O Raman scattering) has been a tool of choice for investigating aqueous lipid suspensions. Recently, there has been interest in supported lipid bilayers (SLBs), where lipids are believed to have fluidities similar to those of free vesicles, and thus have been investigated for applications such as sensors and drug delivery vehicles. Here, Raman spectra of two lipid SLBs on SiO

nanoparticles were obtained. With decreasing nanoparticle size, or for the same nanoparticle size and longer alkyl chain length, the lipids became increasingly interdigitated compared with the normal bilayer structure. 

Nanoparticles are now used in wide range of diverse applications that include drug delivery vehicles, sensors, colloidal dispersions and nanocomposites. For all of these applications, the interphase region, where the surface of the nanoparticle interacts with the surrounding material, becomes critically important and can depend on factors such as the charge, polarity, and degree of hydrophobicity of the nanoparticle. The nanoparticle itself can be composed of organic polymers or inorganic materials such as silica (SiO

). The high surface-to-volume ratio of nanoparticles can be exploited to improve bonding between nanoparticles and the surrounding resin or to increase the dynamic range in sensor applications. Alternatively, the high surface-to-volume ratio can be exploited to investigate the interphase region and the chemical or physical interactions between inorganic surfaces and organic materials. Fourier transform–infrared (FT-IR) and Raman spectroscopy are often employed to obtain information on chemical composition and interactions between materials, conformational properties of molecules, and their state of order. Attenuated total reflectance (ATR)–FT-IR, surface-enhanced Raman spectroscopy, and nonlinear optical techniques such as second harmonic generation can be used to obtain information on surface properties, molecules at surfaces, and their interactions. Raman spectroscopy has recently been used to measure lipid multibilayers on planar supports (1). 

Here, we use conventional Raman spectroscopy to investigate the conformational order, packing, and phase transition behavior of supported lipid bilayers (SLBs) of zwitterionic lipids on silica (SiO

) nanoparticles. Raman spectroscopy is particularly suited to this study because it is well known that both water and SiO

 are weak Raman scatterers but strong IR absorbers. The high surface-to-volume ratio is exploited to investigate single and multiple bilayers of lipids on SiO

 nanoparticles ranging in size from 4–5 to 100 nm in diameter. Since these are monolithic, rigid particles, the effects of curvature on the lipid gel-to-liquid crystal phase transition temperature, and the conformation and packing of the chains on the nanoparticle as a function of size, can be investigated. These investigations are aided by the extensive body of previous literature that relates spectral features in the skeletal and CH bending–stretching regions of alkyl chains to the ratio of 

/gauche conformers and to lateral packing of the chains in both the gel and liquid crystalline phases of lipids (2,3).  

In this work, we present an effect that has not been previously observed spectroscopically, using the conformationally dependent bands in the optical skeletal region and bands in the methylene stretching region that are sensitive to lateral packing order. In particular, zwitterionic 1,2-dipalmitoyl-

-glycero-3-phosphocholine (DPPC) and 1,2-distearoyl-

-glycero-3-phosphocholine (DSPC) SLBs were shown to form increasingly interdigitated structures as the nanoparticle size decreased. A vibration at 1122 cm

  attributed to alkyl chains with a single gauche rotation of the terminal methyl group near the bilayer center — a conformation that permitted better hydrophobic packing of the chains on curved surfaces — was recorded in the Raman spectra of these SLBs. 

The lipids used in this study, 1,2-dipalmitoyl-

-glycero-3-phosphocholine (DSPC), were obtained from Avanti Polar Lipids (Alabaster, Alabama). Snowtex colloidal silica (SiO

) beads with nominal diameters of 4–6, 10–20, 20–30, and 40–50 nm were kindly donated by Nissan Chemical Industries, Ltd. (Tokyo, Japan). Multilamellar vesicles were prepared by first dissolving appropriate amounts of the lipids or lipid mixtures in chloroform, followed by drying with N

/vacuum to form films. The films were redispersed in a 0.1 M, pH 8.0 buffer made from Na

O (PBS), and 75 mM NaCl, and incubated for 2 h above the gel–liquid crystal transition temperature, 

, of the lipids. Small unilamellar vesicles (SUVs) or large unilamellar vesicles (LUVs) were obtained from multilamellar vesicles by subjecting them to five freeze–thaw cycles followed by extrusion (Avanti Mini-Extruder from Avanti Polar Lipids) using a polycarbonate filter with pore sizes of 50 or 100 nm. SUV sizes of approximately 60 and 100 nm were measured by dynamic light scattering (DLS) (4). Adsorption of vesicles onto the beads was accomplished by mixing the SUV and SiO

 dispersions and incubating at temperatures above the 

 of the respective lipids for 2 h (shown schematically in Figure 1). The amount of lipid, in terms of its surface area (SA

) required to form a single bilayer around the beads (SA

 = 1. Here, the suspensions were mixed with excess lipid, SA

> 2, and centrifuged at 3900 rpm (Marathon 3200 centrifuge, Fisher Scientific, Pittsburgh, Pennsylvania) and the supernate decanted. Additional water was added to the pellet, and the centrifuge–washing steps repeated three times. This procedure ensured that the SiO

 nanoparticles were fully covered with lipid, but that only a single bilayer remained, as observed by transmission electron microscopy (TEM) and cryo-TEM data (Figure 1). After the last centrifugation, the pellet was redispersed in 300 μL D

O for the Raman experiments or HPLC-grade water for nano-differential scanning calorimetry (nano-DSC) experiments.  

Figure 1: Schematics of formation of a supported lipid bilayer on SiO2 nanoparticles, and interdigitated, partially interdigitated, and bilayer structures. Cryo-TEM image of a supported lipid bilayer on a 100-nm nanoparticle. 

Raman spectroscopy analysis was performed using a micro-Raman spectrometer (RM 1000, Renishaw, Gloucestershire, United Kingdom) with a 514.5-nm air-cooled Ar ion laser source and an 1800-lines/mm grating polychromator with RenCam CCD detection, providing a resolution of 1 cm

. The laser source was focused on the sample nanoparticle suspension through a long working distance, 50× objective, to a spot diameter of approximately 2 μm, and the Raman signal from the SLBs was collected in backscattering geometry. The samples were studied in standard aluminum pans for DSC (TA Instruments, New Castle, Delaware). A THMS600 heating stage (Linkam Scientific Instruments, Surrey, United Kingdom) was used for temperature control with 0.1 °C accuracy of the nanoparticle suspensions during phase transition studies. In those cases, Raman spectra were recorded after allowing thermal equilibrium to be achieved at every temperature point for 10 min. 

The acquisition time for Raman spectra was 10–20 min, depending on the strength of the Raman signals, until a satisfactory signal-to-noise ratio was achieved. It was found that SLBs in smaller particle size suspensions, because of their higher surface areas per unit volume, produce much stronger Raman signals. Therefore, the longer accumulation times were needed in the case of suspensions of bigger diameter nanoparticles and also when collecting spectra in the fingerprinting region 650–1800 cm

  where the band intensities of SLBs were much weaker than in the stretching region (2700–3200 cm

). Possible laser-heating effects during the Raman spectral collection were eliminated by limiting the argon laser power density to levels where it was proven that the SLB spectra are not affected by prolonged irradiation. Data analysis was performed using the Renishaw Wire 2.0 software. 

Nano-DSC measurements were obtained on a TA Instruments Nano DSC-6300. Samples were scanned at heating–cooling rates of 1 °C/min, using 1–2 mg total lipid in the analyzed samples. 

A representative spectrum with band assignments for DPPC SLBs is shown in Figure 2. The most intense bands in the spectra of molecules including alkanes and lipids with alkyl groups are the CH stretching modes, and therefore there has been much research focused on their interpretation. This spectral region is shown for DSPC multilamellar vesicles and for DSPC SLBs on the 100, 40–50, 20–30, 10–20, and 4–5 nm SiO

 in Figure 3. While the signal-to-noise ratio decreases for DSPC on the largest size SiO

 nanoparticles, the same spectral features are observed for both multilamellar vesicles and SLBs. The CH

 symmetric stretching modes appear at 2871 cm

, with a Fermi resonance (FR) component at 2938 cm

 out-of-plane and in-plane methyl antisymmetric stretches (r

) (5). The methylene vibrations at approximately 2852, 2884, 2900, and 2928 cm

  are sensitive to conformational changes as well as intermolecular interactions of the alky chains of lipids. The ν

) is coupled to rigid rotations–torsional vibrations (6,7), so that it broadens considerably with temperature, and increases continuously in frequency, from 2884 cm

  as gauche conformers are introduced (6). The ν

) contains three components, centered at 2852 cm

), because of extensive Fermi resonance interactions with overtones of the bending modes, and is affected by intra- and inter-molecular interactions.  

Figure 2: Raman spectrum of supported lipid bilayers of DPPC, showing structure of DPPC and band assignments. The intensity in the 650â1800 cm-1 region is shown magnified approximately 5Ã of the original for clarity. 

 vibrations for DPPC on the 40–50 nm SiO

 nanoparticles is shown in Figure 4. As is observed for multilamellar vesicles, with increased temperature, the antisymmetric CH

  and its intensity is reduced compared with one component of the symmetric CH

) vibration increases from 2847 to 2852 cm

 component) increases in intensity with respect to both the ν

Figure 3: C-H region for DSPC multilamellar vesicles (MLVs) and DSPC supported lipid bilayers (SLBs) on SiO2 nanoparticles of different diameters. 

Spectroscopic determination of phase transition temperatures (

) can be derived from intensity ratios of bands in the CH stretching region (8,9). An intensity ratio often used is 

, or alternatively, a lateral order parameter (

 = 1 indicates the highest possible order and 

 = 0 indicates no order (not necessarily the lowest possible), and is based on a comparison of intensity differences in crystalline and liquid hexadecane (3). Both parameters semiquantitatively reflect the lateral packing of the chains. Plots of 

 nanoparticles are presented in Figure 5, along with nano-DSC thermograms of the phase transition temperatures measured calorimetrically. The measurements of 

 based on Raman spectroscopy and nano-DSC are in reasonable agreement, and both transitions also are broader than previously observed for multilamellar vesicles.  

Figure 4: C-H stretching modes of supported lipid bilayers of DPPC on 40â50 nm SiO2 nanoparticles as a function of temperature. 

The lateral packing of the alkyl chains is compared as a function of nanoparticle size in Figure 6 (left) for both DPPC and DSPC. In both cases, lateral packing is observed to increase with decreasing nanoparticle size. This initially seems counterintuitive. However, if the polar headgroups of the inner leaflet of the bilayer pack as on a planar surface, then there is increasing free volume as the curvature of the solid support increases (that is, nanoparticle size decreases). One way to decrease this free volume and increase hydrophobic interactions is for lipid interdigitation, or partial interdigitation, to occur, as shown schematically in Figure 1. Previous investigations have indicated that curvature can induce interdigitation in DPPC vesicles (10) and SLBs (4), and Raman data have shown that there is increased lateral order for interdigitation compared with normal bilayer systems (11). For each nanoparticle size, the lateral order, and thus the interdigitation, is greater for the longer alkyl chain DSPC (18 carbons) compared with DPPC (16 carbons). For a given nanoparticle size, the increased free volume would be greater for the longer length alkyl chain, supporting the view that increased interdigitation would occur.  

Figure 5: (Top) Lateral order parameter as a function of temperature for SLBs on 40â50 and 4â5 nm SiO2. (Bottom) Corresponding nano-DSC thermograms showing calorimetric phase transition temperatures. 

Of further interest is the C-C skeletal region of the alkyl chains, shown in Figure 7 for DPPC multilamellar vesicles and SLBs, where in saturated lipids, three bands are observed at 1060, 1000, and 1127 cm

  in the low temperature gel phase. The 1060/1140 pair is attributed to all 

  to chains with a single gauche defect, although the placement of the defect along the chain is not specified (12). In the high temperature liquid crystalline phase, the intensity of the 1064- and 1130-cm

 band shifts up in frequency, the 1130-cm

  band shifts down in frequency, and the 1101-cm

  band shifts and merges with a new broad intense band at 1090 cm

  (not shown). The amount of alkyl chain disorder is often taken as the relative number of gauche/

 conformers, measured by their Raman intensity ratios 

. This ratio is plotted versus lateral order of the DPPC and DSPC alkyl chains for SLBs as a function of nanoparticle size in Figure 6 (right). For both the DPPC and DSPC, lateral packing increases as the relative number of 

 conformers increases, as expected, except for the smallest nanoparticle size. In addition, lateral order and the relative number of 

 bonds is greater for DSPC compared with DPPC at every nanoparticle size.  

Figure 6: (left) Lateral order of DPPC and DSPC alkyl chains measured by Raman scattering intensity ratio, I[Ïa(CH2)]/I[Ïs(CH2)] = I2884/I2845, as a function of nanoparticle size. (Right) Ratio of gauche/trans conformers in DPPC and DSPC alkyl chains measured by Raman scattering intensity ratio, I[Ï(C-C)G]/I[Ï(C-C)T] = I1090/I1060, as a function lateral order of the alkyl chains for SLBs of different nanoparticle sizes. 

What is of further interest for the DPPC and DSPC SLBs is the presence of a band (or shoulder band) at 1122 cm

, attributed to alkyl chains with a single gauche rotation of the terminal methyl group near the bilayer center, as shown for DPPC in Figure 7. The band is not observed at RT for DPPC multilamellar vesicles, but is observed as a shoulder for the LUVs. The difference between LUVs and SUVs is just their size, both of which are significantly smaller than multilamellar vesicles. This suggests that the 1122-cm

  band has to do with the packing of the alkyl chains. One possibility is that as free volume increases with decreasing size of the vesicles (given that the multilamellar vesicles have almost planar structures with respect to the size of the individual lipids), the ends of the chains kink to fill the free volume that exists between the chains towards the center of the bilayer. The appearance of the 1122-cm

  band is apparent in all of the SLBs, suggesting that on these geometrically fixed structures, kinking of the chains towards the center of the bilayer is another mechanism, along with interdigitation, that fills the free volume between the chains.  

Figure 7: C-C skeletal vibrations for DPPC multilamellar vesicles (MLVs), large unilamellar vesicles (LUVs) and DPPC supported lipid bilayers (SLBs) as a function of SiO2 nanoparticle diameter. There are characteristic vibrations at 1060, 1090, 1121, and 1127 cm-1. 

Raman spectroscopy was shown to be a powerful technique in which to study the interface region of inorganic nanoparticles, particularly ones such as SiO

 that are not strong Raman scatterers. Because water is also a weak Raman scatterer, it is possible to investigate aqueous dispersions of these nanoparticles. Here, we prepared single bilayers of zwitterionic lipids on SiO

 nanoparticles ranging in size from 5 to 100 nm. It was possible to investigate both the CH stretching region, which has the most intense vibrational bands, as well as the weaker bands in the C-C skeletal region. Although the smaller nanoparticles with the highest surface area, and thus the most adsorbed lipid, were most easily observed, Raman spectra from the 100-nm SiO

 could also be obtained. The Raman spectra showed that the lateral packing of the lipids, and the relative number of 

 conformers, increased as the nanoparticle size decreased. Furthermore, both the lateral packing and chain order were greater for the longer chain lipid, DSPC (18-carbon-atom alkyl chain) than for DPPC (16 C). These effects were attributed to increased interdigitation of the two halves of the bilayer with decreasing nanoparticle size. A band at 1122 cm

, attributed to the presence of a terminal methyl group in a gauche orientation, was observed in all the SLBs. Formation of this kink was suggested to be another mechanism by which the alkyl chains increased their hydrophobic interactions, because with increasing curvature of the support there would otherwise be increasing free volume between the chains as the nanoparticle size decreased. 

Biochim. Biophys. Acta, Lipids and Lipid Metabolism 

Biochimica et Biophysica Acta, Biomembranes 

(5) R.G. Snyder, S.L. Hsu, and S. Krimm, 

Spectrochimica Acta Part A - Molecular and Biomolecular Spectroscopy 

(6) Y. Cho, M. Kobayashi, and H. Tadokoro, 

(7) S.L. Wunder, M.I. Bell, and G. Zerbi, 

(9) R.G. Snyder, J.R. Scherer, and B.P. Gaber, 

(10) L.T. Boni, S.R. Minchey, W.R. Perkins, P.L. Ahl, J.L. Slater, M.W. Tate, S.M. Gruner, and A.S. Janoff, 

(11) E.N. Lewis, R. Bittman, and I.W. Levin, 

(12) R.J. Meier, A. Csiszar, and E. Klumpp, 

are with the Department of Chemistry, Temple University, Philadelphia, Pennsylvania. 

is with Centralized Research Facilities, College of Engineering, Drexel University, Philadelphia, Pennsylvania. 

Articles

Raman spectroscopy (weak H2O Raman scattering) has been a tool of choice for investigating aqueous lipid suspensions. Recently, there has been interest in supported lipid bilayers (SLBs), where lipids are believed to have fluidities similar to those of free vesicles, and thus have been investigated for applications such as sensors and drug delivery vehicles. Here, Raman spectra of two lipid SLBs on SiO2 nanoparticles were obtained. With decreasing nanoparticle size, or for the same nanoparticle size and longer alkyl chain length, the lipids became increasingly interdigitated compared with the normal bilayer structure.

Raman Spectroscopy of Supported Lipid Bilayer Nanoparticles

Raman spectroscopy offers a rapid, simple, and nondestructive technique for the identification of counterfeit medicines. The advantages of handheld Raman spectroscopy are that it is easy to use by unskilled personnel and it can identify a test pharmaceutical product on the spot, whether the product is in solid or liquid form. However, these instruments can operate only in reflection mode, and the Raman activity of a sample is often masked by fluorescent species in the sample, especially when the analysis is made in reflection mode. The objective of this work was to compare the use of a handheld and laboratory-based Raman instruments for authentication of pharmaceutical products obtained from the world market.

The problem of counterfeit medicines is a complicated issue and goes way beyond just the poor manufacturing quality of medicines. It is the deliberate and fraudulent labeling of counterfeit medicines that distinguishes them from medicines that are substandard. Not only may the counterfeit product not contain the drug stated on the label, but it might also contain a toxic or even lethal substance. For example, 192,000 people died in China from the use of counterfeit medicines in 2001 (1). Also, polyethylene glycol in counterfeited paracetamol syrup was responsible for the death of children in Nigeria in 1990 (2,3). No country is immune to the problems of counterfeit medicine supply and it is estimated that about 10% of medicines worldwide are counterfeits (4). The supply channel of these counterfeit medicines is believed to come mostly from the Internet and street markets, which are difficult to control. It is estimated that 25% of street market medicines and 60% of Internet medicines are substandard or counterfeits (5). 

These problems highlight the need for fast and simple techniques that can identify counterfeit medicines in places such as hospitals, pharmacies, and markets. Raman spectroscopy offers a rapid, simple, and nondestructive technique for the authentication of medicines. Raman spectroscopy has been used widely in pharmaceutical analysis (6,7), particularly for the identification of counterfeit medicines such as artesunate tablets (8–11), Cialis tablets (12,13), Lipitor tablets (14), and Viagra tablets (13,15). The counterfeit tablets concerned were manufactured using another active pharmaceutical ingredient (API) or excipients including artesunic acid, calcium carbonate, calcite, dipyrone, erythromycin, lovastatin, paracetamol, starch, talc, and titanium dioxide.  

The advantage of the application of Raman spectroscopy to medicines is that it can analyze a product regardless of its physical state: solid, liquid, or film. In addition, Raman spectroscopy offers information about specific components in the sample because it has high structure selectivity. However, a major disadvantage of Raman spectroscopy is that the Raman signal sometimes is masked by the fluorescence in the sample analyzed (16). This can be overcome in two ways: by choosing an excitation wavelength away from the absorption bands of fluorescent species (usually between 785–1064 nm) (4) or by using the transmission mode instead of reflection mode. Newer laboratory-based Raman instruments can operate in both reflection and transmission modes. However, their use is time-consuming because they require bringing the tablet to the laboratory. In addition, they can only be operated by skilled analysts. Thus, they are not convenient for the analysis of potentially counterfeit products on the street market. Handheld Raman instruments offer the advantage of carrying the laboratory to the sample so they save the time of importing the samples to the laboratory. Also, these instruments operate in a simple identification mode so that the analysis can be carried out by unskilled personnel. However, these instruments lack the accuracy of laboratory-based instruments and can operate only in reflection mode. 

The objective of this work was to evaluate the identification of potential counterfeit medicines using a laboratory-based and a handheld Raman instrument. Qualitative comparison using multivariate analysis was used. 

For this study, 67 tablet products, which contained a total of eight APIs, obtained from various different countries were examined (Table I). These APIs were atorvastatin calcium trihydrate, cetirizine hydrochloride, ciprofloxacin hydrochloride, clopidogrel hydrogen sulfate, loratadine, risedronate sodium, valsartan/hydrochlorothiazide, and valsartan. The percentage of API by mass in these tablets ranged from a minimum of 5% m/m to a maximum of 66% m/m. The number of excipients used in the manufacture of the tablets ranged from three to 13, although the major excipients were lactose monohydrate, maize starch, or microcrystalline cellulose. Intact tablets were measured as received with no sample preparation. Powdered tablets were measured in Waters 4-mm glass vials (Waters Corporation, Milford, Massachusetts).  

Table I: Details of the 67 products used in this study			

For the laboratory-based instrument, a Kaiser Raman WorkStation (Kaiser Optical Systems, Inc., Ann Arbor, Michigan) was used. It consisted of a dispersive Raman spectrometer equipped with a diode laser source (785 nm), a charge-coupled device (CCD) detector, and a holographic grating. The Raman spectral range was 142–1898.4 cm

. For the handheld instrument, a Thermo Fisher Scientific TruScan (formerly Ahura Scientific, Inc., Wilmington, Massachusetts) instrument of light weight (1.7 kg) and small dimensions (30 × 15 × 7.6 cm) was used. The instrument could operate in a temperature range of 20–40 °C. It was equipped with a laser source (785 nm) and a 2048-element silicon CCD detector. The Raman spectral range was 250–2875 cm

The Raman spectra of the tablets were measured by both instruments in the reflection mode and baseline corrected. Then, a spectral comparison using correlation in wavelength space (CWS) and principal component analysis (PCA) methods was carried out using Matlab R2007a software. For the CWS method, the correlation coefficient (

) value threshold taken was 0.95 for identification as authentic. For the PCA comparison, the 95% equal frequency ellipse was drawn around the PC scores of each set of tablets. 

The Raman intensity of a tablet is dependent upon the chemistry of the various components in a tablet, although the scale of measurement is arbitrary (17). The tablets used in this study contained different components and differed in shape, color, and physical properties. All of the products measured had Raman scattering except the ones containing clopidogrel hydrogen sulfate. This Raman scattering was variable between the two instruments and was dependent on the concentrations of the Raman scattering species. For instance, Zyrtec tablets contained cetirizine hydrochloride as an API (8% m/m) and a small amount of titanium dioxide in their coating, both of which are Raman active species. When this tablet was measured using the handheld instrument, the spectrum obtained resembled titanium dioxide (Figure 1a–c), whereas the spectrum obtained from the laboratory-based instrument (Figure 1d) resembled cetirizine hydrochloride. Thus, the laboratory-based instrument was better for detecting Raman active species in the core of the tablet.  

Figure 1: Baseline-treated Raman spectra of (a) cetirizine hydrochloride, (b) titanium dioxide, (c) a Zyrtec (cetirizine hydrochloride) tablet measured using the handheld instrument, and (d) the tablet using the laboratory-based instrument.			

However, this was not the case when a high concentration of a Raman active species was present in a tablet. For example, ciprofloxacin hydrochloride is Raman active and present in a concentration of 66% in Ciproxin tablets. Using both instruments, the Ciproxin tablets spectra resembled the spectra of the ciprofloxacin hydrochloride, even though these tablets contained titanium dioxide in their coating (Figure 2).  

Figure 2: Baseline-corrected Raman spectra of (a) ciprofloxacin hydrochloride measured using the handheld instrument, (b) a Ciproxin tablet measured using the handheld instrument, (c) ciprofloxacin hydrochloride measured using the laboratory-based instrument, and (d) a Ciproxin tablet measured using the laboratory-based instrument.			

This showed that the measured Raman activity was dependent on the concentration of the Raman active species. One way this Raman activity could be increased is by crushing the tablets to reduce the effect of the titanium dioxide that was present in the film coat. Figure 3 shows that the Raman scattering intensity of powdered Ciproxin tablet was double that of the intact Ciproxin tablet. This could be an option to increase the scattering intensity of compounds with low Raman activity or present in low concentrations in tablets. However, this only worked with the laboratory-based Raman instrument. Using the handheld instrument, the intact and powdered forms of the tablets had identical spectra.  

Figure 3: Baseline-treated Raman spectra of (a) intact Ciproxin tablet and (b) a powdered Ciproxin tablet measured using the laboratory-based instrument.			

An alternative way of increasing the measured Raman activity is to measure tablets in transmission mode using the laboratory-based instrument, although this was not done in the present work. 

Comparison of Products Containing Different APIs

The authentication of products was tested by comparing 65 out of 67 products (Table I), which contained seven different APIs. Products containing clopidogrel hydrogen sulfate were excluded from this comparison because they did not give any Raman scattering when they were measured in their coated forms. As mentioned previously, the products were purchased from different countries and had variable shapes, sizes, colors, and % m/m of API. The baseline treated spectra of these products obtained from both instruments were compared using CWS and PCA methods. 

The CWS method could not identify all the tablets measured by either instrument. Figure 4 shows the correlation map of the baseline-treated Raman spectra of tablets containing atorvastatin calcium trihydrate (numbers 1 to 13), cetirizine hydrochloride (14 to 20), ciprofloxacin hydrochloride (21 to 37), loratadine (38 to 41), risedronate sodium (42 to 47), valsartan/hydrochlorothiazide (48 to 57), and valsartan (58 to 65) measured using the handheld instrument (Figure 4a) and the laboratory-based instrument (Figure 4b). The correlation coefficient (

) values are displayed in color: negative values in dark blue and the highest positive values in dark red; 

values in between these are in green, yellow, and orange for increasing values of 

. A dark red color was expected to be obtained for tablets containing the same API and a yellow, green, or orange color was expected for tablets containing different APIs. However, this was not the case partly because some of the APIs were present in low concentrations in the tablets and also because of the noise in the spectra of these tablets. Thus, the CWS methods were used to compare both the scattering peaks as well as the noise.  

Figure 4: Correlation map of the baseline-treated Raman spectra of tablets containing atorvastatin calcium trihydrate (product numbers 1â13), cetirizine hydrochloride (numbers 14â20), ciprofloxacin hydrochloride (numbers 21â37), loratadine (numbers 38â41), risedronate sodium (numbers 42â47), valsartan/hydrochlorothiazide (numbers 48â57), and valsartan (numbers 58â65) measured using (a) the handheld instrument and (b) the laboratory-based instrument.			

 values when their spectra were obtained using the handheld instrument. In this case, the lowest 

 values were as low as -0.0017 and were observed in two tablets containing atorvastatin calcium trihydrate. In addition, tablets containing cetirizine hydrochloride had 

 values as low as 0.02. However, the measured Raman signal was not due to the API; it was due to the titanium dioxide in the film coat. High 

 values were seen among some tablets containing ciprofloxacin hydrochloride (

 = 0.96), valsartan/hydrochlorothiazide (

 values of these tablets passed the threshold of 0.95 (Figure 4a). 

On the other hand, the results of the CWS method of comparing the spectra obtained using the laboratory-based instrument were slightly better (Figure 4b). Whereas few low 

 values were obtained for tablets containing the same APIs, most of the tablets of the same API had 

 values above 0.95. For instance, the set of the loratadine-containing tablets all passed (

 > 0.98). This was due to more signal and less noise encountered in the spectra obtained using the laboratory-based instrument. 

Because of the equivocal identifications using the CWS method, a complementary method was needed to exclude the noise from the spectral comparison. PCA was examined as an alternative method because it classifies these tablets in order of decreasing variances. Thus, the most important variances caused by the Raman active species are displayed through the first few principal components; whereas the less important variances (for example, corresponding to spectral noise) are displayed in later principal components. Ciprofloxacin tablets were excluded from the PCA calculation as they were totally discriminated from the other tablet products; so they oriented the principal component scores plot of the other tablet products to one side. When PCA was applied to the spectra of the tablets obtained using the handheld instrument, it could not differentiate between the various products (Figure 5a). Thus, though the first two principal components accounted for 68.65% of the variance, an overlap of principal components was encountered among tablet products containing different APIs. This may be due to the complex matrices and the excipients that these tablets have in common (Table I). However, this was not the case when PCA was applied to the spectra of the same tablet products obtained using the laboratory-based instrument (Figure 5b). A clear discrimination was obtained among the different products when the first two principal components were plotted (82.94% variance). The only overlap observed was between tablet products containing valsartan or valsartan and hydrochlorothiazide. This was because the tablets had a common API (valsartan), similar excipients, and the hydrochlorothiazide was only present at 5% m/m. So for all practical purposes they contained the same Raman active species. Thus, the laboratory-based instrument had a better identification potential than the handheld instrument using the PCA method.  

Figure 5: PCA scores plot of the baseline-treated Raman spectra of tablet products containing the following APIs: atorvastatin calcium trihydrate (blue), cetirizine hydrochloride (red), loratadine (black), risedronate sodium (magenta), valsartan and hydrochlorothiazide (cyan), and valsartan (yellow) measured by the (a) handheld instrument and (b) the laboratory-based instrument, with the 95% equal frequency ellipses drawn around each set.			

To examine the potential of Raman spectroscopy to identify counterfeit medicines, the laboratory-based instrument was evaluated regarding the identification of a previously known counterfeit Plavix (clopidogrel hydrogen sulfate) tablet. The tablet had a thick film coat so that it could not be identified using either the laboratory-based or handheld instrument when measured in its coated form. However, when the coat of this tablet was removed, no signal could be observed using the handheld instrument. A signal was obtained, however, using the laboratory-based instrument. This signal could be easily distinguished from the signal of an authentic Plavix uncoated tablet by visual identification of the peaks (Figure 6). Thus, the laboratory-based instrument was able to identify counterfeit tablets better than the handheld instrument.  

Figure 6: Baseline-treated Raman spectra of an authentic Plavix tablet in its (a) coated and (b) uncoated forms and a counterfeit Plavix tablet in its (c) coated and (d) uncoated forms measured by the laboratory-based instrument.			

This work evaluated the authentication of tablets using handheld and laboratory-based Raman spectroscopic instruments. Whereas handheld Raman instruments offer the advantage of carrying the laboratory to the sample, they still lack the accuracy of identification of the laboratory-based instruments. The laboratory-based instruments can detect Raman active species at lower concentrations; thus, they are more sensitive. Also, they can operate in transmission mode which yields more signal than reflection mode. Spectral visualization would seem to be better than multivariate analysis methods for comparing the Raman spectra of tablet products. The CWS method, however, failed to identify all products correctly when it was applied to the spectra obtained from both instruments. However, the PCA method overcame this problem and was able to identify 68% of the products measured by the laboratory-based instrument. A great overlap was seen among the PCA scores applied to the products measured by the handheld instrument. In addition, unlike the handheld instrument, the laboratory-based instrument was able to identify counterfeit Plavix tablets.  

  (1) A.I. Wertheimer, N.M. Chaney, and T.M. Santella, 

 (3)E. Kamol, "Lace with Poison," The Daily Star (2008) accessed on 09/04/2011.  

 (4) R. Martino, M. Malet-Martino, V. Gilard, and S. Balayssac, 

 (5) D. Taylor, "Trading in False Hope," The School of Pharmacy, University of London (2009).  

 (6) D.E. Bugay and P.A. Martoglio Smith, in 

Clarke's Analysis of Drugs and Poisons, 3rd edition,

 A.C. Moffat, M.D. Osselton, and B. Widdop, Eds. (Pharmaceutical Press, London, 2004), pp. 358–367. 

Pharmaceutical Applications of Raman Spectroscopy,

 S. Sasic, Ed. (John Wiley and Sons Inc., Hoboken, New Jersey, 2008), pp. 1–29. 

 (8) C. Ricci, L. Nyadong, F. Yang, F.M. Fernandez, C.D. Brown, P.N. Newton, and S.G. Kazarian, 

 (9) M. de Veij, P. Vandenabeele, K.A. Hall, F.M. Fernandez, M.D. Green, N.J. White, A.M. Dondrop, P.N. Newton, and L. Moens, 

(10) K.A. Hall, P.N. Newton, M.D. Green, M. de Veij, P. Vandenabeele, D. Pizzanelli, M. Mayxay, A. Dondorp, and F.M. Fernandez, 

(11) C. Ricci, C. Eliasson, N.A. Macleod, P.N. Newton, P. Matousek, and S.G. Kazarian, 

(12) S. Trefi, C. Routaboul, S. Hamieh, V. Gilard, M. Malet-Martino, and R. Martino, 

(13) P.-Y. Sacre, E. Deconinck, T. De Beer, P. Courselle, R. Vancauwenberghe, P. Chiap, J. Crommen, and J.O. De Beer,  

(14) M.G. Orkoula and C.G. Kontoyannis,  

(15) M. de Veij, A. Deneckere, P. Vandenabeele, D. de Kaste, and L. Moens, 

Comparison of Laboratory and Handheld Raman Instruments for the Identification of Counterfeit Medicines

In this article, we present a method that provides prompt detection of the presence of cancer cells inside the 2-mm margin of tissue surrounding the tumor after excision using spatially offset Raman spectroscopy (SORS). SORS was developed to detect subtle changes in soft tissue spectra in the 100–2000 μm range and tested on excised breast tissues. The results display a very high specificity and sensitivity (100% and 95%, respectively) of classification between positive and negative tumor margins. SORS is a clinically feasible method, suitable for the real-time, intraoperative assessment of tumor margins at the micrometer level.

One in eight women is diagnosed with breast cancer today, making it the most frequently diagnosed cancer among women (1). Breast cancer is the second leading cause of cancer-related deaths among women in the United States; however, when diagnosed early, patients with localized breast cancer have a 98% survival rate. Treatment relies on surgery for the complete management of patient care. Of the patients who are treated by surgery, 75% are eligible for breast conserving therapy (BCT), also known as a partial mastectomy or lumpectomy. In this procedure, tumors are first reduced using neoadjuvant therapies and then excised locally to minimize the cosmetic and psychological impact on the patient. This surgery is less invasive than a full mastectomy and has been shown to provide similar long-term survival rates (2).  

Recurrence is directly related to the complete removal of all remnant tumor cells. Thus, the typical clinical standard for a successful partial resection is to have a 1–2 mm negative (tumor-free) margin around the tumor in the excised tissue. Margins therefore play a key role in the prognosis of the patient with respect to local recurrence of breast cancer and are directly correlated to the success of BCT as a treatment modality. This then leads to a critical need for intraoperative evaluation of the resection front so that immediate re-excision of suspicious margins can be performed, minimizing the necessity for a second surgery.  

Any method used to evaluate the surgical margins must be rapid and relatively simple to implement if it is to be used in routine clinical care. The simplest technique for determining margin status is visual inspection of the excised tissue for evidence of tumor, a method that leads to incorrect diagnoses, and therefore repeat surgeries or a higher risk of recurrence in at least 25% of cases (3). Serial sectioning with standard histopathology provides a definitive diagnosis of margin status, but results may take several days to over a week. This delay leaves a patient uncertain and waiting and the results may then lead to a second surgery if tumor-positive margins are found (20–70% of cases). Limitations in current methods therefore emphasize the need for a real-time, intraoperative tool that can accurately determine the status of breast surgical margins. 

In this article, we present the development of spatially offset Raman spectroscopy (SORS) for subsurface assessment of surgical margins in the spatial scale of 100–2000 μm. The scientific goal of the project is to evaluate the ability of SORS to perform biochemical separation of subtly differing tissues in the needed spatial scale such that margin assessment can be performed. The clinical goal of the study is to develop a method to detect the presence of tumor cells within the clinically accepted margin area of 1–2 mm in real time, in the operating room so that the surgeon is provided with real-time feedback and additional tissues may be removed in a single surgical procedure to achieve clear margins and eliminate the need for a second surgery. 

Raman spectroscopy is based on inelastic scattering involving transitions between vibrational energy levels. A Raman spectrum therefore consists of a series of peaks, which represent the different vibrational modes of the scattering molecules. These peaks are spectrally narrow and molecular-specific, such that the observed peaks may be associated with specific bonds in specific molecules. Many biological molecules have distinct Raman spectra that can be used to determine the biochemical composition of a tissue. One particularly relevant biochemical change in cancer cells is an increase in the nucleic acid content that may be correlated with increased proliferation and genetic instability. This change, among others such as changes in glycogen and collagen, can be detected with Raman spectroscopy and used for cancer detection (4,5). 

Many researchers have exploited such cancer-specific signatures with Raman spectroscopy in many organs, including the cervix (6,7), bladder and prostate (8), lung (9), skin (10,11), and the gastrointestinal tract (12–14). Several groups have addressed the application of Raman spectroscopy for breast cancer diagnosis. Alfano and colleagues (15–18) were the first to look at Raman spectroscopy to distinguish normal from malignant breast tissues. More recently, Feld and colleagues (19) performed extensive work on using Raman spectroscopy for breast cancer diagnosis and can discriminate malignant from normal and benign tissues with 94% sensitivity and 96% specificity. It should be noted that all the work described above focuses on breast cancer diagnosis and not on guidance of therapy or margin assessment. 

Beyond the use of Raman spectroscopy, there have been some reports of novel methods for margin assessment, including the use of radiofrequency spectroscopy (20), elastic scattering spectroscopy (21), optical coherence tomography (OCT) (22), and diffuse reflectance spectroscopy (23); however, most of these reports suffer from poor sensitivity or specificity. OCT can provide high-resolution images that can be used for guidance in margin analysis. Recently, Boppart and colleagues (22) showed that OCT performs with an overall accuracy of 75% for determining positive tumor margins. Ramanujam and colleagues (23) have reported using diffuse reflectance spectroscopy for margin assessment achieving an overall accuracy again of 75%. These studies provide improvements in comparison to current clinical standards for intraoperative detection; however, they are limited in their impact on patient care.  

One report on breast margin analysis with Raman spectroscopy was published by Feld and colleagues (24), where normal, fibrocystic, and malignant tissues were classified with an overall accuracy of 93%. However, this report relies on a standard fiber probe configuration that collects signal from only the first several hundred micrometers in depth and does not consider the need for determining negative margins to a depth of 1–2 mm. Whereas Raman spectroscopy can provide specific biochemical information for breast tumor margin analysis, the ideal optical system would be able to resolve this information at depths of at least 2 mm and do so in seconds to minutes in the operating room. 

Optical techniques rely on the penetration of light to obtain depth-related or resolved information. This in turn depends on the wavelength of light under use. Introducing a spatial offset between source and detection elements provides the depth selectivity in spectral measurements needed for this application. Figure 1 demonstrates the photon walk that provides this depth resolution. Larger separations are more likely to detect photons that have traveled deeper into the tissue and have been scattered multiple times compared with smaller separations, which detect photons that have only undergone minimal scattering events and have remained in the superficial layers. Matousek and colleagues first demonstrated this effect by obtaining Raman spectra of diffusely scattering media using a two-layer phantom in which each layer had a chemical with a distinctive spectrum. As the source–detector offset increased, the measured spectra began to resemble a pure spectrum from the bottom layer (25). The same group demonstrated the first biological application of this technique in detecting the strong Raman signature of bone through several millimeters of soft tissue (26). However, the application of SORS was limited to detecting a small area of very strong scatterers, such as bone under soft tissue. The results presented in this article demonstrate the application of SORS to discriminate multiple layers of soft tissue, and more particularly, for detecting cancer through underlying layers of normal tissue on the submillimeter scale.  

Figure 1: Schematic illustrating tumor-positive and tumor-negative margins, defined by the distance between the surgical margin and the tumor boundary.  Overlaid are possible photon migration paths demonstrating the advantage of SORS for this application.			

The fundamental principle of SORS therefore could prove to be very useful for obtaining measurements from a number of tissue depths up to the clinically relevant 1–2 mm in resected partial mastectomy specimens. To demonstrate detection of subtly different soft tissues in depth, a tissue phantom model consisting of a layer of normal human breast tissue was placed between two 100-μm-thick quartz coverslips. The normal layer thickness was varied from 0.5 to 2 mm and placed directly on top of breast cancer tissue sample, which ranged in thickness from 2 to 5 mm. With one excitation fiber (S) to deliver the laser and one fiber (D) to collect the Raman signal, measurements were taken by varying the distance between the source and detector (S, D) fibers ranging from 0.75 to 4.75 mm in 0.5 mm intervals. Figure 2 shows spectra obtained from the experimental setup with 1-mm normal layer thickness, as well as the mean spectrum of normal and tumor layers (measured directly). As spatial offset increases, the spectra begin to resemble the tumor spectrum compared to the normal spectrum.  

Figure 2: Raman spectra from an experimental run with a 1-mm normal layer. Gray boxes highlight regions with the most dramatic changes from normal to tumor signatures as S-D offset (labeled on left) increases.			

The light gray boxes in Figure 2 highlight the spectral regions with the most dramatic changes observed as spatial offset increases. A close-up of these regions is shown in Figure 3. These include the increase of the 1006-cm

  peak generally attributed to phenylalanine, a ratio of the 1303–1265 cm

  peaks, which tends to indicate an increase in protein content, and the increasing width of the amide I peak around 1656 cm

. Other significant changes are a decrease in the relative intensity of the 1445-cm

 deformation peak, a decrease in the 1748-cm

  carbonyl stretch peak, and an increase in the 1156-cm

  carotenoid peak. These results show that SORS can be used to detect spectral contributions from breast tumors under normal breast tissue.  

Figure 3: To aid in the visualization of relevant but subtle spectral changes, zoomed-in versions are shown for the 1006-cm-1 phenylalanine peak, the 1265-cm-1 amide III and 1303-cm-1 lipid peak, the CH2 bend/deformation peak at 1440 cm-1, and the shoulders of the 1656-cm-1 amide I peak.			

To translate this research feasibility into a clinically applicable setup, a fiber-based probe was designed to obtain a sampling depth of 2 mm so that the probe is sensitive to the presence of tumor cells within this depth. Additionally, the source–detector offset was fixed so that only Raman signal from the superficial 2-mm layers of tissue was collected. Based on experimental (27) and computer-simulated results (28), a maximum source–detector offset of 3.5 mm was been determined to provide the collection geometry needed to detect the presence of breast tumors located up to 2 mm beneath normal breast tissue. If a larger offset is used, the measurements could detect tumors from more than 2 mm deep as needed. 

A multiple offset SORS probe was constructed (assembled by EMVision, Loxahatchee, Florida), consisting of a single 400-μm-diameter source fiber on one end, with four (partial) rings of 300-μm-diameter collection fibers (numbered R1, R2, R3, and R4). Inline filters were placed to reject elastically scattered light generated in the fibers. As in a typical clinical Raman setup, the source probe delivered 80 mW of power from a 785-nm diode laser (Innovative Photonics Solutions, Monmouth Junction, New Jersey). The detection fibers delivered light to a near-IR-optimized spectrograph (Princeton Instruments, Princeton, New Jersey), which dispersed the light to be recorded by a deep depletion, thermoelectrically cooled CCD (Princeton Instruments). A 70-μm entrance slit is used and the spectral resolution achieved by the system is about 11 cm

  spectral region. Spectra were calibrated for instrument variation and system response and processed for noise and fluorescence background (26). 

With approval from the Vanderbilt Institutional Review Board (Vanderbilt University, Nashville, Tennessee) and the US Army Medical Research and Materiel Command's Human Research Protection Office, 35 human breast tissue samples were obtained from women undergoing partial mastectomies or full mastectomies. Of those samples, 15 had areas greater than 2 mm of normal tissue with no tumor; the remaining 20 samples had positive margins, defined by less than 2 mm of benign tissue above tumor. To acquire Raman spectra from samples with positive margins, the SORS probe was placed on an area of normal-appearing tissue above the tumor to replicate the standard clinical protocol for tumor margin evaluation. Spectra were recorded for 10–30 s and processed. To compare the spectra to the histopathological evaluation of margin assessment, measurement sites were inked, fixed in formalin, and serially sectioned.  

Two tissue-margin scenarios and their resulting spectra are displayed in Figure 4. The "S" arrow designates the spot of the excitation fiber and R1–R4 indicate the placement of the detection fiber rings. Figure 4a shows the spectra obtained from the tissue section shown in the insert. In this example, the source fiber delivered light to a large fatty region, the area appearing white under H&E stain. The outermost collection fiber (R4) gathered photons that passed through a large tumor section, the darker pink area. Although the spectral changes are subtle, measurements acquired from the rings closer to the tumor were more similar to pure tumor spectra, suggesting that each ring was obtaining information from a different volume of the sample. These changes occur as the source–detector offset increases and are similar to those seen in Figure 2.  

Figure 4: (a) SORS spectra for each detector ring from tissue in inset with a large area of normal fat (white colored area labeled "N") on the right, and solid tumor (darkly stained area labeled "T") on the left. Arrows indicate the placement of the source fiber (S) and each of the detector rings. (b) SORS spectra for each detector ring from tissue in inset with pockets of normal fat near surface of otherwise darkly stained tumor tissue. Arrows again represent placements of fibers.			

Figure 4b demonstrates a case where a clear delineation between normal and tumor does not exist. The source fiber of the probe was placed on the small and narrow benign area near the surface and detection fibers were mostly on the area of the tumor. Accordingly, the spectra obtained from this tissue sample contain features indicative of normal fatty breast tissue from the fiber with the smallest source–detector offset (that is, closest to the excitation source), as well as pure tumor from fibers with larger source–detector offsets. Thus, it is clear that different detector rings are sampling multiple volumes as desired and that the SORS probe can collect data showing biochemical differences at varying depths.   

Clinically, it is only necessary to determine if a given measurement is positive for the presence of tumor cells in the 2-mm probe depth of the tissue. To implement this clinically relevant scheme, spectra from all the rings were averaged to create one spectrum per site measured. Discrimination was performed with sparse multinomial logistic regression (SMLR) (29), a Bayesian machine-learning framework that computes the posterior probability of a spectrum belonging to a tissue class based on a training set. In the case of this binary analysis (either positive or negative margins), whichever class had the higher probability of membership is where the spectrum classified. Table I shows the confusion matrix for classification of these composite spectra using SMLR. These results show that SORS performs at 95% sensitivity and 100% specificity in evaluating breast tumor margins (30).  

Table I:  Confusion matrix for "margin analysis" on in-vitro specimens			

With the promising results from this in-vitro study, patients with breast tumors were recruited to a pilot clinical study, also approved by the Vanderbilt IRB, to determine if SORS can be used to detect positive and negative tumor margins in the operating room on ex-vivo excised specimens during surgery. To standardize the study protocol in using the current SORS probe that measures a single 3.5-mm area at a time, one location was measured from each of the six surfaces of a cuboid resection specimen, as indicated in the insert of Figure 5. These sites were subsequently inked for histological correlation. Figure 5 shows the composite spectra acquired from each of these six sites in a given specimen. SORS spectra indicate the presence of tumor cells within the 2-mm margin in locations 2, 4, and 6, which was confirmed histologically (data not shown). Locations 1, 3, and 5, however, show no cancer within the margins. This example demonstrates the power of SORS for margin analysis in a clinical setting. However, simultaneous or sequential measurement of the entire tissue surface will be required for this technique to become standard clinical practice in the operating room. This work is currently in progress in our laboratory.  

Figure 5: Spectra from six different anatomical positions (from 1 to 6) on an excised lumpectomy from a patient.			

This article demonstrates that SORS can be used to detect the spectral signatures of breast tumors as small as 1–2 mm thick under up to 2 mm of normal breast tissue. This study also shows that SORS can provide resolution on the order of micrometers and can detect layers with subtle differences in biochemical patterns. Being able to collect data from such small areas deeply embedded within samples paves the way for SORS to be used for many biomedical and other applications. For example, when treating patients with severe burns and wounds, it is difficult for medical providers to detect the stage of healing or the presence of infection. SORS could be used for this purpose so doctors could noninvasively detect changes below the surface. Acquiring Raman spectra from various depths in tissue may also make other cancers such as skin, cervical, and colon easier to diagnose. Many nonbiological applications may also benefit from SORS. The Drug Enforcement Agency and Homeland Security could implement SORS screening of unknown substances to obtain vital information from multiple layers and determine if any illegal substances or sources of chemical warfare are present. These are some of the potential uses of SORS. From our intraoperative tumor detection applications to the work in pharmaceutical applications, SORS has opened the door to looking beyond the surface.   

The authors would like to acknowledge the financial support of the Department of Defense Breast Cancer Research Program Idea Award #W81XWH-09-1-0037.  

Breast Cancer Facts & Figures 2009–2010, in American Cancer Society

, American Cancer Society, Inc.: Atlanta (2010).

Raman Spectroscopy: From Benchtop to Bedside, in Biomedical Photonics Handbook

, T. Vo-Dinh, Ed. (CRC Press, Washington, DC, 2003), pp. 30:1-30:27. 

(5) A. Mahadevan-Jansen and R. Richards-Kortum, 

In this article, we present a method that provides prompt detection of the presence of cancer cells inside the 2-mm margin of tissue surrounding the tumor after excision using spatially offset Raman spectroscopy (SORS). SORS was developed to detect subtle changes in soft tissue spectra in the 100–2000 ?m range and tested on excised breast tissues. The results display a very high specificity and sensitivity (100% and 95%, respectively) of classification between positive and negative tumor margins. SORS is a clinically feasible method, suitable for the real-time, intraoperative assessment of tumor margins at the micrometer level.

Special Issues-06-01-2011

Raman Spectroscopy of Supported Lipid Bilayer Nanoparticles

Comparison of Laboratory and Handheld Raman Instruments for the Identification of Counterfeit Medicines

Looking Below the Surface of Breast Tissue During Surgery