2 s per spectrum. In the correlative Raman fluorescence image, the nuclei are shown in blue (recorded with fluorescence microscopy), the nucleoli in green, and the endoplasmic reticula in red (recorded with Raman microscopy). The corresponding Raman spectra are shown in the same colors.


	Figure 3: Showing (a) correlative Raman fluorescence image of primate cells in a cell culture. Blue objects are nuclei recorded by fluorescence microscopy, red objects are endoplasmic reticula, and green circles are nucleoli recorded by Raman microscopy. (b) Raman spectra associated with the image.

	The combination of Raman microscopy, which provides information about the type and distribution of molecules in a sample, and the high-resolution AFM technique, which determines the surface characteristics of a sample, enables the visualization of both chemical and morphological properties.

	Here, the analysis of a 1:1:1 mixture of polystyrene (PS), 2-ethylhexyl acrylate (EHA) and styrene-butadiene rubber (SBR) is shown. For this, a correlative Raman–AFM microscope was used, in which Raman microscopy and AFM technologies are fully integrated.

	The measurement with AFM in intermittent contact or alternating current (AC) mode documents the topography of the polymer mixture (Figure 4a). The simultaneously recorded phase image (Figure 4b) provides information on the viscosity and elasticity of the individual components of the polymer mixture. The confocal Raman image (Figure 4c) shows that PS (red) and EHA (green) are present separately. SBR (purple) partly mixes with EHA (mixture shown in blue). By correlating the Raman image with the AFM image, the chemical information can be linked to the structural information (Figure 4d).


	Figure 4: Correlative, high resolution Raman-atomic force microscopy (AFM) image of a 1:1:1 mixture of polystyrene (PS), 2-ethylhexyl acrylate (EHA), and styrene-butadiene rubber (SBR). The image shows (a) the topography of the polymer mixture determined with AFM in the AC mode; with (b) the phase of the AFM image showing the fine structure of the compound. In (c) a color-coded, confocal Raman image is shown as generated from the Raman spectra, showing the distribution of the polymers: PS (red), EHA (green). SBR (purple), and SBR-EHA mixture (blue). In (d) a correlative Raman-AFM image is shown where the topography and distribution of the different polymers can be visualized.

	Scanning electron microscopy (SEM) is a well-established method for structural surface analysis. By combining Raman imaging with SEM in a correlative microscope, it is possible to combine results of SEM structural analysis with chemical and molecular information from confocal Raman microscopy (5). The sample is placed in the vacuum chamber of the electron microscope. Both analysis methods are then carried out automatically at the same sample location. The obtained SEM and Raman images can then be superimposed.

	In Figure 5, a structure several atoms in thickness comprised of graphene layers was analyzed by correlative Raman–SEM microscopy. The Raman image consists of 22,500 spectra with 50 ms recording time per spectrum. While in the SEM image the contrast between the substrate and the graphene flake is visible, in the Raman image the number of graphene layers and their different orientations can be analyzed. This is not possible with SEM alone.


	Figure 5: Correlative Raman-scanning electron microscopy (SEM) image of a multilayer graphene flake. The different colors show folds and orientations in the graphene that can be identified by Raman spectroscopic analysis.

Raman Particle Identification and Characterization

	High-resolution investigations of particles are of great interest in many fields of application such as environmental science, pharmaceutical research, and many others. Combining a particle analysis tool with the fast, label-free, and nondestructive Raman imaging technique makes it possible to find, classify, and identify particles in a sample according to their size, shape and chemical characteristics. The physical and molecular attributes of the particles in a sample may be correlated and qualitatively and quantitatively evaluated. Figure 6 shows the results of particle analysis carried out on a cosmetic peeling cream sample. Figure 6a shows the overlay of an optical bright field microscope image with the corresponding confocal Raman image. Particles are identified according to their size and shape and further characterized by their molecular properties through confocal Raman imaging. The chemical analysis revealed anatase and boron nitride particles in an oil matrix (Raman spectra shown in Figure 6b). Further evaluation of the results determines the quantitative prevalence of the molecular sample components in the particles (Figure 6c) and also the distribution of chemical compounds correlated to particle size (Figure 6d). In extended analyses, the chemical characteristics of particles could also be linked to parameters such as area, perimeter, bounding box, Feret diameter, aspect ratio, equivalent diameter and many others. This illustrates the potential for comprehensive investigations of particles in many fields of application.


	Figure 6: Particles in a cosmetic peeling cream sample; (a) the optical bright field image overlaid with the confocal Raman image; (b) the corresponding Raman spectra of the molecular components in the sample; (c) a pie chart of the quantitative compound distribution in the sample; and (d) the graphical representation of the correlation between chemical characteristics and particle size.

	(2) J. Toporski, T. Dieing and O. Hollricher, Eds., 

(Springer International Publishing, New York, New York, 2nd ed., 2018).

	(5) G. Wille, C. Lerouge, and U. Schmidt, 

Damon Strom, Sonja Breuninger, Harald Fischer, Andrea Richter, Ute Schmidt, 

 are with WITec GmbH, in Ulm, Germany. Direct correspondence to: 

Five key qualitative factors–speed, sensitivity, resolution, modularity and upgradeability, and combinability–contribute to the quality of confocal Raman imaging microscopes. Using application examples, this article introduces modern Raman imaging and correlative imaging techniques, and presents
state-of-the-art practice examples from polymer research, pharmaceutics, low-dimensional materials research, and life sciences.

Criteria for High-Quality Raman Microscopy

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.

	(1) I. Roca, M. Akova, F. Baquero, J. Carlet, M. Cavaleri, S. Coenen, J. Cohen, D. Findlay, I. Gyssens, O.E. Heure, G. Kahlmeter, H. Kruse, R. Laxminarayan, E. Liébana, L. López-Cerero, A. MacGowan, M. Martins, J. Rodríguez-Bañ o, J.-M. Rolain, C. Segovia, B. Sigauque, E. Taconelli, E. Wellington, and J. Vila, 

	(2) European Antimicrobial Resistance Surveillance Network, 

Surveillance of Antimicrobial Resistance in Europe (

European Centre for Disease Prevention and Control, Solna, Sweden

	(3) Centers for Disease Control and Prevention, 

Antibiotic Resistance Threats in the United States

 (United States Department of Health and Human Services, Washington, D.C.,2013).

	(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

Microplastics have become a growing area of research, as concern over harm to the environment, and potential harm to humans, has increased. Recently, legislation passed in California has mandated the testing for, and reporting of, plastics in drinking water (SB 1422). In this study, Raman spectroscopy is used to characterize two different commercial fabric fiber samples that include synthetic polymer materials. Both macro- and microscopic Raman measurements are carried out, and chemical identification of the individual components is made through database searches. Raman microscopy is demonstrated as a powerful technique for microplastic fiber characterization, especially for samples that contain mixtures of components, including multiple polymers, or additives. 

	The large-scale production and use of plastic dates back to the mid-20th century. Since then, there has been a rapid growth in the production and use of plastics. With the shift from reusable to single-use applications, the amount of plastic produced has only continued to increase over time. In a study by Geyer and associates, it is estimated that, to date, 8300 million metric tons (Mt) of virgin plastics have been produced. Of that amount, only 2500 Mt are still in use. Of the remainder, only 600 Mt have been recycled, while 800 Mt have been incinerated, and 4900 Mt have been discarded. This means that 60% of the plastics ever produced are either accumulating in landfills, or polluting the natural environment (1).

	The same characteristic that makes plastic an appealing choice for many different applications is also what is ultimately causing harm to the natural environment, namely its long lifetime and durability. While materials like paper, cardboard, and wood decompose on a month or year time scale, plastics take tens to hundreds of years to fully decompose (2). In the meantime, weathering causes plastics to break down into smaller fragments, exposing marine life at all stages of the food web, from small organisms like plankton (3), to large mammals like whales (4). Plastics have also been observed in many different marine environments, including coastlines (5), in Arctic sea ice (6), at the sea surface (7), and on the sea floor (8). In an effort to understand the ecological impact of plastics on marine life, research has been undertaken to identify and quantify plastic debris.

	Although plastic debris can come in many sizes, 

 are defined as plastic particulates that are smaller than 5 mm in size. In addition to a range of sizes, they can take on many forms, including fragments, films, and fibers. Fibers have been identified as a significant portion of the microplastics found in the environment. They are shed from textiles, including consumer clothing, during the manufacturing stage, and in the wearing and washing of clothing (9). Studies have been undertaken to tackle the practice of sample collection, preparation, and filtering of microplastics, which is a critical topic (10), but this study focuses on the chemical identification of microplastics.

	Many different methods have been used to characterize microplastics, including standard microscopy techniques to characterize size, shape, and morphology, staining, thermogravimetric analysis, pyrolysis gas chromatography–mass spectrometry (GC-MS), Raman, and Fourier transform–infrared spectroscopy (FT-IR) (11). Both Raman and FT-IR techniques are spectroscopic techniques, sensitive to the vibrational modes of the molecule under study. Selection rules for Raman and infrared (IR) dictate which vibrational modes are observable using each technique, but generally, Raman spectroscopy will provide more vibrational information on the polymer backbone, whereas IR spectroscopy will provide more information on polymer side chains. Because Raman and IR spectroscopy are complementary techniques, ideally, a laboratory would be equipped with both techniques, if resources are available. This article focuses on Raman spectroscopy specifically as an analysis technique for characterizing plastics.

	Because microplastics encompass a wide variety of sample sizes, different sampling techniques can be employed for characterization using Raman spectroscopy. For example, for samples larger than 1 mm, macroscopic measurements can be carried out. The use of a fixed spectrograph with a Raman probe enables measurement in the field, because experimental setups are typically lightweight, compact, and rugged, making them easily transportable. For samples smaller than 1 mm, or for heterogeneous samples with mixtures of polymers or other additives, a Raman microscope is the preferred option, because of its high spatial selectivity.

	Once a spectrum is acquired, the next critical step is to identify exactly which polymers make up the unknown sample. Having a complete, high quality spectral database is critical for carrying out accurate spectral searching and matching. In a typical database, spectra of reference materials are recorded and stored with their chemical and physical properties in the sample record metadata. For microplastics, it is advantageous to build an application library that includes spectra of weathered plastics with their location, preparation, and analysis methods, and to include environmental and health impacts within the metadata. In addition, spectra of different colored plastics, or plastics with additives such as calcium carbonate (CaCO

), are also critical to the correct identification of unknowns.

	In this study, two different fabric (fiber) samples are measured, both acquired from Amazon. Sample 1 is white in color, and identified by its description as "polyester soft felt," comprised of "fabric and rayon, high quality acrylic craft felt." Sample 2 is a patterned fabric sample with white and red areas, identified as being "100% cotton."

	The Raman spectra presented in this paper are collected using two different systems. For macroscopic measurements, a MacroRam benchtop Raman spectrometer (Horiba), equipped with a TouchRaman BallProbe (Marqmetrix), is used. The use of the TouchRaman BallProbe allows spectral measurements to be carried out without the requirement to mount the sample or align the probe. The probe is pressed onto the sample under measurement, and a spectrum is recorded. The MacroRam uses an excitation wavelength of 785 nm, which helps to suppress fluorescence that would otherwise be present at shorter laser wavelength excitation (12). A simplified optical layout of a typical Raman benchtop system with an immersion-touch probe is shown in Figure 1b.


	Figure 1: Optical layouts of typical Raman systems. (a) A typical Raman microscope including objective, filtering, and confocal aperture with focusing optics. (Generally, confocal Raman microscopes are coupled to scanning spectrometers with wavelength tuning capability and multiple lasers and gratings.) (b) A typical benchtop Raman system with immersion or touch probe including fiber optic coupling of laser excitation source with Raman collection optics (generally, benchtop Raman systems are coupled to fixed grating spectrographs for use with single wavelength laser excitation).

	For microscopic measurements, an XploRA Plus (Horiba) confocal Raman microscope was used. This system is equipped with a combination of three different excitation lasers (532 nm, 638 nm, and 785 nm) and four gratings (600 gr, 1200 gr, 1800 gr, and 2400 gr). A 100x magnification objective (numerical aperture of 0.9) is used for all micro-Raman measurements described here. The system is equipped with a deep-cooled SynapseEM electron multiplying charge coupled device (EMCCD), for sensitivity and speed, which is especially critical when carrying out Raman imaging measurements. A simplified optical layout of a typical Raman microscope is shown in Figure 1a.

	For Sample 1, measurements were first carried out using the macroscopic configuration previously described. The recorded Raman spectrum is baseline subtracted, to account for fluorescence background, and fast Fourier transform (FFT) smoothing was carried out to further refine the spectral quality. The resulting processed spectrum is shown in Figure 2. A database search using KnowItAll libraries (BioRad) resulted in a one component match to polyethylene terephthalate (PET). This is not surprising, since 

 § 303.7 (c) as "any long chain synthetic polymer composed of at least 85% by weight of an ester of a substituted aromatic carboxylic acid, including, but not restricted to, substituted terephthalate units."


	Figure 2: Macroscopic Raman spectrum recorded using a macroscopic Raman system, described in the Experimental section, for sample 1 (red), and reference spectrum of polyethylene terephthalate (blue).

	Although the macroscopic measurement confirms that sample 1 contains PET, if there is a combination of different components in the fabric as advertised, Raman microscopy will provide additional spatial information. Using the microscopic configuration described in the Experimental section, 785 nm excitation is used to carry out a line scan measurement across a fiber located in sample 2. An image of the fiber measured is shown in the inset of Figure 3, and the resulting average spectrum is shown in red. The presence of PET in the sample is confirmed (PET reference spectrum in blue, Figure 3), but there is also evidence of cellulose in the sample (cellulose reference spectrum in green, Figure 3). Specifically, the sharp band observed at approximately 1100 cm

 in sample 1 overlaps with the same vibrational band in cellulose. Although this band also overlaps with spectral features in the PET reference spectrum (blue), the relative magnitudes of the two bands around 1100 cm

 in sample 1 are not consistent with PET alone. This result agrees with the fabric description that includes rayon. According to 16 

 is defined as a "manufactured fiber composed of regenerated cellulose."


	Figure 3: Confocal Raman microscope spectrum of sample 1 acquired using a Raman microscope, described in the Experimental section, with a 785 nm laser excitation (red), reference spectrum of microcrystalline cellulose (green), and a reference spectrum of PET (blue). The inset shows the video image of the fiber under study.

	What is not observed in sample 1, but is mentioned in the description of the fabric, is acrylic. According to 16 

 is defined as being "composed of at least 85% by weight of acrylonitrile units." There is no evidence of acrylonitrile (monomer or polymer) in sample 1. If acrylic were present, a band at 2240 cm

 would be observable in the Raman spectrum, corresponding to the nitrile stretching mode in acrylonitrile. No such bands are observed in sample 1, despite a thorough survey of the fabric area.

	For sample 2, macroscopic measurements result in spectra that are too congested for straightforward analysis. Microscopic measurements yield much more information. Preliminary single point measurements, taken at various spots on the sample, indicate the presence of different components. To account for these spatial differences, a Raman map is carried out over a 100 µm × 100 µm area. The results show that there are at least three different components in the fabric, shown as red, green, and blue spectra in Figure 4. Database searches for the three unknown components reveal matches to PET (red), cellulose (blue), and a red pigment (multiple possible database matches, green).


	Figure 4: (a) Raman spectra recorded from sample 2. PET is shown in red, red dye spectrum is shown in green, and cellulose is shown in blue. (b) Pseudo color rendered Raman image of 100 µm x 100 µm area of the sample with color coding corresponding to the three spectra in (a). (c) Video image of sample 2 used for the Raman measurement.

	To assess the spatial heterogeneity in sample 2, the representative spectra recorded from the sample for PET, cellulose, and the unknown red pigment are used as references in a classical least squares (CLS) treatment of the hyperspectral Raman data cube. The resulting rendered Raman image is shown in Figure 4b. When comparing the Raman image with the optical image (Figure 4c), one can see good correlation of the Raman signal with individual fibers in the fabric. Sample 2 was described by the seller as 100% cotton; however, it is readily apparent from the Raman image that there are PET fibers present, as indicated by the bright red areas in Figure 4b.

	The samples studied here serve as a demonstration of how Raman spectroscopy can be applied to the study of microplastics, specifically fiber samples. Both synthetic (PET) and natural (cellulose) fibers are easily identifiable. In addition, Raman spectroscopy can be used to identify additives, including pigments or dyes, as demonstrated in sample 2. Finally, the spatial selectivity that is provided by Raman microscopy allows for characterization of heterogeneous samples that might otherwise be misidentified or mischaracterized using macroscopic techniques.

	(1) R. Geyer, J.R. Jambeck, and K.L. Law, 

	(2) Ocean Conservancy, International Coastal Cleanup (2010 Report: Trash Travels, 2010).

	(3) M. Cole, P. Lindeque, E. Fileman, C. Halsband, R. Goodhead, J. Moger, and T.S. Galloway, 

	(4) A.L. Lusher, G. Hernandez-Milian, J. O'Brien, S. Berrow, I. O'Connor, and R. Officer, 

	(5) S.L. Whitmire, S.J. Van Bloem, and C.A. Toline, 

Marine Debris Program Office of Response and Restoration 

(National Oceanic and Atmospheric Administration Contract GSI-CU-1505, 2017), pg. 23.

	(6) I. Peeken, S. Primpke, B. Beyer, J. Gutermann, C. Katlein, T. Krumpen, M. Bergmann, L. Hehemann, and G. Gerdts, 

National Oceanic and Atmospheric Administration Marine Debris Program 2016 Report on Modeling Oceanic Transport of Floating Marine Debris 

(NOAA Marine Debris Program, Silver Springs, Maryland., 2016), pg. 21.

	(8) A.J. Jamieson, L.S.R. Brooks, W.D.K. Reid, S.B. Piertney, B.E. Narayanaswamy, and T.D. Linley, 

	(9) B.M. Carney Almroth, L. Astrom, S. Roslund, H. Petersson, M. Johansson, and N.-K. Persson, 

	(10) GESAMP, Sources, Fate and Effects of Microplastics in the Marine Environment: Part Two of a Global Assessment, P. J. Kershaw, and C. M. Rochman Eds. IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP/UNDP (Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection. Rep. Stud. GESAMP No. 93, 220, 2016), pp. 104–109.

 M. Bergmann, L. Gutow, and M. Klages, Eds. (Springer, New York, New York, 2015), pp. 201–207.

is the Manager of Raman Applications at Horiba Scientific, in Piscataway, New Jersey. 

is the Raman Project Manager of Raman Applications at Horiba Scientific, in Sunnyvale, California. Direct correspondence to: 

In this study, macro- and microscopic Raman spectroscopy were used to identify different commercial microplastic fibers using measured spectra with database searches. Raman microscopy is demonstrated as a powerful technique for microplastic fiber characterization, especially for samples that contain mixtures of components, including multiple polymers, or additives.

Special Issues-06-01-2019

Vol 34 No s6 Raman Technology for Today's Spectroscopists

Criteria for High-Quality Raman Microscopy

Rapid, Portable Pathogen Detection with Multiplexed SERS-based Nanosensors

Characterizing Microplastic Fibers Using Raman Spectroscopy