David Creasey

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.

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Microplastics in Aquatic Systems – Monitoring Methods and Biological Consequences

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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

Biotechnology, the production of chemicals of choice using microorganisms, is an application very well suited to monitoring with Raman spectroscopy. Raman’s high selectivity and low sensitivity to the solvent, water, can be employed using a simple immersion probe setup to monitor and more tightly control these processes.


We used a Wasatch Photonics Raman spectrometer at 785 nm, fiber-coupled to an external laser and an immersion probe inserted into a stirred solution of 50 g/L glucose and 2 g/L yeast. The fermentation was observed over 14 h, with spectra collected every 2 min at 5 s integration time and an average of 20 scans.


We performed a separate multivariate calibration for glucose in the presence of yeast over the concentration range from 0 to 50 g/L glucose.


We correlated the glucose calibration spectra with the concentration using partial least squares (PLS) regression, yielding a prediction error of 0.7 g/L in the presence of yeast and about 0.1 g/L without yeast. The Raman spectra recorded during the fermentation are shown in Figure 1 together with the pure Raman spectra for the main reactant and product, glucose and ethanol.
Applying the PLS glucose model yielded the prediction of the glucose concentration, which showed a slow decrease with a noise level of below 1 g/L glucose.


Raman spectra recorded during glucose fermentation over 14 h, with time (in seconds) indicated by the color, together with the reactant and product spectra. The glucose peaks around 1100 cm

 decrease, while the ethanol peak at 900 cm

 and the overall yeast background increase.

Principal component analysis (PCA) identified the main spectral trends during the process. The first principal component expressed the remaining glucose level, while the second component captured the simultaneous decrease of glucose (spectrum negative in the corresponding loading) and increase of ethanol (positive spectrum in the loading). The corresponding PCA score therefore allowed the progress of the reaction to be summarized in a single coordinate, and to compare batch to batch.


Additional reaction products could be discovered in the higher principal components; due to its specificity, Raman allows identification. For example, the fourth principle component for the fermentation spectra showed the production of dissolved carbon dioxide. Likewise, unwanted side-products could be discovered and identified during the fermentation.


Using an immersion probe and a Wasatch Photonics Raman spectrometer at 785 nm, we continuously monitor the fermentation of glucose with yeast. Despite the significant yeast background, multivariate regression yields the glucose concentration, while principal component analysis projects the spectra onto a reaction progress coordinate and allows deep insight into the details of the fermentation. The same approach could be used for other processes to harness the strength of Raman spectroscopy.


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Raman spectroscopy’s high analytical selectivity and insensitivity to water is well suited for process monitoring in biotechnology. Here we explore the fermentation of glucose, a common feedstock, with a commonly used microorganism, yeast. Applying multivariate tools, we can monitor the main reactants and products with high sensitivity: glucose, ethanol, and carbon dioxide.

Bioreactor Fermentation Monitoring with  Raman Spectroscopy

Near-infrared (NIR) excitation is often touted as a method of reducing background fluorescence in organic samples, but at considerable sacrifice to signal strength. Is the tradeoff worth it? Here we compare data from bovine bone, monosodium urate crystals isolated from synovial fluid, and demineralized tooth enamel using 785- and 1064-nm Raman spectroscopy to assess factors relevant to data quality. The results indicate that 1064-nm Raman vastly outperforms 785-nm Raman for background fluorescence reduction, even as compared to lengthy sample pretreatment at 785 nm via photobleaching. As cost-effective, sensitive systems for 1064-nm Raman spectroscopy become more widely available, it has become a viable technology for the measurement of a diverse range of biological samples to minimize sample damage and optimize the quality of spectral data.

	Interference from background fluorescence is a common challenge in Raman spectroscopic analysis of organic and biological specimens (1–6). It can degrade the signal-to-noise ratio of the data collected, and in some cases obscures the Raman spectrum entirely. The chemical complexity of biological samples, in particular, presents many possible sources of autofluorescence, from aromatic groups like tryptophan, tyrosine, and phenylalanine in folded proteins (7) to very intense carotenoid bands in blood that interfere in hemoglobin studies (8).

	Although fluorescence can be dealt with via a variety of techniques such as the use of confocal configuration, photobleaching, or chemical bleaching (9), the most effective method of removing or reducing fluorescence has been through excitation at longer wavelengths. While this diminishes the background fluorescence burden, it results in much lower signal intensity (scaling inversely with the fourth order of the excitation wavelength). The use of near-infrared (NIR) excitation at 785 nm has thus been the standard approach in addressing background fluorescence for organic materials because it provides a reasonable compromise between signal level and background fluorescence.

	Photobleaching of samples may be used in tandem with, or in lieu of, 785-nm excitation for fluorescence reduction in biological samples. Though the mechanism is not well understood, prolonged exposure to excitation light often results in a decrease in autofluorescence. Golcuk and colleagues (10) demonstrated the collection of Raman signal from bone tissue with 532-nm laser excitation after samples were subjected to 30–120 min of photobleaching. Even when a reasonable Raman spectrum can be acquired, the signal-to-noise ratio may be improved by using brief photobleaching, as shown in a study of monosodium urate (MSU) crystals isolated from joint fluids (5).

	Similarly, chemical bleaching with agents such as peroxides can reduce interference from fluorescence in some tissues, but the process can take up to several hours and may impact the spectral profile and band ratios (9). It must also be halted in a very narrow window to avoid damaging tissue, such as bone (11). Sample damage and increased data acquisition time are the key limitations associated with both photobleaching and chemical bleaching, particularly in clinical settings.

	Although 785-nm excitation is largely indicated for Raman analysis of biological or organic analytes (12,13), there are circumstances in which such systems fail to acquire adequate Raman spectra because of high fluorescence, as seen with lung (14), liver, and kidney tissues (15). These samples are better served with 1064-nm excitation, as are plant samples studied or classified through the analysis of lignins (16,17).

	The use of 1064-nm excitation results in a dramatic decrease in background for highly fluorescing samples such as biological tissues, edible oils, petrochemicals, polymers, and many inks and pigments. Fourier transform (FT) Raman systems based on 1064-nm excitation have existed for more than two decades, but typically require integration times in excess of 30 min (8,18,19). Dispersive 1064-nm Raman systems provide an alternative, but have historically been limited by the high cost and relatively poor signal-to-noise ratio of cooled InGaAs detector arrays. The emergence of more affordable, higher performance detectors has enabled a burst of commercial dispersive 1064-nm Raman systems in recent years, spurring a variety of new studies (15–17). Acquisition times in dispersive Raman systems can still be moderately long, often 30 s or more, unless a high-throughput 1064-nm system is used. This contributes to noise and can limit applicability, underscoring the need to select a dispersive 1064-nm Raman system carefully.

	Although many individual studies have been undertaken to determine the optimum excitation wavelength for a given sample, few systematic comparisons of 785-nm versus 1064-nm excitation in biological samples have been carried out. The goal of this study was to determine whether 1064-nm excitation presents any significant merits in reducing fluorescence background relative to 785-nm excitation. This knowledge is essential to potential buyers in the market to determine whether there is added value in choosing a dispersive 1064-nm system for biological studies.

	In addition, the uncharted dynamics of photobleaching at 1064-nm excitation in these systems is explored here. The detailed comparison of fluorescence recorded from the same sample set using 785-nm and 1064-nm dispersive Raman systems included analysis of the reduction in fluorescence relative to the duration of photobleaching. 

	Results collected from three biological samples showed significantly lower fluorescence background in Raman spectra collected with 1064-nm excitation as compared to excitation at 785 nm. Furthermore, fluorescence background was found to be more stable with 1064-nm excitation, such that the background reduction with photobleaching was minimal. In short, 1064-nm excitation presented significant merits compared to 785 nm in dealing with background fluorescence.

	Three different types of biological specimens were used in this study: a slice of bovine bone, a sample of monosodium urate crystal that was isolated from human joint fluid (as described in reference 5), and a human tooth (Figures 1a–1c, respectively). Human tissues were collected under the approvals of the appropriate institutional review boards.

	The systems used for measurement included an integrated 785-nm Raman system and a modular 1064-nm system. The 785-nm Raman system (WP 785L, Wasatch Photonics) included a 785-nm laser and an f/1.3 spectrometer optical design integrated with a charge-coupled device (CCD) array cooled to 15 °C to reduce system noise. Integrated free space sampling optics with a 25-mm focal length was used to deliver the laser light to the sample over a 70-µm spot, as well as to collect the Raman signal. 

	The 1064-nm system was an early prototype of a commercial modular Raman system with sample coupling via fiber probe. The spectrometer provided a spectral resolution of 10 cm

 at a 50-µm slit width using a volume phase holographic transmission grating (Wasatch Photonics). It included a TEC-cooled 512-element InGaAs array detector (Du490A-1.7, Andor). Excitation light was provided with an 800-mW, 1064-nm laser (Innovative Photonics Solutions), coupled to a Raman probe with a focal distance of 10 mm (Wasatch Photonics).

	The power used for photobleaching was set at 50 mW at 785 nm; the exposure time was 1 s and the spectra were averaged 10 times, resulting in 10 s total integration time. For the 1064-nm system, a 500-mW laser beam was used for photobleaching, and spectra were acquired as a single scan with a 10-s integration time. The sample was positioned with the laser power set at 5% of the photobleaching level before the start, then aligned at the focal point of the system or probe. The first spectrum was acquired as soon as the photobleaching process began; the ensuing spectra were then recorded at intervals as photobleaching proceeded. Noise levels reported were determined using the standard deviation of the data points in a region free of Raman peaks (1150–1200 cm

	Raman microspectroscopy is used in the study of bone tissue for its ability to provide composition information relevant to biomechanical properties and mineralization processes at micrometer-range spatial resolution (5). High fluorescence background in these samples obscures important Raman bands for mineral and matrix components, however, necessitating the use of chemical treatment or photobleaching at 532-nm (10) or 785-nm excitation (13).

	The dried bovine bone measured in this study yielded significantly higher background fluorescence when illuminated with 785-nm excitation (Figure 2a, top three curves) than with 1064-nm excitation (Figure 2a, bottom three curves). The 1064-nm Raman spectra all appeared to share a common background, seen also in a spectrum taken of a blank glass microscope slide (Figure 2b, central, orange curve). This was concluded to be the 1064-nm system response because it did not change after 20 min of exposure. Possible sources of this system response include silica Raman background from the fiber probe (20), or more likely stray light because of scattered light in the Raman probe, resulting in refinements to the optical design.

	No studies were performed to compare the system response from different samples (which reflect excitation laser back into system); the system response in the case of bovine bone was arbitrarily estimated as 50% of that of glass slides for the purposes of analysis.

	Comparing the baseline of the bone data and the system response, it was concluded that the system response contributed largely to the baseline of the bovine bone that was recorded by the 1064-nm system. Taking into account the difference in laser power and the integration time, the fluorescence  background seen in spectra from the 785-nm system was more than 500 times (in counts) greater than that from the 1064-nm system before photobleaching. An even higher ratio can be deduced if the baseline contributed from the system response is subtracted, as shown in Figure 2b.

	Notwithstanding the initial intensities, the fluorescence level experienced a reduction with photobleaching experiments on bovine bone for both the 785-nm and 1064-nm systems. The rate of photobleaching was much faster at 785 nm, as can be seen in Table I, decreasing by 35% after 15 min versus 15% for 1064 nm.

	MSU crystals are frequently observed in the joint space, and lead to symptoms of gout. Raman spectroscopy is a promising diagnostic technique to identify these crystals, but it suffers from high background fluorescence (5).

	In this study, MSU crystals isolated from synovial fluid and analyzed using Raman spectroscopy presented rapid background reduction in response to photobleaching at 785-nm excitation, while the photobleaching rate in response to the 1064-nm system was low (Figure 3). Total reduction in photobleaching at 1064 nm was only 13% after 15 min, indicating much greater sample stability with light exposure (Table I).

	White lesions on human teeth normally indicated demineralized enamel and have higher concentration of organic phases than healthy enamel. In fact, the progression of carious lesions in teeth can be estimated using the high background in their Raman spectra, as has been shown with 785 nm Raman measurements (12).

	In this study, the tooth had a white lesion on part of the enamel surface, and dark colored spots were present within the white lesion. The white lesion region showed lower mineral intensity but higher fluorescence background (data not shown). Attempts to measure the dark spots within the white lesion with the 785-nm system resulted in intense fluorescence that saturated the system and prevented collection of suitable Raman spectra, even after 1 h of photobleaching at a 0.5-s data integration time. The use of the 1064-nm Raman system, however, resulted in the successful recording of spectra from the same spot (Figure 4). Moderate fluorescence reduction was observed with 1064-nm excitation over time (Table I).

	A direct comparison of fluorescence background in Raman spectroscopic analysis of various biological samples at both 785 nm and 1064 nm showed the longer wavelength to offer significant advantages. Fluorescence background of Raman spectra excited at 1064 nm was more than 500-fold lower than that obtained by 785-nm excitation after differences in excitation energy and acquisition time were taken into account.

	Additionally, autofluorescence from several samples was so intense as to saturate the detector during measurements at 785 nm, whereas excitation at 1064 nm enabled Raman analysis with even the most fluorescent spots encountered. Furthermore, the background reduction with photobleaching was minimal with 1064-nm excitation, resulting in a more stable background with sample exposure.

	Given the current availability of high quality commercial 1064-nm Raman systems at a reasonable cost, users who are facing a diverse set of biological specimens may therefore see greater value in the balance of data quality, acquisition time, and cost offered by 1064-nm Raman as compared to 785-nm systems. Even taking into account established pretreatment methods, 1064-nm excitation presents significant merits over 785-nm excitation in dealing with background fluorescence.

	The authors acknowledge Wasatch Photonics Inc., for their assistance in providing the demo Raman units of 1064 nm; as well as Bolan Li and Anna Akkus for providing the samples. This study was funded by the research grant R01AR057812 (OA) from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) of the National Institutes of Health (NIH).

		A.R. Masri, R.W. Bilger, and R.W. Dibble, 

		A.P. Shreve, N.J. Cherepy, and R.A. Mathies, 

		T-C. Chen, D.A. Shea, and M.D. Morris, 

		C.A. Lieber, H. Wu, and W. Yang, “Tissue Measurement Using 1064 nm Dspersive Raman Spectroscopy,” presented at SPIE BiOS, International Society for Optics and Photonics, San Francisco, California, 2013.

		M.W. Meyer, J.S. Lupoi, and E.A. Smith, 

		J.J. Baraga, M.S. Feld, and R.P. Rava, 

		M.L. Myrick, S.M. Angel, and R. Desiderio, 

 is an Assistant Professor of Physics with the Department of Physics at Jackson State University in Jackson, Mississippi. 

 is a professor with the Departments of Mechanical & Aerospace Engineering, Orthopaedics, and Biomedical Engineering at Case Western Reserve University in Cleveland, Ohio. 

 is the President of Wasatch Photonics in Durham, North Carolina. Direct correspondence to: 

Interference from background fluorescence is a common challenge in Raman analysis. A study of three different types of biological samples was made to compare the ability of 785-nm and 1064-nm excitation to deal with this problem.

David Creasey

Articles

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

Bioreactor Fermentation Monitoring with Raman Spectroscopy

1064-nm Raman: The Right Choice for Biological Samples?