Kristen Frano

Content uniformity testing is a crucial task in pharmaceutical manufacturing, as it ensures that each product that reaches a consumer contains safe dosages of all components. Recently, transmission Raman spectroscopy has been explored as an alternative technique for content uniformity testing, due to its ability to rapidly obtain quantitative measurements representative of the whole volume of a tablet. In this case study, test tablets were created from five different blends of acetaminophen, mannitol, lactose, microcrystalline cellulose, and magnesium stearate. A portable Raman spectrometer connected to a high-efficiency transmission module was used to collect calibration spectra. The spectra were then used to create partial least squares (PLS) models for the acetaminophen and lactose components (ranging from 10-18% w/w and 10.79-28.66% w/w, respectively). Chemometric models can be used for prediction of acetaminophen and lactose conventrations in manufactured tablets to perform fast and accurate at-line content uniformity testing with portable Raman transmission spectroscopy. 

	Content uniformity (CU) testing is a crucial task in pharmaceutical manufacturing, as it ensures that each product that reaches consumers contains a safe dosage of the active pharmaceutical ingredient (API). Traditionally, high performance liquid chromatography (HPLC) is performed off-line in a quality control laboratory to monitor the dosage of finished tablets, due to its sensitivity and the ability to obtain a measurement from the entire volume of the tablet (1,2). However, HPLC analysis is a destructive technique that involves large amounts of solvents and consumables, and often requires highly skilled personnel to perform analyses. Depending on the workload of a QC lab, it may take several hours to complete analyses. Consequently, if a batch is found to be outside of specifications, the process defects leading to the deviation may not be found in a timely manner, potentially causing manufacturing delays for pharmaceutical drugs in heavy demand.

	Analytical methods for CU testing should ideally be fast, noninvasive, and with limited sample preparation. Recently, transmission near infrared (NIR) spectroscopy and transmission Raman spectroscopy have both been explored as alternative methods for nondestructive CU testing with no sample preparation (3-7). Although quick and nondestructive, transmission NIR spectroscopy suffers from poor chemical selectivity, and is sensitive to changes in the testing environment. Transmission Raman spectroscopy, combined with chemometric modeling, is quickly emerging as a valued technique for CU testing, due to its high chemical specificity, which is particularly useful when dealing with complex pharmaceutical formulations that contain multiple components.

	Conventionally, Raman spectrometers collect backscattered signal that is generated upon illumination of the sample surface with a laser. While conventional Raman spectroscopy is quite suitable for identity tests of incoming raw pharmaceutical materials, it does not provide an accurate quantitative measurement representative of the entire volume of a tablet, nor is it able to probe sufficiently beneath layers of coatings on finished tablets. Transmission Raman spectroscopy, however, is able to overcome the limitations of conventional Raman spectroscopy for content uniformity testing. Using transmission Raman spectroscopy, the sample is illuminated at one side, and the Raman signal is collected from the other side. The signal collected contains photons with information that is representative of the full volume of the tablet. Through the use of chemometric models, an accurate quantitative prediction of the content within the tablet can be achieved. This allows for not only reliable content uniformity testing, but can also assist formulation development and detection of pharmaceutical counterfeits.

	Although transmission Raman spectroscopy provides measurements representative of the full volume of a tablet, and is simple to operate with the use of predictive chemometric models, transmission Raman spectroscopy is generally a low- efficiency technique that requires long integration times to obtain sufficient Raman signal. In this case study, we demonstrate proof of concept for CU analysis using a portable Raman spectrometer with a high-efficiency transmission sampling attachment. A series of tablet formulations containing acetaminophen, mannitol, lactose, cellulose, and magnesium stearate were measured on a portable transmission Raman spectrometer. Chemometric models for at-line accurate prediction of acetaminophen and lactose were created using model building software. At-line high-throughput transmission Raman spectroscopy allows for increased efficiency of CU testing, and can quickly catch defects present in the tablet manufacturing process.

	Blended powders and pressed tablets were provided by courtesy of the Engineering Research Center for Structured Organic Particulate Systems of the Department of Chemical and Biochemical Engineering at Rutgers University (Piscataway, New Jersey). Formulations were blended with laboratory grade acetaminophen, mannitol, lactose, microcrystalline cellulose (MCC), and magnesium stearate. Five different blends were created. The concentration of acetaminophen in these blends ranged from 10-18% (w/w), and the concentration of lactose ranged from 10.79-28.66% (w/w). Blends were compressed to create round tablets approximately 6 mm in diameter and 4.5 mm thick using a Gamlen tablet press. Tablets were pressed with an approximate force of 392 kg. Each pill had a typical mass of ~140 mg. Ten tablets were manufactured from each blend. Table I lists the concentration of ingredients for all blends.


	Table I: Concentrations of ingredients in sample tablets (w/w %)

	The QTRam from B&W Tek with BWAnalyst software was used for all measurements. The QTRam uses a transmission Raman attachment for increased photon efficiency, with a laser spot size of ~4 mm. The system contains a 785 nm laser, with a maximum power of ~340 mW. For method development, method validation, and prediction analyses, all measurements used 100% of the maximum laser power, and a 20 second integration time. Measurements were performed on both sides of the tablets for an accurate representation of the full volume of the tablet. BWIQ software was used to create predictive chemometric models.

	Seven tablets from each blend were designated as method samples. Triplicate transmission Raman spectra were collected for each sample. Figure 1 shows representative transmission Raman spectra from a tablet containing 18% acetaminophen (Blend 1) and a tablet containing 28.66% lactose (Blend 4), compared to pure acetaminophen and lactose. Spectral regions that correspond to the signal from acetaminophen and the signal from lactose were isolated for chemometric modeling. The spectral regions used in the model are highlighted in blue and gray for lactose and acetaminophen, respectively. The spectral regions of 1190-1300 cm

 were isolated in the model spectra because their peaks correspond purely to acetaminophen. Spectral regions of 285-500 cm

 were isolated for lactose because their peaks contain contributions from lactose. Samples from all blends show contributions from both acetaminophen and lactose in these spectral regions. Peaks that were attributed to the other components of the blends were not used in the modeling.


	Figure 1: Transmission Raman spectra from two tablet formulation samples compared to acetaminophen and lactose. The blue shaded regions correspond to the areas for lactose modeling. The gray shaded regions correspond to the areas for acetaminophen modeling. Spectra are manually offset.

	The chemometric software randomly chose 70% of the data as calibration spectra, and designated the remaining 30% as validation spectra. A leverage outlier test found no statistical outliers. Several preprocessing steps were applied to the data in order to minimize background effects and baseline offsets. An adaptive, iteratively reweighted penalized least squares (air-PLS) background removal was used in order to mitigate the background effects from the excipients, and a multiplicative scatter correction (MSC) was used to offset effects from baseline differences. A first derivative (Savitzky-Golay, window 11, order 3) was also applied to the data. Figure 2 shows the method spectra after the applied preprocessing steps. A partial least squares (PLS) regression was chosen to model the data.


	Figure 2: Method spectra after air-PLS background correction, MSC, and Savitzky-Golay first derivative.

	Figure 3 shows the plots of the predicted vs. measured values for the acetaminophen and lactose PLS models. Blue samples represent calibration samples, and red samples represent validation samples. Four factors were used in the acetaminophen model, while five factors were used in the lactose model. The R

 coefficient of determination is a measure of how close the data are to the regression line. The R

 value of the calibration spectra for acetaminophen and lactose models were 0.9929 and 0.9915, respectively, indicating good linearity of the models. The root mean square error (RMSE) of the acetaminophen calibration curve was 0.233% and the RMSE of the lactose calibration curve is 0.531%.


	Figure 3: (a) Predicted vs measured plot of the calibration and validation samples for the acetaminophen PLS model. The wavenumber regions used in the model are from 1190-1300 cm

. Four factors are used in the model. (b) Predicted vs measured plot of the calibration and validation samples for the lactose PLS model. The wavenumber regions used in the model are from 285-500 cm

	Before the method can be released to production for CU analysis, method validation must be completed to make sure that the method is suitable for the tablet CU test. According to USP chapter <1125>, CU testing falls under the analytical category I test, which requires method validation on method specificity, linearity, accuracy, precision and range. For independent method validation, two samples were selected for each blend. Triplicate measurements of each sample were acquired on the spectrometer. The specificity validation was checked using the Mahalanobis distance and Q-residual limit.

	Linearity, accuracy, and precision were obtained through the built-in module for method validation. Table II shows the method validation results for the ten validation samples. Both acetaminophen and lactose models show good linearity, with R

 reported as 0.9889 for acetaminophen, and 0.9923 for lactose. Both models also show good precision, with the standard deviations for samples ranging from 0.02-0.17% and 0.13-0.36% for acetaminophen and lactose, respectively. The root mean square error of prediction (RMSEP) was calculated as 0.288% for the acetaminophen model, and 0.504% for the lactose model.


	Table II: Method validation results for acetaminophen and lactose PLS models

	The created chemometric models were used to predict the acetaminophen and lactose contents of new samples in the data analysis software. One tablet per blend was chosen as an independent sample to test the prediction of the acetaminophen and lactose content using the models created. Three spectra were collected for each sample. The prediction results for each tablet are listed in Table III.


	Table III: Prediction results for acetaminophen and lactose

	Portable high-efficiency transmission Raman spectroscopy is an effective tool for rapid, at-line content uniformity testing. We demonstrate through a matrix of tablets of varying compositions that portable transmission Raman is able to build methods that can accurately quantitate the acetaminophen and lactose components. As an alternative method of content uniformity testing, portable transmission Raman spectroscopy provides a simple and nondestructive analysis method, with no solvent consumption and no consumables at the point of measurement.

	(3) N.W. Broad, R.D. Jee, A.C. Moffat and M.R. Smith, 

	(4) C. Peroza Meza, M. A. Santos, and R. J. Romañach, 

	(5) M.D. Hargreaves, N. A. Macleod, M.R. Smith, D. Andrews, S.V. Hammond, and P. Matousek,

	(6) D. Andrews, K. Geentjens, B. Igne, G. McGeorge, A. Owen, N. Pedge, J. Villaumié, and V. Woodward, 

	(7) M. Fransson, J. Johansson, A. Sparén and O. Svensson, 

Kristen Frano, John Maticchio, and Dawn Yang

 are with B&W Tek in Newark, DE. Direct correspondence to: 

Articles

Portable transmission Raman spectroscopy, combined with chemometric modeling, is quickly emerging as a valued technique for content-uniformity testing, given its high chemical specificity, which is particularly useful when dealing with complex pharmaceutical formulations that contain multiple components.

Portable, High-Efficiency Transmission Raman Spectroscopy for At-Line Content Uniformity Testing of Pharmaceutical Tablets

Largely heralded as a "wonder material" since its discovery in 2004, graphene has now entered the era of industrial manufacturing. Although several techniques for large-scale graphene manufacturing have been developed, one important question remains: how to easily and rapidly characterize the quality of graphene and other carbon nanomaterials to effectively monitor and control the production process, where simple and robust analytical tools are required. In this study, three types of materials (graphene powders, graphene powder coated sheets, and carbon nanofibers) were analyzed using portable Raman spectroscopy. This efficient analysis is ideal for at-line or online material analysis for manufacturers of graphene and other carbon nanomaterials to do materials characterization, product quality control, and process monitoring.

	Carbon nanomaterials constitute a variety of carbon allotropes including graphene, graphene oxide, carbon nanotubes, and carbon nanofibers. Each of these materials exhibits unique properties in electrical conductivity, thermal conductivity, and mechanical strength thanks to the distinct structures of each allotrope. For instance, graphene is a two-dimensional (2D) material formed from a hexagonal lattice of carbon atoms, whereas graphite is made up of stacked individual layers of graphene. Graphene's strength, superconductivity, and excellent heat conductivity makes it a very attractive material as a conductor in memory chips and batteries for electronics (1), and its flexibility has the potential to invigorate infrastructure, aeronautics, and biotechnology. Graphene oxide, an alternative form of graphene made from oxidized graphite that has been exfoliated to form a few layers or a single layer, is used to desalinate water and remove radioactive isotopes (2). Carbon nanotubes, which are tubes with walls formed from graphene sheets, have attracted interest from industry to potentially generate high-surface-area catalysts in fuel cells with the addition of different functional groups (3). Carbon nanotubes can either be single-walled nanotubes (SWNTs) or multiwalled nanotubes (MWNTs). Recently, MWNTs have found practical use in lithium-ion batteries to increase the lifespan and capacity of portable electronics (4). SWNTs have been slower to find commercial application, but have potential for use as transistors in microelectronics because of their bandgap and as biosensors because of their biomolecular compatibility (4). A carbon nanofiber consists of graphene layers stacked as cones or plates; recently, carbon nanofiber has found applications in construction materials thanks to its flexibility and durability (5).

	Because of the intense interest surrounding their unique properties and use in many practical applications, graphene and other graphene-based nanomaterials have now entered the era of large-scale manufacturing. Graphene can be made through various processes including

		mechanical exfoliation, a low-cost technique that involves isolating graphene from bulk graphite (6),

		chemical vapor deposition (CVD), which is performed on a relatively larger scale but at a high cost and with expensive equipment (7,8), and

		exfoliation and reduction of graphene oxide, which is synthesized through the oxidation of graphite powder on a larger scale, but can result in extensive defects (9).

	Although recent market forecasts predict the value of the current global graphene market to be only in the low tens of millions of dollars (10), the value of the global graphene market is estimated to reach half a billion dollars by 2025 (11). It may be decades before the potential of these carbon nanomaterials are fully realized, but research and development spending on graphene is likely to reach hundreds of millions of dollars in the coming years.

	To reach these market estimates, one important question for large-scale graphene manufacturing remains: how to easily and rapidly characterize the quality of graphene and other carbon nanomaterials to effectively monitor and control the production process? Raman spectroscopy has been used extensively by carbon nanomaterials research communities in recent years because of its ability to characterize materials from their molecular vibrations (12,13). The Raman spectra of carbon nanomaterials are typically characterized by only three major bands: the G band, the D band, and the 2D band (also known as the G? band). Though simple, the spectra of these nanomaterials are rich in information about their quality and their microstructures as revealed by the peak positions, peak shapes, and peak intensities. The G band appears around 1582 cm

 vibrational mode (13,14); it is an indication of the crystallinity of the material. The dispersion of the G band can be observed in disordered graphene materials, and the dispersion is proportional to the degree of disorder (15). The D band at around 1350 cm

 is attributed to the structural disorder near the edge of the microcrystalline structure that decreases the symmetry of the structure (14). The Raman peak intensity ratio of these two bands, 

, can be used to characterize the degree of disorder of the materials (15). The 2D band appears around 2700 cm

 depending on the laser excitation wavelength and is related to the number of graphene layers (3).

	The Raman spectra of monolayer graphene (red), carbon nanotubes (black), single crystal graphite (green), and carbon black powder (blue) are shown in Figure 1. High-quality graphene is characterized by the sharp, symmetric single peak of the 2D band. The graphite spectrum displays a high amount of order and thus crystallinity characterized by its prominent G band and the absence of a D band. Since graphite is composed of many layers of monolayer graphene, the 2D band observed in the graphite spectrum is much broader and asymmetric than the graphene 2D band, indicating multiple components from several phonon modes. Carbon nanotubes display unique features of the G band. Because of the confinement and curvature of graphene layers in forming nanotubes, the G band becomes asymmetric for MWNT and more likely splits into two bands, the G+ and the G band for SWNT (15). The spectrum of carbon black, which has the lowest amount of crystallinity, displays a strong D band, a broad G band, and a high 

 ratio, indicating a disordered structure.


	Figure 1: Raman spectra of graphene (red), carbon nanotubes (black), graphite (green), and carbon black (blue). The spectra have been manually offset.

	Raman spectroscopy provides rich information about the characteristics of carbon black, graphite, graphene, and other carbon nanomaterials. Although confocal Raman microscopy can provide high-resolution characterization of carbon nanomaterials, the high cost of this instrumentation coupled with the complexity in operation and data interpretation is inadequate for monitoring in a large-scale manufacturing setting, where simple and rapid analytical tools are required. In this study, a high-throughput portable Raman spectrometer is used to characterize three types of materials: graphene powders, carbon nanofibers, and carbon black. This quick analysis can be applied as an at-line or online technique for graphene and other carbon nanomaterials manufacturers to perform materials characterization, product quality control, and process monitoring.

	An i-Raman Pro HT system (B&W Tek) with a laser excitation of 532 nm via a fiber-optic sampling probe (spot size ~100 µm) was used for measurements of all samples. The system uses a high-throughput Raman spectrometer with a back-thinned charged-coupled device (CCD) thermoelectrically cooled to -25 °C. For materials in powdered forms, no microscope was used. A probe holder with an adjustable 

 stage was used to support the fiber-optic probe over an aluminum pan containing a given carbon sample. The 

-focus was used to optimize the working distance from the probe to the sample. BWSpec acquisition software was used for data collection, spectral processing, and programming the automatic calculation of peak intensities and ratios for online measurements or off-line batch processing.

	Three types of carbon nanomaterials were analyzed: graphene powders, carbon nanofibers, and carbon black powders. For graphene powders, a laser power of 40 mW with an integration time of 60 s was used to acquire Raman spectra. For carbon nanofiber and carbon black powder samples, an integration time of 90 s was used with a laser power of ~21 mW. Three spectra were collected for each sample. No spectral averaging was used.


	Figure 2: Raman spectrum before (red trace) and after (blue trace) airPLS baseline correction.

 sample stage of the probe holder, 15 measurements from different locations of one graphene powder were collected to assess the uniformity of the sample. Two types of preprocessing were applied to the spectra prior to the peak ratio calculation. First, an adaptive iteratively reweighted penalized least squares (airPLS) background correction was used to remove any fluorescent background present in the spectra. Figure 2 shows an example of before (red trace) and after (blue trace) baseline correction. A Savitzky-Golay smoothing algorithm with a window size of 7 was also applied to all spectra. Figure 3 shows five manually offset representative spectra from different areas of the graphene powder sample. The D band (1346 cm

) can be observed. A prominent band at 2328 cm

 is the Q-branch Raman peak for atmospheric nitrogen (N

) because of the nitrogen–nitrogen bond (16). The N

Raman peak is observed because of the high-throughput nature of the Raman spectrometer.


	Figure 3: Raman spectra of five locations on a graphene sample. The spectra have been manually offset by 1000 intensity units.

 is an important parameter in assessing the level of disorder in the crystal structure of a sample. Table I presents the calculated peak intensities after background removal and intensity ratios for the D and G bands of the five representative spectra. The 

ratios of the 15 measurements ranged from 0.1246 to 0.1462. The average 

 of all 15 scans was 0.1386, with a percent relative standard deviation (%RSD) of 4.07%. This result indicates a high level of uniformity within the sample.

	Two carbon nanofiber samples and four carbon black samples were also tested. Three spectra were collected at different locations for each sample. For all collected spectra, an airPLS background correction was used to remove any fluorescent background present in the spectra. A Savitzky-Golay smoothing algorithm with a window size of 5 was also applied to all spectra. Figure 4 shows the manually offset representative spectra for each sample. The four carbon black samples display typical carbon black Raman signatures that contain D and G bands but no 2D band. The two carbon nanofiber samples show prominent D bands, indicating a high level of disorder. The G bands in the carbon nanofiber spectra also exhibit some asymmetry. The asymmetry is likely because of the curvature of the nanofiber, with the slight splitting of the G band induced by curvature of graphene layers when forming the nanofiber (15).


	Figure 4: Raman spectra of two carbon nanofiber samples and four carbon black samples. The spectra have been manually offset by 5000 intensity units for clarification.

	BWSpec software was used to automatically calculate the 

 values as spectra were collected. Table II presents the calculated 

 peak intensity ratios measured for the four carbon black samples and two carbon nanofiber samples. The carbon nanofiber sample 2 has the highest level of order of all samples with an average 

 of 0.4706, whereas the carbon nanofiber sample 1 has an especially high level of disorder with an average 

	Raman spectroscopy can also be very useful for identifying contaminants or starting materials in carbon nanomaterial final products. For example, the carbon black 3 and carbon black 4 samples contain up to 10% hematite (Fe

) because of their manufacturing processes. Figure 5 shows the Raman spectra of carbon black 3 and 4 compared to the Raman spectrum of hematite. Raman peaks at 213 cm

 are in good agreement with the Raman spectrum of hematite.


	Figure 5: Raman spectra of carbon black 3 (purple) and carbon black 4 (orange). Raman peaks at 213 cm

 are consistent with a hematite Raman spectrum (black). The spectra have been manually offset by 1000 cm

	Having robust and simple quality control tools for carbon nanomaterials such as portable Raman spectroscopy will help facilitate the future growth of the global graphene market. Although simple, a Raman spectrum of a carbon nanomaterial can provide a vast amount of information to characterize materials including carbon black, graphite, carbon nanotubes, and graphene. Portable Raman spectroscopy is capable of quickly characterizing carbon nanomaterials, revealing critical information regarding material qualities such as sample crystallinity and level of disorder using various parameters such as D- and G-band intensity ratios. Graphene manufacturers can easily use the analysis to obtain at-line or online measurements for material characterization, product quality control, and process monitoring.

	(1) C. Ahn, S.W. Fong, Y. Kim, S. Lee, A. Sood, C.M. Neumann, M. Asheghi, K.E. Goodson, E. Pop, and H.S.P. Wong, 

	(3) C. Luo, H. Xie, Q. Wang, G. Luo, and C. Liu, 

	(4) M.F.L. DeVolder, S.H.Tawfick, R.H. Baughman, andd J. Hart, 

	(7) C. Mattevi, H. Kim, and M. Chhowalla, 

	(9) S. Stankovich, D.A. Dikin, R.D. Piner, K.A. Kohlhaas, A. Kleinhammes, Y. Jia, Y. Wu, S.T. Nguyen, and R.S. Ruoff, 

https://www.grandviewresearch.com/industry-analysis/graphene-industry

	(12) M.S. Dresselhaus, A. Jorio, M. Hofmann, G. Dresselhaus, and R. Saito, 

	(14) A.C. Ferrari, J.C. Meyer, V. Scardaci, C. Casiraghi, M. Lazzeri, F. Mauri, S. Piscanec, D. Jiang, K.S. Novoselov, S. Roth, and A.K. Geim, 

 are with B&W Tek in Newark, Delaware. Direct correspondence to: 

Raman spectroscopy is ideal for at-line or online material analysis for manufacturers of graphene and other carbon nanomaterials to do materials
characterization, product quality control, and process monitoring.



Portable Raman Spectroscopy for At-Line Characterization of Carbon Nanomaterials

Abuse of prescription drugs in the United States has skyrocketed in recent years. The prevalence of prescription drug abuse has also resulted in the emergence of counterfeit pharmaceutical drugs, which often contain no active pharmaceutical ingredient (API) and instead may contain ingredients that are highly potent or dangerous. Because of the pervasiveness of these prescription drugs it is necessary to develop a technique that allows law enforcement officers, forensic analysts, and pharmaceutical scientists to quickly identify a genuine prescription drug. In this study we developed a simple surface-enhanced Raman spectroscopy (SERS)-based method to identify low-dose APIs alprazolam, codeine, oxycodone, and hydrocodone in prescription pharmaceutical drugs using a handheld Raman spectrometer with an on-board matching algorithm.

	Prescription drug abuse has reached epidemic levels in the United States in the past decade. It is estimated that in 2012 at least 16.7 million people over the age of 12 abused prescription drugs, representing a 250% increase in prescription drug abuse over the previous 20 years (1). Furthermore, to keep up with the consumer demand for prescription drugs, drug traffickers produce inexpensive counterfeit substances that contain no active pharmaceutical ingredient (API) and instead may contain harmful substances (2). Recently in the United States, laboratory analyses of seized shipments of what appeared to be the anxiety medication alprazolam showed that they contain no APIs and instead contain fentanyl, a highly potent opioid that is fatal to humans in doses upward of 2 mg, indicating that counterfeit drugs have now become a problem in both developed and developing nations (2). To combat both prescription drug abuse and the prevalence of dangerous counterfeit pharmaceuticals, it is necessary to develop a technique that can quickly confirm the identity of a suspected fake pill. 

	A survey of the literature shows that many traditional analytical methods are used for analyzing suspected counterfeit pharmaceutical drugs. High performance liquid chromatography (HPLC) has customarily been the most popular method in forensic and pharmaceutical laboratories for its ability to quantify APIs as well as impurities in a drug sample (3). In addition, Raman spectroscopy (4–6), near-infrared (NIR) spectroscopy (7–10), thin-layer chromatography (11), nuclear magnetic resonance (NMR) spectroscopy (12), X-ray fluorescence (XRF) spectrometry (13), and liquid chromatography–mass spectrometry (LC–MS) (14) have all been used in recent years to characterize suspected counterfeit drugs. However, chromatography-based techniques require extensive sample preparation that is best suited to highly trained staff in forensic or pharmaceutical laboratories, and some spectroscopic techniques, such as NIR, may require chemometric modeling.

	Combating prescription drug abuse and the prevalence of counterfeit drugs will require collaboration between law enforcement officials, forensic chemists, and pharmaceutical companies. For a method to be a viable analytical technique for confirming a suspected counterfeit pill, it should be simple enough to be performed by nontechnical law enforcement officers, sensitive to detect any low-dose APIs, sufficiently selective to detect the API in a pill matrix that may contain other high-dose APIs and various excipients, and (ideally) portable. Raman spectroscopy is an attractive technique for rapid analysis of these counterfeit drugs. Portable Raman spectrometers have made such advancements in the last decade that handheld Raman spectrometers are now commonly used for applications such as pharmaceutical raw material identification (15), geology (16), and field forensic analysis (17), often by nontechnical end users. Although there are several studies that use Raman spectroscopy as a screening tool for potential counterfeit drugs, these studies typically require the development of chemometric models (for example, principal component analysis) to discriminate counterfeit samples, which are typically not readily available on handheld Raman spectrometers (5). Furthermore, Raman spectroscopy is limited by its inherent insensitivity, given that only one in ~1 million incident photons are Raman scattered. The concentrations of low-dose APIs in prescription drugs are typically too low for normal Raman spectroscopy to detect in a complex matrix, leaving law enforcement officials in the field with no option but to send the evidence back to a central laboratory for testing. 

	Surface-enhanced Raman spectroscopy (SERS) has grown rapidly in its applications in recent years, ranging from areas as diverse as biosensing (18) to art and archeology (19). The recent development of flexible and low-cost paper SERS substrates has enabled SERS to shift from the research laboratory into real-world field detection applications such as explosives detection (20), pesticides detection (21), and controlled narcotics detection (22). The ability of SERS to significantly enhance the Raman signal by a factor of ~10

 while simultaneously quenching molecular fluorescence makes it an attractive technique for identifying low-dose APIs in pharmaceutical drugs. The accessibility, reliability, and simplicity of paper SERS substrates makes them a viable option for the rapid screening of confiscated counterfeit pharmaceutical drugs by local police officers, pharmaceutical technicians, and forensics analysts.

	We have developed a simple SERS-based sampling method using commercially available paper SERS substrates that allow both technical and nontechnical users to quickly investigate confiscated prescription drugs containing the low doses of the APIs oxycodone, hydrocodone, alprazolam, and codeine using a handheld spectrometer. On-board spectral matching of a measured sample to a library spectrum was performed on the handheld instrument using a correlation coefficient algorithm, a common spectroscopy library matching technique (23,24). The ability to identify the low-dose API in a confiscated pharmaceutical pill using a handheld spectrometer allows law-enforcement officials to confirm the identity of a pill on-site before sending the evidence back to a central testing laboratory.

	Samples of four pharmaceutical drugs with low doses of APIs were investigated. A sample of acetaminophen was also analyzed. Table I shows the advertised API dosage information for the pills tested. A one-quarter segment of a pill was typically a sufficient sample mass for all pill types, allowing for the remainder of the pill to be sent to a central laboratory for further testing. Pill segments were placed in individual 2.0-mL plastic centrifuge tubes. Then, ~0.5 mL of acetone was added to the centrifuge tubes. Plastic tubes were shaken until the sample dissolved and the solution looked noticeably cloudy (~30 s). For each tube, a commercially available paper SERS substrate was dipped into the centrifuge tube and allowed to sufficiently interact with the sample solution before analysis.

	A B&W Tek iRaman Plus portable Raman spectrometer using a back-thinned charged-coupled device (CCD) array detector covering 65–3350 cm

 and a 785-nm laser was used to acquire high-quality library spectra of the low-dose pharmaceutical APIs. A fiber-optic sampling probe was used to directly measure the SERS substrates. The laser power was adjusted to ~60–90 mW for all analyses. Typical acquisition times ranged from 10 to 20 s.

	A B&W Tek TacticID handheld Raman spectrometer with 785-nm laser excitation was used for verification of the method. The SERS library spectra collected on the portable Raman spectrometer were imported into the handheld spectrometer. A sampling adapter to hold the paper substrates was used for all handheld Raman measurements. A laser power of ~30 mW was used for all handheld measurements, and typical automatic integration times ranged from 15 to 30 s. To account for sample heterogeneity within the plasmonic region of the substrate, at least two spots were analyzed on each SERS-active region. An on-board matching algorithm was used to match the SERS spectra obtained on the handheld spectrometer to a library spectrum.

	Figure 1 presents the SERS library spectra obtained from the four low dose prescription pills measured on the portable Raman spectrometer. The SERS spectrum from a pill containing alprazolam (Figure 1a) exhibits characteristic SERS peaks at 689 cm

 (s), which are consistent with previously reported SERS spectra of the API alprazolam (25). The SERS spectrum presented in Figure 1b from a pill containing acetaminophen with codeine shows characteristic peaks at 530 cm

 (w), which are consistent with a previously published codeine SERS spectrum (26). The SERS spectrum recorded from a pill containing oxycodone and acetaminophen (Figure 1c) has peaks at 577 cm

 (m), which are consistent with SERS peaks for the oxycodone low-dose API (27). Figure 1d presents the SERS spectrum from a hydrocodone and acetaminophen pill. Observed peaks at 512 cm

 (s) are consistent with the SERS spectrum of the API hydrocodone (26).

	The prescription drugs that contain low doses of oxycodone, codeine, and hydrocodone all include a high dose of acetaminophen. A sample of acetaminophen was also prepared using the SERS protocol. The SERS spectrum of acetaminophen in Figure 1e shows peaks at 796 cm

 (m). These peaks are also observed in the SERS spectra from the pills that contain oxycodone, codeine, and hydrocodone. The peaks at 1094 cm

 labeled with asterisks in Figure 1e are consistent with cellulose in the paper support of the SERS substrate (28).

	The high-quality SERS spectra of the low-dose APIs acquired on the portable Raman spectrometer were imported into a handheld Raman spectrometer as library spectra. The handheld spectrometer also contains thousands of Raman library spectra for narcotics, explosives, excipients, and other general chemicals. To match spectra measured on the handheld spectrometer to library spectra, a correlation coefficient algorithm is used to provide a hit quality index (HQI) value. The equation:

 is the mean-centered library spectrum on board the handheld spectrometer and 

 is the mean-centered spectrum of the unknown sample measured on the handheld spectrometer, yields an HQI match that can range from 0 to 100. An HQI result of 100 represents a perfect match to a library spectrum, while a result approaching 0 presents a poor match. In the operating system of the handheld spectrometer, the user can set an HQI threshold so that only high HQI matches (for example, >90) are shown after a sample scan.

	Figure 2a shows the Raman spectrum from a direct measurement with a handheld spectrometer on the surface of a pill containing a low dose of alprazolam. Raman peaks are present at 356 cm

. No peaks consistent with alprazolam, are present in the Raman spectrum, indicating that normal Raman spectroscopy is too insensitive to identify the API on the surface the pill. On the handheld spectrometer, this spectrum matched to a library spectrum of lactose monohydrate (Figure 2b), one of the inactive ingredients in the pill, with an HQI of 86.7. After the SERS protocol was used on the alprazolam sample and the paper substrate was measured on the handheld spectrometer, SERS peaks consistent with alprazolam were observed at 688 cm

 (Figure 2c). The SERS spectrum measured on the handheld spectrometer matched to the library SERS spectrum of alprazolam with an HQI of 95.9, demonstrating that the SERS protocol can identify a low dose of alprazolam in a pill using a handheld spectrometer, even though the API that makes up just <0.2% (w/w) of the pill.

	For each of the four prescription drugs tested, three pill samples were measured on separate paper SERS substrates. Table II shows the average of the six HQI results of the measured SERS spectra of the four prescription drugs to the on-board SERS library spectra of low-dose APIs. All SERS measurements of the pill samples matched correctly to the corresponding API library spectrum. The second-highest HQI matches for all measurements were below 80, demonstrating the discriminating power of the SERS method between the four low-dose APIs. The standard deviations of the measurements are also included in Table II, showing the reliability of the SERS measurements between samples.

	A SERS-based sampling protocol was developed for the detection of several low-dose APIs in prescription pharmaceutical pills using a handheld spectrometer. The sample preparation for the SERS analysis is simple and can be easily performed by both laboratory technicians and nontechnical law enforcement officers. The SERS method demonstrates high selectivity for the APIs in the pills despite the presence of higher concentration APIs and various excipients. A handheld Raman spectrometer with on-board software processing capabilities was able to match the measured SERS spectrum to a library SERS spectrum of the correct API based on an HQI calculation. The ability to quickly confirm the presence of a low-dose API in a pharmaceutical pill is a valuable tool for local law enforcement, forensic laboratories, and pharmaceutical companies to combat the prevalence of counterfeit pills and prescription drug abuse in their communities.

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 is an Applications Engineer with B&W Tek in Newark, Delaware. 

 is an Applications Manager with B&W Tek. Direct correspondence to: 
	

A simple SERS-based method was used to identify low doses of the APIs alprazolam, codeine, oxycodone, and hydrocodone using a handheld Raman spectrometer.

Kristen Frano

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